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preprint/preprint__0129baf8281eddc2ad657d6e8fa589609bc12adf1490795c312275d391cb9313/preprint__0129baf8281eddc2ad657d6e8fa589609bc12adf1490795c312275d391cb9313.mmd

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+# Micropillar-induced changes in cell nucleus morphology enhance bone regeneration by modulating the secretome  
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+Guillermo Ameer  g- ameer@northwestern.edu  
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+Northwestern University https://orcid.org/0000- 0001- 6023- 048X  Xinlong Wang  Northwestern University https://orcid.org/0000- 0001- 8978- 2851  Yiming Li  Northwestern University https://orcid.org/0000- 0003- 2111- 3939  Zitong Lin  Northwestern University  Indira Pla  Northwestern University  Raju Gajjela  Northwestern University  Basil Mattamana  Northwestern University  Maya Joshi  Northwestern University https://orcid.org/0000- 0002- 6028- 475X  Yugang Liu  Northwestern University https://orcid.org/0000- 0001- 5304- 3459  Huifeng Wang  Northwestern University  Amy Zun  Northwestern University  Hao Wang  The University of Chicago  Ching Wai  Northwestern University  Vasundhara Agrawal  Northwestern University https://orcid.org/0000- 0003- 0913- 9298  Cody Dunton
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+Northwestern University  
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+Chongwen Duan  Northwestern University  
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+Bin Jiang  Northwestern University  
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+Vadim Backman  Northwestern University  https://orcid.org/0000- 0003- 1981- 1818  
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+Tong Chuan He  The University of Chicago Medical Center  
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+Russell Reid  Section of Plastic Surgery, The University of Chicago Medical Centre  
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+Yuan Luo  Northwestern University  https://orcid.org/0000- 0003- 0195- 7456  
+
+## Article  
+
+Keywords:  
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+Posted Date: January 7th, 2025  
+
+DOI: https://doi.org/10.21203/rs.3.rs- 5530535/v1  
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+License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
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+Additional Declarations: There is NO Competing Interest.  
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+Version of Record: A version of this preprint was published at Nature Communications on July 11th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 60760-y.
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+## Microtopography-induced changes in cell nucleus morphology enhance bone regeneration by modulating the cellular secretome  
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+Xinlong Wang \(^{1,2}\) , Yiming Li \(^{3}\) , Zitong Lin \(^{3}\) , Indira Pla \(^{4}\) , Raju Gajjela \(^{4}\) , Basil Baby Mattamana \(^{4}\) , Maya Joshi \(^{1}\) , Yugang Liu \(^{1,2}\) , Huifeng Wang \(^{1,2}\) , Amy B. Zun \(^{1}\) , Hao Wang \(^{5}\) , Ching- Man Wai \(^{6}\) , Vasundhara Agrawal \(^{2,7}\) , Cody L. Dunton \(^{2,7}\) , Chongwen Duan \(^{1,2}\) , Bin Jiang \(^{1,2,8}\) , Vadim Backman \(^{1,2,7,9}\) , Tong- Chuan He \(^{1,5}\) , Russell R. Reid \(^{1,10}\) , Yuan Luo \(^{3,11,12}\) , Guillermo A. Ameer \(^{1,2,7,8,11,13,14*}\)  
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+\(^{1}\) Center for Advanced Regenerative Engineering, Northwestern University, Evanston, IL 60208, USA \(^{2}\) Department of Biomedical Engineering, Northwestern University, Evanston, IL 60208, USA \(^{3}\) Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA \(^{4}\) Proteomics Center of Excellence, Northwestern University, Evanston, IL 60208, USA \(^{5}\) Molecular Oncology Laboratory, Department of Orthopedic Surgery and Rehabilitation Medicine, The University of Chicago Medical Center, Chicago, IL 60637, USA \(^{6}\) Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA \(^{7}\) Center for Physical Genomics and Engineering, Northwestern University, Evanston, IL 60208, USA \(^{8}\) Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA \(^{9}\) Chemistry of Life Process Institute, Northwestern University, Evanston, IL 60208, USA \(^{10}\) Laboratory of Craniofacial Biology and Development, Section of Plastic and Reconstructive Surgery, Department of Surgery, The University of Chicago Medical Center, Chicago, IL 60637, USA \(^{11}\) Northwestern University Clinical and Translational Sciences Institute, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA \(^{12}\) Center for Collaborative AI in Healthcare, Institute for AI in Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA \(^{13}\) International Institute for Nanotechnology, Northwestern University, Evanston, IL 60208, USA \(^{14}\) Simpson Querrey Institute for Bionanotechnology, Northwestern University, Chicago, IL 60611, USA
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+## Abstract  
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+Nuclear morphology, which modulates chromatin architecture, plays a critical role in regulating gene expression and cell functions. While most research has focused on the direct effects of nuclear morphology on cell fate, its impact on the cell secrete and surrounding cells remains largely unexplored, yet is especially crucial for cell- based therapies. In this study, we fabricated implants with a micropillar topography using methacrylated poly(octamethylene citrate)/hydroxyapatite (mPOC/HA) composites to investigate how micropillar- induced nuclear deformation influences cell paracrine signaling for osteogenesis and cranial bone regeneration. In vitro, cells with deformed nuclei showed enhanced secretion of proteins that support extracellular matrix (ECM) organization, which promoted osteogenic differentiation in neighboring human mesenchymal stromal cells (hMSCs). In a mouse model with critical- size cranial defects, nuclear- deformed hMSCs on micropillar mPOC/HA implants elevated Col1a2 expression, contributing to bone matrix formation, and drove cell differentiation toward osteogenic progenitor cells. These findings indicate that micropillars not only enhance the osteogenic differentiation of human mesenchymal stromal cells (hMSCs) but also modulate the secrete, thereby influencing the fate of surrounding cells through paracrine effects.  
+
+## Introduction  
+
+The nucleus is a dynamic organelle that changes its morphology in response to the cell's status. Its morphology has critical influence on nuclear mechanics, chromatin organization, gene expression, cell functionality and disease development.2- 5 Abnormal nuclear morphologies, such as invagination and blebbing, have functional implications in several human disorders, including cancer, accelerated aging, thyroid disorders, and different types of neuro- muscular diseases.6,7 In addition, severe nuclear deformation is also observed during tissue development, cell migration, proliferation, and differentiation.2 Several structural components within the nucleus—including the nuclear envelope, lamins, nuclear actin, and chromatin—work together to determine its shape and structure.8 Although the underlying mechanisms are not yet fully understood, nuclear deformation has been found to affect cell behaviors through mechanotransduction processes.9 In addition, nuclear morphological changes have been reported to affect nuclear membrane tension and unfolding, which regulate the structure of the nuclear pore complex.10 This, in turn, influences the nuclear shuttling of transcription factors (e.g., YAP) and ions (e.g., Ca2+), ultimately impacting cell functions.11,12 In our previous study, we demonstrated that altering nuclear morphology using micropillar topography affects nuclear lamin A/C assembly, which, in turn, influences chromatin tethering, packing, and condensation.13 These changes affect transcriptional accessibility and responsiveness, thereby regulating gene expression and stem cell differentiation.  
+
+To manipulate nuclear morphology, various biophysical tools have been developed, including atomic force microscopy (AFM) nanoindentation, optical, magnetic, and acoustic tweezers, microfluidic devices, micropipette aspiration, plate compression, substrate deformation, and surface topography modulation.14- 21 Among these methods, regulating the surface topography of materials is more accessible and has broader implications for regenerative engineering. One commonly used approach is the fabrication of pillar structures, which are employed to deform cell nuclei and study nuclear properties such as mechanics and deformability.22 These micropillar designs have been utilized to manipulate various cell functions, including migration, adhesion,
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+proliferation, and differentiation. \(^{23 - 26}\) A wide range of materials can be used to create these structures, such as poly- L- lactic acid (PLLA), poly(lactide- co- glycolide) (PLGA), OrmoComp (an organic- inorganic hybrid polymer), and methacrylated poly(octamethylene citrate) (mPOC). \(^{13,26 - 28}\) Among these options, mPOC is particularly suitable for bone regeneration due to its major component, citrate, which acts as a metabolic factor to enhance the osteogenesis of mesenchymal stromal cells (MSCs). \(^{29}\)  
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+Although the influence of nuclear morphogenesis on the functions of individual cells is being intensively investigated, its role in regulating cellular secretion remains unclear. Bioactive molecules secreted by cells are crucial for intercellular communication, affecting various biological processes such as inflammation, cell survival, differentiation, and tissue regeneration. \(^{30,31}\) The success of many cell and exosome- based therapies relies on the cellular secretome. In this study, we fabricated micropillars to manipulate nuclear morphology and investigated their effects on the secretome of human mesenchymal stromal cells (hMSCs). We incorporated hydroxyapatite (HA), the primary inorganic component of native bone tissue, with micropatterned methacrylated poly(octamethylene citrate) (mPOC) to create the micropillars, promoting bone formation. Our results showed that mPOC/HA micropillars facilitated osteogenic differentiation of hMSCs compared to flat mPOC/HA samples in vitro. Secretome analysis revealed that hMSCs with deformed nuclei exhibited higher expression levels of bioactive factors associated with extracellular matrix (ECM) components and organization, as well as ossification. In vivo, both mPOC/HA flat and micropillar scaffolds seeded with hMSCs resulted in new bone formation; however, the micropillar group demonstrated significantly greater new bone volume and regenerated tissue thickness. Spatial transcriptomic analysis further confirmed elevated expression of genes related to the regulation of ECM structures, consistent with the secretome analysis results. These findings suggest that the influence of nuclear deformation on the osteogenesis of hMSCs operates through similar mechanisms in both in vitro and in vivo environments. Therefore, microtopography engineering of scaffold to control nuclear morphology is a promising approach to enhance bone regeneration.  
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+## Results  
+
+## Influence of micropillar structures on physical and chemical properties of mPOC/HA implants  
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+mPOC prepolymer was synthesized according to our previous report, \(^{32}\) and its successful synthesis was confirmed via the nuclear magnetic resonance (1H NMR) spectrum (Fig. S1a- c). The size of HA nanoparticles is around \(100 \mathrm{nm}\) , as characterized by dynamic light scattering (DLS) (Fig. S1d). To mimic the nature of bone composition, \(^{33} 60\%\) (w/w) HA was mixed with mPOC, and the slurry was used to fabricate flat and micropillar implants using a combination of UV lithography and the contact printing method (Fig. 1a). The square micropillars, with dimensions of 5 by 5 in side length and spacing, were fabricated (Fig. 1b). The height of the micropillars is around \(8 \mu \mathrm{m}\) , which can cause significant nuclear deformation (Fig. 1c,d). \(^{27}\) Fourier transform infrared (FTIR) spectrum shows a similar typical peak of functional groups in mPOC and mPOC/HA implants (Fig. S1e). The surface roughness of the implants was scanned using an atomic force microscope (AFM) (Fig. 1e). The analysis result indicates that the topography didn’t affect the surface roughness of the implants (Fig. 1f). Additionally, we tested the hydrophilicity of flat and micropillar implants via
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+water contact angle measurement (Fig. S2). Although, at the initial state, the flat surface was more hydrophilic, there was no significant difference in the water contact angle after a 5- minute stabilization process.  
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+The mechanical properties of the implants were tested using the nano- indentation method. The force- indentation curve of the flat sample has a sharper slope, indicating it is stiffer than the micropillar sample (Fig. S3a). The Young's Modulus of the flat sample \((0.95 \pm 0.12 \mathrm{GPa})\) is significantly higher than that of the micropillars \((0.48 \pm 0.02 \mathrm{GPa})\) and the lateral modulus of the micropillars \((46.88 \pm 1.49 \mathrm{MPa})\) (Fig. S3b,c). However, based on a previous report, the high modulus of the substrates is beyond the threshold that cells can distinguish and does not have an influence on nuclear morphology manipulation. \(^{34,35}\) Accelerated degradation and calcium release tests of the implants were performed in DPBS at \(75^{\circ} \mathrm{C}\) with agitation. \(^{36}\) There is a burst weight loss and calcium release of both flat and micropillar samples at day 1, followed by a gradual change until day 10, and another increase in the degradation and calcium release rate from day 10 to 14 (Fig. 1g,h). The micropillar structure enhanced the degradation and calcium release, but not significantly. According to the images of the samples captured at different time points, the initial burst degradation and calcium release can be attributed to the fast surface erosion of both scaffolds, as many small pores can be observed on their surfaces. From day 10 to 14, scaffolds started break into pieces that may lead to another burst degradation and calcium release (Fig. 1i).
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+<center>Figure 1. Fabrication of surface engineered mPOC/HA implants. a. Illustration shows the combination of UV lithography and contact printing to fabricate free-standing mPOC/HA micropillars. b. SEM image shows the micropillar structures made of mPOC/HA. c. Optical microscope image and d. cross-section analysis of mPOC/HA micropillars. e. Surface scanning of flat and micropillar implants by AFM. f. Surface roughness of flat and micropillar implants. N.S., no significant difference, \(\mathrm{n} = 3\) biological replicates. g. Degradation test and h. calcium release of flat and micropillar mPOC/HA implants. N.S., no significant difference, \(\mathrm{n} = 4\) biological replicates, insert plot shows the initial release of calcium within \(24\mathrm{h}\) . i. Representative images of flat and micropillar implants at different time points after accelerated degradation. </center>
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+## Nuclear deformation facilitates osteogenic differentiation of hMSCs  
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+Nuclear deformation facilitates osteogenic differentiation of hMSCshMSCs were cultured on the flat and micropillar mPOC/HA surfaces in osteogenic medium and stained for F- actin and nuclei after 3 days (Fig. 2a). Noticeable deformation in both the nucleus and cytoskeleton was observed, consistent with mPOC micropillars. The Nuclear shape index (NSI) was calculated to assess the degree of nuclear deformation. A significantly lower NSI value, indicating more severe deformation, was found in the micropillar group (Fig. 2b). Confocal images were then employed to evaluate the 3D geometry of cell nuclei (Fig. 2c). 3D reconstruction analysis revealed that several geometric parameters, including nuclear volume, surface area, and project area, were significantly decreased on micropillars, while nuclear height was significantly increased (Fig. 2d and Fig. S4).  
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+We then investigated the impact of micropillars on cell adhesion, a crucial aspect for manipulating cell function. Initial cell attachment tests revealed that the micropillar structure did not influence cell attachment on the implants (Fig. 2e). SEM imaging of cell adhesion demonstrated that cells formed lamellipodia on flat surfaces but exhibited more filopodia on micropillars (Fig. 2f). Filopodia were observed on the top, side, and bottom of micropillars, indicating that cells were sensing the 2.5D environment using these antennae- like structures. The majority of cells were found to be viable on both flat and micropillar substrates, as evidenced by live/dead staining (Fig. 2g and Fig. S5). While the micropillars reduced cell metabolic activity (Fig. 2h), there was no significant impact on cell proliferation after 3 days of culture (Fig. 2i).  
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+To assess the impact of mPOC/HA micropillars on the osteogenesis of hMSCs, we stained ALP (alkaline phosphate) on a substrate with a combination of half flat and half micropillar structures (Fig. 2j). Quantification results demonstrated a significant increase in ALP activity on the micropillars (Fig. 2k). Furthermore, additional osteogenic differentiation markers of hMSCs, including RUNX2 and osteocalcin (OCN), were quantified through western blot analysis (Fig. 2l). The quantification of these proteins revealed a significant increase in both RUNX2 and OCN in cells on micropillars, confirming that the structures can effectively promote the osteogenic differentiation of hMSCs (Fig. 2m,n).
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+<center>Figure 2. Nuclear deformation promotes osteogenic differentiation of hMSCs. a. Staining of nucleus (green) and F-actin (red) of hMSCs on flat and micropillar mPOC/HA surfaces. Insert: high magnification of cell nucleus. Dashed lines indicate micropillars. b. Analysis of nuclear shape index of hMSCs. \(\mathrm{n} = 117\) (flat) and 132 (pillar) collected from 3 biological replicates, \(\mathrm{***p< 0.0001}\) . c. Orthogonal view of cell nucleus on flat and micropillar surfaces. d. Nuclear volume analysis based on 3D construction of the confocal images of cell nuclei. \(\mathrm{n} = 35\) cells collected from 3 biological replicates, \(\mathrm{***p< 0.0001}\) . e. Initial cell attachment on flat and micropillar surfaces. \(\mathrm{n} = 5\) biological replicates, N.S., no significant difference. f. SEM images show the cell attachment on flat and micropillar mPOC/HA surfaces. g. Live/dead staining of hMSCs on flat and micropillar surfaces at 72 h in osteogenic medium. h. Cell metabolic activity of cells on flat and micropillar surfaces tested by a MTT assay. \(\mathrm{n} = 5\) biological replicates, \(\mathrm{***p< 0.0001}\) . i. Cell proliferation tested via DNA content after 72 h induction. \(\mathrm{n} = 5\) biological replicates, N.S., no significant difference. j. ALP staining of hMSCs on flat and micropillar surfaces after 7 d induction. k. ALP activity test of cells after 7 d osteogenic induction. \(\mathrm{n} = 3\) biological replicates. l. Blot images of osteogenic marker OCN and RUNX2 in cells cultured on flat and micropillar implants. GAPDH is shown as a control. Quantification m. OCN and n. RUNX2 according to western blot tests. \(\mathrm{n} = 3\) biological replicates, \(\mathrm{***p< 0.0001}\) . </center>
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+## Micropillars modulate the secretome of hMSCs that regulate extracellular matrix formation.  
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+Micropillars modulate the secretome of hMSCs that regulate extracellular matrix formation.Previously, we demonstrated the ability of micropillar implants to enhance in vivo bone formation.<sup>13</sup> However, the newly formed bone was not in close contact with the implant. Consequently, we hypothesized that nuclear deformation on micropillars might impact cellular secretion, thereby influencing osteogenesis through paracrine effects. To test this hypothesis, secretome analysis was conducted using medium collected from flat and micropillar samples. Differences in protein secretion levels between the two groups were depicted through principal component analysis (PCA) and a volcano plot, revealing a significant influence of nuclear deformation on the secretome (Fig. 3a,b). Gene ontology (GO) analysis was performed to annotate the significantly altered proteins in relevant processes.<sup>38</sup> Top changes in cellular component, molecular functions, biological processes, and biological pathways indicated that micropillars predominantly affected extracellular matrix (ECM)- related processes (Fig. 3c and Fig. S6- 8). Moreover, ossification and collagen fibril organization were identified as biological processes significantly overrepresented by differentially expressed proteins (Fig. 3d). The heatmap plot of proteins associated with collagen- containing extracellular matrix and ossification showed predominant upregulation on micropillars (Fig. 3e). The linkages of proteins and GO terms in biological process highlighted that ECM organization forms the largest cluster and is closely associated with the ossification process (Fig. 3f).  
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+Reactome pathway analysis was further conducted to assess potential downstream effects of secretome changes on micropillars.<sup>39</sup> Results indicated that pathways related to ECM organization, ECM proteoglycans, and collagen fibril crosslinking were among the top 15 pathways significantly overrepresented by differential expressed pathways (DEP), predominantly showing upregulation (Fig. 3g and Fig. S9). We also noticed an upregulation in the degradation of the ECM on micropillars, indicating enhanced ECM remodeling which a crucial factor for tissue regeneration.<sup>40</sup> These findings suggest that micropillars can influence the ECM formation of hMSCs through paracrine effects. Additionally, we performed proteomic analysis using cells cultured on flat and micropillar mPOC/HA scaffolds (Fig. S10). PCA and volcano plots indicated significant influences of nuclear deformation on protein expression. Pathway analysis revealed significant changes in many cell proliferation- related processes, consistent with previous transcriptomic tests on micropillars.<sup>13</sup>
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+<center>Figure 3. Secretome of hMSCs on flat and micropillar mPOC/HA surfaces. a. PCA plot of differentially expressed proteins secreted by hMSCs on flat and micropillars. Cyan: flat; Red: micropillar. b. Volcano plot of proteins secreted by hMSCs seeded on micropillars compared to the flat surface. Blue dots and orange dots indicate significantly downregulated and upregulated proteins secreted by cells on micropillars compared to those on flat surface. Grey dots indicate </center>
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+non- significantly changed proteins. A threshold of expression greater than 2 times fold- change with \(p< 0.05\) was considered to be significant. Proteins that are related with collagen- ECM pathways are labelled. c. Top 4 significantly enriched GO and Pathways based on their adjusted p- values. d. The most significant enriched GO terms of the biological domain with respect to biological process. e. Heatmap of proteins that are related with collagen- containing extracellular matrix and ossification. F indicates flat samples and P indicates pillar samples, \(n = 3\) biological replicates for each group. f. The linkages of proteins and GO terms in biological process related with collagen fibers, ECM, and ossification as a network. g. Heatmap of top 15 enriched terms plotted based on Reactome pathway analysis.  
+
+## Nuclear deformed cells facilitate osteogenic differentiation of undeformed cells by affecting ECM.  
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+Since the micropillar surfaces can modulate the secrete of hMSCs, we investigated whether the deformed cells could influence the osteogenic differentiation of undeformed cells using a transwell assay (Fig. 4a). The flat and micropillar mPOC/HA surfaces were fabricated at the bottom of cell culture plates to manipulate the nuclear morphology of hMSCs, while undeformed hMSCs were seeded on a transwell membrane with 400 nm nanopores, allowing the exchange of growth factors. After cell attachment, all samples were cultured in osteogenic induction medium. ALP staining of the cells on the transwell membrane showed a higher number of ALP- positive cells when co- cultured with nuclear- deformed cells, indicating enhanced osteogenic differentiation (Fig. 4b,c). Additionally, Alizarin Red S (ARS) staining confirmed increased calcium deposition—a key step in osteogenesis—when the cells were cultured above the micropillar- treated cells (Fig. 4d,e). Based on the secreteome analysis, hMSCs on micropillars appear to promote osteogenesis in the transwell culture by secreting proteins that enhance ECM structure and organization. Collagen staining revealed higher coverage, stronger staining intensity, and more interconnected collagen network structures in the transwell co- cultured with micropillar- treated cells (Fig. 4f,g). In addition, energy dispersive X- ray spectroscopy (EDS) images showed more Ca and P deposition in the transwell co- cultured with micropillar- treated cells (Fig. 4h). Together with the secreteome analysis, these findings suggest that the proteins secreted by cells with deformed nuclei improve ECM organization in undeformed cells, thereby promoting osteogenesis.
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+<center>Figure 4. The paracrine effect of cells with/without nuclear deformation tested through transwell assay. a. Schematic illustration of the experiment setup. b. ALP staining and c. quantification of ALP positive cells on transwell membrane incubated with undeformed and deformed MSCs \((n = 3)\) . d. ARS staining and e. quantification of cells on transwell membrane incubated with undeformed and deformed MSCs \((n = 6)\) . f. Immunofluorescence staining images of collagen in ECM of cells on transwell membrane incubated with undeformed and deformed MSCs. g. The coverage of collagen analyzed according to the staining images \((n = 4)\) . h. EDS images showing Ca, P, and SEM images of cells on transwell membrane incubated with undeformed and deformed MSCs. </center>  
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+## mPOC/HA micropillar implant promotes bone formation in vivo  
+
+To test the in vivo regeneration efficacy of mPOC/HA scaffolds, we created a critical size cranial defect model in nude mice. Two 4 mm diameter critical defects were made on the left and right sides of the skull tissue for the implantation of flat and micropillar scaffolds, respectively (Fig. 5a). The scaffolds were seeded with hMSCs for 24 hours to allow for cell attachment and nuclear deformation (Fig. 5b). After 12 weeks, micro CT was performed to evaluate the bone formation in the living animals. Based on the images, newly formed bone can be observed in the defect area with both flat and micropillar mPOC/HA implants (Fig. 5c and Fig. S11). Comparing this to our previous study using mPOC alone, \(^{13}\) the integration of HA clearly enhanced bone regeneration
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+efficacy in vivo. Furthermore, larger bone segments were observed with the micropillar implant treatment. Quantification results confirmed a significantly increased bone volume with micropillar implant treatment (Fig. 5d).  
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+Histology analysis was further performed to evaluate the influences of flat and micropillar mPOC/HA implants on bone regeneration. Trichrome staining images revealed that defects treated with micropillar implants exhibited more osteoid tissue (Fig. 5e and Fig. S12). Moreover, both flat and micropillar mPOC/HA implants showed evidence of newly formed bone tissue, indicating enhanced bone regeneration compared to the mPOC alone scaffold. As no bone segment was observed with flat mPOC implant treatment. \(^{13}\) The thickness of the regenerated tissue was quantified, and the results demonstrated a significant enhancement with micropillar implant treatment (Fig. 5f). Positive staining of osteogenesis markers, including osteopontin (OPN) and osteocalcin (OCN), was observed throughout the regenerated tissues with both flat and micropillar implants, indicating osteoid tissue formation (Fig. 5g,h). The tissue appeared more compact in the micropillar group compared to the flat group. Furthermore, regenerated bone segments were more frequently observed with micropillar implant treatment.
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+<center>Figure 5. mPOC/HA micropillar implant promotes bone regeneration in vivo. a. Image shows implantation of hMSC seeded flat and micropillar mPOC/HA scaffolds. b. Staining images of nuclei (green) and F-actin (red) of cells on the implants. c. Representative \(\mu \mathrm{CT}\) images of a typical animal implanted with hMSC-seeded flat (left) and micropillar (right) scaffolds at 12-weeks post-surgery. d. Regenerated bone volume in the defect region ( \(\mathrm{n} = 5\) animals). e. Trichrome staining of the defect tissue treated with flat and micropillar implants. f. Average thickness of regenerated tissues with implantation of flat and micropillar scaffolds ( \(\mathrm{n} = 5\) animals). IHC staining of osteogenic marker, g. OPN and h. OCN, in regenerated tissues with flat and micropillar implants. </center>
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+## Micropillar implants facilitated bone regeneration in vivo via regulation of ECM organization and stem cell differentiation.  
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+Histological analyses showed more new bone formation with micropillar implants, although the new bone tissue did not directly interact with the micropillar surfaces. To further investigate the transcription profile of the regenerated tissue, we performed spatial transcriptomics (ST) analyses with both flat and pillar samples (Fig. S13). ST represents a powerful tool to investigate the cellular environment and tissue organization by providing a detailed map of gene expression within the native tissue context. Differential gene expression (DGE) analysis revealed changes in expression levels between the two groups. Although only a few genes showed significant differences, all of them were related to ECM structure or organization (Fig. S13). Notably, the expression of Colla2, critical for type I collagen formation (comprising \(90\%\) of the bone matrix), was enhanced in the micropillar group (Fig. 6a). This expression showed a gradient, increasing toward the dura layer, possibly due to the osteogenic contribution of dura cells. We then plotted a heatmap showing the top 10 up- regulated and down- regulated differentially expressed genes (pillar vs. flat) in comparison with those in native skull bone (Fig. 6b). The heatmap indicated that the tissue regenerated with micropillar implants had expression patterns more similar to native skull bone than the flat group. Gene Ontology (GO) analysis of DGEs was further performed to annotate their relevant biological processes (Fig. 6c). Protein localization to extracellular matrix and crosslinking of collagen fibrils were among the top 5 up- regulated processes in the micropillar group. These results are consistent with the secreteome test, all indicating that micropillar structures can influence ECM organization via paracrine effects.  
+
+To further investigate the relationship between cell type composition and the regenerated tissues, we performed cellular deconvolution on the ST data using single- cell RNA sequencing (scRNA- seq) references from previously published studies. Several major cell lineages involved in bone regeneration were considered when deconvoluting the data (Fig. 6d). The most abundant cell type in regenerated tissues was late mesenchymal progenitor cells (LMPs), followed by MSCs and fibroblasts (Fig. 6e). There were also small proportions of MSC- descendant osteolineage cells (OLCs), osteocytes, osteoblasts, and chondrocytes. LMPs are identified as the late stage of MSCs through osteogenic differentiation. Among all cell types, the proportion of LMPs, which have high expression of marker genes associated with osteoblasts, was significantly increased in regenerated tissues with micropillar implants, indicating that these deformed cells facilitate the differentiation of MSCs toward the osteolineage (Fig. 6f). Additionally, GO analysis of DGEs (LMP versus other cell types) was performed to investigate the roles of LMPs in regenerated tissue. The results suggest that LMPs do not directly contribute to osteogenesis, a role performed by osteoblasts and osteocytes. Instead, LMPs can affect ECM formation, as the process of extracellular matrix organization is one of the top involved pathways (Fig. 6g). Thus, the results indicate that micropillar implants can facilitate skull tissue regeneration by promoting the differentiation of MSCs and ECM organization via paracrine effects.
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+<center>Figure 6. Spatial transcriptomic analysis of tissues regenerated with flat and micropillar implants. a. Spatial plot of Colla2 expression profile in tissues regenerated with flat mPOC/HA implant and micropillar mPOC/HA implant. Arrow indicates enhanced expression around dura layer. b. The heatmap showing the top ten up- and down-regulated DEGs (pillar vs flat) in tissues regenerated with flat mPOC/HA implant, micropillar mPOC/HA implant, and native skull tissue. c. Gene Ontology analysis results based on the top 100 up-regulated genes (pillar vs flat). d. Deconvoluted cell types in each spatial capture location in flat and micropillar groups. Each pie chart shows the deconvoluted cell type proportions of the capture location. e. Bar plots of the cell type proportions in tissues regenerated with flat mPOC/HA implant and micropillar mPOC/HA implant. LMPs, MSCs, and fibroblasts are the predominant cell types. f. Violin plot of the proportion of LMPs in flat and micropillar groups. g. Top enriched processes associated with LMP compared with other cell lineages. LMP: late mesenchymal progenitor cells; MSC: mesenchymal stromal cells; OLC: MSC-descendant osteolineage cells </center>
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+## Discussion  
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+Micropiliars, as a typical topographical feature, have been extensively studied for their ability to regulate cell functions. Recent researches have shown that rigid micropiliars can deform nuclear morphology, which in turn promotes the osteogenic differentiation of mesenchymal stem cells (MSCs), generating significant interest for bone regeneration applications.26,27 Our previous work demonstrated that mPOC micropiliars enhanced bone regeneration in a mouse cranial defect model.13 The mPOC, a citrate- based biomaterial (CBB), is an excellent candidate for bone regeneration because citrate, an important organic component of bone, plays key roles in skeletal development and bone healing by influencing bone matrix formation and the metabolism of bone- related cells.47 In this study, hydroxyapatite (HA) was incorporated into mPOC to further enhance its regenerative potential, leveraging HA's well- known osteoconductive properties.48 Both in vitro and in vivo experiments confirmed that the addition of HA significantly improved bone regeneration compared to mPOC alone.13 Moreover, several products made from CBB/HA composites have recently received FDA clearance, highlighting the promising clinical potential of mPOC/HA micropiliars for bone regeneration applications.49  
+
+Despite recent intensive investigations into nuclear morphogenesis, little is known about its influence on cellular secretion, which can regulate neighboring cells and is critical for regenerative engineering. Previous studies have shown that nuclear mechanotransduction, activated by substrate stiffening or cellular compression, can impact cell secretions.50,51 Here, we found that cells with deformed nuclei exhibited higher expression levels of ECM components and binding proteins that support collagen- enriched ECM organization. Additionally, soluble proteins secreted by these deformed cells were able to diffuse and modulate ECM secretion and organization in neighboring cells, as demonstrated by a transwell assay. The ECM is a complex, dynamic environment with tightly regulated mechanical and biochemical properties that affect essential cell functions, including adhesion, proliferation, and differentiation.52 ECM fiber alignment increases local matrix stiffness, which promotes higher force generation and increases cell stiffness, creating a positive feedback loop between cells and the matrix.53 Furthermore, the organized ECM enhances calcium recruitment and accelerates mineralization, contributing to effective bone regeneration.  
+
+Implantation of the flat and micropillar mPOC/HA scaffolds seeded with MSCs resulted in larger new bone volume formation in vivo compared to previous studies using mPOC alone, a finding likely due to the osteoconductive properties of HA. ST analysis revealed a significant upregulation of genes encoding cartilage oligomeric matrix protein (COMP) and fibromodulin (FMOD) in the micropillar group, consistent with the secreteome analysis. COMP binds to matrix proteins like collagen, enhancing ECM organization and assembly.54 As an ECM protein, COMP also promotes osteogenesis by binding to bone morphogenetic protein 2 (BMP- 2), increasing its local concentration and boosting its biological activity.55 FMOD, with a strong affinity for the HA matrix, helps attenuate osteoclast precursor maturation, thereby influencing osteoblast- osteoclast crosstalk.56 These results suggest that nuclear deformation induced by micropiliars may promote osteogenesis in neighboring cells via matricrine effects.
+
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+
+Despite the enhanced bone regeneration observed, mPOC/HA implants did not achieve complete healing of the cranial defect, likely due to the limited interaction surface of the film scaffold. The influence of the implants, whether through direct chromatin reprogramming guidance or secretome activity, was restricted to cells at the tissue- scaffold interface. Future efforts should focus on the design and fabrication of 3D micropillar implants using additive manufacturing and composite materials to create a more comprehensive 3D cellular microenvironment that promotes bone regeneration. Additionally, the application of micropillars as a platform for delivering bioactive factors could be explored as a strategy to achieve complete cranial bone healing.  
+
+In summary, we investigated the effects of nuclear deformation on the cellular secretome using micropillar implants fabricated from an mPOC/HA composite. The mPOC/HA micropillars demonstrated similar properties to a flat substrate in terms of roughness and degradation but had a substantial impact on cellular and nuclear morphology, cell adhesion, cytoskeletal development, and osteogenic differentiation in hMSCs. Nuclear- deformed cells showed increased secretion of proteins and RNA transcriptions that regulate ECM components and organization, promoting osteogenesis in neighboring cells both in vitro and in vivo. These findings suggest that incorporating microtopography into implants holds significant promise for bone regeneration. This study offers valuable insights for the future design and fabrication of bioactive implants in regenerative engineering.  
+
+## Materials and Methods  
+
+Synthesis and characterization of mPOC pre- polymer.  
+
+The mPOC pre- polymer were synthesized according to a previous report. Briefly, the POC pre- polymer was firstly synthesized by reaction of equal molar of citric acid (Sigma- Aldrich, 251275) and 1,8- octandiol (Sigma- Aldrich, O3303) at \(140^{\circ}\mathrm{C}\) oil bath for 60 min. The product was then purified by precipitation in DI water. After lyophilization, 66g POC pre- polymer was dissolved in 540 ml tetrahydrofuran (THF) and reacted with 0.036 mol imidazole (Sigma- Aldrich, I2399) and 0.4 mol glycidyl methacrylate (Sigma- Aldrich, 151238) at \(60^{\circ}\mathrm{C}\) for 6 h. The final product was then purified by precipitation in DI water and lyophilized for storage at - 20 \(^{\circ}\mathrm{C}\) . Successful synthesis of mPOC pre- polymer was characterized using proton nuclear magnetic resonance (1H- NMR, Bruker A600).  
+
+## Fabrication and characterization of mPOC/HA micropillar scaffolds  
+
+SU- 8 micropillar structures (5x5x8 um) were fabricated according to our previous study. PDMS molds were then fabricated to replicate the invert structures. HA nanoparticles (Sigma- Aldrich, 677418) were mixed with mPOC pre- polymer at weight ratio of 6:4. The \(60\%\) HA was selected to mimic composition of native bone. Photo- initiator (5 mg/ml camphorquinone and ethyl 4- dimethylaminobenzoate) was added to the mPOC/HA slurry. The mixture was then added onto PDMS mold and pressed onto cover glass to prepare free- standing scaffold under exposure with laser (1W, 470 nm). Post- curing of the scaffold was performed in \(80^{\circ}\mathrm{C}\) oven over night. The size of HA nanoparticles was characterized using Dynamic Light Scattering (DLS). The topography of
+
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+
+micropillars was observed using scanning electron microscope (SEM, FEI Quanta 650 ESEM) and characterized using 3D optical microscope (Bruker). Surface roughness of flat and micropillar scaffolds was characterized using atomic force microscope (AFM, Bruker ICON system). The water contact angle was tested using VCA Optima XE system. The compressive modulus of the scaffolds was characterized using a Tribioindenter (Bruker). Based on a previous report, \(^{58}\) the lateral modulus of micropillars was calculated according to the following equations:  
+
+\[k_{L} = \frac{3EI}{L^{3}} (1)\]  
+
+The ' \(\mathrm{kL}\) ' is the lateral stiffness, 'E' is the measured modulus, 'I' is the moment area of inertia, and 'L' is the micropillar height. For square micropillars, 'I' can be described as:  
+
+\[I = \frac{a^{4}}{12} (2)\]  
+
+Where 'a' is the side length of the micropillars. Thus, the lateral modulus of the micropillars ' \(\mathrm{E_{L}}\) ' equals to:  
+
+\[E_{L} = \frac{K_{L}L}{A} (3)\]  
+
+Where 'A' is the cross- section area of micropillars.  
+
+## Degradation and calcium release  
+
+To test the degradation of the mPOC/HA scaffold, the dry weight of mPOC/HA scaffolds at day 0 was recorded as the initial weight. Then the scaffolds were merged in \(1\mathrm{ml}\) DPBS solution in \(75^{\circ}\mathrm{C}\) oven. At each designed time point (1, 2, 3, 5, 7, 10 and 14 d), the scaffolds were rinsed with DI water followed by drying at \(60^{\circ}\mathrm{C}\) . The weight was recorded to calculate the weight loss percentage. The calcium release test was also performed with \(75^{\circ}\mathrm{C}\) DPBS (no calcium, no magnesium). At the designed time points, the elution solution was collected and replaced with fresh DPBS (1 ml). The released calcium was detected with inductively coupled plasma mass spectrometry (ICP- MS, ThermoFisher Element 2). Accumulated calcium release was calculated.  
+
+## Cell culture  
+
+Human mesenchymal stromal cells (hMSCs, PCS- 500- 012) were purchased from the American Type Culture Collection (ATCC) and cultured with the growth medium acquired from ATCC. hMSCs with the passage 4- 6 were seeded onto the flat and micropillar mPOC/HA substrates. To test cell attachment, hMSCs were seeded at 5000 cells/cm \(^2\) and cultured for 3 h followed by PBS rinsing to remove unattached cells. The attached cells were then trypsinized and collected for cell counting. For other experiments, the cells were cultured in growth medium for 24 h to allow cell attachment and spreading followed by incubation with osteogenic induction medium. After 3 d culture, live/dead staining (Thermofisher, L3224), MTT assay (Thermofisher, V13154), and Picogreen assay (Thermofisher, P7589) were performed according to the manufactures' protocol.  
+
+Nuclear morphology analysis
+
+<--- Page Split --->
+
+After one day of culture, the cells were fixed with \(4\%\) paraformaldehyde, and cell nuclei were stained using SYTOX™ Green (ThermoFisher, S7020) according to the manufacture's instruction. The nuclear shape index (NSI) was analyzed to evaluate 2D nuclear deformation. \(^{27}\) The stained cells were then imaged using a confocal microscope (Leica SP8) to acquire their 3D morphology. Cell nuclei were reconstructed using the Fiji ImageJ software (https://imagej.net/Fiji). Cell nuclear volume, surface area, project area, height, and the ratio of surface area to volume were measured using 3D objects counter plugin. More than 30 nuclei from 3 biological replicates were imaged and analyzed to calculate the statistics.  
+
+## Scanning electron microscope  
+
+To visualize cell adhesion on mPOC/HA scaffolds, cells were fixed with \(3\%\) glutaraldehyde (Electron Microscopy Sciences) and rinsed with DI water. Subsequently, the cells underwent dehydration using a series of ethanol concentrations ( \(30\%\) , \(50\%\) , \(70\%\) , \(90\%\) , and \(100\%\) ) for 5 min each, followed by drying using a critical point dryer (Tousimis Samdri) as per the manual. The dehydrated cells were coated with a \(5\mathrm{nm}\) osmium layer and imaged using a scanning electron microscope (SEM, FEI Quanta 650). Captured images were further enhanced for visualization of cellular architecture using Photoshop. Additionally, cells on transwell were imaged using SEM and EDS analysis was performed to evaluate the calcium and phosphate deposition.  
+
+## Osteogenic differentiation  
+
+hMSCs were seeded onto both flat and micropillar mPOC/HA substrates. One- day post- seeding, osteogenic induction medium (Lonza) was applied to prompt the osteogenic differentiation of hMSCs. After 7 days of induction, cells were washed with PBS buffer and fixed with \(4\%\) paraformaldehyde for 10 minutes. Subsequently, the samples were immersed in a solution of 56 mM 2- amino- 2- methyl- 1,3- propanediol (AMP, pH- 9.9), containing \(0.1\%\) naphthol AS- MX phosphate and \(0.1\%\) fast blue RR salt to stain alkaline phosphatase (ALP). Bright- field images were acquired using a Nikon Eclipse TE2000- U inverted microscope. ALP activity was assessed using the ALP assay kit (K422- 500, Biovision) following the provided manual. Briefly, cells cultured in induction medium for 7 days were homogenized using ALP assay buffer. Subsequently, the non- fluorescent substrate 4- Methylumelliferyl phosphate disodium salt (MUP) was mixed with the homogenized samples to generate a fluorescent signal through its cleavage by ALP. Fluorescence intensity was measured using a Cytation 5 imaging reader (BioTek) at \((\mathrm{Ex / Em} = 360 / 440\mathrm{nm})\) . Enzymatic activity was calculated based on the standard curve and normalized to total DNA content, determined by the Quant- iT PicoGreen dsDNA assay (Invitrogen). The expression levels of OCN and RUNX2 were quantified through Western blot analysis. In brief, cell lysis was performed using radioimmunoprecipitation assay (RIPA) buffer. The relative protein quantities were measured using a Cytation 5 imaging reader. Equal amounts of proteins extracted from flat and micropillar samples were loaded onto a NuPAGE 4- 12% Bis- Tris Gel (Invitrogen) and subsequently transferred to nitrocellulose membranes (Bio- rad). Afterward, membranes were blocked with \(5\%\) milk and incubated with primary antibodies (including GAPDH from Abcam, OCN from Cell Signaling, RUNX2 from Santa Cruz) overnight at \(4^{\circ}\mathrm{C}\) with gentle shaking. Following this, secondary antibodies, diluted at a ratio of 1:5000, were applied and incubated with the membranes at room temperature for 1 hour. Protein bands were
+
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+
+visualized using the Azure 600 gel imaging system. The acquired images underwent analysis through the 'Gel Analyzer' tool in ImageJ. The intensity of all target protein bands was initially compared to the corresponding GAPDH, and then normalized against a flat surface, which was set as 1. Statistical calculations were based on three biological replicates.  
+
+Secretome sample preparation: Analysis of secreted proteins is complicated by high concentrations of serum proteins. Our approach reduced initial sample volume to a \(20~\mu \mathrm{l}\) concentrate using a molecular weight cut off filter (50 kDa, Amicon Ultra- 15 centrifugal, Ultracel, Merck). The concentrate above 50KDa was depleted of the most abundant proteins using a High Select HAS / Immunoglobulin Depletion Midi spin column (A36367, Thermo Fisher Scientific), resulting in a filtrate solution (below 50KDa) and a depleted solution per sample. An acetone / TCA (Trichloroacetic acid) protein precipitation was performed on each solution to create protein pellets and an in- solution trypsin digestion was performed on each pellet. \(100~\mu \mathrm{l}\) of re- suspension buffer (8 M urea in \(400~\mathrm{mM}\) ammonium bicarbonate) was added to the pellet and incubated with mixing for 15 minutes. Disulfide bonds were reduced by addition of \(100~\mathrm{mM}\) dithiothreitol and incubated for 45 minutes at \(55~^\circ \mathrm{C}\) . Sulfhydryl groups were alkylated by addition of \(300~\mathrm{mM}\) iodoacetamide and incubated for 45 minutes at \(25~^\circ \mathrm{C}\) shielded from light. Samples were diluted 4- fold with ammonium bicarbonate to reduce the urea concentration below 2 M. Protein digestion was performed by addition of trypsin (MS- grade, Promega) at a 1:50 ratio (enzyme:substrate) and incubated overnight at \(37~^\circ \mathrm{C}\) . Digestion was halted with the addition of \(10\%\) formic acid (FA) to a final concentration of \(0.5\%\) . Peptides were desalted with C18 spin columns (The Nest Group), dried by vacuum centrifugation, and stored at \(- 20~^\circ \mathrm{C}\) . Peptides were resuspended in \(5\%\) ACN (Acetonitrile) / \(0.1\%\) FA for LC- MS analysis. Peptide concentration was quantified using micro BCA (Bicinchoninic acid) protein assay kit (Thermo Scientific, Ref: 23235).  
+
+Proteome sample preparation: Cells were lysed using cell lysis buffer ( \(0.5\%\) SDS, \(50\mathrm{mM}\) Ambi (Ammonium Bicarbonate), \(50\mathrm{mM}\) NaCl (Sodium Chloride), Halt Protease inhibitor). An acetone / TCA protein precipitation was performed on each lysed samples solution to create protein pellets and an in- solution trypsin digestion was performed on each pellet. \(100~\mu \mathrm{l}\) of re- suspension buffer (8 M urea in \(400~\mathrm{mM}\) ammonium bicarbonate) was added to the pellet and incubated with mixing for 15 minutes. Disulfide bonds were reduced by addition of \(100~\mathrm{mM}\) dithiothreitol and incubated for 45 minutes at \(55~^\circ \mathrm{C}\) . Sulfhydryl groups were alkylated by addition of \(300~\mathrm{mM}\) iodoacetamide and incubated for 45 minutes at \(25~^\circ \mathrm{C}\) shielded from light. Samples were diluted 4- fold with ammonium bicarbonate to reduce the urea concentration below 2 M. Protein digestion was performed by addition of trypsin (MS- grade, Promega) at a 1:50 ratio (enzyme:substrate) and incubated overnight at \(37~^\circ \mathrm{C}\) . Digestion was halted with the addition of \(10\%\) formic acid to a final concentration of \(0.5\%\) . Peptides were desalted with C18 spin columns (The Nest Group), dried by vacuum centrifugation, and resuspended in \(5\%\) ACN/ \(0.1\%\) FA for LC- MS analysis. Peptide concentration was quantified using micro BCA Protein Assay Kit (Thermo Scientific, Ref: 23235).  
+
+Liquid Chromatography High Resolution Tandem Mass Spectrometry (LC- HRMS/MS) Analysis: Peptides were analyzed using a Vanquish Neo nano- LC coupled to a Exploris 480 hybrid quadrupole- orbitrap mass spectrometer (Thermo Fisher Scientific, USA). The samples were loaded onto the trap column of \(75\mu \mathrm{m}\) internal diameter (ID) x 2cm length (Acclaim PepMapTM
+
+<--- Page Split --->
+
+100, P/N 164535) and analytical separation was performed using a UHPLC C18 column (15cm length \(\times 75\mu \mathrm{m}\) internal diameter, \(1.7\mu \mathrm{m}\) particle size, Ion Opticks, AUR3- 15075C18). For each run, \(1\mu \mathrm{g}\) of peptide sample was injected. Electrospray ionization was performed using a Nanospray Flex Ion Source (Thermo Fisher, ES071) at a positive static spray voltage of \(2.3\mathrm{kV}\) . Peptides were eluted from the analytical column at a flow rate of \(200\mathrm{nL / min}\) using an increasing organic gradient to separate peptides based on their hydrophobicity. Buffer A was \(0.1\%\) formic acid in Optima LC- MS grade water, and buffer B was \(80\%\) acetonitrile, \(19.9\%\) Optima LC- MS grade water, and \(0.1\%\) formic acid: The method duration was 120 minutes. The mass spectrometer was controlled using Xcalibur and operated in a positive polarity. The full scan (MS1) settings used were: mass range 350- 2000 m/z, RF lens \(60\%\) , orbitrap resolution 120,000, normalized AGC target \(300\%\) , maximum injection time of 25 milliseconds, and a \(5\mathrm{E}^{3}\) intensity threshold. Datadependent acquisition (DDA) by TopN was performed through higher- energy collisional dissociation (HCD) of isolated precursor ions with charges of \(2+\) to \(5+\) inclusive. The MS2 settings were: dynamic exclusion mode duration 30 seconds, mass tolerance 5 ppm (both low and high), 2 second cycle time, isolation window \(1.5\mathrm{m / z}\) , \(30\%\) normalized collision energy, orbitrap resolution 15,000, normalized AGC target \(100\%\) , and maximum injection time of 50 milliseconds.  
+
+Data analysis: Mass spectrometry files (.raw) were converted to Mascot generic format (.mgf) using the Scripps RawConverter program and then analyzed using the Mascot search engine (Matrix Science, version 2.5.1). MS/MS spectra were searched against the SwissProt database of the organism of interest. Search parameters included a fixed modification of cysteine carbamidomethylation, and variable modifications of methionine oxidation, deaminated asparagine and aspartic acid, and acetylated protein N- termini. Two missed tryptic cleavages were permitted. A \(1\%\) false discovery rate (FDR) cutoff was applied at the peptide level. Only proteins with at least two peptides were considered for further study.  
+
+Label- Free Quantification: The samples were acquired on mass spec and the data were searched against a specific database using the MaxQuant application. \(^{59}\) Label- Free Quantification (LFQ) was obtained by LFQ MS1 intensity. The results were filtered with a minimum of 2 unique peptides. Technical replicates were averaged and intensities were Log2 transformed to achieve a normal distribution of the data. Missing values were filtered to keep only proteins quantified in at least 2 samples per group. For statistics, Student t- Test was applied using \(\mathrm{p}< 0.05\) and \(\mathrm{FC} > 2\) to determine which proteins were significantly up- and down- regulated and visualize it by volcano plot. Downstream analyses and visualizations were done using RStudio software (R version 4.3.2, RStudio version 2024.09.0). Principal component analysis (PCA) was done using 'prcomp' R function to visualize a ability of the differential protein expression to distinguish between biological conditions. Heatmap plot was built using 'ComplexHeatmap' R package. GO and Pathways enrichment analysis was done using 'clusterProfiler' R package \(^{60}\) and annotations with adjusted p- values (FDR, Benjamini- Hochberg) \(< 0.05\) were considered significant. Additional packages used include 'org.Hs.eg.db' for human gene annotations and 'enrichplot' for visualization. This analysis considered the entire set of human protein- coding genes as the reference background.
+
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+Transwell assay: The flat and micropillar mPOC/HA surfaces were fabricated in a 24 well plate. The hMSCs were seeded onto the surfaces with 40,000 cells per well. Then a transwell was put in each well and additional hMSCs were seeded inside the transwell (Costar, \(0.4 \mu \mathrm{m}\) polyester membrane) at density of 5,000 cells/ \(\mathrm{cm}^2\) . After cell attachment, osteogenic medium was used to induce osteogenic differentiation of the cells. At 7 days post- induction, the cells on transwell were fixed followed by ALP staining and quantification to investigate the paracrine effect of deformed and undeformed cells on osteogenesis. At 3 weeks post- induction, additional transwells were collected for Alizarin Red S (ARS) staining and quantification to show the calcium deposition influenced by the paracrine effect. At 4 weeks post- induction, the collagen, which is one of the major components in ECM and significantly affected according to the secretome analysis, were stained using anti- collagen antibody (Abcam, ab36064) to investigate the influence of nuclear deformation on ECM organization.  
+
+In vivo implantation: The animal study was approved by the University of Chicago Animal Care and Use Committee following NIH guidance (ACUP#71745). Eight- week- old female athymic nude mice obtained from Harlan Laboratories were used for the study. The animals were housed in a separately air- conditioned cabinet at temperature of \(24 - 26^{\circ}\mathrm{C}\) with 12:12 light:dark cycle. The surgeries were performed according to the previous report61. Briefly, animals were treated with \(2\%\) isoflurane delivered by \(100\% \mathrm{O}_2\) and maintained with \(1 - 1.5\%\) isoflurane for anaesthesia. Two critical- sized defects (4 mm diameter) were created on the left and right side of skull of each animal followed by implantation of hMSCs seeded flat and micropillar scaffolds, respectively. After implantation of scaffolds, a larger mPOC film \((1 \times 1.5 \mathrm{cm}^2)\) was attached to the skull with thrombin/fibrinogen to prevent displacement of implants. Skin tissue was closed with 5- 0 nylon interrupted sutures and removed after 2 weeks. The animals were monitored after anaesthesia hourly until recovery. Buprenorphine \(50 \mu \mathrm{g} \mathrm{kg}^{- 1}\) and meloxicam \(1 \mathrm{mg} \mathrm{kg}^{- 1}\) were used for pain relief.  
+
+Micro- CT: Micro- CT images of cranial were performed on the XCUBE (Molecules NV) by the Integrated Small Animal Imaging Research Resource (iSAIRR) at The University of Chicago. Spiral high- resolution computed tomography acquisitions were performed with an X- ray source of \(50 \mathrm{kVp}\) and \(440 \mu \mathrm{A}\) . Volumetric computed tomography images were reconstructed by applying the iterative image space reconstruction algorithm (ISRA) in a \(400 \times 400 \times 370\) format with voxel dimensions of \(100 \times 100 \times 100 \mu \mathrm{m}^3\) . An Amira software (Thermo Scientific) was used for 3D reconstruction of the skull tissue and to analyse the bone formation in the defect area. Scale bars were used to standardize the images. Defect recovery is defined as \((\mathrm{Vi} - \mathrm{Vd}) / \mathrm{Vi} \times 100\%\) , where Vi and Vd represent defect volume at initial and designed timepoints, respectively.  
+
+Histology analysis: Skull samples were fixed and decalcified in Cal- EX II (Fisher Scientific) for 24 hours, rinsed with PBS, and embedded in paraffin. Tissue sections containing defect sites were cut to \(5 \mu \mathrm{m}\) thickness and stained with H&E and trichrome to assess tissue regeneration. Regenerated tissue thickness was measured using ImageJ, and osteogenesis was evaluated via IHC staining for key osteogenic markers, including OCN and OPN. Mouse skin tissue served as a negative control for all IHC staining.
+
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+
+Spatial transcriptomics: To confirm the RNA quality of each FFPE tissue block, 1- 2 curls (10um thickness each) were used for RNA extraction using Qiagen RNeasy FFPE kit (Qiagen 73504) according to manufactures' protocol. Extracted RNA was examined by Agilent Bioanalyzer RNA pico chip to confirm the \(\mathrm{DV}200 > 30\%\) . Simultaneously, the tissue morphology was examined on HE stained slide to identify region of interest.  
+
+For each FFPE sample, 1 section (5um thickness) was placed on visium slides. Each slide was incubated at \(42^{\circ}\mathrm{C}\) for 3 hours followed by overnight room temperature incubation. Then, the slide was stored at desiccated slide holder until proceeding to deparaffinization.  
+
+The deparaffinization, HE staining and imaging and decrosslinking of tissue slides were performed according to 10x Genomics protocol (CG000409 and CG000407) specific for Visium spatial gene expression for FFPE kit. Then, the slides were proceeded to human probe (v2) hybridization and ligation using 10x Genomics Visium spatial gene expression, \(6.5\mathrm{mm}\) kit (10x Genomics, PN- 1000188). The probes were released from tissue slide and captured on visium slide followed by probe extension. Sequencing libraries were prepared according to manufacturer's protocol. Multiplexed libraries were pooled and sequenced on Novaseq X Plus 10Bflowcell 100 cycles kit with following parameter: 28nt for Read 1 and 90nt for Read 2.  
+
+We visually identified the implant region in each sample. To exclude low quality capture locations, we removed the capture locations with fewer than 500 unique molecular identifiers, fewer than 500 genes, or \(\geq 25\%\) mitochondrial reads. \(^{61}\) We also filtered out the genes that are expressed in fewer than five capture locations. \(^{61}\) After quality control, flat group had 101 capture locations and 12,701 genes, whereas micropillar group had 73 capture locations and 13,371 genes.  
+
+Differential gene expression analysis: To identify the genes differentially expressed in flat and micropillar groups, we performed Wilcoxon rank- sum tests on the merged dataset (174 capture locations) using the FindAllMarkers function in Seurat V3. \(^{62}\) Our testing was limited to the genes present in both implants, detected in a minimum \(1\%\) of cells in either implant, as well as showing at least 0.1 log- fold difference between the two implants.  
+
+Cell type deconvolution: To perform cell typing on our data, we first identified three publicly available bone single- cell RNA sequencing (scRNA- seq) references with annotated cell types. \(^{43 - 45}\) The scRNA- seq references were processed, quality controlled, and merged using Seurat V3. Since our samples are nude mice, we excluded all the immune cells from the merged reference. The final merged scRNA- seq dataset contained a total of 12,717 cells and represented all major cell types present in bone tissues.  
+
+In 10x Visium data, each capture location contains a mixture of cells. \(^{63}\) Therefore, we performed cell type deconvolution to predict the cell type proportions in each capture location using BayesPrism, a Bayesian deconvolution method shown to work on spatial transcriptomics data. \(^{64,65}\) We excluded chromosomes X and Y, ribosomal, and mitochondrial genes from the analysis to reduce batch effects. We also removed the outlier genes with expression greater than \(1\%\) of the total reads in over \(10\%\) of capture locations. To improve cell typing accuracy, we only used the cell type signature genes for deconvolution analysis. The cell type markers were identified based
+
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+
+on the differential expression analysis results on the merged scRNA- seq reference. The predicted cell type proportions with above 0.5 coefficient of variation were clipped to zero to reduce noise.  
+
+Cell- type- based analyses: We performed Wilcoxon rank- sum tests using the deconvoluted cell type proportions to test if certain cell types are more prevalent in one implant than the other. We further examined the association between cell type proportions and gene expression levels in the two implants through Kendall's correlation analyses. All the p- values were adjusted for multiple testing through the false discovery rate approach. The proportions of three cell types (chondrocyte, OLC, and osteocyte) had over 50 significantly positively correlated genes. For each of these cell types, we performed pathway enrichment analysis of the significantly positively correlated genes using Metascape. \(^{66}\)  
+
+Statistical analysis: The results are shown as mean \(\pm\) standard deviation using violin super plots or bar graphs. Statistical analysis was performed using Kyplot software (version 2.0 beta 15). Statistical significance was determined by Student's t- test (flat versus micropillar, two- sided). All experiments presented in the manuscript were repeated at least as two independent experiments with replicates to confirm the results are reproducible.  
+
+## Acknowledgement  
+
+This work was supported by the National Science Foundation (NSF) Emerging Frontiers in Research and Innovation (EFRI) (no. 1830968 to G.A.A.), and National Institutes of Health (NIH) grants U54CA268084 and R01CA228272, NSF grant EFMA- 1830961 (to V.B.). This work was performed as a collaboration between the Center for Advanced Regenerative Engineering (CARE) and the Center for Physical Genomics and Engineering (CPGE) at Northwestern University. This work made use of the EPIC facility, the NUFAB facility, and the BioCryo facility of Northwestern University's NUANCE Center, which has received support from the SHyNE Resource (NSF ECCS- 2025633), the International Institute for Nanotechnology (IIN) and Northwestern's MRSEC programme (NSF DMR- 1720139). Proteomics services were performed by the Northwestern Proteomics Core Facility, generously supported by NCI CCSG P30 CA060553 awarded to the Robert H Lurie Comprehensive Cancer Center, instrumentation award (S10OD025194) from NIH Office of the Director, and the National Resource for Translational and Developmental Proteomics supported by P41 GM108569. We also thank the help from Dr. Hsiu- Ming Tsai at the Department of Radiology, The University of Chicago for microCT imaging. This work also made use of the Northwestern University NUSeq Core and the Biological Imaging Facility (BIF).  
+
+## References  
+
+1 Rippe, K. Dynamic organization of the cell nucleus. Curr. Opin. in Genet. Dev. 17, 373- 380 (2007).  
+2 Kalukula, Y., Stephens, A. D., Lammerding, J. & Gabriele, S. Mechanics and functional consequences of nuclear deformations. Nat. Rev. Mol. Cell Biol. 23, 583- 602 (2022).
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+## Supplementary Files  
+
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+SupplementaryTable1.xlsxSupplementaryTable2.xlsxSupplementaryTable3.xlsxSupplementaryTable4.xlsxSupplementMicrotopographyinducedchangesincellnucleusmorphologyenhanceboneregenerationbymodulatingthecellularsecretome.pdf
+
+<--- Page Split --->

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preprint/preprint__012c200754316231695b8af56a30435ab233e96b827c0370265adf0c1c9cfda9/preprint__012c200754316231695b8af56a30435ab233e96b827c0370265adf0c1c9cfda9.mmd

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+
+# A Transcription Factor Functional Atlas of Germline Development  
+
+Roger Pocock roger.pocock@monash.edu  
+
+Monash University https://orcid.org/0000- 0002- 5515- 3608  
+
+Wei Cao Monash University  
+
+Qi Fan Monash University  
+
+Gemmarie Amparado Monash University  
+
+Dean Begic Monash University  
+
+Rasoul Godini Monash University  
+
+Sandeep Gopal sandeep.gopal@med.lu.se https://orcid.org/0000- 0002- 6706- 6747  
+
+## Resource  
+
+Keywords: Germ line, gametes, transcription factors, RNA interference, Caenorhabditis elegans  
+
+Posted Date: January 26th, 2024  
+
+DOI: https://doi.org/10.21203/rs.3.rs- 3880498/v1  
+
+License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+Additional Declarations: There is NO Competing Interest.  
+
+Version of Record: A version of this preprint was published at Nature Communications on August 11th, 2024. See the published version at https://doi.org/10.1038/s41467- 024- 51212- 0.
+
+<--- Page Split --->
+
+# A Transcription Factor Functional Atlas of Germline Development  
+
+Wei Cao<sup>1#</sup>, Qi Fan<sup>1#</sup>, Gemmarie Amparado<sup>1</sup>, Dean Begic<sup>1</sup>, Rasoul Godini<sup>1</sup>, Sandeep Gopal<sup>1,2\*</sup> and Roger Pocock<sup>1\*</sup>.  
+
+<sup>1</sup>Development and Stem Cells Program, Monash Biomedicine Discovery Institute and Department of Anatomy and Developmental Biology, Monash University, Melbourne, Victoria 3800, Australia.<sup>2</sup>Lund Stem Cell Center, Department of Experimental Medical Science; Lund University; Lund, Sweden.  
+
+<sup>#</sup>Contributed equally to this work.  
+
+<sup>*</sup>Correspondence should be addressed to R.P., W.C. and S.G.  
+
+email: roger.pocock@monash.edu, wei.cao@monash.edu and sandeep.gopal@med.lu.se  
+
+Keywords: Germ line, gametes, transcription factors, RNA interference, Caenorhabditis elegans
+
+<--- Page Split --->
+
+## ABSTRACT  
+
+ABSTRACTFertility requires the faithful proliferation of germ cells and their differentiation into gametes. Controlling these cellular states demands precise timing and expression of gene networks. Transcription factors (TFs) play critical roles in gene expression networks that influence germ cell development. There has, however, been no functional analysis of the entire TF repertoire in controlling in vivo germ cell development. Here, we analyzed germ cell states and germline architecture to systematically investigate the function of 364 germline- expressed TFs in the Caenorhabditis elegans germ line. Using germline- specific knockdown, automated germ cell counting, and high- content analysis of germ cell nuclei and plasma membrane organization, we identify 156 TFs with discrete autonomous germline functions. By identifying TFs that control the germ cell cycle, proliferation, differentiation, germline structure and fertility, we have created an atlas for mechanistic dissection of germ cell behavior and gamete production.
+
+<--- Page Split --->
+
+## INTRODUCTION  
+
+INTRODUCTIONThe production of oocytes and sperm (gametes) from germ cells is required for animal reproductive success. To ensure overall fitness, the balance between germ cell proliferation (self-renewal) and differentiation into gametes must be tightly controlled. Model organism research has previously identified regulatory mechanisms that control gene expression and RNA stability to maintain this balance in germ cell behavior. In the Caenorhabditis elegans model, for example, the PUF- domain RNA binding proteins FBF- 1/2 promote germline stem cell fate, with the mammalian ortholog required for the establishment of female germ cells (Crittenden et al., 2002; Mak et al., 2016). Furthermore, the importance of Notch signaling in establishing and maintaining germline stem cell pools was first discovered in C. elegans (Austin and Kimble, 1987). Thus, the C. elegans germ line is a valuable model for discovering conserved mechanisms governing germ cell behavior and fertility. Here, we map the transcription factor (TF) requirements for germ cell and gamete development in C. elegans.  
+
+The C. elegans germ line originates from a single embryonic blastomere (P4) that divides into two primordial germ cells (Z2 and Z3) (Fukuyama et al., 2006). These embryonic germ cells are arrested at the G2 stage of the cell cycle and are transcriptionally quiescent to preserve germ cell fate and protect against inappropriate expression of somatic genes (Furuhashi et al., 2010; Seydoux et al., 1996). Transcriptional quiescence in these primordial germ cells (PGCs) is maintained by inhibiting RNA Polymerase II (RNA Pol II) and by nucleosome modifications associated with chromatin repression (Batchelder et al., 1999; Furuhashi et al., 2010). After embryos hatch and larvae start to feed, germline transcription is activated, and Z2 and Z3 exit G2 to commence mitosis during larval stage 1 (L1) (Fukuyama et al., 2006). Germ cells continue to proliferate throughout larval development and into adulthood, with meiosis commencing from larval stage 3 (L3) (Hansen et al., 2004). This initial differentiation program generates sperm, that are deposited in the spermatheca, before the germ line switches to producing oocytes in adults (Ellis and Schedl, 2007). The adult germ line is organized in a distal to proximal manner. The distal progenitor zone (PZ) contains stem cells, mitotically dividing germ cells and meiotic S- phase germ cells, which then enter early meiotic prophase (leptotene and zygotene) in the transition zone (TZ). As germ cells move from the TZ they enter meiotic pachytene prior to gametogenesis. As transcription is activated early during larval development, TFs likely play important roles in germ cell transitions and cell behavior during larval and adult development. Indeed, previous studies have identified key TFs that regulate germ cell and gamete development (Chi and Reinke, 2006; Curran et al., 2009; Edwards et al., 2021; Rodriguez- Crespo et al., 2022). However, no
+
+<--- Page Split --->
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+systematic in vivo assessment of TF function in the C. elegans germ line, or in any organism, has been conducted. As a result, we sought to identify the TFs required for C. elegans germline development.  
+
+Of the 875 TFs encoded by the C. elegans genome, we identified 364 TFs that are expressed in the germ line (Cao et al., 2017; Jimeno- Martin et al., 2022; Tzur et al., 2018). As most of these TFs are also expressed in the soma, we dissected their germline functions by exploiting a germline- specific RNA- mediated interference (RNAi) approach (Cao et al., 2017; Serizay et al., 2020; Zou et al., 2019). Following germline- specific RNAi silencing from the L1 larval stage, we performed semi- automated cell counting and high- content analysis of germ line architecture to interrogate a broad spectrum of readouts in adult germ lines. This analysis encompassed the 'distal germ line' where mitotic germ cells, Notch signaling, and early meiotic transition were analyzed; and the 'proximal germ line' where meiotic progression, oocyte/sperm production and germline structure were analyzed. We identified 156 TFs that regulate germ cell development, including 8 TFs that are essential for fertility. Our research provides a TF functional atlas of germ cell development, which is a valuable resource and experimental platform for understanding how specific germ cell fate decisions and gamete production are controlled.
+
+<--- Page Split --->
+
+## RESULTS  
+
+## Transcription Factor Germline Profiling and Genetic Screen Validation  
+
+To identify germline- expressed TFs (GTFs), we extruded wild- type adult hermaphrodite germ lines and performed RNA- sequencing from extracted RNA (Figure 1A and Table S1). Combining our germline transcriptome dataset with previously published germline transcriptome studies identified 364 GTFs (Tables S1- 2) (Cao et al., 2017; Tzur et al., 2018). These GTFs were detected in all three of our RNA sequencing samples, both germ lines analyzed in Tzur et al., or GTFs with \(>10\) counts per million in Cao et al. (Table S2) (Cao et al., 2017; Tzur et al., 2018). Of the 364 GTFs, 150 have been ascribed a broad C. elegans reproduction function (sterility/fertility/brood size) (Table S2), however, the precise germline function of these TFs is not well understood.  
+
+By analyzing published TF expression profiles, we found that most GTFs are also expressed in the soma (Table S2) (Cao et al., 2017; Serizay et al., 2020). Therefore, to circumvent potential somatic effects of TF silencing, we utilized a germline- specific RNA- mediated interference (RNAi) approach to identify germline- specific TF functions (Zou et al., 2019). Animals lacking the RDE- 1 Argonaute are systemically RNAi- deficient (Tabara et al., 1999). In our genetic screen, we utilized a rde- 1(mkc36) indel mutant in which RDE- 1 is re- supplied under the germline- specific sun- 1 promoter and 3' untranslated region (UTR) - thus enabling germline- specific RNAi (Zou et al., 2019). To test the efficacy of this strain for germline- specific RNAi by feeding, we silenced the RNA Polymerase II encoding gene ama- 1 from the first larval stage (L1) for 66 hrs (Timmons and Fire, 1998). sun- 1p::rde- 1; rde- 1(mkc36) animals fed with ama- 1 RNAi bacteria grew to adulthood however their germ lines did not develop, while ama- 1 RNAi wild- type animals arrested as young larvae (Figure S1A). Thus, germline- specific inhibition of transcription prevents germline development without overtly affecting somatic development. As additional proof- of- concept for the germline- specific RNAi approach, we silenced two genes that autonomously control germline development - gld- 1 (encodes an RNA binding protein) and mog- 7 (encodes a splicing factor). Consistent with previous studies, gld- 1 RNAi causes proximal germline tumors (86% of germlines are tumorous; n=51) and mog- 7 RNAi causes spermatogenic germ lines (32% of germlines are spermatogenic; n=66) in sun- 1p::rde- 1; rde- 1(mkc36) animals (Figure S1A- B) (Cao et al., 2021; Kerins et al., 2010; Nayak et al., 2005). Prior to embarking on a large- scale germline- specific screen, we needed to confirm that somatic RNAi was inhibited in sun- 1p::rde- 1; rde- 1(mkc36) animals. We examined somatic phenotypes caused by RNAi silencing of hypodermal (bli- 3), intestinal (elt- 2), and muscle (unc- 112) genes. We found that knockdown of these genes from the
+
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+L1 stage caused robust somatic phenotypes in wild-type but not in sun- 1p::rde- 1; rde- 1(mkc36) animals, as shown previously (Figure S1C) (Kumsta and Hansen, 2012; Zou et al., 2019).  
+
+To enable functional mapping of the 364 GTFs, we obtained RNAi feeding clones: 315 from previously generated RNAi libraries and 49 that we cloned (Table S3) (Jimeno- Martin et al., 2022; Kamath et al., 2003; Rual et al., 2004). All RNAi clones were confirmed by sequencing prior to analysis. We profiled GTF function in the germ line by feeding L1 hermaphrodites with Escherichia coli HT115 bacteria expressing individual TF RNAi clones and imaged and analyzed one- day adult germ lines (Figure 1A). We screened GTF function in hermaphrodite germline development using two parallel approaches for distal and proximal germline analysis (Figure 1A). Distal germline analysis was performed with confocal imaging and semi- automated cell counting of extruded germ lines of the 3xflag::syg- 1; sun- 1p::rde- 1; rde- 1(mkc36) strain that we confirmed exhibits wild- type germ cell number and brood size (Figure 1A and S2). Following GTF RNAi silencing from the L1 stage, we quantified germ cell number in the PZ and TZ by DAPI staining (PZ and TZ size), plus visualized M- phase chromosomes (anti- phospho- histone H3 (pH3) staining) and Notch signaling (SYGL- 1 immunofluorescence) in adults (Figure 1A) (Gopal et al., 2017). For proximal germline analysis, we examined gonad architecture, meiotic cell behavior and gamete development using a transgenic strain co- expressing fluorescent markers targeting chromosomes (mCherry- histone H2B) and plasma membrane (GFP- PI4, 5P2) (Figure 1A and S3). This approach for analyzing the proximal germ line was previously used for a group of 554 essential genes (Green et al., 2011), however, their work analyzed less than 3% of GTFs and used the RNAi soaking method (not germline- specific) from the L4 stage. Thus, our work is distinct to this previous study with regards to genes investigated, phenotypes analyzed and methods/timing of gene silencing.  
+
+## Profiling Transcription Factor Function in the Germ Line  
+
+To determine the in vivo functional contributions of TFs in the germ line, we performed two independent reverse genetic screens (as described above) to study the distal and proximal germ line. The results of the two screens identified 156 TFs that are important for germline development (Tables S4- 6). Among these GTFs, 82 have previously reported germline phenotypes in C. elegans (Table S7). In both screens, silencing of 8 GTFs resulted in immature germ lines that did not generate embryos in one- day adults (Tables S6), detailed analysis of which will be discussed later. Silencing of an additional 10 GTFs resulted in 20- 100% immature germ lines in the proximal screen only, among which 6 also caused distal phenotypes (Table S5). We hypothesize that the
+
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+sterility phenotype of these 10 genes is likely a synthetic effect of the RNAi silencing and transgenic reporters (mCherry- histone H2B and plasma membrane GFP- PI4, 5P2) used in the proximal screen.  
+
+Beyond of the sterility phenotype, we used strict phenotypic criteria to furnish a robust inventory of key GTFs: distal germline analysis ( \(\geq 20\%\) significant change in germ cell number of PZ, TZ, and SYGL- 1+ region compared to control) and proximal germline analysis ( \(\geq 4\) of 8 germlines exhibiting a phenotype). Using these criteria, we found that RNAi silencing of 128 TFs caused germline phenotypes (88 in the distal screen and 52 in the proximal screen), 12 of which caused phenotypes in both screens (Figure 1B- D and Tables S4- 6). We classified TFs that control germline development based on broad DNA- binding domain families and found no obvious overrepresentation of TF family that is functionally required for germline development (Figure 1E). We wondered whether the TFs we identified in our primary screen were previously associated with germ cell/gamete development and reproduction. We thus compared our list of TFs hits to genes associated with the 'reproduction' gene ontology (GO) terms (GO:0019952, GO:0050876) in mice, rats and humans (https://www.flyrnai.org/cgi- bin/DRSC_orthologs.pl). We found that 88 GTFs we identified to regulate C. elegans germline development in our primary screens have mammalian orthologs, of which 43 are associated with germ cell/gamete development and reproduction GO terms (Figure 1 and Table S8).  
+
+## Transcriptional Control of Distal Germ Cell Behavior  
+
+At the distal progenitor zone, maintenance of the germline stem cell (GSC) pool is influenced by interaction between Notch ligands (LAG- 2/APX- 1), expressed on the somatic distal tip cell, and the GLP- 1 Notch receptor expressed on the surface of germ cells (Austin and Kimble, 1987, 1989; Gao and Kimble, 1995; Henderson et al., 1994; Tax et al., 1994). This Notch ligand- receptor interaction promotes GSC self- renewal by regulating the two redundantly acting post- transcriptional regulators SYGL- 1 and LST- 1 (Chen et al., 2020; Ferdous et al., 2023; Haupt et al., 2019; Shin et al., 2017).  
+
+Our primary screen identified 23 GTFs that when silenced have a \(\geq 20\%\) change in progenitor zone (PZ) size (Figure 1 and Table S4). To identify high- confidence regulators of germline development, we performed a secondary screen of these hits in triplicate (Figure 2A and Figure S4). We found that silencing of 11 GTFs robustly repeated our initial findings with significant changes in PZ size (Figure 2A and Figure S4). These factors include two nuclear hormone
+
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+receptors (NHR- 84 and NHR- 146), HSF- 1/HSF1, DAF- 19/RFX, LET- 607/CREB, F54F2.9/DNAJC1, ZK546.5, XND- 1, ZTF- 23, C02F5.12 and C30G4.4. Of these 11 TFs, only HSF- 1 and XND- 1 have known roles in C. elegans germ cell proliferation (Edwards et al., 2021; Mainpal et al., 2015).  
+
+The PZ houses self- renewing stem cells, germ cells undergoing final round of the mitotic cell cycle, and germ cells in meiotic S- phase (Fox and Schedl, 2015). To better understand the cellular and molecular effects underpinning the changes in PZ size, we analyzed the roles of these 11 TFs in Notch signaling and the mitotic cell cycle (Figure 2A- H and Figures S4- 5). First, we used a glp- 1(ar202gf) temperature- sensitive gain- of- function mutant that at the permissive temperature of \(15^{\circ}C\) generates \(\sim 25\%\) more PZ germ cells than wild- type animals (Figure 2B- C). We found that RNAi silencing of hsf- 1 and xnd- 1 decreased the number of PZ germ cells in glp- 1(ar202gf) animals at the permissive temperature (Figure 2B- C). This suggests that these TFs are required for excessive germ cell proliferation in glp- 1(ar202gf) animals. In contrast, RNAi silencing of C02F5.12, C30G4.4, daf- 19, F54F2.9 and ZK546.5 increases PZ size in wild- type animals but not in glp- 1(ar202) gain- of- function animals, suggesting that these genes function in the glp- 1 pathway (Figure 2A- B). Further, glp- 1 is epistatic to let- 607, nhr- 84, ztf- 23 and nhr- 146 as glp- 1(ar202) gain- of- function animals have an increase in PZ size irrespective of silencing these GTFs (Figure 2A- B).  
+
+Maintenance of the GSC pool in the PZ is regulated by two direct GLP- 1 target genes SYGL- 1 and LST- 1 (Chen et al., 2020; Shin et al., 2017). SYGL- 1 and LST- 1 proteins are present in partially overlapping gradients through the GSC pool (Shin et al., 2017). Previous studies showed that overexpression of SYGL- 1 or LST- 1 can expand the GSC pool (Shin et al., 2017) and sygl- 1 RNAi knockdown can reduce the PZ length (Kershner et al., 2014). Therefore, we quantified endogenous 3xFLAG::SYGL- 1 and 3xFLAG::LST- 1 after silencing of the 11 GTFs that control PZ size to determine their potential roles in regulating Notch signaling (Figure 2D- E and Figure S4B- D). We found that RNAi silencing of C02F5.12 and ZK546.5 increased the number of SYGL- 1+ germ cells, consistent with their knockdown leading to an increase in PZ size (Figure 2A, D- E). This implies that C02F5.12 and ZK546.5 inhibit Notch signaling. Given that RNAi silencing of C02F5.12 and ZK546.5 did not affect the glp- 1(gf) increase of PZ size, they may function upstream of GLP- 1. We also found that RNAi silencing of hsf- 1 and xnd- 1 decreased the number of SYGL- 1+ germ cells, consistent with their knockdown to leading to a decrease in PZ size (Figure 2A, D- E). This suggests that HSF- 1 and XND- 1 promote Notch- signaling. Given that HSF- 1 and
+
+<--- Page Split --->
+
+XND- 1 are required for glp- 1(gf) induced increase in PZ size, they likely function downstream of GLP- 1. Interestingly, both HSF- 1 and XND- 1 have ChIP peaks in the promoter regions of sygl- 1 (ENCSR512EIF, ENCSR327NRA), suggesting a direct regulatory relationship with this Notch target gene (Consortium, 2012; Luo et al., 2020). In contrast to the SYGL- 1+ analysis, we did not detect changes in LST- 1+ in the PZ after silencing these 11 GTFs (Figure S4C- D). These data suggest that the change in PZ size following silencing of C02F5.12, ZK546.5, hsf- 1 and xnd- 1 may be due to altered Notch- dependent SYGL- 1 regulation.  
+
+PZ size is also impacted by the mitotic cell cycle that is genetically separable to Notch signaling (Fox and Schedl, 2015). Within the PZ, a cohort of germ cells act as stem cells that self- renew by mitosis or differentiate into gametes (Crittenden et al., 2006; Crittenden et al., 1994). To examine whether any of the 11 GTFs impact progression through the cell cycle, we quantified cell proliferation using 5- ethynyl- 2'- deoxyuridine (EdU) staining (Figure 2F- G) (Fox and Schedl, 2015; Seidel and Kimble, 2015). We silenced each GTF from the L1 stage, fed the resultant adults with EdU- labelled bacteria for 4 hrs or 10 hrs, and then counted EdU+ germ cells (Figure 2F- G). Using these data, we calculated the rate of proliferation as the number of germ cells incorporating EdU per hour (Figure 2F- G). As previously reported, control animals had a proliferation rate of \(\sim 23\) cells/hour (Figure 2F) (Fox and Schedl, 2015). We found that RNAi silencing of C02F5.12, daf- 19 and ZK546.5 increased the germ cell proliferation rate, which may contribute to increases in PZ size observed by increasing number of mitotic cells (Figure 2F- G). Taken together, these results reveal key TFs that control PZ size, likely through fine- tuning Notch signaling, cell cycle dynamics and possibly other mechanisms.  
+
+Antibody staining to detect phosphorylation of histone H3 (pH3) at serine 10 is used to mark mitotic M- phase germ cells (Crittenden et al., 2017; Hsu et al., 2000). No pH3+ cells are observed after proliferative zone cells have entered meiosis (Fox and Schedl, 2015). Therefore, changes in the number of pH3+ germ cells could indicate changes in the total number of mitotic germ cells, or changes in M- phase length with respect to the entire cell cycle. We found that of the 11 GTFs analyzed, only hsf- 1 silencing increased the number of pH3+ germ cells (Figure S5A- B). This suggests that hsf- 1 silencing increases mitotic germ cell number or M- phase length, or potentially accelerates other cell cycle phases. However, further investigation of hsf- 1 functions in mitosis and early meiosis is required to explain the contradictory results of decreased PZ size, decreased SYGL- 1+ cells, and unchanged proliferation rate following hsf- 1 silencing. A previous study found that germline- specific HSF- 1 protein depletion from hatching causes a severe reduction in germ
+
+<--- Page Split --->
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+cells and depletion from the mid- L3 stage causes a decrease in EdU labeled germ cells (Edwards et al., 2021). These contradictory findings suggest the effect of HSF- 1 knockdown on the germ line is dosage or developmental stage dependent. Another method frequently used to assess germline proliferation is the mitotic index (MI), which is the percentage of PZ cells that are M- phase (Crittenden et al., 2023). In addition to hsf- 1, we found silencing of F54F2.9 and nhr- 146 caused changes in MI without changing the absolute number of pH3+ cells (Figure S5A- C). Although silencing of F54F2.9 and nhr- 146 caused changes in PZ size, there were no changes in the number of SYGL- 1+ cells or proliferation rate (Figure 2D and F). Thus, it is possible that these genes are not involved in mitosis, and the change of MI is a result of changes of overall PZ size which also include meiotic M- phase cells.  
+
+Proximal to the PZ, the germline TZ houses a cohort of crescent- shaped nuclei that is characteristic of early meiotic prophase (leptotene and zygotene) germ cells (Hubbard, 2007). We found that some GTFs are also required for normal TZ size (ZK546.5, HSF- 1/HSF1, XND- 1, ZTF- 23 and NHR- 146), suggesting that meiotic prophase progression and/or chromosome pairing may be perturbed following silencing of these GTFs (Figure S5D- E). Interestingly, the changes in TZ size paralleled changes in PZ size – ZK546.5 silencing increased PZ and TZ size; hsf- 1, xnd- 1, ztf- 23 and nhr- 146 silencing decreased PZ and TZ size (Figure 2H). This suggests that when these genes are silenced, germ cell output from the PZ impacts TZ size. Finally, we found that 5 out of the 11 GTFs that are important for distal germ cell behavior also impact the proximal germline. ZK546.5 silencing caused incomplete spermatogenesis (7/8 germ lines), ztf- 23 and C02F5.12 silencing caused changes in the number of single array or budded oocytes, and C30G4.4 and nhr- 146 silencing caused germ cell multinucleation (Table S5). Thus, these GTFs likely play roles in both mitotic and meiotic germ cell behavior. Taken together, our distal germline screen discovered multiple GTFs that control germ cell behavior.  
+
+## Transcriptional Control of Meiotic Cell Behavior and Gamete Development  
+
+As germ cells progress proximally, they mature into sperm during late larval development before committing to the oogenic program. To examine the function of GTFs in these processes we utilized a transgenic strain in which fluorescent markers targeting chromosomes (mCherry- histone H2B) and plasma membrane (GFP- PI4, 5P2) enable gonad architecture to be visualized (Figure 1A and S3) (Essex et al., 2009; Green et al., 2011). We identified GTF functions in the proximal germ line including germline structure (small germ line, narrow rachis), meiosis (multinucleation, meiotic progression and apoptosis), sperm production and behavior
+
+<--- Page Split --->
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+(spermatogenesis, sperm localization), and oocyte production (germ cell expansion, number of oocytes, oocyte budding and vesiculation) (Figure S3 and Table S5). The most prominent phenotype we observed was multinucleation of germ cells and thus we focused our subsequent analysis on this phenotype.  
+
+The C. elegans hermaphrodite gonad is a syncytium with germ cell nuclei partially enclosed by a plasma membrane. Multinucleated germ cells (MNCs) can occur in both wild- type and some mutant animals at varying frequency (Raiders et al., 2018). Persistent MNCs would generate multinuclear oocytes and infertile embryos and are therefore cleared by apoptosis prior to generating gametes. Earlier studies showed that failed germ cell division in the PZ and an unstable rachis can cause multinucleation, while in other cases, including in wild- type animals, MNCs form at specific germline structures called folds (Lan et al., 2019; Raiders et al., 2018). In our primary screen, we identified 26 GTFs that have \(>25\%\) increase in germ lines with MNCs compared to control animals (Table S5- 6). To confirm this result, we selected 10 GTFs with the strongest penetrant phenotypes (DMD- 7/DMRTC1, BAZ- 2/BAZ2A, JUN- 1/JUN, MTER- 4, LIN- 26, MADF- 5, NHR- 40/HNF4, NHR- 114/NR1H3, NHR- 161/PPAR- alpha, and UNC- 62/MEIS) and repeated RNAi silencing in triplicate (Figure 3). We confirmed that silencing of all 10 GTFs increased MNCs in both one- day and two- day adults (Figure 3A- B and Figure S6A- B). Of these TFs, only NHR- 114 had previously been associated with germ cell multinucleation in C. elegans (Gracida and Eckmann, 2013). We used confocal microscopy to analyze the PZ of one- day adults following GTF silencing and detected no MNCs, suggesting that MNC formation is not due to aberrant mitotic division, as shown previously (data not shown) (Lan et al., 2019).  
+
+As part of normal germline development, multinucleate cells are removed by physiological apoptosis to prevent them from potentially generating multipoid oocytes (Gumienny et al., 1999; Raiders et al., 2018). The CED- 3 executioner caspase is essential for physiological germline apoptosis (Gumienny et al., 1999), and thus germ lines lacking ced- 3 contain many MNCs (Raiders et al., 2018). To determine whether regulation of germ cell multinucleation by GTFs is dependent on CED- 3, we silenced the 10 GTFs that inhibit MNC formation in ced- 3 mutant animals. As the ced- 3 gene is linked to the pie- 1p::mCherry::his- 58 transgene that is used to count multinucleation events, we used CRISPR/Cas9 genome editing to generate a 3951bp ced- 3(rp190) deletion allele (Figure S7). These animals exhibit overt persistent apoptotic corpses confirming defective apoptosis (Figure S7B- C). Further, we did not detect apoptosis in the germ lines of ced- 3(rp190) animals, and loss of CED- 3 caused an increase in multinucleated germ cells
+
+<--- Page Split --->
+
+(Figure 3B- D), confirming that apoptosis is involved in the clearance of multinucleated germ cells (Raiders et al., 2018).  
+
+We next counted germline MNCs in ced- 3(rp190) two- day adults after GTF RNAi silencing from the L1 stage (Figure 3E). We found that RNAi silencing of jun- 1, mter- 4, lin- 26, nhr- 40, nhr- 114, nhr- 161, and unc- 62 caused no change in MNC number in the ced- 3(rp190) background, suggesting that silencing these GTFs lead to increase in multinucleated cells by interfering with apoptosis. In contrast, silencing of dmd- 7, baz- 2 and madv- 5 increased MNCs independent of loss of ced- 3, suggesting that these GTFs promote mono- nucleation through mechanisms that are independent of apoptosis. However, caspase- 3 can play roles outside apoptosis (Dehkordi et al., 2022; Eskandari and Eaves, 2022), therefore it is possible that CED- 3 is involved in multinucleation formation through other mechanisms. In addition, we observed that MNCs are primarily located in the distal half of the pachytene region in wild- type and ced- 3(rp190) animals (Figure 3B), suggesting that mechanisms independent of physiological apoptosis can remove/resolve multinucleation.  
+
+A previous study revealed that meiotic germline structure impacts the location and occurrence of multinucleation (Raiders et al., 2018). During proliferation, folds in the germline syncytium are formed and multinucleate cells occur during fold eversion (Raiders et al., 2018). As the size and complexity of germline folds is correlated with multinucleation, we assessed whether the 10 GTFs with high numbers of MNCs regulate germline folding. We used phalloidin to stain the germline actin cytoskeleton after RNAi silencing of these GTFs and measured germline folding by counting the rows and numbers of cells protruding inwards from the single layer of cells that form the tubelike structure in the pachytene region (Figure 3F- G and Figure S6C). We found that RNAi silencing of dmd- 7, baz- 2, mter- 4, lin- 26, nhr- 114 and nhr- 161 caused excess germline folding that may result in the increased multinucleation observed (Figure 3F- G and Figure S6C). Taken together, these data reveal 10 GTFs that act in the germline to maintain uninuclear germ cells. Cell behavior is likely controlled by these TFs through gene networks connected to the apoptotic pathway and germline morphogenesis.  
+
+## Essential TFs for Germline Development  
+
+Our initial distal germline screen identified 8 TFs (CDC- 5L, ZNF- 622, VEP- 1, F23B12.7, F33H1.4, F58G1.2, REPO- 1, and TBP- 1) that when silenced from the L1 stage result in small germ lines that do not generate embryos in one- day adults (Table S4). These TFs are mostly
+
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+uncharacterized but have predicted general functions in controlling gene expression (Table S4). Analysis of broods after silencing these TFs from the L1 stage showed almost complete sterility (Figure 4A). An exception was repo- 1 RNAi, which generated \(\sim 230(\pm 30.47)\) progeny, however most resultant embryos did not hatch (Figure 4A). To investigate when these TFs are required during development, we performed temporal germline analysis by silencing each TF from the L1 stage and examining germline size and morphology at each larval stage through to adult (Figure 4B and S8).  
+
+We found that germline size and morphology were mostly intact during early larval development (L1 through L3) following TF silencing (Figure S8). However, from the L4 stage, TF silencing began to reveal germline defects - silencing of all 8 essential GTFs caused a significant decrease in germline length (Figure 4C). Of note, cdc- 5L and znf- 622 silencing resulted in small germ lines ( \(\sim 50\%\) the length of controls) that contained large nuclei suggesting arrest in the G2 stage of the cell cycle (Figure 4C- D and S8). We continued to monitor germline development for all 8 TF RNAi experiments during the first three days of adulthood to determine the effect on sperm, oocyte, and embryo development (Figure 4E). We found that silencing of some genes prevented or delayed sperm generation (cdc- 5L, znf- 622, vep- 1 and F23B12.7), with all genes defective or delayed in oocyte production and embryogenesis (Figure 4E). In addition, silencing of cdc- 5L, znf- 622, vep- 1 and tbp- 1 caused sterility in three- day adults (Figure 4E). We wondered whether these 8 TFs are continuously required for germline development or perform functions during larval development to enable fertility in adults. We explored this by performing germline- specific RNAi silencing from the mid- L4 stage at which gonad development and spermatogenesis are largely complete (Figure 4F). We found that silencing of repo- 1, F33H1.4, F23B12.7, F58G1.2, tbp- 1 and vep- 1 had similar broods to control animals, albeit with different degrees of embryonic lethality (Figure 4F). In contrast, cdc- 5L and znf- 622 silencing caused approximately \(>50\%\) reduction in brood size (with cdc- 5L RNAi causing \(100\%\) embryonic lethality), suggesting post- developmental roles in germline maintenance (Figure 4F).  
+
+To examine the function of the essential GTFs in the oogenic germline, we quantified distal germ cell number in one- day adults following RNAi from the L4 stage (Figure 4G- J). We identified subtle changes in PZ/TZ size, pH3+ cells, and SYGL- 1+ staining (Figure 4G- J). However, these changes are negligible compared to the detrimental effect of silencing these GTFs from the L1 stage (Figure 4A), suggesting that the germ cell cycle and transition to meiosis are largely intact. Further, the minor changes in distal germ line cell numbers suggest that the brood size decrease
+
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+
+we observed following cdc- 5L and znf- 622 silencing are likely caused by defects in gametogenesis. Taken together, we have identified multiple TFs that control the timing and function of gamete generation and subsequent fertility in C. elegans.
+
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+
+## DISCUSSION  
+
+In this study, we profiled the function of every germline- expressed TF in C. elegans to gain a comprehensive understanding of transcriptional regulation of germline development. To identify autonomous regulatory functions, we first determined that 364 TFs are germline expressed and then depleted their expression specifically in the germ line. We used two independent screening approaches to investigate TF functions from the proliferative germ cell stage to gamete production. Using high- content imaging, semi- automated germ cell counting and cell behavior analysis of \(>6000\) germ lines and \(>3\) million germ cells, we identified 156 TFs that influence germline development, \(>75\%\) of which are undescribed. Furthermore, we identified 8 factors that are essential for fertility. As a result, this study provides a rich resource for mechanistic dissection of TF function in germ cells and potentially new opportunities for manipulating cell fates.  
+
+Previous C. elegans forward and reverse genetic screening approaches identified genes that cause sterility or reduced brood sizes (Argon and Ward, 1980; Hirsh and Vanderslice, 1976; Maeda et al., 2001). Further, a large- scale screen of essential genes used the RNAi soaking method to examine proximal germline development (Green et al., 2011); however, \(< 3\%\) of these genes overlapped with our screen. Whether phenotypes observed in these previous studies result from autonomous germline functions or somatic deficits is, however, unclear. Thus, our use of a germline- specific RNAi approach to survey autonomous TF functions, allows for a more refined and robust evaluation of critical germline regulators.  
+
+Our primary screen identified 8 factors that are essential for fertility: CDC- 5L, ZNF- 622, VEP- 1, F23B12.7, F33H1.4, F58G1.2, REPO- 1, and TBP- 1. Loss of these factors had previously produced sterile/fertility phenotypes in genetic mutants and/or large- scale RNAi screens (Fraser et al., 2000; Kamath et al., 2003; Kerins et al., 2010; Simmer et al., 2003; Sonnichsen et al., 2005; Sun et al., 2011). Our analysis shows that these factors act autonomously and predominantly during larval development to enable germline function as we observed robust sterility when these factors were silenced from hatching, but not when silenced late in development. Major exceptions were silencing of cdc- 5L, repo- 1 and znf- 622 which caused reduced brood sizes and high proportions of dead eggs even when they were silenced late in development. Previous studies have identified conserved germline functions in mammals for some of these factors. Porcine CDC- 5L is required for fertility and oocyte maturation (Liu et al., 2018), which is consistent with the oogenesis defect induced by C. elegans cdc- 5L silencing. In addition, murine BDP1 is required for spermatogonial stem cell gene expression (Sisakhtnezhad and Heshmati, 2018), which may
+
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+be the cause of sterility and delayed sperm development when the BDP1 C. elegans ortholog VEP- 1 is silenced. Further, the zinc- finger protein CTCF is critical for oocyte development in mice through regulation of chromatin architecture (Wan et al., 2008), which may also impact the oocyte developmental defects we observed in the C. elegans ortholog F58G1.2.  
+
+We also identified 128 TFs that control germ cell behavior in the distal germ line, including multiple TFs previously reported. For example, our screen confirmed previous studies identifying a zinc- finger TF LSL- 1 that limits TZ size, and the heat shock factor HSF- 1 and chromatin factor XND- 1 that promote germ cell proliferation in the PZ (Edwards et al., 2021; Mainpal et al., 2015; Rodriguez- Crespo et al., 2022). Our secondary screen focused on the TFs important for controlling PZ size to define whether these factors potentially control Notch signaling and/or mitotic cell cycle - two critical determinants of PZ germ cell behavior. Our analysis identified 5 TFs that limit and 6 TFs that promote PZ size. Key findings from this work suggest that HSF- 1 and XND- 1 are required for excess germ cell proliferation caused by GLP- 1/Notch overexpression. Further, loss of the zinc- finger TFs C02F5.12 and ZK546.5 enhance expression of the GLP- 1 target gene SYGL- 1 and germ cell proliferation rate, suggesting that these genes act in the Notch signaling pathway. Interestingly, C02F5.12 is a paralog of ZIM- 1, ZIM- 2, ZIM- 3, and HIM- 8 that are required for chromosome pairing (Phillips and Dernburg, 2006). However, as with C02F5.12, we found that loss of ZIM- 2 additionally limits PZ size suggesting a pre- meiotic function for these proteins. Further, ZK546.5 also limits size of the TZ - a phenotype it shares with its paralog LSL- 1. Finally, we found that the RFX- type TF DAF- 19 limits the germ cell proliferation in C. elegans. Interaction analysis predicts that DAF- 19 physically interacts with the serine-threonine kinase ATL- 1 and checkpoint kinase CHKR- 1, two kinases that are required for fertility, suggesting a potential mechanism for DAF- 19 function in the germline (Garcia- Muse and Boulton, 2005). A mouse ortholog of DAF- 19, RFX2, is expressed in the testis and required for spermatogenesis (Wu et al., 2016). Thus, future analysis of DAF- 19 functions in sperm development in C. elegans hermaphrodites and males is warranted.  
+
+Meiotic development in the C. elegans germline is predominantly regulated by post- transcriptional networks governed by the redundant GLD- 1, GLD- 2, and SCF<sup>PROM- 1</sup> proteins (Francis et al., 1995; Jantsch et al., 2007; Kadyk and Kimble, 1998; Nayak et al., 2002). However, the pachytene region is transcriptionally active prior to the transcriptional silencing that occurs in oocytes (Gibert et al., 1984; Starck, 1977). Our analysis of TF function in the proximal germline identified functions in spermatogenesis, oogenesis, gonad structure and meiotic progression. However, the most
+
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+common phenotype we observed was increased multinucleation of meiotic cells where silencing of 26 GTFs caused a robust phenotype. We focused on the 10 GTFs with the most penetrant multinucleation phenotype for secondary analysis. Previous studies revealed that folding of the germline structure and apoptosis impacts multinucleation (Raiders et al., 2018). In consensus with this, we found that silencing of 6/10 of these GTFs (DMD- 7, BAZ- 2, MTER- 4, LIN- 26, NHR- 114 and NHR- 161) caused excess folding in the germline syncytium that may contribute to the multinucleation phenotype. Furthermore, we found that increase in multinucleation caused by silencing of 7/10 of these GTFs is dependent on the presence of the apoptotic executioner caspase, CED- 3. Caspase- 3 is a well- known essential apoptosis inducer, but it can also regulate non- apoptotic processes including axonal differentiation, growth, routing, regeneration and degeneration (Dehkordi et al., 2022). Considering that the molecular mechanism of multinucleation formation and removal is unknown, it is challenging to speculate if CED- 3 only controls this event by inducing apoptosis, or it has additional functions.  
+
+Overall, our detailed map of TF function throughout the C. elegans germ line is a valuable resource for mechanistic studies of germ cell and gamete development. Our discovery of multiple TFs with specific functions in the developing germ line suggests that many distinct pathways act to fine- tune gene networks and cell behavior. We anticipate that specific TFs will act downstream of key signaling pathways (e.g., TGF, mTOR, AMPK and insulin signaling) to integrate environmental and genetic cues for optimizing germ cell proliferation, meiotic transition, and progeny production. Together, these transcriptional outputs, when combined, likely provide robustness to the process of delivering the next generation.  
+
+## Limitations of the study  
+
+We wish to highlight two limitations of our study. First, categorization of TFs was based on the presence of DNA binding domains (Jimeno- Martin et al., 2022). However, these factors may also control other aspects of gene expression such as RNA splicing (CDC- 5L) and translation (ZNF- 622), thus care must be taken when planning mechanistic dissection of their function. Second, we found that the transgenic strain we used to analyze the proximal germ line (OD95) identified more TFs that cause a sterility phenotype than the strain used for distal analysis. This OD95 strain expresses two independently integrated transgenes to mark germ cell nuclei and membranes that may cause sensitivity to certain genetic perturbations.
+
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+## Acknowledgements  
+
+AcknowledgementsWe thank members of the Pocock and Gopal laboratories for advice and comments on the manuscript. RNA sequencing and Bioinformatics performed at Monash Micromon Genomics and Monash Bioinformatics Platform. Imaging performed at Monash Microimaging. We thank Nuria Flames for their kind gift of TF RNAi clones. Some strains were provided by the Caenorhabditis Genetics Center (University of Minnesota), which is funded by the NIH Office of Research Infrastructure Programs (P40 OD010440).  
+
+## Funding  
+
+FundingThis work was supported by the following grants: Australian Research Council DE190100174 (SG) and DP200103293 (RP); National Health and Medical Research Council GNT1161439 (S.G.), GNT1105374 (RP), GNT1137645 (RP) and GNT2018825 (RP, WC and QF).  
+
+## Author Contributions  
+
+Conceptualization: RP, SG  Methodology: RP, SG, WC, QF, RG  Investigation: RP, SG, WC, QF, RG, GA, DB  Visualization: RP, SG, WC, QF, RG, GA, DB Funding acquisition: SG, RP  Project administration: RP, SG, WC  Supervision: RP, SG, WC  Writing - original draft: RP  Writing - review & editing: RP, SG, WC, QF, RG, GA, DB  
+
+## Competing interests  
+
+Authors declare that they have no competing interests.  
+
+## Data and materials availability  
+
+All data is available in the main text, supplementary materials, and source data. No accession codes, unique identifiers, or weblinks are in our study and there are no restrictions on data availability. Materials are available upon request from Roger Pocock.
+
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+## FIGURE LEGENDS  
+
+## Figure 1. Phenotypic Profiling of Germline Transcription Factor Function  
+
+(A) Schematic of the C. elegans hermaphrodite germline (left), identification of germline-expressed TFs by RNA sequencing (center), and representative images and phenotypic readouts for high-content distal and proximal germline analysis (right). The distal germ line is labelled with DAPI (nuclei), SYGL-1 (Notch target gene) and pH3 (M-phase chromosomes). PZ = progenitor zone, TZ = transition zone. The proximal germ line is marked with transgenic fluorophores: red = nuclei; green = plasma membrane. Scale bars = 20 μm.  
+
+(B- D) Heatmaps showing phenotypic overview for TF silencing causing distal (B \(\geq 20\%\) change compared to control), proximal (C \(\geq 50\%\) of animals with the phenotype) or distal and proximal germline phenotypes (D). (B) TF family categories (colored boxes); progenitor zone (PZ), transition zone (TZ) and SYGL- \(1^{+}\) region phenotypes shown as percentage change compared to control (blue \(=\) decrease; red \(=\) increase); germline expression of each TF (grey bars \(=\) log2 CPM); mammalian ortholog associated with the reproduction GO term (blue boxes \(=\) No, black boxes \(=\) Yes).  
+
+(C) Labelling as in B, except purple \(=\) proximal phenotype; blue \(=\) no phenotype detected. Phenotypes shown in this figure: small germ line, rachis defect (narrow or wide rachis), meiotic defect (multinucleated cells, abnormal meiotic progression), apoptosis, oocyte defect (delayed expansion at the turn, difference in single-array oocyte number, difference in budded oocyte number, vesiculation), sperm defect (mislocated sperm, incomplete spermatogenesis). (D) Labelling as in B and C. Note: the heatmaps do not include essential GTFs identified in the screens – this analysis is detailed in Figure 4 and Table S4.  
+
+(E) TF family distribution of the 875 C. elegans TFs, 96 TFs that regulate the distal germ line or are essential for germline development, and 52 TFs that regulate the proximal germline. TF families shown in this figure: bZIP = basic leucine zipper domain, HD = homeodomain, HMG = High mobility group box domain, MYB= myeloblastosis viral oncogene homolog, NHR = nuclear hormone receptors, T-box, WH = winged helix, ZF = zinc finger.  
+
+## Figure 2. TF Control of Distal Germ Cell Behavior  
+
+(A) Quantification of nuclei number in the PZ of 3xflag::syg1-1; sun-1p::rde-1; rde-1(mkc36) one-day adults. n = 23-24. Green dashed line = average of the control.
+
+<--- Page Split --->
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+(B and C) Quantification of nuclei number (B) and confocal micrographs (C) of one-day adult germline PZ of sun- 1p::rde- 1; rde- 1(mkc36) treated with control RNAi and glp- 1(ar202); sun- 1p::rde- 1; rde- 1(mkc36) treated with control or experimental RNAi. \(\mathsf{n} = 29 - 34\) . (D and E) Quantification of SYGL- 1+ nuclei number (D) and confocal micrographs of germline PZ (E) of 3xflag::syg- 1; sun- 1p::rde- 1; rde- 1(mkc36) one- day adults. \(\mathsf{n} = 23 - 24\) . Green dashed line \(=\) average of the control (D); yellow dashed line \(=\) PZ/TZ boundary (E - left) and white line \(=\) SYGL- 1 boundary (E - right). (F) Proliferation rates of the C. elegans germ line after GTF RNAi. RNAi was performed from the L1 stage for 66 hrs before commencing EdU labelling, and germ lines collected and imaged after 4 and 10 hrs. The 3xflag::syg- 1; sun- 1p::rde- 1; rde- 1(mkc36) strain was used in this experiment. Results of three independent experiments were shown. \(\mathsf{n} = 9 - 13\) for each experiment. (G) Confocal micrographs showing DAPI and EdU+ nuclei after 4 and 10 hrs of EdU labelling. (H) Heatmap showing the effect of silencing 11 GTF on proliferation rate, mitotic index, and nuclei numbers of PZ, TZ, SYGL- 1+ and pH3+.  
+
+RNAi was performed from the L1 stage. Data was generated from three independent experiments, and results were normalized to respective controls in (A) (B) and (D). \(P\) values assessed comparing to control RNAi by multiple unpaired t- test with no correction for multiple comparison (A, B, D and F). Error bars indicate SEM. Scale bars \(= 20 \mu \mathrm{m}\) .  
+
+## Figure 3. TF Control of Meiotic Germ Cell Behavior  
+
+(A) Quantification of multinucleated germ cells of pie-1p::mCherry::his-58; pie-1p::GFP::PH(PLC1delta1); sun-1p::rde-1; rde-1(mkc36) two-day adults following RNAi from the L1 stage. Data was generated from three independent experiments. \(\mathsf{n} = 30 - 31\) . \(P\) values assessed by one-way ANOVA with no correction for multiple comparison.  
+
+(B) Fluorescence micrographs of wild-type or ced-3(rp190) two-day adult germ lines in the pie-1p::mCherry::his-58; pie-1p::GFP::PH(PLC1delta1); sun-1p::rde-1; rde-1(mkc36) strain. Control RNAi and RNAi of dmd-7 and baz-2 were applied from the L1 stage in wild-type animals. Dash lines = border between the TZ (above) and pachytene region (below) of the germ line. Asterisks = multinucleated cells; triangles = apoptotic cells. Scale bar = 20 μm.  
+
+(C and D) Quantification of apoptotic germ cells (C) and multinucleated germ cells (D) in two- day adults of pie-1p::mCherry::his-58; pie-1p::GFP::PH(PLC1delta1); sun-1p::rde-1; rde-1(mkc36)
+
+<--- Page Split --->
+
+strain (wild- type and ced- 3(rp190) animals). Data generated from three independent experiments. \(\mathsf{n} = 30 - 33\) . \(P\) values assessed by unpaired t- test.  
+
+(E) Quantification of multinucleated germ cells in ced-3(rp190), pie-1p::mCherry::his-58; pie-1p::GFP::PH(PLC1delta1); sun-1p::rde-1; rde-1(mkc36) two-day adults following RNAi from the L1 stage. Data generated from three independent experiments. \(\mathsf{n} = 30\) . \(P\) values assessed by one- way ANOVA with no correction for multiple comparison.  
+
+(F and G) Confocal micrographs of DAPI (white) and phalloidin (pink) staining (F) and quantification of germline folding events in the pachytene region (G) of sun- 1p::rde- 1; rde- 1(mkc36) one- day adults. Images for control RNAi (L4440) and lin- 26 RNAi are shown in F (note - only one plane is shown). Data was generated from four independent experiments. \(\mathsf{n} = 24 - 36\) . Yellow dashed line = border between the TZ (right) and pachytene region (left) of the germ line. \(P\) values assessed by one- way ANOVA with no correction for multiple comparison.  
+
+RNAi was performed from the L1 stage. Error bars indicate SEM. Scale bars = 20 μm.  
+
+## Figure 4. Essential TFs act Late in Germline Development to Control Fertility  
+
+(A) Quantification of hatched larvae and dead eggs (brood size) following germline-specific RNAi from the L1 stage. Data generated from three independent experiments. \(\mathsf{n} = 15 - 18\) .  
+
+(B) Timeline of temporal germline analysis showing the time and respective life stages when germline imaging and analysis were performed following egg-laying on control and RNAi plates (RNAi from the L1 stage).  
+
+(C) Quantification of germline length of pie-1p::mCherry::his-58; sun-1p::rde-1; rde-1(mkc36) animals at the L4 stage following RNAi from the L1 stage. Data was generated from three independent experiments, and results normalized to respective controls. \(\mathsf{n} = 26 - 30\) . \(P\) values assessed by one- way ANOVA with no correction for multiple comparison. Error bars indicate SEM.  
+
+(D) DIC (left) and fluorescent micrographs (right) of pie-1p::mCherry::his-58; sun-1p::rde-1; rde-1(mkc36) germ lines at the L4 stage following control, cdc-5L and znf-622 RNAi. Blue arrow = vulva. Scale bar = 20 μm.  
+
+(E) Heatmap showing the percentage of germ lines producing sperm, oocytes and embryos in day 1, day 2 and day 3 of adulthood following germline-specific RNAi from the L1 stage. For day 2 and day 3 adult analysis, worms were selected from day 1 sterile animals, except for the control group, and incubated for another 48 hrs or 72hrs prior to analysis. Data was generated from three independent experiments. \(\mathsf{n} = 30\) .
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+<--- Page Split --->
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+(F) Quantification of hatched larvae and dead eggs (brood size) following germline-specific RNAi from the L4 stage. Data generated from three independent experiments. \(\mathsf{n} = 17 - 18\) . \(P\) values assessed by one-way ANOVA with no correction for multiple comparison. (G-J) Quantification of nuclei number of PZ (G), TZ (H), pH3+ (I) and SYGL-1+ (J) of 3xflag::syg1- 1; sun-1p::rde- 1; rde- 1(mkc36) one-day adult germ lines following RNAi treatment from the L4 stage. \(\mathsf{n} = 22 - 24\) . \(P\) values assessed by one-way ANOVA with no correction for multiple comparison. Error bars indicate SEM.
+
+<--- Page Split --->
+
+## REFERENCES  
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+
+<--- Page Split --->
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+## Figures  
+
+![](images/Figure_1.jpg)
+
+<center>Figure 1 </center>  
+
+Phenotypic Profiling of Germline Transcription Factor Function
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+<--- Page Split --->
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+(A) Schematic of the C. elegans hermaphrodite germline (left), identification of germline-expressed TFs by RNA sequencing (center), and representative images and phenotypic readouts for high-content distal and proximal germline analysis (right). The distal germ line is labelled with DAPI (nuclei), SYGL-1 (Notch target gene) and pH3 (M-phase chromosomes). PZ = progenitor zone, TZ = transition zone. The proximal germ line is marked with transgenic fluorophores: red = nuclei; green = plasma membrane. Scale bars = 20 μm.  
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+(B- D) Heatmaps showing phenotypic overview for TF silencing causing distal (B \(\geq 20\%\) change compared to control), proximal (C \(\geq 50\%\) of animals with the phenotype) or distal and proximal germline phenotypes (D). (B) TF family categories (colored boxes); progenitor zone (PZ), transition zone (TZ) and SYGL-1+ region phenotypes shown as percentage change compared to control (blue = decrease; red = increase); germline expression of each TF (grey bars = log2 CPM); mammalian ortholog associated with the reproduction GO term (blue boxes = No, black boxes = Yes).  
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+(C) Labelling as in B, except purple = proximal phenotype; blue = no phenotype detected. Phenotypes shown in this figure: small germ line, rachis defect (narrow or wide rachis), meiotic defect (multinucleated cells, abnormal meiotic progression), apoptosis, oocyte defect (delayed expansion at the turn, difference in single-array oocyte number, difference in budded oocyte number, vesiculation), sperm defect (mislocated sperm, incomplete spermatogenesis). (D) Labelling as in B and C. Note: the heatmaps do not include essential GTFs identified in the screens – this analysis is detailed in Figure 4 and Table S4.  
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+(E) TF family distribution of the 875 C. elegans TFs, 96 TFs that regulate the distal germ line or are essential for germline development, and 52 TFs that regulate the proximal germline.  
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+TF families shown in this figure: bZIP = basic leucine zipper domain, HD = homeodomain, HMG = High mobility group box domain, MYB= myeloblastosis viral oncogene homolog, NHR = nuclear hormone receptors, T-box, WH = winged helix, ZF = zinc finger.
+
+<--- Page Split --->
+![](images/Figure_2.jpg)
+
+<center>Figure 2 </center>  
+
+TF Control of Distal Germ Cell Behavior  
+
+(A) Quantification of nuclei number in the PZ of 3xflag::syg1-1; sun-1p::rde-1; rde-1(mkc36) one-day adults. \(\mathrm{n} = 23 - 24\) . Green dashed line = average of the control.
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+<--- Page Split --->
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+(B and C) Quantification of nuclei number (B) and confocal micrographs (C) of one-day adult germline PZ of sun-1p::rde-1; rde-1(mkc36) treated with control RNAi and glp-1(ar202); sun-1p::rde-1; rde-1(mkc36) treated with control or experimental RNAi. \(n = 29 - 34\) .  
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+(D and E) Quantification of SYGL- 1+ nuclei number (D) and confocal micrographs of germline PZ  
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+(E) of 3xflag::syg1- 1; sun-1p::rde- 1; rde- 1(mkc36) one-day adults. \(n = 23 - 24\) . Green dashed line = average of the control (D); yellow dashed line = PZ/TZ boundary (E - left) and white line = SYGL- 1 boundary (E - right).  
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+(F) Proliferation rates of the C. elegans germ line after GTF RNAi. RNAi was performed from the L1 stage for 66 hrs before commencing EdU labelling, and germ lines collected and imaged after 4 and 10 hrs. The 3xflag::syg1- 1; sun-1p::rde- 1; rde- 1(mkc36) strain was used in this experiment. Results of three independent experiments were shown. \(n = 9 - 13\) for each experiment.  
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+(G) Confocal micrographs showing DAPI and EdU+ nuclei after 4 and 10 hrs of EdU labelling.  
+
+(H) Heatmap showing the effect of silencing 11 GTF on proliferation rate, mitotic index, and nuclei numbers of PZ, TZ, SYGL-1+ and pH3+.  
+
+RNAi was performed from the L1 stage. Data was generated from three independent experiments, and results were normalized to respective controls in (A) (B) and (D). P values assessed comparing to control RNAi by multiple unpaired t-test with no correction for multiple comparison (A, B, D and F). Error bars indicate SEM. Scale bars = 20 μm.
+
+<--- Page Split --->
+![](images/Figure_3.jpg)
+
+<center>Figure 3 </center>  
+
+TF Control of Meiotic Germ Cell Behavior  
+
+(A) Quantification of multinucleated germ cells of pie-1p::mCherry::his-58; pie-1p::GFP::PH(PLC1delta1); sun-1p::rde-1; rde-1(mkc36) two-day adults following RNAi from the L1 stage. Data was generated from
+
+<--- Page Split --->
+
+three independent experiments. \(n = 30 - 31\) . P values assessed by one- way ANOVA with no correction for multiple comparison.  
+
+(B) Fluorescence micrographs of wild-type or ced-3(rp190) two-day adult germ lines in the pie-1p::mCherry::his-58; pie-1p::GFP::PH(PLC1delta1); sun-1p::rde-1; rde-1(mkc36) strain. Control RNAi and RNAi of dmd-7 and baz-2 were applied from the L1 stage in wild-type animals. Dash lines = border between the TZ (above) and pachytene region (below) of the germ line. Asterisks = multinucleated cells; triangles = apoptotic cells. Scale bar = 20 μm.  
+
+(C and D) Quantification of apoptotic germ cells (C) and multinucleated germ cells (D) in two- day adults of pie-1p::mCherry::his-58; pie-1p::GFP::PH(PLC1delta1); sun-1p::rde-1; rde-1(mkc36) strain (wild-type and ced-3(rp190) animals). Data generated from three independent experiments. \(n = 30 - 33\) . P values assessed by unpaired t-test.  
+
+(E) Quantification of multinucleated germ cells in ced-3(rp190), pie-1p::mCherry::his-58; pie-1p::GFP::PH(PLC1delta1); sun-1p::rde-1; rde-1(mkc36) two-day adults following RNAi from the L1 stage. Data generated from three independent experiments. \(n = 30\) . P values assessed by one-way ANOVA with no correction for multiple comparison.  
+
+(F and G) Confocal micrographs of DAPI (white) and phalloidin (pink) staining (F) and quantification of germline folding events in the pachytene region (G) of sun-1p::rde-1; rde-1(mkc36) one-day adults. Images for control RNAi (L4440) and lin-26 RNAi are shown in F (note - only one plane is shown). Data was generated from four independent experiments. \(n = 24 - 36\) . Yellow dashed line = border between the TZ (right) and pachytene region (left) of the germ line. P values assessed by one-way ANOVA with no correction for multiple comparison.  
+
+RNAi was performed from the L1 stage. Error bars indicate SEM. Scale bars = 20 μm.
+
+<--- Page Split --->
+![](images/Figure_4.jpg)
+
+<center>Figure 4 </center>  
+
+Essential TFs act Late in Germline Development to Control Fertility  
+
+(A) Quantification of hatched larvae and dead eggs (brood size) following germline-specific RNAi from the L1 stage. Data generated from three independent experiments. \(n = 15 - 18\) .
+
+<--- Page Split --->
+
+(B) Timeline of temporal germline analysis showing the time and respective life stages when germline imaging and analysis were performed following egg-laying on control and RNAi plates (RNAi from the L1 stage).  
+
+(C) Quantification of germline length of pie-1p::mCherry::his-58; sun-1p::rde-1; rde-1(mkc36) animals at the L4 stage following RNAi from the L1 stage. Data was generated from three independent experiments, and results normalized to respective controls. \(\mathrm{n} = 26 - 30\) . P values assessed by one-way ANOVA with no correction for multiple comparison. Error bars indicate SEM.  
+
+(D) DIC (left) and fluorescent micrographs (right) of pie-1p::mCherry::his-58; sun-1p::rde-1; rde-1(mkc36) germ lines at the L4 stage following control, cdc-5L and znf-622 RNAi. Blue arrow = vulva. Scale bar = 20 μm.  
+
+(E) Heatmap showing the percentage of germ lines producing sperm, oocytes and embryos in day 1, day 2 and day 3 of adulthood following germline-specific RNAi from the L1 stage. For day 2 and day 3 adult analysis, worms were selected from day 1 sterile animals, except for the control group, and incubated for another 48 hrs or 72hrs prior to analysis. Data was generated from three independent experiments. \(\mathrm{n} = 30\) .  
+
+(F) Quantification of hatched larvae and dead eggs (brood size) following germline-specific RNAi from the L4 stage. Data generated from three independent experiments. \(\mathrm{n} = 17 - 18\) . P values assessed by one-way ANOVA with no correction for multiple comparison.  
+
+(G-J) Quantification of nuclei number of PZ (G), TZ (H), pH3+ (I) and SYGL- 1+ (J) of 3xflag::syg1- 1; sun- 1p::rde- 1; rde- 1(mkc36) one-day adult germ lines following RNAi treatment from the L4 stage. \(\mathrm{n} = 22 - 24\) .  
+
+P values assessed by one-way ANOVA with no correction for multiple comparison. Error bars indicate SEM.  
+
+## Supplementary Files  
+
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+- FigureS1.pdf- FigureS2.pdf- FigureS3.pdf- FigureS4.pdf- FigureS5.pdf- FigureS6.pdf- FigureS7.pdf- FigureS8.pdf
+
+<--- Page Split --->
+
+- TableS1.Germlinetranscriptomeanalysis.xlsx- TableS2WormGTFexpressionandreproductivefunctions.xlsx- TableS3RNAiplasmidusedinthisstudy.xlsx- TableS4Distalgermlineanalysis.xlsx- TableS5ProximalgermlineanalysisV6.xlsx- TableS6156GTFswithgermlinefunctions.xlsx- TableS7PreviouslyreportedphenotypesinC.elegans.xlsx- TableS8GTFfunctionsinotherorganismsGOanalysis.xlsx- TableS9Strainsusedinthisstudy.xlsx- TableS10Oligosusedinthisstudy.xlsx- TableS11Sourcedata.xlsx
+
+<--- Page Split --->

preprint/preprint__012c200754316231695b8af56a30435ab233e96b827c0370265adf0c1c9cfda9/preprint__012c200754316231695b8af56a30435ab233e96b827c0370265adf0c1c9cfda9_det.mmd

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+<|ref|>title<|/ref|><|det|>[[44, 108, 935, 175]]<|/det|>
+# A Transcription Factor Functional Atlas of Germline Development  
+
+<|ref|>text<|/ref|><|det|>[[44, 196, 310, 241]]<|/det|>
+Roger Pocock roger.pocock@monash.edu  
+
+<|ref|>text<|/ref|><|det|>[[44, 268, 580, 288]]<|/det|>
+Monash University https://orcid.org/0000- 0002- 5515- 3608  
+
+<|ref|>text<|/ref|><|det|>[[44, 293, 220, 333]]<|/det|>
+Wei Cao Monash University  
+
+<|ref|>text<|/ref|><|det|>[[44, 339, 228, 378]]<|/det|>
+Qi Fan Monash University  
+
+<|ref|>text<|/ref|><|det|>[[44, 384, 232, 424]]<|/det|>
+Gemmarie Amparado Monash University  
+
+<|ref|>text<|/ref|><|det|>[[44, 430, 220, 470]]<|/det|>
+Dean Begic Monash University  
+
+<|ref|>text<|/ref|><|det|>[[44, 476, 220, 516]]<|/det|>
+Rasoul Godini Monash University  
+
+<|ref|>text<|/ref|><|det|>[[44, 522, 180, 562]]<|/det|>
+Sandeep Gopal sandeep.gopal@med.lu.se https://orcid.org/0000- 0002- 6706- 6747  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 608, 128, 625]]<|/det|>
+## Resource  
+
+<|ref|>text<|/ref|><|det|>[[44, 645, 866, 666]]<|/det|>
+Keywords: Germ line, gametes, transcription factors, RNA interference, Caenorhabditis elegans  
+
+<|ref|>text<|/ref|><|det|>[[44, 683, 330, 703]]<|/det|>
+Posted Date: January 26th, 2024  
+
+<|ref|>text<|/ref|><|det|>[[44, 721, 475, 741]]<|/det|>
+DOI: https://doi.org/10.21203/rs.3.rs- 3880498/v1  
+
+<|ref|>text<|/ref|><|det|>[[44, 758, 914, 801]]<|/det|>
+License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+<|ref|>text<|/ref|><|det|>[[44, 819, 534, 839]]<|/det|>
+Additional Declarations: There is NO Competing Interest.  
+
+<|ref|>text<|/ref|><|det|>[[42, 874, 936, 918]]<|/det|>
+Version of Record: A version of this preprint was published at Nature Communications on August 11th, 2024. See the published version at https://doi.org/10.1038/s41467- 024- 51212- 0.
+
+<--- Page Split --->
+<|ref|>title<|/ref|><|det|>[[183, 83, 819, 102]]<|/det|>
+# A Transcription Factor Functional Atlas of Germline Development  
+
+<|ref|>text<|/ref|><|det|>[[70, 278, 932, 331]]<|/det|>
+Wei Cao<sup>1#</sup>, Qi Fan<sup>1#</sup>, Gemmarie Amparado<sup>1</sup>, Dean Begic<sup>1</sup>, Rasoul Godini<sup>1</sup>, Sandeep Gopal<sup>1,2\*</sup> and Roger Pocock<sup>1\*</sup>.  
+
+<|ref|>text<|/ref|><|det|>[[70, 377, 935, 496]]<|/det|>
+<sup>1</sup>Development and Stem Cells Program, Monash Biomedicine Discovery Institute and Department of Anatomy and Developmental Biology, Monash University, Melbourne, Victoria 3800, Australia.<sup>2</sup>Lund Stem Cell Center, Department of Experimental Medical Science; Lund University; Lund, Sweden.  
+
+<|ref|>text<|/ref|><|det|>[[70, 575, 368, 593]]<|/det|>
+<sup>#</sup>Contributed equally to this work.  
+
+<|ref|>text<|/ref|><|det|>[[70, 608, 630, 626]]<|/det|>
+<sup>*</sup>Correspondence should be addressed to R.P., W.C. and S.G.  
+
+<|ref|>text<|/ref|><|det|>[[70, 642, 880, 660]]<|/det|>
+email: roger.pocock@monash.edu, wei.cao@monash.edu and sandeep.gopal@med.lu.se  
+
+<|ref|>text<|/ref|><|det|>[[70, 836, 928, 856]]<|/det|>
+Keywords: Germ line, gametes, transcription factors, RNA interference, Caenorhabditis elegans
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[71, 84, 185, 101]]<|/det|>
+## ABSTRACT  
+
+<|ref|>text<|/ref|><|det|>[[69, 107, 888, 398]]<|/det|>
+ABSTRACTFertility requires the faithful proliferation of germ cells and their differentiation into gametes. Controlling these cellular states demands precise timing and expression of gene networks. Transcription factors (TFs) play critical roles in gene expression networks that influence germ cell development. There has, however, been no functional analysis of the entire TF repertoire in controlling in vivo germ cell development. Here, we analyzed germ cell states and germline architecture to systematically investigate the function of 364 germline- expressed TFs in the Caenorhabditis elegans germ line. Using germline- specific knockdown, automated germ cell counting, and high- content analysis of germ cell nuclei and plasma membrane organization, we identify 156 TFs with discrete autonomous germline functions. By identifying TFs that control the germ cell cycle, proliferation, differentiation, germline structure and fertility, we have created an atlas for mechanistic dissection of germ cell behavior and gamete production.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[71, 84, 228, 101]]<|/det|>
+## INTRODUCTION  
+
+<|ref|>text<|/ref|><|det|>[[70, 108, 935, 398]]<|/det|>
+INTRODUCTIONThe production of oocytes and sperm (gametes) from germ cells is required for animal reproductive success. To ensure overall fitness, the balance between germ cell proliferation (self-renewal) and differentiation into gametes must be tightly controlled. Model organism research has previously identified regulatory mechanisms that control gene expression and RNA stability to maintain this balance in germ cell behavior. In the Caenorhabditis elegans model, for example, the PUF- domain RNA binding proteins FBF- 1/2 promote germline stem cell fate, with the mammalian ortholog required for the establishment of female germ cells (Crittenden et al., 2002; Mak et al., 2016). Furthermore, the importance of Notch signaling in establishing and maintaining germline stem cell pools was first discovered in C. elegans (Austin and Kimble, 1987). Thus, the C. elegans germ line is a valuable model for discovering conserved mechanisms governing germ cell behavior and fertility. Here, we map the transcription factor (TF) requirements for germ cell and gamete development in C. elegans.  
+
+<|ref|>text<|/ref|><|det|>[[68, 424, 936, 916]]<|/det|>
+The C. elegans germ line originates from a single embryonic blastomere (P4) that divides into two primordial germ cells (Z2 and Z3) (Fukuyama et al., 2006). These embryonic germ cells are arrested at the G2 stage of the cell cycle and are transcriptionally quiescent to preserve germ cell fate and protect against inappropriate expression of somatic genes (Furuhashi et al., 2010; Seydoux et al., 1996). Transcriptional quiescence in these primordial germ cells (PGCs) is maintained by inhibiting RNA Polymerase II (RNA Pol II) and by nucleosome modifications associated with chromatin repression (Batchelder et al., 1999; Furuhashi et al., 2010). After embryos hatch and larvae start to feed, germline transcription is activated, and Z2 and Z3 exit G2 to commence mitosis during larval stage 1 (L1) (Fukuyama et al., 2006). Germ cells continue to proliferate throughout larval development and into adulthood, with meiosis commencing from larval stage 3 (L3) (Hansen et al., 2004). This initial differentiation program generates sperm, that are deposited in the spermatheca, before the germ line switches to producing oocytes in adults (Ellis and Schedl, 2007). The adult germ line is organized in a distal to proximal manner. The distal progenitor zone (PZ) contains stem cells, mitotically dividing germ cells and meiotic S- phase germ cells, which then enter early meiotic prophase (leptotene and zygotene) in the transition zone (TZ). As germ cells move from the TZ they enter meiotic pachytene prior to gametogenesis. As transcription is activated early during larval development, TFs likely play important roles in germ cell transitions and cell behavior during larval and adult development. Indeed, previous studies have identified key TFs that regulate germ cell and gamete development (Chi and Reinke, 2006; Curran et al., 2009; Edwards et al., 2021; Rodriguez- Crespo et al., 2022). However, no
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[70, 82, 934, 152]]<|/det|>
+systematic in vivo assessment of TF function in the C. elegans germ line, or in any organism, has been conducted. As a result, we sought to identify the TFs required for C. elegans germline development.  
+
+<|ref|>text<|/ref|><|det|>[[68, 181, 936, 497]]<|/det|>
+Of the 875 TFs encoded by the C. elegans genome, we identified 364 TFs that are expressed in the germ line (Cao et al., 2017; Jimeno- Martin et al., 2022; Tzur et al., 2018). As most of these TFs are also expressed in the soma, we dissected their germline functions by exploiting a germline- specific RNA- mediated interference (RNAi) approach (Cao et al., 2017; Serizay et al., 2020; Zou et al., 2019). Following germline- specific RNAi silencing from the L1 larval stage, we performed semi- automated cell counting and high- content analysis of germ line architecture to interrogate a broad spectrum of readouts in adult germ lines. This analysis encompassed the 'distal germ line' where mitotic germ cells, Notch signaling, and early meiotic transition were analyzed; and the 'proximal germ line' where meiotic progression, oocyte/sperm production and germline structure were analyzed. We identified 156 TFs that regulate germ cell development, including 8 TFs that are essential for fertility. Our research provides a TF functional atlas of germ cell development, which is a valuable resource and experimental platform for understanding how specific germ cell fate decisions and gamete production are controlled.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[72, 84, 168, 101]]<|/det|>
+## RESULTS  
+
+<|ref|>sub_title<|/ref|><|det|>[[72, 108, 747, 127]]<|/det|>
+## Transcription Factor Germline Profiling and Genetic Screen Validation  
+
+<|ref|>text<|/ref|><|det|>[[70, 133, 936, 325]]<|/det|>
+To identify germline- expressed TFs (GTFs), we extruded wild- type adult hermaphrodite germ lines and performed RNA- sequencing from extracted RNA (Figure 1A and Table S1). Combining our germline transcriptome dataset with previously published germline transcriptome studies identified 364 GTFs (Tables S1- 2) (Cao et al., 2017; Tzur et al., 2018). These GTFs were detected in all three of our RNA sequencing samples, both germ lines analyzed in Tzur et al., or GTFs with \(>10\) counts per million in Cao et al. (Table S2) (Cao et al., 2017; Tzur et al., 2018). Of the 364 GTFs, 150 have been ascribed a broad C. elegans reproduction function (sterility/fertility/brood size) (Table S2), however, the precise germline function of these TFs is not well understood.  
+
+<|ref|>text<|/ref|><|det|>[[68, 350, 936, 892]]<|/det|>
+By analyzing published TF expression profiles, we found that most GTFs are also expressed in the soma (Table S2) (Cao et al., 2017; Serizay et al., 2020). Therefore, to circumvent potential somatic effects of TF silencing, we utilized a germline- specific RNA- mediated interference (RNAi) approach to identify germline- specific TF functions (Zou et al., 2019). Animals lacking the RDE- 1 Argonaute are systemically RNAi- deficient (Tabara et al., 1999). In our genetic screen, we utilized a rde- 1(mkc36) indel mutant in which RDE- 1 is re- supplied under the germline- specific sun- 1 promoter and 3' untranslated region (UTR) - thus enabling germline- specific RNAi (Zou et al., 2019). To test the efficacy of this strain for germline- specific RNAi by feeding, we silenced the RNA Polymerase II encoding gene ama- 1 from the first larval stage (L1) for 66 hrs (Timmons and Fire, 1998). sun- 1p::rde- 1; rde- 1(mkc36) animals fed with ama- 1 RNAi bacteria grew to adulthood however their germ lines did not develop, while ama- 1 RNAi wild- type animals arrested as young larvae (Figure S1A). Thus, germline- specific inhibition of transcription prevents germline development without overtly affecting somatic development. As additional proof- of- concept for the germline- specific RNAi approach, we silenced two genes that autonomously control germline development - gld- 1 (encodes an RNA binding protein) and mog- 7 (encodes a splicing factor). Consistent with previous studies, gld- 1 RNAi causes proximal germline tumors (86% of germlines are tumorous; n=51) and mog- 7 RNAi causes spermatogenic germ lines (32% of germlines are spermatogenic; n=66) in sun- 1p::rde- 1; rde- 1(mkc36) animals (Figure S1A- B) (Cao et al., 2021; Kerins et al., 2010; Nayak et al., 2005). Prior to embarking on a large- scale germline- specific screen, we needed to confirm that somatic RNAi was inhibited in sun- 1p::rde- 1; rde- 1(mkc36) animals. We examined somatic phenotypes caused by RNAi silencing of hypodermal (bli- 3), intestinal (elt- 2), and muscle (unc- 112) genes. We found that knockdown of these genes from the
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[70, 83, 933, 128]]<|/det|>
+L1 stage caused robust somatic phenotypes in wild-type but not in sun- 1p::rde- 1; rde- 1(mkc36) animals, as shown previously (Figure S1C) (Kumsta and Hansen, 2012; Zou et al., 2019).  
+
+<|ref|>text<|/ref|><|det|>[[68, 153, 936, 670]]<|/det|>
+To enable functional mapping of the 364 GTFs, we obtained RNAi feeding clones: 315 from previously generated RNAi libraries and 49 that we cloned (Table S3) (Jimeno- Martin et al., 2022; Kamath et al., 2003; Rual et al., 2004). All RNAi clones were confirmed by sequencing prior to analysis. We profiled GTF function in the germ line by feeding L1 hermaphrodites with Escherichia coli HT115 bacteria expressing individual TF RNAi clones and imaged and analyzed one- day adult germ lines (Figure 1A). We screened GTF function in hermaphrodite germline development using two parallel approaches for distal and proximal germline analysis (Figure 1A). Distal germline analysis was performed with confocal imaging and semi- automated cell counting of extruded germ lines of the 3xflag::syg- 1; sun- 1p::rde- 1; rde- 1(mkc36) strain that we confirmed exhibits wild- type germ cell number and brood size (Figure 1A and S2). Following GTF RNAi silencing from the L1 stage, we quantified germ cell number in the PZ and TZ by DAPI staining (PZ and TZ size), plus visualized M- phase chromosomes (anti- phospho- histone H3 (pH3) staining) and Notch signaling (SYGL- 1 immunofluorescence) in adults (Figure 1A) (Gopal et al., 2017). For proximal germline analysis, we examined gonad architecture, meiotic cell behavior and gamete development using a transgenic strain co- expressing fluorescent markers targeting chromosomes (mCherry- histone H2B) and plasma membrane (GFP- PI4, 5P2) (Figure 1A and S3). This approach for analyzing the proximal germ line was previously used for a group of 554 essential genes (Green et al., 2011), however, their work analyzed less than 3% of GTFs and used the RNAi soaking method (not germline- specific) from the L4 stage. Thus, our work is distinct to this previous study with regards to genes investigated, phenotypes analyzed and methods/timing of gene silencing.  
+
+<|ref|>sub_title<|/ref|><|det|>[[70, 698, 614, 717]]<|/det|>
+## Profiling Transcription Factor Function in the Germ Line  
+
+<|ref|>text<|/ref|><|det|>[[70, 722, 936, 914]]<|/det|>
+To determine the in vivo functional contributions of TFs in the germ line, we performed two independent reverse genetic screens (as described above) to study the distal and proximal germ line. The results of the two screens identified 156 TFs that are important for germline development (Tables S4- 6). Among these GTFs, 82 have previously reported germline phenotypes in C. elegans (Table S7). In both screens, silencing of 8 GTFs resulted in immature germ lines that did not generate embryos in one- day adults (Tables S6), detailed analysis of which will be discussed later. Silencing of an additional 10 GTFs resulted in 20- 100% immature germ lines in the proximal screen only, among which 6 also caused distal phenotypes (Table S5). We hypothesize that the
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[70, 82, 934, 152]]<|/det|>
+sterility phenotype of these 10 genes is likely a synthetic effect of the RNAi silencing and transgenic reporters (mCherry- histone H2B and plasma membrane GFP- PI4, 5P2) used in the proximal screen.  
+
+<|ref|>text<|/ref|><|det|>[[68, 181, 935, 546]]<|/det|>
+Beyond of the sterility phenotype, we used strict phenotypic criteria to furnish a robust inventory of key GTFs: distal germline analysis ( \(\geq 20\%\) significant change in germ cell number of PZ, TZ, and SYGL- 1+ region compared to control) and proximal germline analysis ( \(\geq 4\) of 8 germlines exhibiting a phenotype). Using these criteria, we found that RNAi silencing of 128 TFs caused germline phenotypes (88 in the distal screen and 52 in the proximal screen), 12 of which caused phenotypes in both screens (Figure 1B- D and Tables S4- 6). We classified TFs that control germline development based on broad DNA- binding domain families and found no obvious overrepresentation of TF family that is functionally required for germline development (Figure 1E). We wondered whether the TFs we identified in our primary screen were previously associated with germ cell/gamete development and reproduction. We thus compared our list of TFs hits to genes associated with the 'reproduction' gene ontology (GO) terms (GO:0019952, GO:0050876) in mice, rats and humans (https://www.flyrnai.org/cgi- bin/DRSC_orthologs.pl). We found that 88 GTFs we identified to regulate C. elegans germline development in our primary screens have mammalian orthologs, of which 43 are associated with germ cell/gamete development and reproduction GO terms (Figure 1 and Table S8).  
+
+<|ref|>sub_title<|/ref|><|det|>[[70, 574, 576, 594]]<|/det|>
+## Transcriptional Control of Distal Germ Cell Behavior  
+
+<|ref|>text<|/ref|><|det|>[[70, 599, 936, 766]]<|/det|>
+At the distal progenitor zone, maintenance of the germline stem cell (GSC) pool is influenced by interaction between Notch ligands (LAG- 2/APX- 1), expressed on the somatic distal tip cell, and the GLP- 1 Notch receptor expressed on the surface of germ cells (Austin and Kimble, 1987, 1989; Gao and Kimble, 1995; Henderson et al., 1994; Tax et al., 1994). This Notch ligand- receptor interaction promotes GSC self- renewal by regulating the two redundantly acting post- transcriptional regulators SYGL- 1 and LST- 1 (Chen et al., 2020; Ferdous et al., 2023; Haupt et al., 2019; Shin et al., 2017).  
+
+<|ref|>text<|/ref|><|det|>[[70, 795, 935, 914]]<|/det|>
+Our primary screen identified 23 GTFs that when silenced have a \(\geq 20\%\) change in progenitor zone (PZ) size (Figure 1 and Table S4). To identify high- confidence regulators of germline development, we performed a secondary screen of these hits in triplicate (Figure 2A and Figure S4). We found that silencing of 11 GTFs robustly repeated our initial findings with significant changes in PZ size (Figure 2A and Figure S4). These factors include two nuclear hormone
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[70, 82, 934, 177]]<|/det|>
+receptors (NHR- 84 and NHR- 146), HSF- 1/HSF1, DAF- 19/RFX, LET- 607/CREB, F54F2.9/DNAJC1, ZK546.5, XND- 1, ZTF- 23, C02F5.12 and C30G4.4. Of these 11 TFs, only HSF- 1 and XND- 1 have known roles in C. elegans germ cell proliferation (Edwards et al., 2021; Mainpal et al., 2015).  
+
+<|ref|>text<|/ref|><|det|>[[69, 205, 936, 546]]<|/det|>
+The PZ houses self- renewing stem cells, germ cells undergoing final round of the mitotic cell cycle, and germ cells in meiotic S- phase (Fox and Schedl, 2015). To better understand the cellular and molecular effects underpinning the changes in PZ size, we analyzed the roles of these 11 TFs in Notch signaling and the mitotic cell cycle (Figure 2A- H and Figures S4- 5). First, we used a glp- 1(ar202gf) temperature- sensitive gain- of- function mutant that at the permissive temperature of \(15^{\circ}C\) generates \(\sim 25\%\) more PZ germ cells than wild- type animals (Figure 2B- C). We found that RNAi silencing of hsf- 1 and xnd- 1 decreased the number of PZ germ cells in glp- 1(ar202gf) animals at the permissive temperature (Figure 2B- C). This suggests that these TFs are required for excessive germ cell proliferation in glp- 1(ar202gf) animals. In contrast, RNAi silencing of C02F5.12, C30G4.4, daf- 19, F54F2.9 and ZK546.5 increases PZ size in wild- type animals but not in glp- 1(ar202) gain- of- function animals, suggesting that these genes function in the glp- 1 pathway (Figure 2A- B). Further, glp- 1 is epistatic to let- 607, nhr- 84, ztf- 23 and nhr- 146 as glp- 1(ar202) gain- of- function animals have an increase in PZ size irrespective of silencing these GTFs (Figure 2A- B).  
+
+<|ref|>text<|/ref|><|det|>[[69, 574, 936, 914]]<|/det|>
+Maintenance of the GSC pool in the PZ is regulated by two direct GLP- 1 target genes SYGL- 1 and LST- 1 (Chen et al., 2020; Shin et al., 2017). SYGL- 1 and LST- 1 proteins are present in partially overlapping gradients through the GSC pool (Shin et al., 2017). Previous studies showed that overexpression of SYGL- 1 or LST- 1 can expand the GSC pool (Shin et al., 2017) and sygl- 1 RNAi knockdown can reduce the PZ length (Kershner et al., 2014). Therefore, we quantified endogenous 3xFLAG::SYGL- 1 and 3xFLAG::LST- 1 after silencing of the 11 GTFs that control PZ size to determine their potential roles in regulating Notch signaling (Figure 2D- E and Figure S4B- D). We found that RNAi silencing of C02F5.12 and ZK546.5 increased the number of SYGL- 1+ germ cells, consistent with their knockdown leading to an increase in PZ size (Figure 2A, D- E). This implies that C02F5.12 and ZK546.5 inhibit Notch signaling. Given that RNAi silencing of C02F5.12 and ZK546.5 did not affect the glp- 1(gf) increase of PZ size, they may function upstream of GLP- 1. We also found that RNAi silencing of hsf- 1 and xnd- 1 decreased the number of SYGL- 1+ germ cells, consistent with their knockdown to leading to a decrease in PZ size (Figure 2A, D- E). This suggests that HSF- 1 and XND- 1 promote Notch- signaling. Given that HSF- 1 and
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[70, 82, 935, 250]]<|/det|>
+XND- 1 are required for glp- 1(gf) induced increase in PZ size, they likely function downstream of GLP- 1. Interestingly, both HSF- 1 and XND- 1 have ChIP peaks in the promoter regions of sygl- 1 (ENCSR512EIF, ENCSR327NRA), suggesting a direct regulatory relationship with this Notch target gene (Consortium, 2012; Luo et al., 2020). In contrast to the SYGL- 1+ analysis, we did not detect changes in LST- 1+ in the PZ after silencing these 11 GTFs (Figure S4C- D). These data suggest that the change in PZ size following silencing of C02F5.12, ZK546.5, hsf- 1 and xnd- 1 may be due to altered Notch- dependent SYGL- 1 regulation.  
+
+<|ref|>text<|/ref|><|det|>[[69, 279, 936, 619]]<|/det|>
+PZ size is also impacted by the mitotic cell cycle that is genetically separable to Notch signaling (Fox and Schedl, 2015). Within the PZ, a cohort of germ cells act as stem cells that self- renew by mitosis or differentiate into gametes (Crittenden et al., 2006; Crittenden et al., 1994). To examine whether any of the 11 GTFs impact progression through the cell cycle, we quantified cell proliferation using 5- ethynyl- 2'- deoxyuridine (EdU) staining (Figure 2F- G) (Fox and Schedl, 2015; Seidel and Kimble, 2015). We silenced each GTF from the L1 stage, fed the resultant adults with EdU- labelled bacteria for 4 hrs or 10 hrs, and then counted EdU+ germ cells (Figure 2F- G). Using these data, we calculated the rate of proliferation as the number of germ cells incorporating EdU per hour (Figure 2F- G). As previously reported, control animals had a proliferation rate of \(\sim 23\) cells/hour (Figure 2F) (Fox and Schedl, 2015). We found that RNAi silencing of C02F5.12, daf- 19 and ZK546.5 increased the germ cell proliferation rate, which may contribute to increases in PZ size observed by increasing number of mitotic cells (Figure 2F- G). Taken together, these results reveal key TFs that control PZ size, likely through fine- tuning Notch signaling, cell cycle dynamics and possibly other mechanisms.  
+
+<|ref|>text<|/ref|><|det|>[[69, 647, 936, 914]]<|/det|>
+Antibody staining to detect phosphorylation of histone H3 (pH3) at serine 10 is used to mark mitotic M- phase germ cells (Crittenden et al., 2017; Hsu et al., 2000). No pH3+ cells are observed after proliferative zone cells have entered meiosis (Fox and Schedl, 2015). Therefore, changes in the number of pH3+ germ cells could indicate changes in the total number of mitotic germ cells, or changes in M- phase length with respect to the entire cell cycle. We found that of the 11 GTFs analyzed, only hsf- 1 silencing increased the number of pH3+ germ cells (Figure S5A- B). This suggests that hsf- 1 silencing increases mitotic germ cell number or M- phase length, or potentially accelerates other cell cycle phases. However, further investigation of hsf- 1 functions in mitosis and early meiosis is required to explain the contradictory results of decreased PZ size, decreased SYGL- 1+ cells, and unchanged proliferation rate following hsf- 1 silencing. A previous study found that germline- specific HSF- 1 protein depletion from hatching causes a severe reduction in germ
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[69, 82, 936, 325]]<|/det|>
+cells and depletion from the mid- L3 stage causes a decrease in EdU labeled germ cells (Edwards et al., 2021). These contradictory findings suggest the effect of HSF- 1 knockdown on the germ line is dosage or developmental stage dependent. Another method frequently used to assess germline proliferation is the mitotic index (MI), which is the percentage of PZ cells that are M- phase (Crittenden et al., 2023). In addition to hsf- 1, we found silencing of F54F2.9 and nhr- 146 caused changes in MI without changing the absolute number of pH3+ cells (Figure S5A- C). Although silencing of F54F2.9 and nhr- 146 caused changes in PZ size, there were no changes in the number of SYGL- 1+ cells or proliferation rate (Figure 2D and F). Thus, it is possible that these genes are not involved in mitosis, and the change of MI is a result of changes of overall PZ size which also include meiotic M- phase cells.  
+
+<|ref|>text<|/ref|><|det|>[[69, 352, 936, 693]]<|/det|>
+Proximal to the PZ, the germline TZ houses a cohort of crescent- shaped nuclei that is characteristic of early meiotic prophase (leptotene and zygotene) germ cells (Hubbard, 2007). We found that some GTFs are also required for normal TZ size (ZK546.5, HSF- 1/HSF1, XND- 1, ZTF- 23 and NHR- 146), suggesting that meiotic prophase progression and/or chromosome pairing may be perturbed following silencing of these GTFs (Figure S5D- E). Interestingly, the changes in TZ size paralleled changes in PZ size – ZK546.5 silencing increased PZ and TZ size; hsf- 1, xnd- 1, ztf- 23 and nhr- 146 silencing decreased PZ and TZ size (Figure 2H). This suggests that when these genes are silenced, germ cell output from the PZ impacts TZ size. Finally, we found that 5 out of the 11 GTFs that are important for distal germ cell behavior also impact the proximal germline. ZK546.5 silencing caused incomplete spermatogenesis (7/8 germ lines), ztf- 23 and C02F5.12 silencing caused changes in the number of single array or budded oocytes, and C30G4.4 and nhr- 146 silencing caused germ cell multinucleation (Table S5). Thus, these GTFs likely play roles in both mitotic and meiotic germ cell behavior. Taken together, our distal germline screen discovered multiple GTFs that control germ cell behavior.  
+
+<|ref|>sub_title<|/ref|><|det|>[[70, 722, 785, 741]]<|/det|>
+## Transcriptional Control of Meiotic Cell Behavior and Gamete Development  
+
+<|ref|>text<|/ref|><|det|>[[70, 747, 936, 914]]<|/det|>
+As germ cells progress proximally, they mature into sperm during late larval development before committing to the oogenic program. To examine the function of GTFs in these processes we utilized a transgenic strain in which fluorescent markers targeting chromosomes (mCherry- histone H2B) and plasma membrane (GFP- PI4, 5P2) enable gonad architecture to be visualized (Figure 1A and S3) (Essex et al., 2009; Green et al., 2011). We identified GTF functions in the proximal germ line including germline structure (small germ line, narrow rachis), meiosis (multinucleation, meiotic progression and apoptosis), sperm production and behavior
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[70, 83, 935, 177]]<|/det|>
+(spermatogenesis, sperm localization), and oocyte production (germ cell expansion, number of oocytes, oocyte budding and vesiculation) (Figure S3 and Table S5). The most prominent phenotype we observed was multinucleation of germ cells and thus we focused our subsequent analysis on this phenotype.  
+
+<|ref|>text<|/ref|><|det|>[[68, 205, 936, 620]]<|/det|>
+The C. elegans hermaphrodite gonad is a syncytium with germ cell nuclei partially enclosed by a plasma membrane. Multinucleated germ cells (MNCs) can occur in both wild- type and some mutant animals at varying frequency (Raiders et al., 2018). Persistent MNCs would generate multinuclear oocytes and infertile embryos and are therefore cleared by apoptosis prior to generating gametes. Earlier studies showed that failed germ cell division in the PZ and an unstable rachis can cause multinucleation, while in other cases, including in wild- type animals, MNCs form at specific germline structures called folds (Lan et al., 2019; Raiders et al., 2018). In our primary screen, we identified 26 GTFs that have \(>25\%\) increase in germ lines with MNCs compared to control animals (Table S5- 6). To confirm this result, we selected 10 GTFs with the strongest penetrant phenotypes (DMD- 7/DMRTC1, BAZ- 2/BAZ2A, JUN- 1/JUN, MTER- 4, LIN- 26, MADF- 5, NHR- 40/HNF4, NHR- 114/NR1H3, NHR- 161/PPAR- alpha, and UNC- 62/MEIS) and repeated RNAi silencing in triplicate (Figure 3). We confirmed that silencing of all 10 GTFs increased MNCs in both one- day and two- day adults (Figure 3A- B and Figure S6A- B). Of these TFs, only NHR- 114 had previously been associated with germ cell multinucleation in C. elegans (Gracida and Eckmann, 2013). We used confocal microscopy to analyze the PZ of one- day adults following GTF silencing and detected no MNCs, suggesting that MNC formation is not due to aberrant mitotic division, as shown previously (data not shown) (Lan et al., 2019).  
+
+<|ref|>text<|/ref|><|det|>[[70, 648, 936, 914]]<|/det|>
+As part of normal germline development, multinucleate cells are removed by physiological apoptosis to prevent them from potentially generating multipoid oocytes (Gumienny et al., 1999; Raiders et al., 2018). The CED- 3 executioner caspase is essential for physiological germline apoptosis (Gumienny et al., 1999), and thus germ lines lacking ced- 3 contain many MNCs (Raiders et al., 2018). To determine whether regulation of germ cell multinucleation by GTFs is dependent on CED- 3, we silenced the 10 GTFs that inhibit MNC formation in ced- 3 mutant animals. As the ced- 3 gene is linked to the pie- 1p::mCherry::his- 58 transgene that is used to count multinucleation events, we used CRISPR/Cas9 genome editing to generate a 3951bp ced- 3(rp190) deletion allele (Figure S7). These animals exhibit overt persistent apoptotic corpses confirming defective apoptosis (Figure S7B- C). Further, we did not detect apoptosis in the germ lines of ced- 3(rp190) animals, and loss of CED- 3 caused an increase in multinucleated germ cells
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[71, 83, 932, 127]]<|/det|>
+(Figure 3B- D), confirming that apoptosis is involved in the clearance of multinucleated germ cells (Raiders et al., 2018).  
+
+<|ref|>text<|/ref|><|det|>[[68, 155, 935, 447]]<|/det|>
+We next counted germline MNCs in ced- 3(rp190) two- day adults after GTF RNAi silencing from the L1 stage (Figure 3E). We found that RNAi silencing of jun- 1, mter- 4, lin- 26, nhr- 40, nhr- 114, nhr- 161, and unc- 62 caused no change in MNC number in the ced- 3(rp190) background, suggesting that silencing these GTFs lead to increase in multinucleated cells by interfering with apoptosis. In contrast, silencing of dmd- 7, baz- 2 and madv- 5 increased MNCs independent of loss of ced- 3, suggesting that these GTFs promote mono- nucleation through mechanisms that are independent of apoptosis. However, caspase- 3 can play roles outside apoptosis (Dehkordi et al., 2022; Eskandari and Eaves, 2022), therefore it is possible that CED- 3 is involved in multinucleation formation through other mechanisms. In addition, we observed that MNCs are primarily located in the distal half of the pachytene region in wild- type and ced- 3(rp190) animals (Figure 3B), suggesting that mechanisms independent of physiological apoptosis can remove/resolve multinucleation.  
+
+<|ref|>text<|/ref|><|det|>[[68, 475, 936, 791]]<|/det|>
+A previous study revealed that meiotic germline structure impacts the location and occurrence of multinucleation (Raiders et al., 2018). During proliferation, folds in the germline syncytium are formed and multinucleate cells occur during fold eversion (Raiders et al., 2018). As the size and complexity of germline folds is correlated with multinucleation, we assessed whether the 10 GTFs with high numbers of MNCs regulate germline folding. We used phalloidin to stain the germline actin cytoskeleton after RNAi silencing of these GTFs and measured germline folding by counting the rows and numbers of cells protruding inwards from the single layer of cells that form the tubelike structure in the pachytene region (Figure 3F- G and Figure S6C). We found that RNAi silencing of dmd- 7, baz- 2, mter- 4, lin- 26, nhr- 114 and nhr- 161 caused excess germline folding that may result in the increased multinucleation observed (Figure 3F- G and Figure S6C). Taken together, these data reveal 10 GTFs that act in the germline to maintain uninuclear germ cells. Cell behavior is likely controlled by these TFs through gene networks connected to the apoptotic pathway and germline morphogenesis.  
+
+<|ref|>sub_title<|/ref|><|det|>[[71, 820, 463, 839]]<|/det|>
+## Essential TFs for Germline Development  
+
+<|ref|>text<|/ref|><|det|>[[70, 845, 934, 913]]<|/det|>
+Our initial distal germline screen identified 8 TFs (CDC- 5L, ZNF- 622, VEP- 1, F23B12.7, F33H1.4, F58G1.2, REPO- 1, and TBP- 1) that when silenced from the L1 stage result in small germ lines that do not generate embryos in one- day adults (Table S4). These TFs are mostly
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[69, 82, 935, 250]]<|/det|>
+uncharacterized but have predicted general functions in controlling gene expression (Table S4). Analysis of broods after silencing these TFs from the L1 stage showed almost complete sterility (Figure 4A). An exception was repo- 1 RNAi, which generated \(\sim 230(\pm 30.47)\) progeny, however most resultant embryos did not hatch (Figure 4A). To investigate when these TFs are required during development, we performed temporal germline analysis by silencing each TF from the L1 stage and examining germline size and morphology at each larval stage through to adult (Figure 4B and S8).  
+
+<|ref|>text<|/ref|><|det|>[[68, 278, 936, 742]]<|/det|>
+We found that germline size and morphology were mostly intact during early larval development (L1 through L3) following TF silencing (Figure S8). However, from the L4 stage, TF silencing began to reveal germline defects - silencing of all 8 essential GTFs caused a significant decrease in germline length (Figure 4C). Of note, cdc- 5L and znf- 622 silencing resulted in small germ lines ( \(\sim 50\%\) the length of controls) that contained large nuclei suggesting arrest in the G2 stage of the cell cycle (Figure 4C- D and S8). We continued to monitor germline development for all 8 TF RNAi experiments during the first three days of adulthood to determine the effect on sperm, oocyte, and embryo development (Figure 4E). We found that silencing of some genes prevented or delayed sperm generation (cdc- 5L, znf- 622, vep- 1 and F23B12.7), with all genes defective or delayed in oocyte production and embryogenesis (Figure 4E). In addition, silencing of cdc- 5L, znf- 622, vep- 1 and tbp- 1 caused sterility in three- day adults (Figure 4E). We wondered whether these 8 TFs are continuously required for germline development or perform functions during larval development to enable fertility in adults. We explored this by performing germline- specific RNAi silencing from the mid- L4 stage at which gonad development and spermatogenesis are largely complete (Figure 4F). We found that silencing of repo- 1, F33H1.4, F23B12.7, F58G1.2, tbp- 1 and vep- 1 had similar broods to control animals, albeit with different degrees of embryonic lethality (Figure 4F). In contrast, cdc- 5L and znf- 622 silencing caused approximately \(>50\%\) reduction in brood size (with cdc- 5L RNAi causing \(100\%\) embryonic lethality), suggesting post- developmental roles in germline maintenance (Figure 4F).  
+
+<|ref|>text<|/ref|><|det|>[[70, 770, 935, 914]]<|/det|>
+To examine the function of the essential GTFs in the oogenic germline, we quantified distal germ cell number in one- day adults following RNAi from the L4 stage (Figure 4G- J). We identified subtle changes in PZ/TZ size, pH3+ cells, and SYGL- 1+ staining (Figure 4G- J). However, these changes are negligible compared to the detrimental effect of silencing these GTFs from the L1 stage (Figure 4A), suggesting that the germ cell cycle and transition to meiosis are largely intact. Further, the minor changes in distal germ line cell numbers suggest that the brood size decrease
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[70, 82, 933, 152]]<|/det|>
+we observed following cdc- 5L and znf- 622 silencing are likely caused by defects in gametogenesis. Taken together, we have identified multiple TFs that control the timing and function of gamete generation and subsequent fertility in C. elegans.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[72, 84, 200, 101]]<|/det|>
+## DISCUSSION  
+
+<|ref|>text<|/ref|><|det|>[[70, 107, 936, 350]]<|/det|>
+In this study, we profiled the function of every germline- expressed TF in C. elegans to gain a comprehensive understanding of transcriptional regulation of germline development. To identify autonomous regulatory functions, we first determined that 364 TFs are germline expressed and then depleted their expression specifically in the germ line. We used two independent screening approaches to investigate TF functions from the proliferative germ cell stage to gamete production. Using high- content imaging, semi- automated germ cell counting and cell behavior analysis of \(>6000\) germ lines and \(>3\) million germ cells, we identified 156 TFs that influence germline development, \(>75\%\) of which are undescribed. Furthermore, we identified 8 factors that are essential for fertility. As a result, this study provides a rich resource for mechanistic dissection of TF function in germ cells and potentially new opportunities for manipulating cell fates.  
+
+<|ref|>text<|/ref|><|det|>[[70, 378, 936, 570]]<|/det|>
+Previous C. elegans forward and reverse genetic screening approaches identified genes that cause sterility or reduced brood sizes (Argon and Ward, 1980; Hirsh and Vanderslice, 1976; Maeda et al., 2001). Further, a large- scale screen of essential genes used the RNAi soaking method to examine proximal germline development (Green et al., 2011); however, \(< 3\%\) of these genes overlapped with our screen. Whether phenotypes observed in these previous studies result from autonomous germline functions or somatic deficits is, however, unclear. Thus, our use of a germline- specific RNAi approach to survey autonomous TF functions, allows for a more refined and robust evaluation of critical germline regulators.  
+
+<|ref|>text<|/ref|><|det|>[[70, 598, 936, 914]]<|/det|>
+Our primary screen identified 8 factors that are essential for fertility: CDC- 5L, ZNF- 622, VEP- 1, F23B12.7, F33H1.4, F58G1.2, REPO- 1, and TBP- 1. Loss of these factors had previously produced sterile/fertility phenotypes in genetic mutants and/or large- scale RNAi screens (Fraser et al., 2000; Kamath et al., 2003; Kerins et al., 2010; Simmer et al., 2003; Sonnichsen et al., 2005; Sun et al., 2011). Our analysis shows that these factors act autonomously and predominantly during larval development to enable germline function as we observed robust sterility when these factors were silenced from hatching, but not when silenced late in development. Major exceptions were silencing of cdc- 5L, repo- 1 and znf- 622 which caused reduced brood sizes and high proportions of dead eggs even when they were silenced late in development. Previous studies have identified conserved germline functions in mammals for some of these factors. Porcine CDC- 5L is required for fertility and oocyte maturation (Liu et al., 2018), which is consistent with the oogenesis defect induced by C. elegans cdc- 5L silencing. In addition, murine BDP1 is required for spermatogonial stem cell gene expression (Sisakhtnezhad and Heshmati, 2018), which may
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[70, 82, 934, 177]]<|/det|>
+be the cause of sterility and delayed sperm development when the BDP1 C. elegans ortholog VEP- 1 is silenced. Further, the zinc- finger protein CTCF is critical for oocyte development in mice through regulation of chromatin architecture (Wan et al., 2008), which may also impact the oocyte developmental defects we observed in the C. elegans ortholog F58G1.2.  
+
+<|ref|>text<|/ref|><|det|>[[68, 201, 936, 744]]<|/det|>
+We also identified 128 TFs that control germ cell behavior in the distal germ line, including multiple TFs previously reported. For example, our screen confirmed previous studies identifying a zinc- finger TF LSL- 1 that limits TZ size, and the heat shock factor HSF- 1 and chromatin factor XND- 1 that promote germ cell proliferation in the PZ (Edwards et al., 2021; Mainpal et al., 2015; Rodriguez- Crespo et al., 2022). Our secondary screen focused on the TFs important for controlling PZ size to define whether these factors potentially control Notch signaling and/or mitotic cell cycle - two critical determinants of PZ germ cell behavior. Our analysis identified 5 TFs that limit and 6 TFs that promote PZ size. Key findings from this work suggest that HSF- 1 and XND- 1 are required for excess germ cell proliferation caused by GLP- 1/Notch overexpression. Further, loss of the zinc- finger TFs C02F5.12 and ZK546.5 enhance expression of the GLP- 1 target gene SYGL- 1 and germ cell proliferation rate, suggesting that these genes act in the Notch signaling pathway. Interestingly, C02F5.12 is a paralog of ZIM- 1, ZIM- 2, ZIM- 3, and HIM- 8 that are required for chromosome pairing (Phillips and Dernburg, 2006). However, as with C02F5.12, we found that loss of ZIM- 2 additionally limits PZ size suggesting a pre- meiotic function for these proteins. Further, ZK546.5 also limits size of the TZ - a phenotype it shares with its paralog LSL- 1. Finally, we found that the RFX- type TF DAF- 19 limits the germ cell proliferation in C. elegans. Interaction analysis predicts that DAF- 19 physically interacts with the serine-threonine kinase ATL- 1 and checkpoint kinase CHKR- 1, two kinases that are required for fertility, suggesting a potential mechanism for DAF- 19 function in the germline (Garcia- Muse and Boulton, 2005). A mouse ortholog of DAF- 19, RFX2, is expressed in the testis and required for spermatogenesis (Wu et al., 2016). Thus, future analysis of DAF- 19 functions in sperm development in C. elegans hermaphrodites and males is warranted.  
+
+<|ref|>text<|/ref|><|det|>[[70, 771, 936, 914]]<|/det|>
+Meiotic development in the C. elegans germline is predominantly regulated by post- transcriptional networks governed by the redundant GLD- 1, GLD- 2, and SCF<sup>PROM- 1</sup> proteins (Francis et al., 1995; Jantsch et al., 2007; Kadyk and Kimble, 1998; Nayak et al., 2002). However, the pachytene region is transcriptionally active prior to the transcriptional silencing that occurs in oocytes (Gibert et al., 1984; Starck, 1977). Our analysis of TF function in the proximal germline identified functions in spermatogenesis, oogenesis, gonad structure and meiotic progression. However, the most
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[69, 82, 936, 399]]<|/det|>
+common phenotype we observed was increased multinucleation of meiotic cells where silencing of 26 GTFs caused a robust phenotype. We focused on the 10 GTFs with the most penetrant multinucleation phenotype for secondary analysis. Previous studies revealed that folding of the germline structure and apoptosis impacts multinucleation (Raiders et al., 2018). In consensus with this, we found that silencing of 6/10 of these GTFs (DMD- 7, BAZ- 2, MTER- 4, LIN- 26, NHR- 114 and NHR- 161) caused excess folding in the germline syncytium that may contribute to the multinucleation phenotype. Furthermore, we found that increase in multinucleation caused by silencing of 7/10 of these GTFs is dependent on the presence of the apoptotic executioner caspase, CED- 3. Caspase- 3 is a well- known essential apoptosis inducer, but it can also regulate non- apoptotic processes including axonal differentiation, growth, routing, regeneration and degeneration (Dehkordi et al., 2022). Considering that the molecular mechanism of multinucleation formation and removal is unknown, it is challenging to speculate if CED- 3 only controls this event by inducing apoptosis, or it has additional functions.  
+
+<|ref|>text<|/ref|><|det|>[[69, 426, 936, 619]]<|/det|>
+Overall, our detailed map of TF function throughout the C. elegans germ line is a valuable resource for mechanistic studies of germ cell and gamete development. Our discovery of multiple TFs with specific functions in the developing germ line suggests that many distinct pathways act to fine- tune gene networks and cell behavior. We anticipate that specific TFs will act downstream of key signaling pathways (e.g., TGF, mTOR, AMPK and insulin signaling) to integrate environmental and genetic cues for optimizing germ cell proliferation, meiotic transition, and progeny production. Together, these transcriptional outputs, when combined, likely provide robustness to the process of delivering the next generation.  
+
+<|ref|>sub_title<|/ref|><|det|>[[72, 650, 301, 667]]<|/det|>
+## Limitations of the study  
+
+<|ref|>text<|/ref|><|det|>[[69, 673, 936, 865]]<|/det|>
+We wish to highlight two limitations of our study. First, categorization of TFs was based on the presence of DNA binding domains (Jimeno- Martin et al., 2022). However, these factors may also control other aspects of gene expression such as RNA splicing (CDC- 5L) and translation (ZNF- 622), thus care must be taken when planning mechanistic dissection of their function. Second, we found that the transgenic strain we used to analyze the proximal germ line (OD95) identified more TFs that cause a sterility phenotype than the strain used for distal analysis. This OD95 strain expresses two independently integrated transgenes to mark germ cell nuclei and membranes that may cause sensitivity to certain genetic perturbations.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[72, 84, 263, 102]]<|/det|>
+## Acknowledgements  
+
+<|ref|>text<|/ref|><|det|>[[70, 107, 935, 250]]<|/det|>
+AcknowledgementsWe thank members of the Pocock and Gopal laboratories for advice and comments on the manuscript. RNA sequencing and Bioinformatics performed at Monash Micromon Genomics and Monash Bioinformatics Platform. Imaging performed at Monash Microimaging. We thank Nuria Flames for their kind gift of TF RNAi clones. Some strains were provided by the Caenorhabditis Genetics Center (University of Minnesota), which is funded by the NIH Office of Research Infrastructure Programs (P40 OD010440).  
+
+<|ref|>sub_title<|/ref|><|det|>[[71, 281, 155, 299]]<|/det|>
+## Funding  
+
+<|ref|>text<|/ref|><|det|>[[71, 305, 934, 373]]<|/det|>
+FundingThis work was supported by the following grants: Australian Research Council DE190100174 (SG) and DP200103293 (RP); National Health and Medical Research Council GNT1161439 (S.G.), GNT1105374 (RP), GNT1137645 (RP) and GNT2018825 (RP, WC and QF).  
+
+<|ref|>sub_title<|/ref|><|det|>[[71, 403, 277, 421]]<|/det|>
+## Author Contributions  
+
+<|ref|>text<|/ref|><|det|>[[70, 427, 732, 618]]<|/det|>
+Conceptualization: RP, SG  Methodology: RP, SG, WC, QF, RG  Investigation: RP, SG, WC, QF, RG, GA, DB  Visualization: RP, SG, WC, QF, RG, GA, DB Funding acquisition: SG, RP  Project administration: RP, SG, WC  Supervision: RP, SG, WC  Writing - original draft: RP  Writing - review & editing: RP, SG, WC, QF, RG, GA, DB  
+
+<|ref|>sub_title<|/ref|><|det|>[[71, 649, 268, 666]]<|/det|>
+## Competing interests  
+
+<|ref|>text<|/ref|><|det|>[[70, 673, 561, 692]]<|/det|>
+Authors declare that they have no competing interests.  
+
+<|ref|>sub_title<|/ref|><|det|>[[71, 722, 362, 740]]<|/det|>
+## Data and materials availability  
+
+<|ref|>text<|/ref|><|det|>[[70, 747, 934, 815]]<|/det|>
+All data is available in the main text, supplementary materials, and source data. No accession codes, unique identifiers, or weblinks are in our study and there are no restrictions on data availability. Materials are available upon request from Roger Pocock.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[72, 84, 253, 101]]<|/det|>
+## FIGURE LEGENDS  
+
+<|ref|>sub_title<|/ref|><|det|>[[70, 108, 770, 127]]<|/det|>
+## Figure 1. Phenotypic Profiling of Germline Transcription Factor Function  
+
+<|ref|>text<|/ref|><|det|>[[69, 133, 935, 275]]<|/det|>
+(A) Schematic of the C. elegans hermaphrodite germline (left), identification of germline-expressed TFs by RNA sequencing (center), and representative images and phenotypic readouts for high-content distal and proximal germline analysis (right). The distal germ line is labelled with DAPI (nuclei), SYGL-1 (Notch target gene) and pH3 (M-phase chromosomes). PZ = progenitor zone, TZ = transition zone. The proximal germ line is marked with transgenic fluorophores: red = nuclei; green = plasma membrane. Scale bars = 20 μm.  
+
+<|ref|>text<|/ref|><|det|>[[69, 283, 935, 451]]<|/det|>
+(B- D) Heatmaps showing phenotypic overview for TF silencing causing distal (B \(\geq 20\%\) change compared to control), proximal (C \(\geq 50\%\) of animals with the phenotype) or distal and proximal germline phenotypes (D). (B) TF family categories (colored boxes); progenitor zone (PZ), transition zone (TZ) and SYGL- \(1^{+}\) region phenotypes shown as percentage change compared to control (blue \(=\) decrease; red \(=\) increase); germline expression of each TF (grey bars \(=\) log2 CPM); mammalian ortholog associated with the reproduction GO term (blue boxes \(=\) No, black boxes \(=\) Yes).  
+
+<|ref|>text<|/ref|><|det|>[[69, 457, 935, 622]]<|/det|>
+(C) Labelling as in B, except purple \(=\) proximal phenotype; blue \(=\) no phenotype detected. Phenotypes shown in this figure: small germ line, rachis defect (narrow or wide rachis), meiotic defect (multinucleated cells, abnormal meiotic progression), apoptosis, oocyte defect (delayed expansion at the turn, difference in single-array oocyte number, difference in budded oocyte number, vesiculation), sperm defect (mislocated sperm, incomplete spermatogenesis). (D) Labelling as in B and C. Note: the heatmaps do not include essential GTFs identified in the screens – this analysis is detailed in Figure 4 and Table S4.  
+
+<|ref|>text<|/ref|><|det|>[[69, 629, 935, 747]]<|/det|>
+(E) TF family distribution of the 875 C. elegans TFs, 96 TFs that regulate the distal germ line or are essential for germline development, and 52 TFs that regulate the proximal germline. TF families shown in this figure: bZIP = basic leucine zipper domain, HD = homeodomain, HMG = High mobility group box domain, MYB= myeloblastosis viral oncogene homolog, NHR = nuclear hormone receptors, T-box, WH = winged helix, ZF = zinc finger.  
+
+<|ref|>sub_title<|/ref|><|det|>[[70, 777, 545, 795]]<|/det|>
+## Figure 2. TF Control of Distal Germ Cell Behavior  
+
+<|ref|>text<|/ref|><|det|>[[70, 801, 933, 844]]<|/det|>
+(A) Quantification of nuclei number in the PZ of 3xflag::syg1-1; sun-1p::rde-1; rde-1(mkc36) one-day adults. n = 23-24. Green dashed line = average of the control.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[68, 82, 936, 430]]<|/det|>
+(B and C) Quantification of nuclei number (B) and confocal micrographs (C) of one-day adult germline PZ of sun- 1p::rde- 1; rde- 1(mkc36) treated with control RNAi and glp- 1(ar202); sun- 1p::rde- 1; rde- 1(mkc36) treated with control or experimental RNAi. \(\mathsf{n} = 29 - 34\) . (D and E) Quantification of SYGL- 1+ nuclei number (D) and confocal micrographs of germline PZ (E) of 3xflag::syg- 1; sun- 1p::rde- 1; rde- 1(mkc36) one- day adults. \(\mathsf{n} = 23 - 24\) . Green dashed line \(=\) average of the control (D); yellow dashed line \(=\) PZ/TZ boundary (E - left) and white line \(=\) SYGL- 1 boundary (E - right). (F) Proliferation rates of the C. elegans germ line after GTF RNAi. RNAi was performed from the L1 stage for 66 hrs before commencing EdU labelling, and germ lines collected and imaged after 4 and 10 hrs. The 3xflag::syg- 1; sun- 1p::rde- 1; rde- 1(mkc36) strain was used in this experiment. Results of three independent experiments were shown. \(\mathsf{n} = 9 - 13\) for each experiment. (G) Confocal micrographs showing DAPI and EdU+ nuclei after 4 and 10 hrs of EdU labelling. (H) Heatmap showing the effect of silencing 11 GTF on proliferation rate, mitotic index, and nuclei numbers of PZ, TZ, SYGL- 1+ and pH3+.  
+
+<|ref|>text<|/ref|><|det|>[[70, 451, 935, 546]]<|/det|>
+RNAi was performed from the L1 stage. Data was generated from three independent experiments, and results were normalized to respective controls in (A) (B) and (D). \(P\) values assessed comparing to control RNAi by multiple unpaired t- test with no correction for multiple comparison (A, B, D and F). Error bars indicate SEM. Scale bars \(= 20 \mu \mathrm{m}\) .  
+
+<|ref|>sub_title<|/ref|><|det|>[[70, 576, 559, 595]]<|/det|>
+## Figure 3. TF Control of Meiotic Germ Cell Behavior  
+
+<|ref|>text<|/ref|><|det|>[[70, 601, 936, 696]]<|/det|>
+(A) Quantification of multinucleated germ cells of pie-1p::mCherry::his-58; pie-1p::GFP::PH(PLC1delta1); sun-1p::rde-1; rde-1(mkc36) two-day adults following RNAi from the L1 stage. Data was generated from three independent experiments. \(\mathsf{n} = 30 - 31\) . \(P\) values assessed by one-way ANOVA with no correction for multiple comparison.  
+
+<|ref|>text<|/ref|><|det|>[[70, 700, 936, 820]]<|/det|>
+(B) Fluorescence micrographs of wild-type or ced-3(rp190) two-day adult germ lines in the pie-1p::mCherry::his-58; pie-1p::GFP::PH(PLC1delta1); sun-1p::rde-1; rde-1(mkc36) strain. Control RNAi and RNAi of dmd-7 and baz-2 were applied from the L1 stage in wild-type animals. Dash lines = border between the TZ (above) and pachytene region (below) of the germ line. Asterisks = multinucleated cells; triangles = apoptotic cells. Scale bar = 20 μm.  
+
+<|ref|>text<|/ref|><|det|>[[70, 825, 935, 869]]<|/det|>
+(C and D) Quantification of apoptotic germ cells (C) and multinucleated germ cells (D) in two- day adults of pie-1p::mCherry::his-58; pie-1p::GFP::PH(PLC1delta1); sun-1p::rde-1; rde-1(mkc36)
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[70, 82, 933, 125]]<|/det|>
+strain (wild- type and ced- 3(rp190) animals). Data generated from three independent experiments. \(\mathsf{n} = 30 - 33\) . \(P\) values assessed by unpaired t- test.  
+
+<|ref|>text<|/ref|><|det|>[[70, 132, 934, 225]]<|/det|>
+(E) Quantification of multinucleated germ cells in ced-3(rp190), pie-1p::mCherry::his-58; pie-1p::GFP::PH(PLC1delta1); sun-1p::rde-1; rde-1(mkc36) two-day adults following RNAi from the L1 stage. Data generated from three independent experiments. \(\mathsf{n} = 30\) . \(P\) values assessed by one- way ANOVA with no correction for multiple comparison.  
+
+<|ref|>text<|/ref|><|det|>[[70, 230, 934, 374]]<|/det|>
+(F and G) Confocal micrographs of DAPI (white) and phalloidin (pink) staining (F) and quantification of germline folding events in the pachytene region (G) of sun- 1p::rde- 1; rde- 1(mkc36) one- day adults. Images for control RNAi (L4440) and lin- 26 RNAi are shown in F (note - only one plane is shown). Data was generated from four independent experiments. \(\mathsf{n} = 24 - 36\) . Yellow dashed line = border between the TZ (right) and pachytene region (left) of the germ line. \(P\) values assessed by one- way ANOVA with no correction for multiple comparison.  
+
+<|ref|>text<|/ref|><|det|>[[70, 379, 839, 399]]<|/det|>
+RNAi was performed from the L1 stage. Error bars indicate SEM. Scale bars = 20 μm.  
+
+<|ref|>sub_title<|/ref|><|det|>[[70, 429, 805, 449]]<|/det|>
+## Figure 4. Essential TFs act Late in Germline Development to Control Fertility  
+
+<|ref|>text<|/ref|><|det|>[[70, 454, 935, 499]]<|/det|>
+(A) Quantification of hatched larvae and dead eggs (brood size) following germline-specific RNAi from the L1 stage. Data generated from three independent experiments. \(\mathsf{n} = 15 - 18\) .  
+
+<|ref|>text<|/ref|><|det|>[[70, 504, 934, 573]]<|/det|>
+(B) Timeline of temporal germline analysis showing the time and respective life stages when germline imaging and analysis were performed following egg-laying on control and RNAi plates (RNAi from the L1 stage).  
+
+<|ref|>text<|/ref|><|det|>[[70, 577, 934, 694]]<|/det|>
+(C) Quantification of germline length of pie-1p::mCherry::his-58; sun-1p::rde-1; rde-1(mkc36) animals at the L4 stage following RNAi from the L1 stage. Data was generated from three independent experiments, and results normalized to respective controls. \(\mathsf{n} = 26 - 30\) . \(P\) values assessed by one- way ANOVA with no correction for multiple comparison. Error bars indicate SEM.  
+
+<|ref|>text<|/ref|><|det|>[[70, 700, 934, 769]]<|/det|>
+(D) DIC (left) and fluorescent micrographs (right) of pie-1p::mCherry::his-58; sun-1p::rde-1; rde-1(mkc36) germ lines at the L4 stage following control, cdc-5L and znf-622 RNAi. Blue arrow = vulva. Scale bar = 20 μm.  
+
+<|ref|>text<|/ref|><|det|>[[70, 775, 934, 893]]<|/det|>
+(E) Heatmap showing the percentage of germ lines producing sperm, oocytes and embryos in day 1, day 2 and day 3 of adulthood following germline-specific RNAi from the L1 stage. For day 2 and day 3 adult analysis, worms were selected from day 1 sterile animals, except for the control group, and incubated for another 48 hrs or 72hrs prior to analysis. Data was generated from three independent experiments. \(\mathsf{n} = 30\) .
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[66, 81, 936, 280]]<|/det|>
+(F) Quantification of hatched larvae and dead eggs (brood size) following germline-specific RNAi from the L4 stage. Data generated from three independent experiments. \(\mathsf{n} = 17 - 18\) . \(P\) values assessed by one-way ANOVA with no correction for multiple comparison. (G-J) Quantification of nuclei number of PZ (G), TZ (H), pH3+ (I) and SYGL-1+ (J) of 3xflag::syg1- 1; sun-1p::rde- 1; rde- 1(mkc36) one-day adult germ lines following RNAi treatment from the L4 stage. \(\mathsf{n} = 22 - 24\) . \(P\) values assessed by one-way ANOVA with no correction for multiple comparison. Error bars indicate SEM.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[119, 84, 260, 101]]<|/det|>
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+Rual, J.F., Ceron, J., Koreth, J., Hao, T., Nicot, A.S., Hirozane- Kishikawa, T., Vandenhaute, J., Orkin, S.H., Hill, D.E., van den Heuvel, S., et al. (2004). Toward improving Caenorhabditis elegans phenome mapping with an ORFeome- based RNAi library. Genome Res 14, 2162- 2168.  
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+Seidel, H.S., and Kimble, J. (2015). Cell- cycle quiescence maintains Caenorhabditis elegans germline stem cells independent of GLP- 1/Notch. Elife 4.  
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+Serizay, J., Dong, Y., Janes, J., Chesney, M., Cerrato, C., and Ahringer, J. (2020). Distinctive regulatory architectures of germline- active and somatic genes in C. elegans. Genome Res 30, 1752- 1765.  
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+Sisakhtnezhad, S., and Heshmati, P. (2018). Comparative analysis of single- cell RNA sequencing data from mouse spermatogonial and mesenchymal stem cells to identify differentially expressed genes and transcriptional regulators of germline cells. J Cell Physiol 233, 5231- 5242.  
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+Sonnichsen, B., Koski, L.B., Walsh, A., Marschall, P., Neumann, B., Brehm, M., Alleaume, A.M., Artelt, J., Bettencourt, P., Cassin, E., et al. (2005). Full- genome RNAi profiling of early embryogenesis in Caenorhabditis elegans. Nature 434, 462- 469.  
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+Sun, Y., Yang, P., Zhang, Y., Bao, X., Li, J., Hou, W., Yao, X., Han, J., and Zhang, H. (2011). A genome- wide RNAi screen identifies genes regulating the formation of P bodies in C. elegans and their functions in NMD and RNAi. Protein Cell 2, 918- 939.  
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+Wan, L.B., Pan, H., Hannenhalli, S., Cheng, Y., Ma, J., Fedorow, A., Lobanenkov, V., Latham, K.E., Schultz, R.M., and Bartolomei, M.S. (2008). Maternal depletion of CTCF reveals multiple functions during oocyte and preimplantation embryo development. Development 135, 2729- 2738.  
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+Wu, Y., Hu, X., Li, Z., Wang, M., Li, S., Wang, X., Lin, X., Liao, S., Zhang, Z., Feng, X., et al. (2016). Transcription Factor RFX2 Is a Key Regulator of Mouse Spermiogenesis. Sci Rep 6, 20435.  
+
+<|ref|>text<|/ref|><|det|>[[118, 573, 866, 606]]<|/det|>
+Zou, L., Wu, D., Zang, X., Wang, Z., Wu, Z., and Chen, D. (2019). Construction of a germline- specific RNAi tool in C. elegans. Sci Rep 9, 2354.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[43, 44, 144, 68]]<|/det|>
+## Figures  
+
+<|ref|>image<|/ref|><|det|>[[60, 100, 700, 720]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[43, 852, 115, 870]]<|/det|>
+<center>Figure 1 </center>  
+
+<|ref|>text<|/ref|><|det|>[[43, 894, 585, 912]]<|/det|>
+Phenotypic Profiling of Germline Transcription Factor Function
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[40, 44, 950, 180]]<|/det|>
+(A) Schematic of the C. elegans hermaphrodite germline (left), identification of germline-expressed TFs by RNA sequencing (center), and representative images and phenotypic readouts for high-content distal and proximal germline analysis (right). The distal germ line is labelled with DAPI (nuclei), SYGL-1 (Notch target gene) and pH3 (M-phase chromosomes). PZ = progenitor zone, TZ = transition zone. The proximal germ line is marked with transgenic fluorophores: red = nuclei; green = plasma membrane. Scale bars = 20 μm.  
+
+<|ref|>text<|/ref|><|det|>[[40, 196, 951, 331]]<|/det|>
+(B- D) Heatmaps showing phenotypic overview for TF silencing causing distal (B \(\geq 20\%\) change compared to control), proximal (C \(\geq 50\%\) of animals with the phenotype) or distal and proximal germline phenotypes (D). (B) TF family categories (colored boxes); progenitor zone (PZ), transition zone (TZ) and SYGL-1+ region phenotypes shown as percentage change compared to control (blue = decrease; red = increase); germline expression of each TF (grey bars = log2 CPM); mammalian ortholog associated with the reproduction GO term (blue boxes = No, black boxes = Yes).  
+
+<|ref|>text<|/ref|><|det|>[[40, 347, 952, 504]]<|/det|>
+(C) Labelling as in B, except purple = proximal phenotype; blue = no phenotype detected. Phenotypes shown in this figure: small germ line, rachis defect (narrow or wide rachis), meiotic defect (multinucleated cells, abnormal meiotic progression), apoptosis, oocyte defect (delayed expansion at the turn, difference in single-array oocyte number, difference in budded oocyte number, vesiculation), sperm defect (mislocated sperm, incomplete spermatogenesis). (D) Labelling as in B and C. Note: the heatmaps do not include essential GTFs identified in the screens – this analysis is detailed in Figure 4 and Table S4.  
+
+<|ref|>text<|/ref|><|det|>[[41, 521, 899, 566]]<|/det|>
+(E) TF family distribution of the 875 C. elegans TFs, 96 TFs that regulate the distal germ line or are essential for germline development, and 52 TFs that regulate the proximal germline.  
+
+<|ref|>text<|/ref|><|det|>[[41, 582, 928, 650]]<|/det|>
+TF families shown in this figure: bZIP = basic leucine zipper domain, HD = homeodomain, HMG = High mobility group box domain, MYB= myeloblastosis viral oncogene homolog, NHR = nuclear hormone receptors, T-box, WH = winged helix, ZF = zinc finger.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[45, 37, 803, 780]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[42, 800, 117, 820]]<|/det|>
+<center>Figure 2 </center>  
+
+<|ref|>text<|/ref|><|det|>[[42, 845, 384, 864]]<|/det|>
+TF Control of Distal Germ Cell Behavior  
+
+(A) Quantification of nuclei number in the PZ of 3xflag::syg1-1; sun-1p::rde-1; rde-1(mkc36) one-day adults. \(\mathrm{n} = 23 - 24\) . Green dashed line = average of the control.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[41, 45, 951, 111]]<|/det|>
+(B and C) Quantification of nuclei number (B) and confocal micrographs (C) of one-day adult germline PZ of sun-1p::rde-1; rde-1(mkc36) treated with control RNAi and glp-1(ar202); sun-1p::rde-1; rde-1(mkc36) treated with control or experimental RNAi. \(n = 29 - 34\) .  
+
+<|ref|>text<|/ref|><|det|>[[41, 128, 884, 150]]<|/det|>
+(D and E) Quantification of SYGL- 1+ nuclei number (D) and confocal micrographs of germline PZ  
+
+<|ref|>text<|/ref|><|det|>[[41, 166, 940, 233]]<|/det|>
+(E) of 3xflag::syg1- 1; sun-1p::rde- 1; rde- 1(mkc36) one-day adults. \(n = 23 - 24\) . Green dashed line = average of the control (D); yellow dashed line = PZ/TZ boundary (E - left) and white line = SYGL- 1 boundary (E - right).  
+
+<|ref|>text<|/ref|><|det|>[[41, 250, 951, 339]]<|/det|>
+(F) Proliferation rates of the C. elegans germ line after GTF RNAi. RNAi was performed from the L1 stage for 66 hrs before commencing EdU labelling, and germ lines collected and imaged after 4 and 10 hrs. The 3xflag::syg1- 1; sun-1p::rde- 1; rde- 1(mkc36) strain was used in this experiment. Results of three independent experiments were shown. \(n = 9 - 13\) for each experiment.  
+
+<|ref|>text<|/ref|><|det|>[[41, 355, 844, 377]]<|/det|>
+(G) Confocal micrographs showing DAPI and EdU+ nuclei after 4 and 10 hrs of EdU labelling.  
+
+<|ref|>text<|/ref|><|det|>[[41, 393, 896, 436]]<|/det|>
+(H) Heatmap showing the effect of silencing 11 GTF on proliferation rate, mitotic index, and nuclei numbers of PZ, TZ, SYGL-1+ and pH3+.  
+
+<|ref|>text<|/ref|><|det|>[[41, 454, 955, 543]]<|/det|>
+RNAi was performed from the L1 stage. Data was generated from three independent experiments, and results were normalized to respective controls in (A) (B) and (D). P values assessed comparing to control RNAi by multiple unpaired t-test with no correction for multiple comparison (A, B, D and F). Error bars indicate SEM. Scale bars = 20 μm.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[60, 80, 808, 737]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[44, 802, 117, 821]]<|/det|>
+<center>Figure 3 </center>  
+
+<|ref|>text<|/ref|><|det|>[[44, 845, 400, 864]]<|/det|>
+TF Control of Meiotic Germ Cell Behavior  
+
+<|ref|>text<|/ref|><|det|>[[42, 882, 940, 925]]<|/det|>
+(A) Quantification of multinucleated germ cells of pie-1p::mCherry::his-58; pie-1p::GFP::PH(PLC1delta1); sun-1p::rde-1; rde-1(mkc36) two-day adults following RNAi from the L1 stage. Data was generated from
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[42, 45, 940, 88]]<|/det|>
+three independent experiments. \(n = 30 - 31\) . P values assessed by one- way ANOVA with no correction for multiple comparison.  
+
+<|ref|>text<|/ref|><|det|>[[42, 105, 945, 218]]<|/det|>
+(B) Fluorescence micrographs of wild-type or ced-3(rp190) two-day adult germ lines in the pie-1p::mCherry::his-58; pie-1p::GFP::PH(PLC1delta1); sun-1p::rde-1; rde-1(mkc36) strain. Control RNAi and RNAi of dmd-7 and baz-2 were applied from the L1 stage in wild-type animals. Dash lines = border between the TZ (above) and pachytene region (below) of the germ line. Asterisks = multinucleated cells; triangles = apoptotic cells. Scale bar = 20 μm.  
+
+<|ref|>text<|/ref|><|det|>[[42, 234, 943, 322]]<|/det|>
+(C and D) Quantification of apoptotic germ cells (C) and multinucleated germ cells (D) in two- day adults of pie-1p::mCherry::his-58; pie-1p::GFP::PH(PLC1delta1); sun-1p::rde-1; rde-1(mkc36) strain (wild-type and ced-3(rp190) animals). Data generated from three independent experiments. \(n = 30 - 33\) . P values assessed by unpaired t-test.  
+
+<|ref|>text<|/ref|><|det|>[[42, 339, 947, 428]]<|/det|>
+(E) Quantification of multinucleated germ cells in ced-3(rp190), pie-1p::mCherry::his-58; pie-1p::GFP::PH(PLC1delta1); sun-1p::rde-1; rde-1(mkc36) two-day adults following RNAi from the L1 stage. Data generated from three independent experiments. \(n = 30\) . P values assessed by one-way ANOVA with no correction for multiple comparison.  
+
+<|ref|>text<|/ref|><|det|>[[42, 444, 937, 580]]<|/det|>
+(F and G) Confocal micrographs of DAPI (white) and phalloidin (pink) staining (F) and quantification of germline folding events in the pachytene region (G) of sun-1p::rde-1; rde-1(mkc36) one-day adults. Images for control RNAi (L4440) and lin-26 RNAi are shown in F (note - only one plane is shown). Data was generated from four independent experiments. \(n = 24 - 36\) . Yellow dashed line = border between the TZ (right) and pachytene region (left) of the germ line. P values assessed by one-way ANOVA with no correction for multiple comparison.  
+
+<|ref|>text<|/ref|><|det|>[[42, 597, 780, 618]]<|/det|>
+RNAi was performed from the L1 stage. Error bars indicate SEM. Scale bars = 20 μm.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[45, 30, 700, 789]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[44, 800, 118, 819]]<|/det|>
+<center>Figure 4 </center>  
+
+<|ref|>text<|/ref|><|det|>[[44, 844, 620, 864]]<|/det|>
+Essential TFs act Late in Germline Development to Control Fertility  
+
+<|ref|>text<|/ref|><|det|>[[42, 881, 930, 925]]<|/det|>
+(A) Quantification of hatched larvae and dead eggs (brood size) following germline-specific RNAi from the L1 stage. Data generated from three independent experiments. \(n = 15 - 18\) .
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[42, 45, 949, 111]]<|/det|>
+(B) Timeline of temporal germline analysis showing the time and respective life stages when germline imaging and analysis were performed following egg-laying on control and RNAi plates (RNAi from the L1 stage).  
+
+<|ref|>text<|/ref|><|det|>[[42, 128, 952, 217]]<|/det|>
+(C) Quantification of germline length of pie-1p::mCherry::his-58; sun-1p::rde-1; rde-1(mkc36) animals at the L4 stage following RNAi from the L1 stage. Data was generated from three independent experiments, and results normalized to respective controls. \(\mathrm{n} = 26 - 30\) . P values assessed by one-way ANOVA with no correction for multiple comparison. Error bars indicate SEM.  
+
+<|ref|>text<|/ref|><|det|>[[42, 234, 949, 300]]<|/det|>
+(D) DIC (left) and fluorescent micrographs (right) of pie-1p::mCherry::his-58; sun-1p::rde-1; rde-1(mkc36) germ lines at the L4 stage following control, cdc-5L and znf-622 RNAi. Blue arrow = vulva. Scale bar = 20 μm.  
+
+<|ref|>text<|/ref|><|det|>[[42, 317, 952, 428]]<|/det|>
+(E) Heatmap showing the percentage of germ lines producing sperm, oocytes and embryos in day 1, day 2 and day 3 of adulthood following germline-specific RNAi from the L1 stage. For day 2 and day 3 adult analysis, worms were selected from day 1 sterile animals, except for the control group, and incubated for another 48 hrs or 72hrs prior to analysis. Data was generated from three independent experiments. \(\mathrm{n} = 30\) .  
+
+<|ref|>text<|/ref|><|det|>[[42, 446, 945, 512]]<|/det|>
+(F) Quantification of hatched larvae and dead eggs (brood size) following germline-specific RNAi from the L4 stage. Data generated from three independent experiments. \(\mathrm{n} = 17 - 18\) . P values assessed by one-way ANOVA with no correction for multiple comparison.  
+
+<|ref|>text<|/ref|><|det|>[[42, 529, 944, 572]]<|/det|>
+(G-J) Quantification of nuclei number of PZ (G), TZ (H), pH3+ (I) and SYGL- 1+ (J) of 3xflag::syg1- 1; sun- 1p::rde- 1; rde- 1(mkc36) one-day adult germ lines following RNAi treatment from the L4 stage. \(\mathrm{n} = 22 - 24\) .  
+
+<|ref|>text<|/ref|><|det|>[[42, 590, 928, 633]]<|/det|>
+P values assessed by one-way ANOVA with no correction for multiple comparison. Error bars indicate SEM.  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 656, 312, 683]]<|/det|>
+## Supplementary Files  
+
+<|ref|>text<|/ref|><|det|>[[44, 707, 768, 727]]<|/det|>
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+<|ref|>text<|/ref|><|det|>[[60, 745, 200, 950]]<|/det|>
+- FigureS1.pdf- FigureS2.pdf- FigureS3.pdf- FigureS4.pdf- FigureS5.pdf- FigureS6.pdf- FigureS7.pdf- FigureS8.pdf
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[58, 46, 600, 330]]<|/det|>
+- TableS1.Germlinetranscriptomeanalysis.xlsx- TableS2WormGTFexpressionandreproductivefunctions.xlsx- TableS3RNAiplasmidusedinthisstudy.xlsx- TableS4Distalgermlineanalysis.xlsx- TableS5ProximalgermlineanalysisV6.xlsx- TableS6156GTFswithgermlinefunctions.xlsx- TableS7PreviouslyreportedphenotypesinC.elegans.xlsx- TableS8GTFfunctionsinotherorganismsGOanalysis.xlsx- TableS9Strainsusedinthisstudy.xlsx- TableS10Oligosusedinthisstudy.xlsx- TableS11Sourcedata.xlsx
+
+<--- Page Split --->

preprint/preprint__014d74e44dae70e5db80998cebc90ca95b419b71bced776056d3779c43cd19fb/images_list.json

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preprint/preprint__014d7fcc701aeaec731ab407447a9cff88ca28668ba3db6be184e70f89ec55ce/images_list.json

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+[
+  {
+    "type": "image",
+    "img_path": "images/Figure_1.jpg",
+    "caption": "Fig. 1. Fe monodispersion on ZnCr2O4 spinel. HADDF-STEM images of (a) 4.48% Fe-Zn/Cr and (b) 7.78% Fe-Zn/Cr and corresponding EDS mappings of Fe element. (c) HADDF intensity",
+    "footnote": [],
+    "bbox": [
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+      ]
+    ],
+    "page_idx": 7
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_2.jpg",
+    "caption": "Fig. 2. Catalyst evaluation of monodispersed and enriched Fe. (a) Catalyst evaluation of pristine \\(\\mathrm{ZnCr_2O_4}\\) and different Fe-Zn/Cr with H-ZSM-5; reaction conditions: \\(350^{\\circ}\\mathrm{C}\\) , 2.0 MPa, space velocity \\(= 600\\mathrm{ml}\\mathrm{h}^{-1}\\cdot \\mathrm{g}_{\\mathrm{cat}}^{-1}\\) , \\(\\mathrm{CO:H_2 = 1:1}\\) . (b) The optimal space time yield (STY) of aromatics for different catalysts (note that the STYs were obtained at the optimized reaction conditions for each catalyst and the corresponding reaction conditions are summarized in table S3). (c) TOF and aromatic selectivity as functions of Fe contents in \\(\\mathrm{ZnCr_2O_4}\\) and reaction conditions are similar as A. (d) Comparison between reported Fe-based catalysts and isolated Fe-Zn/Cr + H-ZSM-5 catalyst on syngas conversion to aromatics. (e) Stability test of \\(4.48\\%\\) Fe-Zn/Cr + H-ZSM-5 catalytic system for \\(100\\mathrm{h}\\) .",
+    "footnote": [],
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+    ],
+    "page_idx": 12
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_3.jpg",
+    "caption": "Fig. 3. Understanding the role of monodispersed Fe in catalytic activity. (a) Thermodynamic phase diagram of Fe-doped \\(\\mathrm{ZnCr_2O_4}\\) (111) surfaces at varying temperatures and CO partial pressures \\((P_{\\mathrm{CO}})\\) (b) iDPC-STEM images of \\(4.48\\%\\) Fe-Zn/Cr. (c) iDPC-STEM intensity profiles along the corresponding dashed lines in B. (d) O 1s XPS spectra of \\(\\mathrm{ZnCr_2O_4}\\) and different Fe-Zn/Cr samples. (e) Projected electric field strength and vector map of \\(4.48\\%\\) Fe-Zn/Cr. (f) Differential charge density (cyan and yellow represent charge depletion and accumulation, respectively; the cutoff of the density-difference isosurface is \\(0.11\\mathrm{e~Å}^{-3}\\) ).",
+    "footnote": [],
+    "bbox": [
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+    ],
+    "page_idx": 15
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_4.jpg",
+    "caption": "Fig. 4. The mechanism study of monodispersed Fe & pristine \\(\\mathrm{ZnCr_2O_4}\\) in syngas conversion.",
+    "footnote": [],
+    "bbox": [
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preprint/preprint__014d7fcc701aeaec731ab407447a9cff88ca28668ba3db6be184e70f89ec55ce/preprint__014d7fcc701aeaec731ab407447a9cff88ca28668ba3db6be184e70f89ec55ce.mmd

@@ -0,0 +1,196 @@
+
+# Accelerating Syngas-to-Aromatic Conversion via Spontaneously Monodispersed Fe in ZnCr2O4 Spinel  
+
+Guo Tian Tsinghua University Xinyan Liu University of Electronic Science and Technology of China Chenxi Zhang ( cxzhang@mail.tsinghua.edu.cn ) Tsinghua University https://orcid.org/0000- 0002- 1708- 9449 Xiaoyu Fan Tsinghua University Hao Xiong Tsinghua University https://orcid.org/0000- 0001- 5479- 7754 Xiao Chen Tsinghua University https://orcid.org/0000- 0003- 1104- 6146 Wenzheng Li Tsinghua University Binhang Yan Tsinghua University https://orcid.org/0000- 0003- 2833- 8022 Lan Zhang Beijing University of Technology Ning Wang Beijing University of Technology Hong- Jie Peng University of Electronic Science and Technology of China https://orcid.org/0000- 0002- 4183- 703X Fei Wei Tsinghua University https://orcid.org/0000- 0002- 1422- 9784  
+
+# Article  
+
+# Keywords:  
+
+Posted Date: May 13th, 2022  
+
+DOI: https://doi.org/10.21203/rs.3.rs- 1607273/v1
+
+<--- Page Split --->
+
+License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+Version of Record: A version of this preprint was published at Nature Communications on September 22nd, 2022. See the published version at https://doi.org/10.1038/s41467-022-33217-9.
+
+<--- Page Split --->
+
+# Title: Accelerating Syngas-to-Aromatic Conversion via Spontaneously Monodispersed Fe in ZnCr₂O₄ Spinel  
+
+Authors: Guo Tian<sup>†</sup>, Xinyan Liu<sup>†</sup>, Chenxi Zhang<sup>†</sup>, Xiaoyu Fan<sup>†</sup>, Hao Xiong<sup>†</sup>, Xiao Chen<sup>†</sup>,  
+
+Wenzheng Li<sup>†</sup>, Binhang Yan<sup>†</sup>, Lan Zhang<sup>3</sup>, Ning Wang<sup>3</sup>, Hong-Jie Peng<sup>2</sup>, Fei Wei<sup>1\*</sup>  
+
+# Affiliations:  
+
+1. Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology,  
+
+Department of Chemical Engineering, Tsinghua University, Beijing, China, 100084  
+
+2. Institute of Fundamental and Frontier Sciences, University of Electronic Science and  
+
+Technology of China, Chengdu 611731, Sichuan, China  
+
+3. Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China  
+
+† These authors contributed equally to this work  
+
+\* Corresponding authors Fei Wei, email: wf- dce@tsinghua.edu.cn, Chenxi Zhang, email:  
+
+cxzhang@mail.tsinghua.edu.cn, Xiao Chen, email: chenx123@tsinghua.edu.cn, Hong-Jie Peng:  
+
+hjpeng@uestc.edu.cn.
+
+<--- Page Split --->
+
+## Abstract:  
+
+Spontaneous monodispersion of Fe species and their stabilization in reactive atmospheres remain a key challenge in catalytic syngas chemistry. In this study, we present a catalyst with spontaneously monodispersed Fe on \(\mathrm{ZnCr_2O_4}\) , which shows remarkable performance in the syngas- to- aromatic reaction when coupled with a H- ZSM- 5 zeolite. Monodispersed Fe increases the turnover frequency from 0.54 to \(2.48\mathrm{h}^{-1}\) without sacrificing the record high selectivity total aromatic ( \(80\% - 90\%\) ) at a single pass. This is enabled by the activation of CO and \(\mathrm{H}_2\) at oxygen vacancy nearest to the isolated Fe site and the prevention of carbide formation. Atomic precise characterization and theoretical calculations shed light on the origin and implications of spontaneous Fe monodispersion.  
+
+One Sentence Summary: Highly monodispersed Fe is realized on a \(\mathrm{ZnCr_2O_4}\) spinel and stabilized under a highly reactive atmosphere, showing record high catalytic performance in the syngas- to- aromatic reaction.
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+## Main Text:  
+
+## Introduction  
+
+The spontaneous dispersion of catalytically active species on a support has been widely investigated in heterogeneous catalysis due to interactions between the species and support \(^{1,2}\) . Highly dispersed or, more strictly, monodispersed active sites endow the supported catalysts with unique catalytic properties. Spontaneous monodispersion can also improve atomic efficiency. Metal oxides with weak internal cohesive energy, e.g., \(\mathrm{MoO}_3\) and \(\mathrm{NiO}\) , can be easily dispersed on supports such as \(\gamma\) - \(\mathrm{Al}_2\mathrm{O}_3\) and \(\mathrm{TiO}_2\) , manifesting as prototype designs of spontaneously dispersed active sites. Such spontaneous dispersion is driven by negative Gibbs free energy \((\Delta G < 0)\) of breaking internal bonds of oxides being dispersed and forming new bonds between dispersed species and support. From this thermodynamics perspective, the monodispersion of species with strong self- interaction, such as Fe and Pt, presents a daunting challenge in heterogeneous catalysis \(^{3- 5}\) .  
+
+Confining Fe and Pt precursors into the cages of nanoporous materials is an effective means of preparing monodispersed catalysts that is paramount in many sectors of heterogeneous catalysis \(^{6}\) . However, owing to the high surface energy, monodispersed Fe and Pt species tend to migrate and agglomerate under reaction conditions, especially at elevated temperatures, which inevitably cause catalyst deactivation. For instance, Han et al. \(^{7}\) found that the formation of Fe nanoclusters resulted in the deactivation of monodispersed Fe- N- C catalysts in proton exchange membrane fuel cells. Improving the metal- support interaction using an oxygen- donor support was considered an efficient method to stabilize monodispersed species \(^{8}\) . Recently, Nie et al. \(^{9}\) demonstrated how atomically dispersed ionic \(\mathrm{Pt}^{2 + }\) was stabilized by \(\mathrm{CeO}_2\) to endure steam treatment, presenting a feasible way against catalyst sintering. In addition to agglomeration, reactive atmosphere such as
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+syngas can induce irreversible phase transition from metal to metal carbides, e.g. Fe to \(\mathrm{Fe_xC_y}\) , and the as- generated carbides further complicate the reaction pathways of syngas conversion and result in unwanted products \(^{10,11}\) . Therefore, how to stabilize monodispersed Fe species and to avoid \(\mathrm{Fe_xC_y}\) formation in a reactive atmosphere has become a major bottleneck in catalytic syngas conversion.  
+
+In this study, we rationalized \(\mathrm{ZnCr_2O_4}\) spinel with abundant oxygen vacancies (Ov) as an oxygen- donor support. Fe could be spontaneously monodispersed on \(\mathrm{ZnCr_2O_4}\) via a simple impregnation method when the content of Fe was controlled less than 5 wt%. The octahedral site preference energies from the literature \(^{12}\) are in the order of \(\mathrm{Fe^{3 + } < Fe^{2 + } < Cr^{3 + } < Zn^{2 + }}\) , indicating that \(\mathrm{Fe^{2 + }}\) or \(\mathrm{Fe^{3 + }}\) prefers to occupy the octahedral sites, whereas \(\mathrm{Zn^{2 + }}\) tends to occupy the tetrahedral sites in the spinel structure. The spontaneously monodispersed Fe was found not only thermodynamically stable at high temperature but also resistive to carbonization in a reducing atmosphere. When coupled with a H- ZSM- 5 zeolite as a test, \(\mathrm{ZnCr_2O_4}\) with monodispersed Fe showed significantly improved syngas conversion efficiency (45%) with a turnover frequency (TOF) increased from 0.54 to 2.48 h\(^{- 1}\) ; while the high selectivity towards aromatics was maintained by preventing the formation of \(\mathrm{Fe_xC_y}\) . Moreover, the composite catalyst exhibited superior stability as no sign of decay in either the activity or total aromatic selectivity was observed for more than 100 h reaction at 350°C and 2.0 MPa. Density functional theory (DFT) calculations revealed that monodispersed Fe sites in \(\mathrm{ZrCr_2O_4}\) greatly reduced energy barriers of forming formaldehyde (H\(_2\)CO) and methanol (CH\(_3\)OH) from syngas, which were previously identified as key intermediates leading to aromatics \(^{13- 15}\) . This study not only demonstrates a promising monodispersed catalyst that is highly active, selective and stable in reactive syngas atmosphere but also sheds a light on spontaneous monodispersion of catalytically active species for heterogeneous catalysis.
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+## Results and Discussion  
+
+## Identification of spontaneously monodispersed and enriched Fe  
+
+\(Z n C r_{2}O_{4}\) spinels with increasing amounts of Fe (denoted as \(X\%\) Fe- Zn/Cr where X refers to the weight percentage of Fe) were prepared for investigation (supplementary fig.1). The identical crystal structure of \(Z n F e_{2}O_{4}\) and \(Z n C r_{2}O_{4}\) , as well as the similar atomic weights of Fe and Cr, makes it very challenging to identify the state of Fe in \(Z n C r_{2}O_{4}\) 16,17. High- angle annular dark- field scanning transmission electron microscopy (HADDF- STEM) images of different Fe- Zn/Cr all show well- identified atomic lattices of the spinel [001] and [220] surfaces (Figs. 1a,b and supplementary figs.2, 3). Nevertheless, the Fe, Cr, and Zn atoms cannot be distinguished only by the contrast of images. To probe the element distribution, HADDF energy dispersive spectroscopy (EDS) mapping at the atomic scale was employed. Elemental composition through EDS analysis agrees well with the quantitative inductively coupled plasma optical emission spectroscopy results (supplementary fig.4). When the amount of Fe is less than \(4.48 \mathrm{wt}\%\) , distribution of Fe is uniform across the catalyst surface. In contrast, Fe is enriched at the catalyst edges when its amount further increases to \(7.78 \mathrm{wt}\%\) . Furthermore, the spinel structure also exist at the edges, indicating that the Fe- enriched region might be assigned to \(Z n F e_{2}O_{4}\) (supplementary fig.5). This is further verified by DFT simulation of the [220] surfaces of \(Z n C r_{2}O_{4}\) and \(Z n F e_{2}O_{4}\) , which show that the Cr (16d)- Cr (16d) distance is a \(\sim 6\%\) longer than the Fe (16d)- Fe (16d) distance (supplementary fig.6). Such a difference aligns with the analysis of HADDF intensity profiles of a Fe- enriched region (arrow 1) and a Fe- deficient region (arrow 2), respectively (Fig. 1c). Distances between two adjacent 16d sites in the two region are \(0.278 \mathrm{nm}\) and \(0.294 \mathrm{nm}\) , respectively. Therefore, it is inferred that when the Fe amount \(< 4.48 \mathrm{wt}\%\) , it is feasible to monodisperse Fe on the \(Z n C r_{2}O_{4}\) matrix spontaneously; when the Fe amount increases to \(7.78 \mathrm{wt}\%\) , Fe starts to accumulate at the edge of the catalyst surface, likely in form of \(Z n F e_{2}O_{4}\) .
+
+<--- Page Split --->
+![](images/Figure_1.jpg)
+
+<center>Fig. 1. Fe monodispersion on ZnCr2O4 spinel. HADDF-STEM images of (a) 4.48% Fe-Zn/Cr and (b) 7.78% Fe-Zn/Cr and corresponding EDS mappings of Fe element. (c) HADDF intensity </center>
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+profile of two lines at the selected regions shown in the inset HADDF- STEM image. Atomic structure of \(\mathrm{Zn(Cr, Fe)_2O_4}\) spinel is also shown as an inset to illustrate the distance between two adjacent (16d) sites. (d) Fe K- edge X- ray absorption near- edge structure (XANES) profiles of different Fe- Zn/Cr and some standard samples. (e) The first two shells, Fe- O and Fe- Cr/Fe- Fe fittings, of the Fe K- edge Fourier transform- extended X- ray absorption fine structure (FT- EXAFS) spectra of different Fe- Zn/Cr. (f,g) Fe K- edge wavelet transform (WT)- EXAFS spectra of \(4.48\%\) Fe- Zn/Cr and \(7.78\%\) Fe- Zn/Cr, respectively. (h) DFT calculated substitution energies of Fe in the 4a site and 16d sites (including isolated, adjacent and multiple sites) of the \(\mathrm{ZnCr_2O_4}\) matrix. (i) Structure model of pristine \(\mathrm{ZnCr_2O_4}\) showing different sites. (j) Schematic illustration showing how monodispersed Fe endures reactive atmosphere and enriched Fe suffer from phase transition to \(\mathrm{Fe_xC_y}\) during syngas conversion.  
+
+Since electron microscopy analysis only reveals local structure information, XANES and EXAFS based on synchrotron radiation were performed to unveil the coordination structures more globally. The Fe XANES edges of different Fe- Zn/Cr is between those of \(\mathrm{Fe_2O_3}\) and \(\mathrm{FeO}\) , indicating that the average valence state of Fe is between \(+2\) and \(+3\) (Fig. 1d and supplementary fig. 7). FT- EXAFS spectra of different Fe- Zn/Cr reveals a Fe- O distance without phase correction \((1.62 \AA)\) similar to that of \(\mathrm{ZnFe_2O_4}\) (Fig. 1e and supplementary fig. 8a). While for the peak at around \(2.8 \AA\) , Fe- Zn/Cr samples with a Fe content \(\leq 4.48 \mathrm{wt\%}\) show a bond length closer to \(\mathrm{Fe_3O_4}\) and \(7.78\%\) Fe- Zn/Cr is closer to \(\mathrm{ZnFe_2O_4}\) . Although FT- EXAFS cannot distinguish the separate peaks with an atomic number of \(\pm 2\) , e.g. Fe- Fe and Fe- Cr, such a difference is coincided with the DFT calculations (supplementary fig.6) and HADDF- STEM observation (Fig. 1c), suggesting that only the Fe- O- Cr structure exists in spontaneously monodispersed Fe samples (<4.48 wt%),
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+whereas the Fe- O- Cr and Fe- O- Fe structures coexist in the enriched Fe sample (7.78 wt%). Further Fe 2p X- ray photoelectron spectroscopy (XPS) analysis shown in supplementary fig. 8b supports the above deduction from FT- EXAFS \(^{18,19}\) .  
+
+The subtle difference in local coordination structures between samples with monodispersed and enriched Fe is further confirmed using WT- EXAFS (Figs. 1f, 1g and supplementary fig. 9). In general, the WT- EXAFS heat maps show two scattering centers, which are associated with Fe- O and Fe- Cr/Fe- Fe, Fe- Fe, respectively. The k- axis location of the second scattering center shifts to higher values with increasing Fe amounts (6.46 Å \(^{- 1}\) , 6.49 Å \(^{- 1}\) , 6.73 Å \(^{- 1}\) and 6.83 Å \(^{- 1}\) when the Fe amount is 2.89%, 4.48%, 7.78%, and 100% (i.e. ZnFe \(_2\) O \(_4\) )), corresponding to shortened bond lengths (supplementary fig. 10). This aligns well with the transition from longer Fe- Cr to shorter Fe- Fe. The FT- EXAFS and WT- EXAFS results were further fitted using optimized doped Fe- Zn/Cr systems (Fig. 1e and supplementary fig. 11). Fitted results show that the coordination numbers (CNs) of Fe- O (i.e. the nearest neighboring shell) are more than 4 for all Fe- Zn/Cr samples, verifying the 16d site as Fe- substitution site because 4a site’s CN is less than 4 (table S1) \(^{20}\) . All the fitting results including The CN, bond distances (R), and Debye–Waller factor (σ²) are listed in Table S1 supplementary Materials.  
+
+The above electron microscopic and synchrotron radiation- based spectroscopic characterizations experimentally validates the monodispersion of Fe when the Fe content is below 4.48 wt%. We then performed DFT calculations to understand the origin of Fe monodispersion. Energies that are required for Fe substituting Cr or Zn atoms at different sites on the (111) surface of ZnCr \(_2\) O \(_4\) are shown in Fig. 1h, along with the atomic surface model of ZnCr \(_2\) O \(_4\) (111) surface shown in Fig. 1i (calculation details are presented in Supplementary Materials). Notably, substitution of Cr by Fe at the (16d) site is thermodynamically preferred (−2.43 eV) over substitution of Zn at the (4a) site (3.47 eV) (with structures shown in supplementary figs. 12a, b).
+
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+When more Cr (16d) sites are considered for Fe substitution, it is found that Fe preferrs to substitute a (16d) site far from the existing doping site rather than an adjacent one, with a substitution energy being \(0.96\mathrm{eV}\) more negative (with structures shown in supplementary fig. 12c, d). Namely, isolated (16d) sites are preferably occupied upon Fe doping. Note that calculated substitution energies at isolated (16d) sites remain negative when the doping concentration of Fe increases from \(6.25\%\) to \(25\%\) (normalized to all the surface metal atoms of spinel (111) surface), providing theoretical insights into spontaneous monodispersion of Fe on \(\mathrm{ZnCr_2O_4}\) . By using DFT calculation, we also investigated the stability of isolated Fe doping site in a reactive syngas condition. The formation energies of \(\mathrm{Fe_5C_2}\) (as a representative \(\mathrm{Fe_xC_y}\) phase) from Fe in \(\mathrm{ZnFe_2O_4}\) and monodispersed Fe in \(\mathrm{ZnCr_2O_4}\) are \(- 3.17\mathrm{eV}\) and \(1.25\mathrm{eV}\) and per Fe atom, respectively, indicating the strong tendency of \(\mathrm{ZnFe_2O_4}\) and high resistance of Fe- Zn/Cr to form carbides. Therefore, we propose a catalyst evolution scene herein (Fig. 1j). In a oxide- zeolite (OX- ZEO) tandem catalytic system for syngas conversion, the oxide catalyzes the formation of oxygenates such as \(\mathrm{H_2CO}\) and \(\mathrm{CH_3OH}\) from syngas and the zeolite further selectively catalyzes these oxygenates to produce aromatics \(^{21 - 23}\) . The Fe- enriched oxide catalysts, however, suffers from irreversible phase transition to \(\mathrm{Fe_xC_y}\) , which follows Anderson- Schulz- Flory theory and inevitably leads to alkanes with uncontrolled carbon atoms per molecule. In contrast, the monodispersed Fe with superior stability against sintering and carbonization could enable stable aromatics formation from syngas.  
+
+## Catalyst performance of monodispersed and enriched Fe in \(\mathrm{ZnCr_2O_4}\) matrix coupled with H-ZSM-5 for syngas conversion  
+
+To validate above prospect on the superior stability of monodispersed Fe in \(\mathrm{ZnCr_2O_4}\) through theoretical prediction, different Fe- Zn/Cr oxides were coupled with H- ZSM- 5 as tandem catalysts for selective syngas conversion to aromatics. Fig. 2a shows the catalyst evaluation of Fe- Zn/Crvs.
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+pristine \(\mathrm{ZnCr_2O_4}\) (denoted as \(\mathrm{Zn / Cr}\) ) at a reaction conditions of \(2.0\mathrm{MPa}\) \(\mathrm{CO:H_2 = 1:1)}\) , \(350^{\circ}\) C and \(600\mathrm{ml}\mathrm{h}^{- 1}\cdot \mathrm{g}_{\mathrm{cat}}^{- 1}\) . CO conversion increases from \(13\%\) using pristine \(\mathrm{Zn / Cr}\) to \(45\%\) using \(4.48\%\) Fe- Zn/Cr with monodispersed Fesites; meanwhile, the total aromatic selectivity remains as high as \(80\%\) with various alkanes as minor side products. Since the conversion processes in tandem, i.e. syngas to oxygenates and oxygenates to aromatics, are spatially separated, the enhanced CO conversion is attributed to the activation effect of Fe dopants on CO and \(\mathrm{H_2}\) to accelerate oxygenates formation. In situ diffuse reflectance infrared Fourier transform spectroscopy results shows that signal intensities of oxygenate intermediates are at least an order of magnitude higher on \(4.48\%\) Fe- Zn/Cr than on pristine \(\mathrm{Zn / Cr}\) , corroborating the above analysis (fig. S13). \(7.78\%\) Fe- Zn/Cr with enriched Fe increases the CO conversion to \(51\%\) but sacrifices the total aromatic selectivity from \(80\%\) to \(44\%\) , along with significantly higher yields of \(\mathrm{CH_4}\) and \(\mathrm{C_2 - C_4}\) alkanes. Since these alkanes are typical products of \(\mathrm{Fe_xC_y}\) - catalyzed syngas conversion \(^{24,25}\) , we characterized spent \(7.78\%\) Fe- Zn/Cr using HADDF- STEM and observed \(\mathrm{Fe_xC_y}\) phases (fig. S14). The \(\mathrm{Fe_xC_y}\) phases likely originated from the Fe- O- Fe structure present in the enriched Fe parts of \(7.78\%\) Fe- Zn/Cr and this is rationalized by the negative formation energy of carbides from \(\mathrm{ZnFe_2O_4}\) in former theoretical prediction \(^{24,25}\) .
+
+<--- Page Split --->
+![](images/Figure_2.jpg)
+
+<center>Fig. 2. Catalyst evaluation of monodispersed and enriched Fe. (a) Catalyst evaluation of pristine \(\mathrm{ZnCr_2O_4}\) and different Fe-Zn/Cr with H-ZSM-5; reaction conditions: \(350^{\circ}\mathrm{C}\) , 2.0 MPa, space velocity \(= 600\mathrm{ml}\mathrm{h}^{-1}\cdot \mathrm{g}_{\mathrm{cat}}^{-1}\) , \(\mathrm{CO:H_2 = 1:1}\) . (b) The optimal space time yield (STY) of aromatics for different catalysts (note that the STYs were obtained at the optimized reaction conditions for each catalyst and the corresponding reaction conditions are summarized in table S3). (c) TOF and aromatic selectivity as functions of Fe contents in \(\mathrm{ZnCr_2O_4}\) and reaction conditions are similar as A. (d) Comparison between reported Fe-based catalysts and isolated Fe-Zn/Cr + H-ZSM-5 catalyst on syngas conversion to aromatics. (e) Stability test of \(4.48\%\) Fe-Zn/Cr + H-ZSM-5 catalytic system for \(100\mathrm{h}\) . </center>  
+
+As a typical tandem reaction over a bifunctional OX- ZEO catalyst, it has been reported that and low temperature and high pressure are detrimental to aromatics selectivity and CO conversion, respectively \(^{26,27}\) . In this study, these two problems were tackled by monodispersion of Fe on \(\mathrm{ZnCr_2O_4}\) . A low reaction temperature of \(275^{\circ}\mathrm{C}\) was previously found to increase the single pass
+
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+aromatic selectivity to \(\sim 82\% - 84\%\) using unmodified \(\mathrm{ZnCr_2O_4 + H - ZSM - 5}\) catalysts; while \(4.48\%\) Fe- Zn/Cr profoundly promotes the aromatic selectivity up to \(88\%\) to \(91\%\) , a value that has rarely been achieved (supplementary figs. 15a,b). It means that even at a low temperature, the monodispersed Fe is able to activate CO and \(\mathrm{H}_2\) to produce oxygenate intermediates, which further diffuse into the pores of zeolites to initiate C- C coupling and subsequent aromatics formation \(^{22,28}\) . In addition to low temperature performance, \(\mathrm{ZnCr_2O_4}\) with monodispersed Fe also exhibit outstanding high pressure performance (supplementary figs. 15c,d). The CO conversion increases remarkably from \(44\%\) to \(68\%\) , whereas selectivity towards \(\mathrm{CH_4}\) and \(\mathrm{C}_2\mathrm{- C}_4\) side products is only marginally increased by \(< 1\%\) . By analyzing the Fe contents and STYs of aromatics, it is found that STYs increase with higher Fe amounts only when Fe remains monodispersed and once Fe is further enriched to form Fe- O- Fe structure prone to carbonization, STYs of aromatics deteriorate due to the emergence of conventional Fischer- Tropsch pathways (Fig. 2b and supplementary fig. 16a) \(^{29}\) . This trend is also notable when projecting TOF and total aromatic selectivity to the Fe content; monodispersed Fe increases the TOF of syngas conversion and maintains high selectivity towards aromatics, whereas enriched Fe sacrifices the aromatic selectivity despite the higher TOF (Fig. 2c). As shown in Fig. 2d and supplementary fig. 16b, the outstanding ability of our monodispersed Fe- Zn/Cr based OX- ZEO catalysts to achieve high CO conversion and high aromatics selectivity simultaneously is further illustrated through comparison with several recently reported catalysts for syngas conversion process \(^{30 - 34}\) . Further, the long- term stability test of \(4.48\%\) Fe- Zn/Cr + H- ZSM- 5 shows no signs of decay in either the activity or total aromatic selectivity for more than \(100\mathrm{~h}\) at \(350^{\circ}\mathrm{C}\) and \(2.0\mathrm{MPa}\) and stable CO conversion of \(\sim 45\%\) is achieved (Fig. 2e).
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+## Mechanistic investigation of monodispersed Fe in enhanced catalytic activity  
+
+The above catalysis evaluation demonstrates the unique ability of monodispersed Fe to catalyze syngas to form reactive oxygenates. To understand the origin of such enhanced activity, we further performed a series of combinatorial mechanistic investigations. Previous demonstrations have suggested surface \(\mathrm{Ov}\) as possible active sites for CO activation \(^{35,36}\) . However, there is a lack of high- precision characterization of \(\mathrm{Ov}^{37}\) . DFT calculations were firstly performed to assess the equilibrium \(\mathrm{Ov}\) concentration in monodispersed Fe- Zn/Cr samples. Using spinel (111) surface as a model surface, the formation energies of \(\mathrm{Ov}\) with different concentrations and spatial locations (i.e., surface and subsurface layer) were calculated through either CO or \(\mathrm{H}_{2}\) reduction (calculation details are presented in Supplementary Materials). Similar to a previous study, CO reduction was found to be the major driving force for \(\mathrm{Ov}\) formation \(^{13}\) (supplementary fig. 17). The phase diagram shows that \(\mathrm{Ov}\) preferably presents in the surface layer with a concentration of 0.25 monolayer (ML) under the experimental condition (Fig. 3a). The atomic evidence of surface \(\mathrm{Ov}\) was obtained by using the integrated differential phase- contrast STEM (iDPC- STEM) technique. Fig. 3b depicts the [111] surface of 4.48 % Fe- Zn/Cr and some \(\mathrm{Ov}\) can be identified with atomic precision. A microquantitative intensity statistic analysis was subsequently performed by applying line scan to the iDPC- STEM images. The non- periodic variations in image contrast relating to O demonstrate the existence of \(\mathrm{Ov}\) and its concentration is estimated to be 25%–28%, similar to the theoretically predicted \(\mathrm{Ov}\) concentration (Fig. 3c). O1s XPS spectra of different Fe- Zn/Cr samples further show that Fe doping only alters surface \(\mathrm{Ov}\) concentration by insignificant amounts (Fig. 3d). Electron paramagnetic resonance spectroscopy also quantitatively evidences the existence of \(\mathrm{Ov}\) (supplementary figs. 18a, b). It is found that there are not much changes in the \(\mathrm{Ov}\) content.
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+![](images/Figure_3.jpg)
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+<center>Fig. 3. Understanding the role of monodispersed Fe in catalytic activity. (a) Thermodynamic phase diagram of Fe-doped \(\mathrm{ZnCr_2O_4}\) (111) surfaces at varying temperatures and CO partial pressures \((P_{\mathrm{CO}})\) (b) iDPC-STEM images of \(4.48\%\) Fe-Zn/Cr. (c) iDPC-STEM intensity profiles along the corresponding dashed lines in B. (d) O 1s XPS spectra of \(\mathrm{ZnCr_2O_4}\) and different Fe-Zn/Cr samples. (e) Projected electric field strength and vector map of \(4.48\%\) Fe-Zn/Cr. (f) Differential charge density (cyan and yellow represent charge depletion and accumulation, respectively; the cutoff of the density-difference isosurface is \(0.11\mathrm{e~Å}^{-3}\) ). </center>  
+
+Since the \(\mathrm{O}_{\mathrm{V}}\) concentration remains marginally affected by Fe doping, it might not be the reason for the enhanced catalytic activity. We then focused on the effect of local electronic structures and performed a charge density analysis by combining electric field imaging and theoretical calculations. As shown in Fig. 3e and supplementary figs. 18c,d, some local bright spots (in red circle) appear despite the surrounding uniform intensity, implying the possible charge transfer between isolated Fe and surrounding O and \(\mathrm{O}_{\mathrm{V}}\) 38. This is corroborated by the theoretically
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+calculated charge density (Fig. 3f). Clearly, charges on Ov and O nearest to the Fe dopant increase in Fe- doped \(\mathrm{ZnCr_2O_4}\) with respect to pristine \(\mathrm{ZnCr_2O_4}\) . The locally modulated charge density could account for the difference in catalytic activity between monodispersed Fe- Zn/Cr and Zn/Cr. A series of experiments were conducted to prove the promoted activation effect of Fe dopants on CO and \(\mathrm{H}_2\) (supplementary figs. 19, 20).  
+
+CO hydrogenation reaction pathways were investigated using DFT calculations. By taking the aforementioned structural characterization and theoretical analysis into consideration, undoped and isolated Fe- doped \(\mathrm{ZnCr_2O_4}\) (111) surfaces with 1/4 ML Ov on the surface were constructed for simulations. The gas phase energies of CO and \(\mathrm{H}_2\) are adjusted using the actual experimental conditions. On both surfaces, our calculations support the stepwise hydrogenation mechanism in syngas conversion (Fig. 4a) \(^{13,15}\) . In more details, the reaction starts with CO adsorption at Ov (Fig. 4b). Next, \(\mathrm{H}_2\) dissociates near the surface Ov and \(\mathrm{H}^*\) is adsorbed on top of an O near Fe (16d). Subsequently, adsorbed CO\* is stepwise hydrogenated to form formyl (CHO\*), formaldehyde (CH2O\*), methoxyl (CH3O\*), and finally CH3OH. It is found that pristine \(\mathrm{ZnCr_2O_4}\) (111) with Ov is mainly limited by the weak adsorption of \(\mathrm{H}^*\) with the presence of CHO\*, as well as strong adsorption of CH3O\*. The incorporation of isolated Fe effectively stabilizes the state of CHO\* + H\*, making the hydrogenation step to form H2CO\* energetically exothermic, while destabilizes the state of CH3O\* + H\*, thereby facilitating the formation of CH3OH. From both aspects, monodispersed Fe improves the ability of pristine \(\mathrm{ZnCr_2O_4}\) to produce key oxygenate intermediates of H2CO and CH3OH from syngas. These key intermediates further participate in complicated oxygenate- to- aromatics reactions occurring in the pores of H- ZSM- 5 \(^{39}\) .
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+![](images/Figure_4.jpg)
+
+<center>Fig. 4. The mechanism study of monodispersed Fe & pristine \(\mathrm{ZnCr_2O_4}\) in syngas conversion. </center>  
+
+(a) Gibbs free energy diagrams of syngas conversion to \(\mathrm{CH_3OH}\) on pristine and Fe-doped \(\mathrm{ZnCr_2O_4}\) (111) surfaces with 1/4 ML \(\mathrm{Ov}\) . The reaction images are shown in the inset of a; Zn: green, Cr: blue, Fe: yellow, O: red, C: brown, and H: white. (b) The atomic structures of the corresponding states in a.  
+
+## Conclusion  
+
+In this study, we demonstrated that the spinel structure of \(\mathrm{ZnCr_2O_4}\) can disperse strong self- interaction metal, Fe, spontaneously due to the thermodynamically stable state caused by the interaction between Fe and \(\mathrm{ZnCr_2O_4}\) , showing the highest performance in syngas- to- aromatic reaction. By combining high- precision microscopic and macroscopic characterizations and theory calculations, monodispersed Fe activates the surrounding \(\mathrm{Ov}\) , thereby activating the CO and \(\mathrm{H_2}\)
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+while avoiding the formation of \(\mathrm{Fe_xC_y}\) and maintaining stability under a reducing atmosphere. The TOF increased from 0.54 to \(2.48 \mathrm{h}^{- 1}\) without sacrificing the aromatic selectivity in monodispersed Fe samples. Our monodispersed Fe in the \(\mathrm{ZnCr_2O_4}\) catalyst showed \(81.4\%\) aromatic selectivity at a single- pass CO conversion of \(68.4\%\) , which is the best catalyst evaluation among all Fe- based catalysts during the syngas- to- aromatic reaction. DFT calculations revealed that the monodispersion of Fe in 16d sites lowered the key determining step of \(\mathrm{HCO^* + H^* - H_2CO^*}\) , thereby accelerating the C1 oxygenate intermediates, which made them diffuse quickly into the pores of H- ZSM- 5 to initiate the C- C coupling toward the aromatics. This study provides a prototype for rationally tailoring single atom Fe in syngas chemistry to obtain targeted catalytic reactivity and avoid the formation of \(\mathrm{Fe_xC_y}\) .
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+
+<--- Page Split --->
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+
+## Acknowledgements:  
+
+This work is supported by the National Natural Science Foundation of China (21908125 and 22005170), the National Key Research and Development Program of China (2018YFB0604801), the Key Research and Development Program of Inner Mongolia and Ordos, and CNPC Innovation Funds.  
+
+Author contributions: G.T and X.L contributed equally. All the authors approved the final version of the manuscript.  
+
+Competing financial interests: The authors declare no competing financial interests.  
+
+Data and materials availability: All data are available in the manuscript or the supplementary materials.  
+
+Supplementary Information:  
+
+Materials and Methods  
+
+Figs. S1 to S20  
+
+Table S1 to S4  
+
+References (1- 9)
+
+<--- Page Split --->
+
+## Supplementary Files  
+
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+SupplementaryInformationSubmitNatureCommunication.pdf
+
+<--- Page Split --->

preprint/preprint__014d7fcc701aeaec731ab407447a9cff88ca28668ba3db6be184e70f89ec55ce/preprint__014d7fcc701aeaec731ab407447a9cff88ca28668ba3db6be184e70f89ec55ce_det.mmd

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+<|ref|>title<|/ref|><|det|>[[44, 108, 888, 208]]<|/det|>
+# Accelerating Syngas-to-Aromatic Conversion via Spontaneously Monodispersed Fe in ZnCr2O4 Spinel  
+
+<|ref|>text<|/ref|><|det|>[[44, 230, 588, 777]]<|/det|>
+Guo Tian Tsinghua University Xinyan Liu University of Electronic Science and Technology of China Chenxi Zhang ( cxzhang@mail.tsinghua.edu.cn ) Tsinghua University https://orcid.org/0000- 0002- 1708- 9449 Xiaoyu Fan Tsinghua University Hao Xiong Tsinghua University https://orcid.org/0000- 0001- 5479- 7754 Xiao Chen Tsinghua University https://orcid.org/0000- 0003- 1104- 6146 Wenzheng Li Tsinghua University Binhang Yan Tsinghua University https://orcid.org/0000- 0003- 2833- 8022 Lan Zhang Beijing University of Technology Ning Wang Beijing University of Technology Hong- Jie Peng University of Electronic Science and Technology of China https://orcid.org/0000- 0002- 4183- 703X Fei Wei Tsinghua University https://orcid.org/0000- 0002- 1422- 9784  
+
+<|ref|>title<|/ref|><|det|>[[44, 825, 100, 841]]<|/det|>
+# Article  
+
+<|ref|>title<|/ref|><|det|>[[44, 862, 135, 879]]<|/det|>
+# Keywords:  
+
+<|ref|>text<|/ref|><|det|>[[44, 899, 297, 917]]<|/det|>
+Posted Date: May 13th, 2022  
+
+<|ref|>text<|/ref|><|det|>[[44, 936, 473, 954]]<|/det|>
+DOI: https://doi.org/10.21203/rs.3.rs- 1607273/v1
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[42, 44, 911, 87]]<|/det|>
+License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+<|ref|>text<|/ref|><|det|>[[42, 123, 914, 167]]<|/det|>
+Version of Record: A version of this preprint was published at Nature Communications on September 22nd, 2022. See the published version at https://doi.org/10.1038/s41467-022-33217-9.
+
+<--- Page Split --->
+<|ref|>title<|/ref|><|det|>[[72, 80, 875, 135]]<|/det|>
+# Title: Accelerating Syngas-to-Aromatic Conversion via Spontaneously Monodispersed Fe in ZnCr₂O₄ Spinel  
+
+<|ref|>text<|/ref|><|det|>[[111, 220, 884, 243]]<|/det|>
+Authors: Guo Tian<sup>†</sup>, Xinyan Liu<sup>†</sup>, Chenxi Zhang<sup>†</sup>, Xiaoyu Fan<sup>†</sup>, Hao Xiong<sup>†</sup>, Xiao Chen<sup>†</sup>,  
+
+<|ref|>text<|/ref|><|det|>[[111, 264, 779, 285]]<|/det|>
+Wenzheng Li<sup>†</sup>, Binhang Yan<sup>†</sup>, Lan Zhang<sup>3</sup>, Ning Wang<sup>3</sup>, Hong-Jie Peng<sup>2</sup>, Fei Wei<sup>1\*</sup>  
+
+<|ref|>title<|/ref|><|det|>[[113, 338, 217, 355]]<|/det|>
+# Affiliations:  
+
+<|ref|>text<|/ref|><|det|>[[111, 370, 812, 392]]<|/det|>
+1. Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology,  
+
+<|ref|>text<|/ref|><|det|>[[111, 405, 808, 426]]<|/det|>
+Department of Chemical Engineering, Tsinghua University, Beijing, China, 100084  
+
+<|ref|>text<|/ref|><|det|>[[111, 440, 815, 460]]<|/det|>
+2. Institute of Fundamental and Frontier Sciences, University of Electronic Science and  
+
+<|ref|>text<|/ref|><|det|>[[111, 475, 590, 495]]<|/det|>
+Technology of China, Chengdu 611731, Sichuan, China  
+
+<|ref|>text<|/ref|><|det|>[[111, 510, 869, 531]]<|/det|>
+3. Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China  
+
+<|ref|>text<|/ref|><|det|>[[111, 584, 496, 604]]<|/det|>
+† These authors contributed equally to this work  
+
+<|ref|>text<|/ref|><|det|>[[111, 626, 820, 647]]<|/det|>
+\* Corresponding authors Fei Wei, email: wf- dce@tsinghua.edu.cn, Chenxi Zhang, email:  
+
+<|ref|>text<|/ref|><|det|>[[111, 661, 870, 682]]<|/det|>
+cxzhang@mail.tsinghua.edu.cn, Xiao Chen, email: chenx123@tsinghua.edu.cn, Hong-Jie Peng:  
+
+<|ref|>text<|/ref|><|det|>[[111, 696, 290, 715]]<|/det|>
+hjpeng@uestc.edu.cn.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[115, 81, 197, 98]]<|/det|>
+## Abstract:  
+
+<|ref|>text<|/ref|><|det|>[[111, 113, 885, 415]]<|/det|>
+Spontaneous monodispersion of Fe species and their stabilization in reactive atmospheres remain a key challenge in catalytic syngas chemistry. In this study, we present a catalyst with spontaneously monodispersed Fe on \(\mathrm{ZnCr_2O_4}\) , which shows remarkable performance in the syngas- to- aromatic reaction when coupled with a H- ZSM- 5 zeolite. Monodispersed Fe increases the turnover frequency from 0.54 to \(2.48\mathrm{h}^{-1}\) without sacrificing the record high selectivity total aromatic ( \(80\% - 90\%\) ) at a single pass. This is enabled by the activation of CO and \(\mathrm{H}_2\) at oxygen vacancy nearest to the isolated Fe site and the prevention of carbide formation. Atomic precise characterization and theoretical calculations shed light on the origin and implications of spontaneous Fe monodispersion.  
+
+<|ref|>text<|/ref|><|det|>[[112, 464, 884, 552]]<|/det|>
+One Sentence Summary: Highly monodispersed Fe is realized on a \(\mathrm{ZnCr_2O_4}\) spinel and stabilized under a highly reactive atmosphere, showing record high catalytic performance in the syngas- to- aromatic reaction.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[115, 82, 211, 99]]<|/det|>
+## Main Text:  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 150, 225, 168]]<|/det|>
+## Introduction  
+
+<|ref|>text<|/ref|><|det|>[[111, 184, 886, 551]]<|/det|>
+The spontaneous dispersion of catalytically active species on a support has been widely investigated in heterogeneous catalysis due to interactions between the species and support \(^{1,2}\) . Highly dispersed or, more strictly, monodispersed active sites endow the supported catalysts with unique catalytic properties. Spontaneous monodispersion can also improve atomic efficiency. Metal oxides with weak internal cohesive energy, e.g., \(\mathrm{MoO}_3\) and \(\mathrm{NiO}\) , can be easily dispersed on supports such as \(\gamma\) - \(\mathrm{Al}_2\mathrm{O}_3\) and \(\mathrm{TiO}_2\) , manifesting as prototype designs of spontaneously dispersed active sites. Such spontaneous dispersion is driven by negative Gibbs free energy \((\Delta G < 0)\) of breaking internal bonds of oxides being dispersed and forming new bonds between dispersed species and support. From this thermodynamics perspective, the monodispersion of species with strong self- interaction, such as Fe and Pt, presents a daunting challenge in heterogeneous catalysis \(^{3- 5}\) .  
+
+<|ref|>text<|/ref|><|det|>[[111, 568, 886, 902]]<|/det|>
+Confining Fe and Pt precursors into the cages of nanoporous materials is an effective means of preparing monodispersed catalysts that is paramount in many sectors of heterogeneous catalysis \(^{6}\) . However, owing to the high surface energy, monodispersed Fe and Pt species tend to migrate and agglomerate under reaction conditions, especially at elevated temperatures, which inevitably cause catalyst deactivation. For instance, Han et al. \(^{7}\) found that the formation of Fe nanoclusters resulted in the deactivation of monodispersed Fe- N- C catalysts in proton exchange membrane fuel cells. Improving the metal- support interaction using an oxygen- donor support was considered an efficient method to stabilize monodispersed species \(^{8}\) . Recently, Nie et al. \(^{9}\) demonstrated how atomically dispersed ionic \(\mathrm{Pt}^{2 + }\) was stabilized by \(\mathrm{CeO}_2\) to endure steam treatment, presenting a feasible way against catalyst sintering. In addition to agglomeration, reactive atmosphere such as
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[111, 78, 884, 239]]<|/det|>
+syngas can induce irreversible phase transition from metal to metal carbides, e.g. Fe to \(\mathrm{Fe_xC_y}\) , and the as- generated carbides further complicate the reaction pathways of syngas conversion and result in unwanted products \(^{10,11}\) . Therefore, how to stabilize monodispersed Fe species and to avoid \(\mathrm{Fe_xC_y}\) formation in a reactive atmosphere has become a major bottleneck in catalytic syngas conversion.  
+
+<|ref|>text<|/ref|><|det|>[[111, 252, 886, 904]]<|/det|>
+In this study, we rationalized \(\mathrm{ZnCr_2O_4}\) spinel with abundant oxygen vacancies (Ov) as an oxygen- donor support. Fe could be spontaneously monodispersed on \(\mathrm{ZnCr_2O_4}\) via a simple impregnation method when the content of Fe was controlled less than 5 wt%. The octahedral site preference energies from the literature \(^{12}\) are in the order of \(\mathrm{Fe^{3 + } < Fe^{2 + } < Cr^{3 + } < Zn^{2 + }}\) , indicating that \(\mathrm{Fe^{2 + }}\) or \(\mathrm{Fe^{3 + }}\) prefers to occupy the octahedral sites, whereas \(\mathrm{Zn^{2 + }}\) tends to occupy the tetrahedral sites in the spinel structure. The spontaneously monodispersed Fe was found not only thermodynamically stable at high temperature but also resistive to carbonization in a reducing atmosphere. When coupled with a H- ZSM- 5 zeolite as a test, \(\mathrm{ZnCr_2O_4}\) with monodispersed Fe showed significantly improved syngas conversion efficiency (45%) with a turnover frequency (TOF) increased from 0.54 to 2.48 h\(^{- 1}\) ; while the high selectivity towards aromatics was maintained by preventing the formation of \(\mathrm{Fe_xC_y}\) . Moreover, the composite catalyst exhibited superior stability as no sign of decay in either the activity or total aromatic selectivity was observed for more than 100 h reaction at 350°C and 2.0 MPa. Density functional theory (DFT) calculations revealed that monodispersed Fe sites in \(\mathrm{ZrCr_2O_4}\) greatly reduced energy barriers of forming formaldehyde (H\(_2\)CO) and methanol (CH\(_3\)OH) from syngas, which were previously identified as key intermediates leading to aromatics \(^{13- 15}\) . This study not only demonstrates a promising monodispersed catalyst that is highly active, selective and stable in reactive syngas atmosphere but also sheds a light on spontaneous monodispersion of catalytically active species for heterogeneous catalysis.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[115, 81, 310, 100]]<|/det|>
+## Results and Discussion  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 115, 648, 135]]<|/det|>
+## Identification of spontaneously monodispersed and enriched Fe  
+
+<|ref|>text<|/ref|><|det|>[[111, 145, 886, 904]]<|/det|>
+\(Z n C r_{2}O_{4}\) spinels with increasing amounts of Fe (denoted as \(X\%\) Fe- Zn/Cr where X refers to the weight percentage of Fe) were prepared for investigation (supplementary fig.1). The identical crystal structure of \(Z n F e_{2}O_{4}\) and \(Z n C r_{2}O_{4}\) , as well as the similar atomic weights of Fe and Cr, makes it very challenging to identify the state of Fe in \(Z n C r_{2}O_{4}\) 16,17. High- angle annular dark- field scanning transmission electron microscopy (HADDF- STEM) images of different Fe- Zn/Cr all show well- identified atomic lattices of the spinel [001] and [220] surfaces (Figs. 1a,b and supplementary figs.2, 3). Nevertheless, the Fe, Cr, and Zn atoms cannot be distinguished only by the contrast of images. To probe the element distribution, HADDF energy dispersive spectroscopy (EDS) mapping at the atomic scale was employed. Elemental composition through EDS analysis agrees well with the quantitative inductively coupled plasma optical emission spectroscopy results (supplementary fig.4). When the amount of Fe is less than \(4.48 \mathrm{wt}\%\) , distribution of Fe is uniform across the catalyst surface. In contrast, Fe is enriched at the catalyst edges when its amount further increases to \(7.78 \mathrm{wt}\%\) . Furthermore, the spinel structure also exist at the edges, indicating that the Fe- enriched region might be assigned to \(Z n F e_{2}O_{4}\) (supplementary fig.5). This is further verified by DFT simulation of the [220] surfaces of \(Z n C r_{2}O_{4}\) and \(Z n F e_{2}O_{4}\) , which show that the Cr (16d)- Cr (16d) distance is a \(\sim 6\%\) longer than the Fe (16d)- Fe (16d) distance (supplementary fig.6). Such a difference aligns with the analysis of HADDF intensity profiles of a Fe- enriched region (arrow 1) and a Fe- deficient region (arrow 2), respectively (Fig. 1c). Distances between two adjacent 16d sites in the two region are \(0.278 \mathrm{nm}\) and \(0.294 \mathrm{nm}\) , respectively. Therefore, it is inferred that when the Fe amount \(< 4.48 \mathrm{wt}\%\) , it is feasible to monodisperse Fe on the \(Z n C r_{2}O_{4}\) matrix spontaneously; when the Fe amount increases to \(7.78 \mathrm{wt}\%\) , Fe starts to accumulate at the edge of the catalyst surface, likely in form of \(Z n F e_{2}O_{4}\) .
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[117, 150, 880, 808]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[111, 840, 884, 896]]<|/det|>
+<center>Fig. 1. Fe monodispersion on ZnCr2O4 spinel. HADDF-STEM images of (a) 4.48% Fe-Zn/Cr and (b) 7.78% Fe-Zn/Cr and corresponding EDS mappings of Fe element. (c) HADDF intensity </center>
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[111, 80, 886, 450]]<|/det|>
+profile of two lines at the selected regions shown in the inset HADDF- STEM image. Atomic structure of \(\mathrm{Zn(Cr, Fe)_2O_4}\) spinel is also shown as an inset to illustrate the distance between two adjacent (16d) sites. (d) Fe K- edge X- ray absorption near- edge structure (XANES) profiles of different Fe- Zn/Cr and some standard samples. (e) The first two shells, Fe- O and Fe- Cr/Fe- Fe fittings, of the Fe K- edge Fourier transform- extended X- ray absorption fine structure (FT- EXAFS) spectra of different Fe- Zn/Cr. (f,g) Fe K- edge wavelet transform (WT)- EXAFS spectra of \(4.48\%\) Fe- Zn/Cr and \(7.78\%\) Fe- Zn/Cr, respectively. (h) DFT calculated substitution energies of Fe in the 4a site and 16d sites (including isolated, adjacent and multiple sites) of the \(\mathrm{ZnCr_2O_4}\) matrix. (i) Structure model of pristine \(\mathrm{ZnCr_2O_4}\) showing different sites. (j) Schematic illustration showing how monodispersed Fe endures reactive atmosphere and enriched Fe suffer from phase transition to \(\mathrm{Fe_xC_y}\) during syngas conversion.  
+
+<|ref|>text<|/ref|><|det|>[[112, 531, 886, 907]]<|/det|>
+Since electron microscopy analysis only reveals local structure information, XANES and EXAFS based on synchrotron radiation were performed to unveil the coordination structures more globally. The Fe XANES edges of different Fe- Zn/Cr is between those of \(\mathrm{Fe_2O_3}\) and \(\mathrm{FeO}\) , indicating that the average valence state of Fe is between \(+2\) and \(+3\) (Fig. 1d and supplementary fig. 7). FT- EXAFS spectra of different Fe- Zn/Cr reveals a Fe- O distance without phase correction \((1.62 \AA)\) similar to that of \(\mathrm{ZnFe_2O_4}\) (Fig. 1e and supplementary fig. 8a). While for the peak at around \(2.8 \AA\) , Fe- Zn/Cr samples with a Fe content \(\leq 4.48 \mathrm{wt\%}\) show a bond length closer to \(\mathrm{Fe_3O_4}\) and \(7.78\%\) Fe- Zn/Cr is closer to \(\mathrm{ZnFe_2O_4}\) . Although FT- EXAFS cannot distinguish the separate peaks with an atomic number of \(\pm 2\) , e.g. Fe- Fe and Fe- Cr, such a difference is coincided with the DFT calculations (supplementary fig.6) and HADDF- STEM observation (Fig. 1c), suggesting that only the Fe- O- Cr structure exists in spontaneously monodispersed Fe samples (<4.48 wt%),
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[111, 78, 884, 170]]<|/det|>
+whereas the Fe- O- Cr and Fe- O- Fe structures coexist in the enriched Fe sample (7.78 wt%). Further Fe 2p X- ray photoelectron spectroscopy (XPS) analysis shown in supplementary fig. 8b supports the above deduction from FT- EXAFS \(^{18,19}\) .  
+
+<|ref|>text<|/ref|><|det|>[[111, 184, 886, 622]]<|/det|>
+The subtle difference in local coordination structures between samples with monodispersed and enriched Fe is further confirmed using WT- EXAFS (Figs. 1f, 1g and supplementary fig. 9). In general, the WT- EXAFS heat maps show two scattering centers, which are associated with Fe- O and Fe- Cr/Fe- Fe, Fe- Fe, respectively. The k- axis location of the second scattering center shifts to higher values with increasing Fe amounts (6.46 Å \(^{- 1}\) , 6.49 Å \(^{- 1}\) , 6.73 Å \(^{- 1}\) and 6.83 Å \(^{- 1}\) when the Fe amount is 2.89%, 4.48%, 7.78%, and 100% (i.e. ZnFe \(_2\) O \(_4\) )), corresponding to shortened bond lengths (supplementary fig. 10). This aligns well with the transition from longer Fe- Cr to shorter Fe- Fe. The FT- EXAFS and WT- EXAFS results were further fitted using optimized doped Fe- Zn/Cr systems (Fig. 1e and supplementary fig. 11). Fitted results show that the coordination numbers (CNs) of Fe- O (i.e. the nearest neighboring shell) are more than 4 for all Fe- Zn/Cr samples, verifying the 16d site as Fe- substitution site because 4a site’s CN is less than 4 (table S1) \(^{20}\) . All the fitting results including The CN, bond distances (R), and Debye–Waller factor (σ²) are listed in Table S1 supplementary Materials.  
+
+<|ref|>text<|/ref|><|det|>[[111, 636, 886, 902]]<|/det|>
+The above electron microscopic and synchrotron radiation- based spectroscopic characterizations experimentally validates the monodispersion of Fe when the Fe content is below 4.48 wt%. We then performed DFT calculations to understand the origin of Fe monodispersion. Energies that are required for Fe substituting Cr or Zn atoms at different sites on the (111) surface of ZnCr \(_2\) O \(_4\) are shown in Fig. 1h, along with the atomic surface model of ZnCr \(_2\) O \(_4\) (111) surface shown in Fig. 1i (calculation details are presented in Supplementary Materials). Notably, substitution of Cr by Fe at the (16d) site is thermodynamically preferred (−2.43 eV) over substitution of Zn at the (4a) site (3.47 eV) (with structures shown in supplementary figs. 12a, b).
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[110, 78, 886, 699]]<|/det|>
+When more Cr (16d) sites are considered for Fe substitution, it is found that Fe preferrs to substitute a (16d) site far from the existing doping site rather than an adjacent one, with a substitution energy being \(0.96\mathrm{eV}\) more negative (with structures shown in supplementary fig. 12c, d). Namely, isolated (16d) sites are preferably occupied upon Fe doping. Note that calculated substitution energies at isolated (16d) sites remain negative when the doping concentration of Fe increases from \(6.25\%\) to \(25\%\) (normalized to all the surface metal atoms of spinel (111) surface), providing theoretical insights into spontaneous monodispersion of Fe on \(\mathrm{ZnCr_2O_4}\) . By using DFT calculation, we also investigated the stability of isolated Fe doping site in a reactive syngas condition. The formation energies of \(\mathrm{Fe_5C_2}\) (as a representative \(\mathrm{Fe_xC_y}\) phase) from Fe in \(\mathrm{ZnFe_2O_4}\) and monodispersed Fe in \(\mathrm{ZnCr_2O_4}\) are \(- 3.17\mathrm{eV}\) and \(1.25\mathrm{eV}\) and per Fe atom, respectively, indicating the strong tendency of \(\mathrm{ZnFe_2O_4}\) and high resistance of Fe- Zn/Cr to form carbides. Therefore, we propose a catalyst evolution scene herein (Fig. 1j). In a oxide- zeolite (OX- ZEO) tandem catalytic system for syngas conversion, the oxide catalyzes the formation of oxygenates such as \(\mathrm{H_2CO}\) and \(\mathrm{CH_3OH}\) from syngas and the zeolite further selectively catalyzes these oxygenates to produce aromatics \(^{21 - 23}\) . The Fe- enriched oxide catalysts, however, suffers from irreversible phase transition to \(\mathrm{Fe_xC_y}\) , which follows Anderson- Schulz- Flory theory and inevitably leads to alkanes with uncontrolled carbon atoms per molecule. In contrast, the monodispersed Fe with superior stability against sintering and carbonization could enable stable aromatics formation from syngas.  
+
+<|ref|>sub_title<|/ref|><|det|>[[113, 742, 883, 795]]<|/det|>
+## Catalyst performance of monodispersed and enriched Fe in \(\mathrm{ZnCr_2O_4}\) matrix coupled with H-ZSM-5 for syngas conversion  
+
+<|ref|>text<|/ref|><|det|>[[113, 811, 884, 900]]<|/det|>
+To validate above prospect on the superior stability of monodispersed Fe in \(\mathrm{ZnCr_2O_4}\) through theoretical prediction, different Fe- Zn/Cr oxides were coupled with H- ZSM- 5 as tandem catalysts for selective syngas conversion to aromatics. Fig. 2a shows the catalyst evaluation of Fe- Zn/Crvs.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[110, 78, 886, 630]]<|/det|>
+pristine \(\mathrm{ZnCr_2O_4}\) (denoted as \(\mathrm{Zn / Cr}\) ) at a reaction conditions of \(2.0\mathrm{MPa}\) \(\mathrm{CO:H_2 = 1:1)}\) , \(350^{\circ}\) C and \(600\mathrm{ml}\mathrm{h}^{- 1}\cdot \mathrm{g}_{\mathrm{cat}}^{- 1}\) . CO conversion increases from \(13\%\) using pristine \(\mathrm{Zn / Cr}\) to \(45\%\) using \(4.48\%\) Fe- Zn/Cr with monodispersed Fesites; meanwhile, the total aromatic selectivity remains as high as \(80\%\) with various alkanes as minor side products. Since the conversion processes in tandem, i.e. syngas to oxygenates and oxygenates to aromatics, are spatially separated, the enhanced CO conversion is attributed to the activation effect of Fe dopants on CO and \(\mathrm{H_2}\) to accelerate oxygenates formation. In situ diffuse reflectance infrared Fourier transform spectroscopy results shows that signal intensities of oxygenate intermediates are at least an order of magnitude higher on \(4.48\%\) Fe- Zn/Cr than on pristine \(\mathrm{Zn / Cr}\) , corroborating the above analysis (fig. S13). \(7.78\%\) Fe- Zn/Cr with enriched Fe increases the CO conversion to \(51\%\) but sacrifices the total aromatic selectivity from \(80\%\) to \(44\%\) , along with significantly higher yields of \(\mathrm{CH_4}\) and \(\mathrm{C_2 - C_4}\) alkanes. Since these alkanes are typical products of \(\mathrm{Fe_xC_y}\) - catalyzed syngas conversion \(^{24,25}\) , we characterized spent \(7.78\%\) Fe- Zn/Cr using HADDF- STEM and observed \(\mathrm{Fe_xC_y}\) phases (fig. S14). The \(\mathrm{Fe_xC_y}\) phases likely originated from the Fe- O- Fe structure present in the enriched Fe parts of \(7.78\%\) Fe- Zn/Cr and this is rationalized by the negative formation energy of carbides from \(\mathrm{ZnFe_2O_4}\) in former theoretical prediction \(^{24,25}\) .
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[133, 82, 876, 370]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[113, 416, 884, 712]]<|/det|>
+<center>Fig. 2. Catalyst evaluation of monodispersed and enriched Fe. (a) Catalyst evaluation of pristine \(\mathrm{ZnCr_2O_4}\) and different Fe-Zn/Cr with H-ZSM-5; reaction conditions: \(350^{\circ}\mathrm{C}\) , 2.0 MPa, space velocity \(= 600\mathrm{ml}\mathrm{h}^{-1}\cdot \mathrm{g}_{\mathrm{cat}}^{-1}\) , \(\mathrm{CO:H_2 = 1:1}\) . (b) The optimal space time yield (STY) of aromatics for different catalysts (note that the STYs were obtained at the optimized reaction conditions for each catalyst and the corresponding reaction conditions are summarized in table S3). (c) TOF and aromatic selectivity as functions of Fe contents in \(\mathrm{ZnCr_2O_4}\) and reaction conditions are similar as A. (d) Comparison between reported Fe-based catalysts and isolated Fe-Zn/Cr + H-ZSM-5 catalyst on syngas conversion to aromatics. (e) Stability test of \(4.48\%\) Fe-Zn/Cr + H-ZSM-5 catalytic system for \(100\mathrm{h}\) . </center>  
+
+<|ref|>text<|/ref|><|det|>[[112, 764, 884, 890]]<|/det|>
+As a typical tandem reaction over a bifunctional OX- ZEO catalyst, it has been reported that and low temperature and high pressure are detrimental to aromatics selectivity and CO conversion, respectively \(^{26,27}\) . In this study, these two problems were tackled by monodispersion of Fe on \(\mathrm{ZnCr_2O_4}\) . A low reaction temperature of \(275^{\circ}\mathrm{C}\) was previously found to increase the single pass
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[110, 78, 886, 837]]<|/det|>
+aromatic selectivity to \(\sim 82\% - 84\%\) using unmodified \(\mathrm{ZnCr_2O_4 + H - ZSM - 5}\) catalysts; while \(4.48\%\) Fe- Zn/Cr profoundly promotes the aromatic selectivity up to \(88\%\) to \(91\%\) , a value that has rarely been achieved (supplementary figs. 15a,b). It means that even at a low temperature, the monodispersed Fe is able to activate CO and \(\mathrm{H}_2\) to produce oxygenate intermediates, which further diffuse into the pores of zeolites to initiate C- C coupling and subsequent aromatics formation \(^{22,28}\) . In addition to low temperature performance, \(\mathrm{ZnCr_2O_4}\) with monodispersed Fe also exhibit outstanding high pressure performance (supplementary figs. 15c,d). The CO conversion increases remarkably from \(44\%\) to \(68\%\) , whereas selectivity towards \(\mathrm{CH_4}\) and \(\mathrm{C}_2\mathrm{- C}_4\) side products is only marginally increased by \(< 1\%\) . By analyzing the Fe contents and STYs of aromatics, it is found that STYs increase with higher Fe amounts only when Fe remains monodispersed and once Fe is further enriched to form Fe- O- Fe structure prone to carbonization, STYs of aromatics deteriorate due to the emergence of conventional Fischer- Tropsch pathways (Fig. 2b and supplementary fig. 16a) \(^{29}\) . This trend is also notable when projecting TOF and total aromatic selectivity to the Fe content; monodispersed Fe increases the TOF of syngas conversion and maintains high selectivity towards aromatics, whereas enriched Fe sacrifices the aromatic selectivity despite the higher TOF (Fig. 2c). As shown in Fig. 2d and supplementary fig. 16b, the outstanding ability of our monodispersed Fe- Zn/Cr based OX- ZEO catalysts to achieve high CO conversion and high aromatics selectivity simultaneously is further illustrated through comparison with several recently reported catalysts for syngas conversion process \(^{30 - 34}\) . Further, the long- term stability test of \(4.48\%\) Fe- Zn/Cr + H- ZSM- 5 shows no signs of decay in either the activity or total aromatic selectivity for more than \(100\mathrm{~h}\) at \(350^{\circ}\mathrm{C}\) and \(2.0\mathrm{MPa}\) and stable CO conversion of \(\sim 45\%\) is achieved (Fig. 2e).
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[113, 80, 757, 100]]<|/det|>
+## Mechanistic investigation of monodispersed Fe in enhanced catalytic activity  
+
+<|ref|>text<|/ref|><|det|>[[111, 111, 886, 835]]<|/det|>
+The above catalysis evaluation demonstrates the unique ability of monodispersed Fe to catalyze syngas to form reactive oxygenates. To understand the origin of such enhanced activity, we further performed a series of combinatorial mechanistic investigations. Previous demonstrations have suggested surface \(\mathrm{Ov}\) as possible active sites for CO activation \(^{35,36}\) . However, there is a lack of high- precision characterization of \(\mathrm{Ov}^{37}\) . DFT calculations were firstly performed to assess the equilibrium \(\mathrm{Ov}\) concentration in monodispersed Fe- Zn/Cr samples. Using spinel (111) surface as a model surface, the formation energies of \(\mathrm{Ov}\) with different concentrations and spatial locations (i.e., surface and subsurface layer) were calculated through either CO or \(\mathrm{H}_{2}\) reduction (calculation details are presented in Supplementary Materials). Similar to a previous study, CO reduction was found to be the major driving force for \(\mathrm{Ov}\) formation \(^{13}\) (supplementary fig. 17). The phase diagram shows that \(\mathrm{Ov}\) preferably presents in the surface layer with a concentration of 0.25 monolayer (ML) under the experimental condition (Fig. 3a). The atomic evidence of surface \(\mathrm{Ov}\) was obtained by using the integrated differential phase- contrast STEM (iDPC- STEM) technique. Fig. 3b depicts the [111] surface of 4.48 % Fe- Zn/Cr and some \(\mathrm{Ov}\) can be identified with atomic precision. A microquantitative intensity statistic analysis was subsequently performed by applying line scan to the iDPC- STEM images. The non- periodic variations in image contrast relating to O demonstrate the existence of \(\mathrm{Ov}\) and its concentration is estimated to be 25%–28%, similar to the theoretically predicted \(\mathrm{Ov}\) concentration (Fig. 3c). O1s XPS spectra of different Fe- Zn/Cr samples further show that Fe doping only alters surface \(\mathrm{Ov}\) concentration by insignificant amounts (Fig. 3d). Electron paramagnetic resonance spectroscopy also quantitatively evidences the existence of \(\mathrm{Ov}\) (supplementary figs. 18a, b). It is found that there are not much changes in the \(\mathrm{Ov}\) content.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[130, 117, 880, 440]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[112, 454, 884, 680]]<|/det|>
+<center>Fig. 3. Understanding the role of monodispersed Fe in catalytic activity. (a) Thermodynamic phase diagram of Fe-doped \(\mathrm{ZnCr_2O_4}\) (111) surfaces at varying temperatures and CO partial pressures \((P_{\mathrm{CO}})\) (b) iDPC-STEM images of \(4.48\%\) Fe-Zn/Cr. (c) iDPC-STEM intensity profiles along the corresponding dashed lines in B. (d) O 1s XPS spectra of \(\mathrm{ZnCr_2O_4}\) and different Fe-Zn/Cr samples. (e) Projected electric field strength and vector map of \(4.48\%\) Fe-Zn/Cr. (f) Differential charge density (cyan and yellow represent charge depletion and accumulation, respectively; the cutoff of the density-difference isosurface is \(0.11\mathrm{e~Å}^{-3}\) ). </center>  
+
+<|ref|>text<|/ref|><|det|>[[112, 696, 884, 892]]<|/det|>
+Since the \(\mathrm{O}_{\mathrm{V}}\) concentration remains marginally affected by Fe doping, it might not be the reason for the enhanced catalytic activity. We then focused on the effect of local electronic structures and performed a charge density analysis by combining electric field imaging and theoretical calculations. As shown in Fig. 3e and supplementary figs. 18c,d, some local bright spots (in red circle) appear despite the surrounding uniform intensity, implying the possible charge transfer between isolated Fe and surrounding O and \(\mathrm{O}_{\mathrm{V}}\) 38. This is corroborated by the theoretically
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[111, 78, 884, 240]]<|/det|>
+calculated charge density (Fig. 3f). Clearly, charges on Ov and O nearest to the Fe dopant increase in Fe- doped \(\mathrm{ZnCr_2O_4}\) with respect to pristine \(\mathrm{ZnCr_2O_4}\) . The locally modulated charge density could account for the difference in catalytic activity between monodispersed Fe- Zn/Cr and Zn/Cr. A series of experiments were conducted to prove the promoted activation effect of Fe dopants on CO and \(\mathrm{H}_2\) (supplementary figs. 19, 20).  
+
+<|ref|>text<|/ref|><|det|>[[111, 253, 885, 799]]<|/det|>
+CO hydrogenation reaction pathways were investigated using DFT calculations. By taking the aforementioned structural characterization and theoretical analysis into consideration, undoped and isolated Fe- doped \(\mathrm{ZnCr_2O_4}\) (111) surfaces with 1/4 ML Ov on the surface were constructed for simulations. The gas phase energies of CO and \(\mathrm{H}_2\) are adjusted using the actual experimental conditions. On both surfaces, our calculations support the stepwise hydrogenation mechanism in syngas conversion (Fig. 4a) \(^{13,15}\) . In more details, the reaction starts with CO adsorption at Ov (Fig. 4b). Next, \(\mathrm{H}_2\) dissociates near the surface Ov and \(\mathrm{H}^*\) is adsorbed on top of an O near Fe (16d). Subsequently, adsorbed CO\* is stepwise hydrogenated to form formyl (CHO\*), formaldehyde (CH2O\*), methoxyl (CH3O\*), and finally CH3OH. It is found that pristine \(\mathrm{ZnCr_2O_4}\) (111) with Ov is mainly limited by the weak adsorption of \(\mathrm{H}^*\) with the presence of CHO\*, as well as strong adsorption of CH3O\*. The incorporation of isolated Fe effectively stabilizes the state of CHO\* + H\*, making the hydrogenation step to form H2CO\* energetically exothermic, while destabilizes the state of CH3O\* + H\*, thereby facilitating the formation of CH3OH. From both aspects, monodispersed Fe improves the ability of pristine \(\mathrm{ZnCr_2O_4}\) to produce key oxygenate intermediates of H2CO and CH3OH from syngas. These key intermediates further participate in complicated oxygenate- to- aromatics reactions occurring in the pores of H- ZSM- 5 \(^{39}\) .
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[149, 99, 819, 420]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[112, 453, 884, 475]]<|/det|>
+<center>Fig. 4. The mechanism study of monodispersed Fe & pristine \(\mathrm{ZnCr_2O_4}\) in syngas conversion. </center>  
+
+<|ref|>text<|/ref|><|det|>[[112, 487, 884, 610]]<|/det|>
+(a) Gibbs free energy diagrams of syngas conversion to \(\mathrm{CH_3OH}\) on pristine and Fe-doped \(\mathrm{ZnCr_2O_4}\) (111) surfaces with 1/4 ML \(\mathrm{Ov}\) . The reaction images are shown in the inset of a; Zn: green, Cr: blue, Fe: yellow, O: red, C: brown, and H: white. (b) The atomic structures of the corresponding states in a.  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 697, 212, 714]]<|/det|>
+## Conclusion  
+
+<|ref|>text<|/ref|><|det|>[[112, 730, 884, 891]]<|/det|>
+In this study, we demonstrated that the spinel structure of \(\mathrm{ZnCr_2O_4}\) can disperse strong self- interaction metal, Fe, spontaneously due to the thermodynamically stable state caused by the interaction between Fe and \(\mathrm{ZnCr_2O_4}\) , showing the highest performance in syngas- to- aromatic reaction. By combining high- precision microscopic and macroscopic characterizations and theory calculations, monodispersed Fe activates the surrounding \(\mathrm{Ov}\) , thereby activating the CO and \(\mathrm{H_2}\)
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[111, 78, 886, 415]]<|/det|>
+while avoiding the formation of \(\mathrm{Fe_xC_y}\) and maintaining stability under a reducing atmosphere. The TOF increased from 0.54 to \(2.48 \mathrm{h}^{- 1}\) without sacrificing the aromatic selectivity in monodispersed Fe samples. Our monodispersed Fe in the \(\mathrm{ZnCr_2O_4}\) catalyst showed \(81.4\%\) aromatic selectivity at a single- pass CO conversion of \(68.4\%\) , which is the best catalyst evaluation among all Fe- based catalysts during the syngas- to- aromatic reaction. DFT calculations revealed that the monodispersion of Fe in 16d sites lowered the key determining step of \(\mathrm{HCO^* + H^* - H_2CO^*}\) , thereby accelerating the C1 oxygenate intermediates, which made them diffuse quickly into the pores of H- ZSM- 5 to initiate the C- C coupling toward the aromatics. This study provides a prototype for rationally tailoring single atom Fe in syngas chemistry to obtain targeted catalytic reactivity and avoid the formation of \(\mathrm{Fe_xC_y}\) .
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[56, 88, 886, 888]]<|/det|>
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+
+<--- Page Split --->
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+367 18 Gao, W. et al. Capsule-like zeolite catalyst fabricated by solvent-free strategy for para- 368 Xylene formation from CO2 hydrogenation. Applied Catalysis B: Environmental 303, 369 120906 (2022). 370 19 Han, W. et al. Research progresses in the hydrogenation of carbon dioxide to certain 371 hydrocarbon products. Journal of Fuel Chemistry and Technology 49, 1609- 1619 (2021). 372 20 Li, A. et al. Enhancing the stability of cobalt spinel oxide towards sustainable oxygen 373 evolution in acid. Nature Catalysis, 1- 10 (2022). 374 21 Jiao, F. et al. Selective conversion of syngas to light olefins. Science 351, 1065- 1068 375 (2016). 376 22 Zhou, W. et al. New horizon in C1 chemistry: breaking the selectivity limitation in 377 transformation of syngas and hydrogenation of CO2 into hydrocarbon chemicals and fuels. 378 Chem Soc Rev 48, 3193- 3228, doi:10.1039/c8cs00502h (2019). 379 23 Li, Z. et al. Highly Selective Conversion of Carbon Dioxide to Aromatics over Tandem 380 Catalysts. Joule 3, 570- 583, doi:10.1016/j.joule.2018.10.027 (2019). 381 24 Song, C. et al. Photothermal conversion of CO2 with tunable selectivity using Fe- based 382 catalysts: from oxide to carbide. ACS Catalysis 10, 10364- 10374 (2020). 383 25 Li, Z. et al. Fe- Based Catalysts for the Direct Photohydrogenation of CO2 to Value- Added 384 Hydrocarbons. Advanced Energy Materials 11, 2002783 (2021). 385 26 Liu, C. et al. Constructing directional component distribution in a bifunctional catalyst to 386 boost the tandem reaction of syngas conversion. Chem Catalysis 1, 896- 907, 387 doi:10.1016/j.checat.2021.06.016 (2021). 388 27 Arslan, M. T. et al. Highly Selective Conversion of CO2 or CO into Precursors for 389 Kerosene- Based Aviation Fuel via an Aldol- Aromatic Mechanism. ACS Catalysis 12, 390 2023- 2033 (2022). 391 28 Zhou, C. et al. Highly Active ZnO- ZrO2 Aerogels Integrated with H- ZSM- 5 for Aromatics 392 Synthesis from Carbon Dioxide. ACS Catalysis 10, 302- 310, 393 doi:10.1021/acscatal.9b04309 (2019). 394 29 Zhu, J. et al. Dynamic structural evolution of iron catalysts involving competitive oxidation 395 and carburization during CO2 hydrogenation. Science Advances 8, eabm3629 (2022). 396 30 Zhao, B. et al. Direct Transformation of Syngas to Aromatics over Na- Zn- Fe 5 C 2 and 397 Hierarchical HZSM- 5 Tandem Catalysts. Chem 3, 323- 333, 398 doi:10.1016/j.chempr.2017.06.017 (2017). 399 31 Wei, J. et al. Directly converting CO2 into a gasoline fuel. Nature communications 8, 1- 9 400 (2017). 401 32 Geng, S., Jiang, F., Xu, Y. & Liu, X. iron- based Fischer- Tropsch synthesis for the efficient 402 conversion of carbon dioxide into Isoparaffins. ChemCatChem 8, 1303- 1307 (2016). 403 33 Wang, X. et al. Synthesis of isoalkanes over a core (Fe- Zn- Zr)- shell (zeolite) catalyst by 404 CO 2 hydrogenation. Chemical Communications 52, 7352- 7355 (2016). 405 34 Dai, C. et al. Hydrogenation of CO2 to Aromatics over Fe- K/Alkaline Al2O3 and P/ZSM- 406 5 Tandem Catalysts. Industrial & Engineering Chemistry Research 59, 19194- 19202 407 (2020).
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[57, 80, 886, 343]]<|/det|>
+408 35 Wang, Y. et al. Visualizing Element Migration over Bifunctional Metal- Zeolite Catalysts 409 and its Impact on Catalysis. Angew Chem Int Ed Engl 60, 17735- 17743, 410 doi:10.1002/anie.202107264 (2021). 411 36 Pan, X., Jiao, F., Miao, D. & Bao, X. Oxide- Zeolite- Based Composite Catalyst Concept 412 That Enables Syngas Chemistry beyond Fischer- Tropsch Synthesis. Chemical Reviews 413 121, 6588- 6609 (2021). 414 37 Wang, S. et al. Selective conversion of CO2 into propene and butene. Chem 6, 3344- 3363 415 (2020). 416 38 Shibata, N. et al. Electric field imaging of single atoms. Nature communications 8, 1- 7 417 (2017). 418 39 Arslan, M. T. et al. Selective Conversion of Syngas into Tetramethylbenzene via an Aldol- 419 Aromatic Mechanism. ACS Catalysis 10, 2477- 2488, doi:10.1021/acscatal.9b03417 420 (2020). 421  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 408, 285, 425]]<|/det|>
+## Acknowledgements:  
+
+<|ref|>text<|/ref|><|det|>[[115, 427, 883, 504]]<|/det|>
+This work is supported by the National Natural Science Foundation of China (21908125 and 22005170), the National Key Research and Development Program of China (2018YFB0604801), the Key Research and Development Program of Inner Mongolia and Ordos, and CNPC Innovation Funds.  
+
+<|ref|>text<|/ref|><|det|>[[115, 525, 883, 565]]<|/det|>
+Author contributions: G.T and X.L contributed equally. All the authors approved the final version of the manuscript.  
+
+<|ref|>text<|/ref|><|det|>[[115, 584, 789, 604]]<|/det|>
+Competing financial interests: The authors declare no competing financial interests.  
+
+<|ref|>text<|/ref|><|det|>[[115, 623, 883, 662]]<|/det|>
+Data and materials availability: All data are available in the manuscript or the supplementary materials.  
+
+<|ref|>text<|/ref|><|det|>[[115, 682, 360, 700]]<|/det|>
+Supplementary Information:  
+
+<|ref|>text<|/ref|><|det|>[[115, 703, 300, 720]]<|/det|>
+Materials and Methods  
+
+<|ref|>text<|/ref|><|det|>[[115, 723, 240, 739]]<|/det|>
+Figs. S1 to S20  
+
+<|ref|>text<|/ref|><|det|>[[115, 743, 238, 758]]<|/det|>
+Table S1 to S4  
+
+<|ref|>text<|/ref|><|det|>[[115, 763, 250, 779]]<|/det|>
+References (1- 9)
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[44, 42, 312, 70]]<|/det|>
+## Supplementary Files  
+
+<|ref|>text<|/ref|><|det|>[[44, 93, 765, 113]]<|/det|>
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+<|ref|>text<|/ref|><|det|>[[60, 130, 612, 150]]<|/det|>
+SupplementaryInformationSubmitNatureCommunication.pdf
+
+<--- Page Split --->

preprint/preprint__015d965c0cb0c74c387f9596e3f91c0197ff39a7333b1c0b5bef96993bcbbd18/images_list.json

@@ -0,0 +1,47 @@
+[
+  {
+    "type": "image",
+    "img_path": "images/Figure_1.jpg",
+    "caption": "Figure 1. Chirality at different length scales. (a) Bright-Field STEM image showing the stack of 16 STO/16 PTO/16 STO with SRO buffer layer on DSO substrate. The red regions near the center of the PTO layer indicate the position of the vortex core approximately. (b) Vector mapping of the local displacements of A sites of highlighted region in (a) overlaid on HAADF-STEM image. The local red- and blue contrast at the center of the PTO layer indicates the local non-zero curl of displacement. The net lateral polarization resulting from vortex off-centering is indicated at the top. The dotted black line shows the center of the PTO layer. (c) Dark field TEM image along [110]o direction displaying long tubular vortex structure with domain walls shown as white dashed lines.",
+    "footnote": [],
+    "bbox": [
+      [
+        125,
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+    "type": "image",
+    "img_path": "images/Figure_2.jpg",
+    "caption": "Figure 2. Identification of polarization using 4D-STEM mapping. (a) Schematic showing the e-beam scanned in 4D STEM mode across the vortex sample in in-plane geometry. (b) Virtual dark field image of the vortex region obtained via integrating the intensity of disk 3 from the mean diffraction pattern in (c). Zoomed-in images of the (d) dash-dot region (e) solid line region showing lateral (P [001]o) and axial (P [110]o) polarization maps in vortices. \\(\\alpha , \\beta ,\\) and \\(\\gamma\\) domain walls can be identified. \\(\\alpha\\) domain wall (magenta curve) has an anti-parallel lateral (P [001]o) component, \\(\\beta\\) (green curve) domain wall has an anti-parallel axial component (P [110]o), \\(\\gamma\\) has both axial and lateral components antiparallel across the domain wall.",
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+    "caption": "Figure 3. Identification of triple point topologies and chiral domain walls. (a) The helicity map formed from Figure 2b shows left and right-handed domains separated by \\(\\alpha\\) (magenta) and \\(\\beta\\) (green). Additionally, achiral domain walls (black line) also coexist. The resultant triple point topologies formed due to the co-existence of chiral and achiral domain walls are shown in encircled regions. The sense of rotation of these triple point topologies is indicated in the encircled region. (b-c) Possible pairs of triple points. (b) represents triple points with opposite sense of rotation with the point of inversion along \\(\\beta\\) , \\(\\gamma\\) , and \\(\\alpha\\) (c) represents triple points with the same sense of rotation. The green ticks on the side show what has been observed in experiments.",
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preprint/preprint__015d965c0cb0c74c387f9596e3f91c0197ff39a7333b1c0b5bef96993bcbbd18/preprint__015d965c0cb0c74c387f9596e3f91c0197ff39a7333b1c0b5bef96993bcbbd18.mmd

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+
+# The emergence of three-dimensional chiral domain walls in polar vortices  
+
+Sandhya Susarla ( \(\boxed{ \begin{array}{r l} \end{array} }\) sandhya.susarla@asu.edu) Arizona State University https://orcid.org/0000- 0003- 1773- 0993  
+
+Shang- Lin Hsu University of california, Berkeley  
+
+Fernando Gomez- Ortiz Universidad de Cantabria https://orcid.org/0000- 0002- 7203- 8476  
+
+Pablo Garcia- Fernandez Universidad de Cantabria https://orcid.org/0000- 0002- 4901- 0811  
+
+Benjamin Savitzky Cornell University  
+
+SUJIT DAS Indian Institute of Science, Bangalore https://orcid.org/0000- 0001- 9823- 0207  
+
+Piush Behera University of California, Berkeley  
+
+Javier Junquera Universidad de Cantabria, Cantabria Campus Internacional https://orcid.org/0000- 0002- 9957- 8982  
+
+Peter Erucis Lawrence Berkeley National Laboratory https://orcid.org/0000- 0002- 6762- 9976  
+
+Ramamoorthy Ramesh Rice University  
+
+Colin Ophus National Center for Electron Microscopy Facility, Molecular Foundry, Lawrence Berkeley National Laboratory  
+
+Article  
+
+Keywords:  
+
+Posted Date: February 8th, 2023  
+
+DOI: https://doi.org/10.21203/rs.3.rs- 2551328/v1  
+
+License: \(\circledcirc\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License
+
+<--- Page Split --->
+
+Version of Record: A version of this preprint was published at Nature Communications on July 25th, 2023. See the published version at https://doi.org/10.1038/s41467-023-40009-2.
+
+<--- Page Split --->
+
+## The emergence of three-dimensional chiral domain walls in polar vortices  
+
+Sandhya Susarla \(^{1,2,\mathrm{S}\# *}\) , Shanglin Hsu \(^{1,2\#}\) , Fernando Gómez- Ortiz \(^{4}\) , Pablo García- Fernández \(^{4}\) , Benjamin H. Savitzky \(^{1}\) , Sujit Das \(^{2,6}\) , Piush Behera \(^{3}\) , Javier Junquera \(^{4}\) , Peter Ercius \(^{1}\) , Ramamoorthy Ramesh \(^{1,2,5,7,8*}\) , Colin Ophus \(^{1*}\)  
+
+1: National Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA 94720  
+2: Materials Sciences Division, Lawrence Berkeley Laboratory, Berkeley CA, USA 94720  
+3: Department of Materials Science & Engineering, University of California, Berkeley, CA, USA 94720  
+4: Departmento de Ciencias de la Tierra y Física de la Materia Condensada, Universidad de Cantabria, Cantabria Campus Internacional Santander, Spain, 39005  
+5: Department of Physics, University of California, Berkeley Berkeley, CA, USA 94720  
+6: Material Research Centre, Indian Institute of Science, Bangalore, 560012  
+7: Department of Physics, Rice University, Houston, TX, USA, 77005  
+8: Department of Materials Science and Nanoengineering, Houston, TX, USA, 77005 #Equal contribution.  
+\$ Present address: School for Engineering of Matter, Transport, and Energy, Arizona State University  
+
+Corresponding authors: sandhya.susarla@asu.edu, ramamoorthy.ramesh@rice.edu, and clophus@lbl.gov  
+
+## Abstract  
+
+Chirality or handedness of a material can be used as an order parameter to uncover emergent electronic properties for quantum information science. Conventionally, chirality is found in naturally occurring biomolecules and magnetic materials. Chirality can be engineered in a topological polar vortex ferroelectric/dielectric system via atomic- scale symmetry- breaking operations. We use four- dimensional scanning transmission electron microscopy (4D- STEM) to map out topology- driven three- dimensional domain walls, where the handedness of two neighbor topological domains change or remain the same. The nature of the domain walls is governed by the interplay of local perpendicular (lateral) and parallel (axial) polarization with respect to the tubular vortex structures. Unique symmetry- breaking operations and the finite nature of domain walls results in a triple point at the junction of chiral and achiral domain walls. The unconventional nature of the domain walls with triple point pairs may result in unique electrostatic and magnetic properties potentially useful for quantum sensing applications.
+
+<--- Page Split --->
+
+## Introduction  
+
+Chirality is a unique topological feature that drives many- body interactions in naturally occurring organic molecules and proteins \(^{1}\) , subatomic particle physics \(^{2}\) , and solid- state physics \(^{3}\) . The symmetries in a chiral system are configured in such a way that its mirror image cannot be superimposed on itself, manifesting a handedness to the system as exemplified by screws and our own hands. Chirality also exists at the microscale/nanoscale level in inorganic and organic materials such as liquid crystals \(^{4}\) , spin textures in ferromagnets \(^{5,6}\) , amino acids, and D/L- glucose molecules \(^{1}\) with applications in spin selectivity- based quantum sensing \(^{7}\) , non- linear optics \(^{8}\) , and biosensing applications \(^{9}\) . However, there are very few examples of chiral inorganic ferroelectric crystals which could have fundamentally different domain textures \(^{10 - 13}\) . Over the past few years, novel polarization textures in ferroelectrics such as merons \(^{14}\) , polar flux- closure domains \(^{15,16}\) , vortices \(^{17}\) , bubble domains \(^{18,19}\) , super crystals \(^{20,21}\) and skyrmions \(^{22}\) have been engineered in oxide superlattices, emerging from the careful interplay of elastic, electrostatic and gradient energies of electric dipoles. The electric dipole arrangement and complex orbital hybridization in these systems have been probed by the x- ray scattering techniques \(^{23}\) , scanning transmission electron microscopy (STEM) \(^{17}\) - electron energy loss spectroscopy (EELS) \(^{24}\) , phase- field simulations \(^{12,14,15,17,20}\) and atomistic first- and second- principles calculations \(^{17,18,20,22,23}\) . Surprisingly, the presence of chirality has been observed in one such topological texture i.e. polar vortices in \(\mathrm{PbTiO_3 / SrTiO_3}\) superlattices from resonant soft x- ray scattering (RXS) \(^{23}\) , second harmonic generation (SHG) and second principles calculations \(^{25}\) . The presence of chirality in polar vortices is an emergent phenomenon because none of the parent compounds such as \(\mathrm{SrTiO_3}\) or \(\mathrm{PbTiO_3}\) are known to be chiral. It has been recently shown experimentally and theoretically that the presence of chirality in these systems might be due to different sources.
+
+<--- Page Split --->
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+First, the strongest one is the coexistence of vortices with an axial component of the polarization, perpendicular to the vortex plane \(^{26}\) . The second factor is the buckling of the vortices (i.e., a staggered vortex configuration where the center of the clockwise and counterclockwise vortices are located at different heights) combined with different sizes of the up and down domains results in a chiral structure, although its strength is smaller than in the first scenario. This last source of chirality can be reversed by external electric fields. The first experimental demonstration was in \(^{25}\) , where chirality switches in a reversible, deterministic, and non- destructive fashion over mesoscale regions \(^{25}\) .  
+
+Without any prior knowledge, one would expect the as- grown sample as a racemic mixture, i.e., equal amounts of left- handed and right- handed domain enantiomers, where chirality within each domain comes from a combination of the two sources described above. To have a non- destructive switchable chirality, it is essential to understand the role of the domain walls separating the enantiomers. In other words, what local physical parameters play a role when the handedness in neighboring domains changes? This includes the offset between the center of the cores, the axial component at the center of the clockwise/counterclockwise vortices, the sense of rotation of the vortices when they merge at the domain wall, the presence of dislocations, or the combined effect of all of them. A proper understanding of how the left/right- handed domains evolve at the nanoscale is crucial to design new electrically switchable chiral devices that can be measured without scientifically sophisticated techniques. Indeed, a proper answer to this question will pave the way for the use of these chiral textures in next- generation technologies.  
+
+Four- dimensional (4D)- STEM can precisely measure strain, and thus spontaneous polarization in ferroelectrics due to the violation of Friedel's Law \(^{27 - 29}\) . This makes 4D- STEM a unique tool to probe polarization in three dimensions and understand emergent chirality in
+
+<--- Page Split --->
+
+polarization vortices. In the current work, we have used 4D- STEM to probe three- dimensional domain walls in polar vortices in oxide superlattices and understand the nano- scale nature of chirality. We find that both achiral and chiral domain walls coexist in the same system. The chiral to achiral domain wall transition is driven by the change in local axial and lateral polarization direction across the domain wall. We have discovered new pair of triple points with the opposite/same sense of rotation at the junction of achiral and chiral domain walls. Finally, we unravel all the possible configurations of chiral and achiral domain walls in this system through different symmetry- breaking operations.  
+
+## Results  
+
+Trilayer \((\mathrm{SrTiO}_3)_{16} / (\mathrm{PbTiO}_3)_{16} / (\mathrm{SrTiO}_3)_{16}\) (STO/PTO/STO) were grown on orthorhombic \(\mathrm{DyScO}_3\) (DSO) \([110]_{0}\) substrates with \(\mathrm{SrRuO}_3\) as a buffer layer using reflection high- energy electron diffraction (RHEED)- assisted pulsed- laser deposition (PLD) (Figure 1A and Supplementary Information). The polar vortex phase in this system is stabilized as a consequence of the interplay between depolarization energy at the PTO/STO interfaces, elastic energy from the tensile strain imposed by the DSO substrate, and the gradient energy in the ferroelectric \(^{17}\) .  
+
+A direct way to measure the polar textures in vortex topologies is through electron microscopy using atomic resolution images. We have used different types of STEM and TEM techniques to characterize the exact positions of the vortex centers. Figure 1a shows a low- magnification bright- field STEM image of the superlattice trilayer with an SRO buffer layer along the \([1\bar{1} 0]_{0}\) zone axis. The diffraction contrast in BF- STEM allows us to directly locate the vortex center as dark contrast; marked using red circles in Figure 1a. To precisely understand the polarization or displacement texture around each vortex center, we obtained the A- site
+
+<--- Page Split --->
+
+displacement vector maps at atomic resolution via gaussian fitting at A- sites (Methods, supplementary information). Figure 1b shows the High angle annular dark field (HAADF)- STEM image of trilayer STO/PTO/STO along \([1\bar{1} 0]_{0}\) zone axis where brighter regions are PTO and darker regions are STO. Overlaid yellow arrows show clockwise and counterclockwise rotating curls in the displacement of the A- cation in PTO/STO superlattices. The coexistence of the concomitant non- zero curl of polarization (red/blue contrast) with an alternating axial component of the polarization (perpendicular to the plane defined by the vortices) is the first symmetry- breaking operation that results in emergent chirality in an otherwise non- chiral system. The non- zero curl is larger in continuously rotating polarization textures such as polar vortices \(^{25,27}\) , merons \(^{30}\) , and skyrmions \(^{22}\) than in other polarization textures such as flux closure domains \(^{15,16}\) where the curl vanished in the central regions with \(180^{0}\) domain walls. Additionally, we observe that the cores of the polarization curls (indicated as blue/red contrast) are not located exactly at the center of the PTO layer, but follow a zig- zag type pattern, giving rise to a net in- plane polarization rotation along \([001]_{0}\) (lateral component) indicated as \(\mathrm{P}_{\mathrm{x}}\) in Figure 1b. This buckling, combined with a small difference in the size of the up and down domains, is the second symmetry- breaking operation that results in net chirality; in agreement with previous observations \(^{25}\) .
+
+<--- Page Split --->
+![](images/Figure_1.jpg)
+
+<center>Figure 1. Chirality at different length scales. (a) Bright-Field STEM image showing the stack of 16 STO/16 PTO/16 STO with SRO buffer layer on DSO substrate. The red regions near the center of the PTO layer indicate the position of the vortex core approximately. (b) Vector mapping of the local displacements of A sites of highlighted region in (a) overlaid on HAADF-STEM image. The local red- and blue contrast at the center of the PTO layer indicates the local non-zero curl of displacement. The net lateral polarization resulting from vortex off-centering is indicated at the top. The dotted black line shows the center of the PTO layer. (c) Dark field TEM image along [110]o direction displaying long tubular vortex structure with domain walls shown as white dashed lines. </center>  
+
+The various permutations and combinations of atomic scale symmetry- breaking operations such as a non- zero curl of polarization together with the presence of an axial component, and the buckling of the vortices that yield a non- zero polarization component along [001]o, result in different types of domains walls at the mesoscale. We can visualize the mesoscale domain walls in these topological structures by imaging the vortices along the [110]o zone axis using weak beam dark field TEM (Figure 1c). We can observe the tubular nature of vortex textures by long bright and dark stripes regions in the image. Additionally, we observe different domain wall features (indicated as white dashed lines) cutting across vortex tubes. Overall, if we combine our observations from DF- TEM and HR- STEM, we observe a three-
+
+<--- Page Split --->
+
+dimensional structure in the PbTiO₃ layer sandwiched between SrTiO₃ layers where the polarization curls follow a tubular pattern (Figure 2a). Unfortunately, the images formed by weak beam dark field, HRSTEM, and BF- STEM are merely atomic projections and cannot give us an estimate of physical quantities such as polarization and chirality. On the other hand, 4D- STEM allows us to collect a diffraction pattern at each probe position, which can then be used to create precise maps of physical quantities. For the present experiment, we performed 4D- STEM imaging on a trilayer STO/PTO/STO along the [110]₀ zone axis (Figure 2a). We used a probe size of \(\sim 7 \text{Å}\) , larger than the STO/PTO unit cell dimensions ( \(\sim 4 \text{Å}\) ) to remove the atomic- resolution signal and to estimate the polarization at unit cell resolution. The 4D- STEM analysis was carried out in open source py4DSTEM analysis package ³¹. We define the [110]₀ direction as axial and [001]₀ direction as lateral. The rotation calibration was performed between the real space and diffraction space to determine the lateral and axial axis. Details are given in the supplementary information. For initial visualization of the polar textures, we created a virtual dark field image using the [2\(\bar{1}\)0]₀ disk (disk 3) as shown in Figure 2b- c. The polarization from the PTO layer can be determined qualitatively by subtracting opposite Friedel pair disks due to the violation of Friedel’s law ²⁷-²⁹. The polarization maps corresponding to regions delimited by rectangles in Figure 2b are shown in Figure 2d- e. We observe alternate longitudinal red and blue stripes representing positive and negative polarization in both the lateral ([001]₀) and axial [110]₀ directions with domain walls as seen from the weak beam dark field images marked with green and magenta lines in Figure 2d and with a black line in Figure 2e. We observe that the axial polarization magnitude is relatively smaller than the lateral polarization in agreement with the predictions from previous second principles calculations ²³,²⁵.
+
+<--- Page Split --->
+![](images/Figure_2.jpg)
+
+<center>Figure 2. Identification of polarization using 4D-STEM mapping. (a) Schematic showing the e-beam scanned in 4D STEM mode across the vortex sample in in-plane geometry. (b) Virtual dark field image of the vortex region obtained via integrating the intensity of disk 3 from the mean diffraction pattern in (c). Zoomed-in images of the (d) dash-dot region (e) solid line region showing lateral (P [001]o) and axial (P [110]o) polarization maps in vortices. \(\alpha , \beta ,\) and \(\gamma\) domain walls can be identified. \(\alpha\) domain wall (magenta curve) has an anti-parallel lateral (P [001]o) component, \(\beta\) (green curve) domain wall has an anti-parallel axial component (P [110]o), \(\gamma\) has both axial and lateral components antiparallel across the domain wall. </center>  
+
+We track the relative domain shift across the wall by the black dotted line as shown in Figure 2d- e. In Figure 2d, we observe a \(\alpha\) - domain wall (magenta line) where the lateral polarization shifts whereas the axial polarization remain the same, as shown in Figure 3. We also detect a \(\beta\) - domain wall (green line), where the axial polarization shifts, but the lateral polarization remains the same. Finally, in Figure 2e there is a third domain wall configuration i.e., a \(\gamma\) - domain wall (black line) as well where both the lateral and axial polarization shift. We verified this observation with average line profiles across the domain walls (Supplementary
+
+<--- Page Split --->
+
+information, Figure S1). To detect whether a change of chirality occurs at these domain walls, a way to quantify the chirality is required. The order parameter that best captures the breakdown of chiral symmetry is the helicity H of the chiral field. In our case, the chiral field is the polarization, and for the helicity, we borrow the definition from fluid dynamics:  
+
+\[\mathcal{H} = \int \vec{p}\cdot \left(\vec{\nabla}\times \vec{p}\right)d^{3}r, \quad (1)\]  
+
+where \(\vec{p}\) is the local value of polarization. Note that \(\mathcal{H}\) changes sign upon a mirror symmetry reflection \(^{32,33}\) . A nonzero helicity means chirality or lack of mirror symmetry of the polarization texture: right (left) handedness can be associated with positive (negative) values of \(\mathcal{H}\) . Assuming a vortex structure where the polarization lines in the plane defined by the vortices are closed, and that we can measure the axial and lateral components of the polarization at the topmost \(\mathrm{PbTiO_3}\) layer, then the previous equation can be estimated by  
+
+\[\mathcal{H} = 2\cdot < p_{lateral} > < p_{axial} > \quad (2)\]  
+
+where \(p_{lateral}\) and \(p_{axial}\) are polarization along lateral and axial directions (Supplementary information, Figure S2). Using this equation we can understand the nature of domain walls found in Figure 2d- e. For the \(\alpha /\beta\) domain wall, only one of the lateral/axial polarization sign change across the domain wall. This causes a change in the overall sign of helicity, thus making them chiral domain walls. On the other hand, the \(\gamma\) - domain wall has both a lateral and axial polarization switch, which doesn't change the overall helicity of the system, thus making it an achiral domain wall.
+
+<--- Page Split --->
+![](images/Figure_3.jpg)
+
+<center>Figure 3. Identification of triple point topologies and chiral domain walls. (a) The helicity map formed from Figure 2b shows left and right-handed domains separated by \(\alpha\) (magenta) and \(\beta\) (green). Additionally, achiral domain walls (black line) also coexist. The resultant triple point topologies formed due to the co-existence of chiral and achiral domain walls are shown in encircled regions. The sense of rotation of these triple point topologies is indicated in the encircled region. (b-c) Possible pairs of triple points. (b) represents triple points with opposite sense of rotation with the point of inversion along \(\beta\) , \(\gamma\) , and \(\alpha\) (c) represents triple points with the same sense of rotation. The green ticks on the side show what has been observed in experiments. </center>  
+
+We can use the qualitative lateral and axial polarization data from 4D- STEM and create a map of the helicity over a large scale using the helicity equation as shown in Figure 3. The red and blue regions in the chirality maps indicate different signs of helicity in the system, making them left- handed and right- handed chiral domains separated by the \(\alpha\) or \(\beta\) domain walls. We find that most of these chiral and achiral domain walls were not visible in the virtual dark field images. Further, we also observe a unique triple point topology at the mesoscale whenever the two types of chiral domain walls meet an achiral domain wall as seen from black- encircled areas, thus forming a quasi- 1D defect in the network of chiral and achiral domain walls. These triple points tend to exist in pairs and exhibit a sense of rotation via the transition from \(\alpha\) to \(\beta\) to \(\gamma\) domain wall and vice- versa (Figure 3, S3- S4), similar to what has been observed previously in the trimerized domain walls in hexagonal manganates \(^{34}\) , vortex- antivortex phases in intercalated Vander- Waal ferromagnets \(^{6}\) , and ferroelectric vortex cores in BiFeO \(_{3}\) \(^{35}\) . The sense of rotation in
+
+<--- Page Split --->
+
+a triple point can be the same or opposite depending on the arrangement of \(\alpha , \beta\) or \(\gamma\) domain walls. Figure 3b,c illustrates this situation. Whenever a pair of domain walls \((\beta , \gamma\) or \(\alpha , \gamma\) or \(\alpha , \beta\) ) break inversion symmetry across \(\alpha , \beta\) or \(\gamma\) domains, we form triple point pairs with opposite sense of rotation. If the inversion symmetry across \(\alpha , \beta\) or \(\gamma\) domains is not broken, we form triple point pairs with the same sense of rotation. The origin of triple point pairs can be understood by the following hypothesis. Consider the example of the first triple point pair in Figure 3b. If this particular type of triple point has to be isolated, then \(\beta\) boundary would infinitely separate the positive and negative chirality regions. If \(\beta\) boundary is not infinite, then it has to meet somewhere an \(\alpha\) boundary to continue separating the positive and negative chirality regions. Now, if a \(\beta\) boundary (change in axial polarization) meets an \(\alpha\) boundary (change in lateral polarization), a \(\gamma\) boundary appears (change in both lateral and axial polarization). In such a scenario, we get another triple point in the vicinity of the first triple point thus explaining the origins of pairs for the majority of cases. \(\alpha / \beta\) boundary could be isolated only in very special circumstances when they are born/die at the surface or they are infinitely long.  
+
+![PLACEHOLDER_12_0]
+
+
+<--- Page Split --->
+
+Figure 4. Types of chiral domain walls. (a) Four possible combinations of alternating clockwise/counterclockwise vortices are displaced, Top and bottom cartoons differ by the direction of the axial component of the polarization (red dot and blue cross). Left and right cartoons differ by the curl of the polarization. The chirality for each type of domain is indicated by a sketched hand. The possible domain walls between these configurations are marked as type \(\alpha /\alpha^{\prime}\) , \(\beta /\beta^{\prime}\) , and \(\gamma /\gamma^{\prime}\) . \(\alpha /\alpha^{\prime}\) and \(\beta /\beta^{\prime}\) domain walls change the chirality at the domain wall. \(\gamma /\gamma^{\prime}\) domains preserve the chirality. (b- d) Three- dimensional representation of three types of chiral domain walls observed by 4D- STEM measurements.  
+
+From a theoretical perspective, there are three symmetry- breaking operations for the formation of domain walls, 1) Change in the lateral polarization direction, 2) Change in axial polarization direction, and 3) Change in the net lateral polarization due to the vortex core shifting away from the center. If we consider all three factors, then we expect to have six types of domain walls as shown in Figure 4. The first pair of chiral domain walls, \(\alpha /\alpha^{\prime}\) , results from a combination of factors 1 and 3. The second chiral domain wall pair, \(\beta /\beta^{\prime}\) , results from a combination of factors 2 and 3. The third achiral domain wall pair results from all three factors. Unfortunately, it is challenging to measure the quantitative net lateral component in the vortices due to the very low sensitivity of electron scattering to sample changes along the beam direction. Due to this, \(\alpha /\alpha^{\prime}\) , \(\beta /\beta^{\prime}\) , and \(\gamma /\gamma^{\prime}\) are degenerate in this 4D- STEM and thus we observe only three types of domain walls. This has been consistent in multiple such 4D STEM as observed in Figure S3- S4. Future studies, such as depth sectioning or sample tilting experiments, may be able to probe this variation \(^{36}\) .  
+
+## Discussion  
+
+We have unraveled the nanoscale three- dimensional domain wall network in topological polar vortices using quantitative 4D- STEM techniques. The polar vortex oxide superlattice has emergent chirality through different symmetry- breaking operations in the non- zero curl of polarization along with the alternate axial polarization component and vortex buckling- induced net- in plane rotation or lateral polarization component. The interplay of these symmetry- breaking
+
+<--- Page Split --->
+
+operations results in the formation of two types of chiral and an achiral domain wall within tubular vortex topologies. Topology- driven domain wall existence in our work is unusual in comparison to other electrostatic wall conditions- driven domain walls in the ferroic materials \(^{16,34}\) . The finite nature of chiral and achiral domain walls results in the formation of unique triple points whenever these domain walls intersect. The most probable existence of these points is in pairs with the same/different handedness, similar to multiferroic materials such as barium hexaferrite \(^{37}\) and \(\mathrm{BiFeO_3}^{35}\) . To our best understanding, such an unconventional scenario has not been seen yet in improper ferroics literature. We hope that our studies could inspire future experiments to understand the electronic and magnetic transport at these triple points within the network of chiral and achiral domain walls in polar vortices oxide superlattices.  
+
+## Materials and Methods  
+
+Synthesis: \([(PbTiO_3)_{16} / (SrTiO_3)_{16}]\) trilayer with \(\mathrm{SrRuO_3}\) buffer layer was synthesized on single- crystalline \(\mathrm{DyScO_3}\) (011) substrates via reflection high- energy electron diffraction (RHEED)- assisted pulsed- laser deposition (KrF laser). The \(\mathrm{PbTiO_3}\) and the \(\mathrm{SrTiO_3}\) layers were grown at \(610^{\circ}\mathrm{C}\) in \(100\mathrm{mTorr}\) oxygen pressure.  
+
+HR- STEM vector mapping: The vector mapping was performed via Gaussian fitting of A site atomic positions on the drift- corrected HR- STEM images \(^{38}\) . First, all the A- sites in the drift- corrected images were identified using “Atomap” atom finding tool \(^{39}\) . Once the atoms were identified, the atomic planes were divided into different zone axis such as along \([001]_0\) and \([001]_0\) . The deviation in local A- displacement was found by taking the difference between the local A site displacement and the corresponding average displacement in the local zone axis plane. The displacement vectors were further interpolated into a grid Cartesian grid and then
+
+<--- Page Split --->
+
+differentiated to obtain strain tensor maps. The infinitesimal rotation or the curl of the displacement of vortices was calculated using the following equation:  
+
+\[\theta = \frac{1}{2}\left(\frac{\partial u}{\partial y} -\frac{\partial v}{\partial x}\right)\]  
+
+The color bar in the curl of displacement plot is plotted with respect to the mean intensities in the \(\mathrm{PbTiO_3}\) layer.  
+
+4D STEM analysis: All 4D STEM experiments were carried out on TEAM I microscope (aberration- corrected Thermo Fisher Scientific Titan 80- 300) using a Gatan K3 direct detection camera located at the end of a Gatan Continuum imaging filter. The microscope was operated at \(300\mathrm{kV}\) with a probe current of \(100\mathrm{pA}\) . The probe semi- angle used for the measurement was 2 mrad. Diffraction patterns were collected using a step size of \(1\mathrm{nm}\) with 514 by 399 scan positions. The K3 camera was used in full- frame electron counting mode with a binning of 4 pixels by 4 pixels and a camera length of \(1.05\mathrm{mm}\) . The exposure time for each diffraction pattern was \(47\mathrm{ms}\) . The 4D STEM analysis was carried out using the py4DSTEM modules. Briefly, rotation calibration was performed between the diffraction and image plane to identify the right orientation of the zone axis. For that process, the defocused image in the Ronchigram was compared to the focused scan image and the relative orientation of the two images was compared. Once the zone axis was identified, all the disks in the diffraction pattern at each probe position were fitted using the disk fitting function. The so- called polarization maps were generated by taking the normalized intensity difference between the opposite Friedel pair disks. Subsequently, the signal- to- noise in these polarization maps was improved by using a combination of low- pass and high- pass Gaussian filters. By using a high- pass Gaussian filter, we also minimized the dominating thickness contrast.
+
+<--- Page Split --->
+
+## References:  
+
+1. Sowa, W. Synthesis of L-glucurone. Conversion of D-glucose into L-glucose. Can. J. Chem. 47, 3931–3934 (1969).  
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+
+## Acknowledgments  
+
+All the electron microscopy experiments were carried out at the National Center for Electron Microscopy (NCEM) located in the Molecular Foundry user facility at Lawrence Berkeley National Laboratory. Work at the Molecular Foundry was supported by the Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE- AC02- 05CH11231. S.S. and R.R. are supported by the DOE Quantum Materials Program. CO acknowledges support from a DOE Early Career Research Award. F.G.- O., P.G.- F., and J.J. acknowledge financial support from Grant No. PGC2018- 096955- B- C41 funded by
+
+<--- Page Split --->
+
+MCIN/AEI/10.13039/501100011033 and by ERDF "A way of making Europe," by the European Union. F.G.- O. acknowledges financial support from Grant No. FPU18/04661 funded by MCIN/AEI/10.13039/501100011033. BHS was supported by the Toyota Research Institute. National Institute of Health Research UK.  
+
+Author contributions: S.S., R.R., and C.O. conceived the idea, and designed the experiments. S.S. analyzed the 4D STEM datasets, made the figures and wrote the initial draft of the manuscript. S.L. performed the 4D STEM experiments. B.H. provided inputs for the scripts of the 4D STEM analysis. F.G.O, P.G. F, and J.J helped in providing intellectual inputs regarding the origin of chiral domain walls and triple points. P.B. and P.E. participated in revising the manuscript. R.R. and C.O. supervised the project.  
+
+Competing interests: Authors declare that they have no competing interests.  
+
+Data and materials availability: All data are available in the main text or the supplementary materials.  
+
+## Supplementary Materials.  
+
+All the supplementary materials is available in supplemental information.
+
+<--- Page Split --->
+
+## Supplementary Files  
+
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+- Sl.pdf
+
+<--- Page Split --->

preprint/preprint__015d965c0cb0c74c387f9596e3f91c0197ff39a7333b1c0b5bef96993bcbbd18/preprint__015d965c0cb0c74c387f9596e3f91c0197ff39a7333b1c0b5bef96993bcbbd18_det.mmd

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+<|ref|>title<|/ref|><|det|>[[44, 107, 936, 175]]<|/det|>
+# The emergence of three-dimensional chiral domain walls in polar vortices  
+
+<|ref|>text<|/ref|><|det|>[[44, 195, 621, 236]]<|/det|>
+Sandhya Susarla ( \(\boxed{ \begin{array}{r l} \end{array} }\) sandhya.susarla@asu.edu) Arizona State University https://orcid.org/0000- 0003- 1773- 0993  
+
+<|ref|>text<|/ref|><|det|>[[44, 241, 336, 282]]<|/det|>
+Shang- Lin Hsu University of california, Berkeley  
+
+<|ref|>text<|/ref|><|det|>[[44, 288, 633, 330]]<|/det|>
+Fernando Gomez- Ortiz Universidad de Cantabria https://orcid.org/0000- 0002- 7203- 8476  
+
+<|ref|>text<|/ref|><|det|>[[44, 335, 631, 377]]<|/det|>
+Pablo Garcia- Fernandez Universidad de Cantabria https://orcid.org/0000- 0002- 4901- 0811  
+
+<|ref|>text<|/ref|><|det|>[[44, 382, 209, 422]]<|/det|>
+Benjamin Savitzky Cornell University  
+
+<|ref|>text<|/ref|><|det|>[[44, 428, 738, 470]]<|/det|>
+SUJIT DAS Indian Institute of Science, Bangalore https://orcid.org/0000- 0001- 9823- 0207  
+
+<|ref|>text<|/ref|><|det|>[[44, 475, 336, 515]]<|/det|>
+Piush Behera University of California, Berkeley  
+
+<|ref|>text<|/ref|><|det|>[[44, 521, 923, 563]]<|/det|>
+Javier Junquera Universidad de Cantabria, Cantabria Campus Internacional https://orcid.org/0000- 0002- 9957- 8982  
+
+<|ref|>text<|/ref|><|det|>[[44, 568, 756, 609]]<|/det|>
+Peter Erucis Lawrence Berkeley National Laboratory https://orcid.org/0000- 0002- 6762- 9976  
+
+<|ref|>text<|/ref|><|det|>[[44, 614, 247, 654]]<|/det|>
+Ramamoorthy Ramesh Rice University  
+
+<|ref|>text<|/ref|><|det|>[[44, 660, 886, 723]]<|/det|>
+Colin Ophus National Center for Electron Microscopy Facility, Molecular Foundry, Lawrence Berkeley National Laboratory  
+
+<|ref|>text<|/ref|><|det|>[[44, 764, 101, 782]]<|/det|>
+Article  
+
+<|ref|>text<|/ref|><|det|>[[44, 802, 137, 820]]<|/det|>
+Keywords:  
+
+<|ref|>text<|/ref|><|det|>[[44, 840, 323, 860]]<|/det|>
+Posted Date: February 8th, 2023  
+
+<|ref|>text<|/ref|><|det|>[[44, 879, 473, 899]]<|/det|>
+DOI: https://doi.org/10.21203/rs.3.rs- 2551328/v1  
+
+<|ref|>text<|/ref|><|det|>[[44, 916, 909, 958]]<|/det|>
+License: \(\circledcirc\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[42, 100, 907, 142]]<|/det|>
+Version of Record: A version of this preprint was published at Nature Communications on July 25th, 2023. See the published version at https://doi.org/10.1038/s41467-023-40009-2.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[189, 97, 808, 116]]<|/det|>
+## The emergence of three-dimensional chiral domain walls in polar vortices  
+
+<|ref|>text<|/ref|><|det|>[[175, 121, 865, 176]]<|/det|>
+Sandhya Susarla \(^{1,2,\mathrm{S}\# *}\) , Shanglin Hsu \(^{1,2\#}\) , Fernando Gómez- Ortiz \(^{4}\) , Pablo García- Fernández \(^{4}\) , Benjamin H. Savitzky \(^{1}\) , Sujit Das \(^{2,6}\) , Piush Behera \(^{3}\) , Javier Junquera \(^{4}\) , Peter Ercius \(^{1}\) , Ramamoorthy Ramesh \(^{1,2,5,7,8*}\) , Colin Ophus \(^{1*}\)  
+
+<|ref|>text<|/ref|><|det|>[[173, 192, 872, 472]]<|/det|>
+1: National Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA 94720  
+2: Materials Sciences Division, Lawrence Berkeley Laboratory, Berkeley CA, USA 94720  
+3: Department of Materials Science & Engineering, University of California, Berkeley, CA, USA 94720  
+4: Departmento de Ciencias de la Tierra y Física de la Materia Condensada, Universidad de Cantabria, Cantabria Campus Internacional Santander, Spain, 39005  
+5: Department of Physics, University of California, Berkeley Berkeley, CA, USA 94720  
+6: Material Research Centre, Indian Institute of Science, Bangalore, 560012  
+7: Department of Physics, Rice University, Houston, TX, USA, 77005  
+8: Department of Materials Science and Nanoengineering, Houston, TX, USA, 77005 #Equal contribution.  
+\$ Present address: School for Engineering of Matter, Transport, and Energy, Arizona State University  
+
+<|ref|>text<|/ref|><|det|>[[115, 488, 810, 524]]<|/det|>
+Corresponding authors: sandhya.susarla@asu.edu, ramamoorthy.ramesh@rice.edu, and clophus@lbl.gov  
+
+<|ref|>sub_title<|/ref|><|det|>[[116, 549, 191, 565]]<|/det|>
+## Abstract  
+
+<|ref|>text<|/ref|><|det|>[[115, 583, 883, 793]]<|/det|>
+Chirality or handedness of a material can be used as an order parameter to uncover emergent electronic properties for quantum information science. Conventionally, chirality is found in naturally occurring biomolecules and magnetic materials. Chirality can be engineered in a topological polar vortex ferroelectric/dielectric system via atomic- scale symmetry- breaking operations. We use four- dimensional scanning transmission electron microscopy (4D- STEM) to map out topology- driven three- dimensional domain walls, where the handedness of two neighbor topological domains change or remain the same. The nature of the domain walls is governed by the interplay of local perpendicular (lateral) and parallel (axial) polarization with respect to the tubular vortex structures. Unique symmetry- breaking operations and the finite nature of domain walls results in a triple point at the junction of chiral and achiral domain walls. The unconventional nature of the domain walls with triple point pairs may result in unique electrostatic and magnetic properties potentially useful for quantum sensing applications.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[115, 91, 225, 109]]<|/det|>
+## Introduction  
+
+<|ref|>text<|/ref|><|det|>[[112, 120, 886, 880]]<|/det|>
+Chirality is a unique topological feature that drives many- body interactions in naturally occurring organic molecules and proteins \(^{1}\) , subatomic particle physics \(^{2}\) , and solid- state physics \(^{3}\) . The symmetries in a chiral system are configured in such a way that its mirror image cannot be superimposed on itself, manifesting a handedness to the system as exemplified by screws and our own hands. Chirality also exists at the microscale/nanoscale level in inorganic and organic materials such as liquid crystals \(^{4}\) , spin textures in ferromagnets \(^{5,6}\) , amino acids, and D/L- glucose molecules \(^{1}\) with applications in spin selectivity- based quantum sensing \(^{7}\) , non- linear optics \(^{8}\) , and biosensing applications \(^{9}\) . However, there are very few examples of chiral inorganic ferroelectric crystals which could have fundamentally different domain textures \(^{10 - 13}\) . Over the past few years, novel polarization textures in ferroelectrics such as merons \(^{14}\) , polar flux- closure domains \(^{15,16}\) , vortices \(^{17}\) , bubble domains \(^{18,19}\) , super crystals \(^{20,21}\) and skyrmions \(^{22}\) have been engineered in oxide superlattices, emerging from the careful interplay of elastic, electrostatic and gradient energies of electric dipoles. The electric dipole arrangement and complex orbital hybridization in these systems have been probed by the x- ray scattering techniques \(^{23}\) , scanning transmission electron microscopy (STEM) \(^{17}\) - electron energy loss spectroscopy (EELS) \(^{24}\) , phase- field simulations \(^{12,14,15,17,20}\) and atomistic first- and second- principles calculations \(^{17,18,20,22,23}\) . Surprisingly, the presence of chirality has been observed in one such topological texture i.e. polar vortices in \(\mathrm{PbTiO_3 / SrTiO_3}\) superlattices from resonant soft x- ray scattering (RXS) \(^{23}\) , second harmonic generation (SHG) and second principles calculations \(^{25}\) . The presence of chirality in polar vortices is an emergent phenomenon because none of the parent compounds such as \(\mathrm{SrTiO_3}\) or \(\mathrm{PbTiO_3}\) are known to be chiral. It has been recently shown experimentally and theoretically that the presence of chirality in these systems might be due to different sources.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 88, 879, 354]]<|/det|>
+First, the strongest one is the coexistence of vortices with an axial component of the polarization, perpendicular to the vortex plane \(^{26}\) . The second factor is the buckling of the vortices (i.e., a staggered vortex configuration where the center of the clockwise and counterclockwise vortices are located at different heights) combined with different sizes of the up and down domains results in a chiral structure, although its strength is smaller than in the first scenario. This last source of chirality can be reversed by external electric fields. The first experimental demonstration was in \(^{25}\) , where chirality switches in a reversible, deterministic, and non- destructive fashion over mesoscale regions \(^{25}\) .  
+
+<|ref|>text<|/ref|><|det|>[[113, 368, 883, 771]]<|/det|>
+Without any prior knowledge, one would expect the as- grown sample as a racemic mixture, i.e., equal amounts of left- handed and right- handed domain enantiomers, where chirality within each domain comes from a combination of the two sources described above. To have a non- destructive switchable chirality, it is essential to understand the role of the domain walls separating the enantiomers. In other words, what local physical parameters play a role when the handedness in neighboring domains changes? This includes the offset between the center of the cores, the axial component at the center of the clockwise/counterclockwise vortices, the sense of rotation of the vortices when they merge at the domain wall, the presence of dislocations, or the combined effect of all of them. A proper understanding of how the left/right- handed domains evolve at the nanoscale is crucial to design new electrically switchable chiral devices that can be measured without scientifically sophisticated techniques. Indeed, a proper answer to this question will pave the way for the use of these chiral textures in next- generation technologies.  
+
+<|ref|>text<|/ref|><|det|>[[114, 785, 875, 876]]<|/det|>
+Four- dimensional (4D)- STEM can precisely measure strain, and thus spontaneous polarization in ferroelectrics due to the violation of Friedel's Law \(^{27 - 29}\) . This makes 4D- STEM a unique tool to probe polarization in three dimensions and understand emergent chirality in
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 88, 881, 354]]<|/det|>
+polarization vortices. In the current work, we have used 4D- STEM to probe three- dimensional domain walls in polar vortices in oxide superlattices and understand the nano- scale nature of chirality. We find that both achiral and chiral domain walls coexist in the same system. The chiral to achiral domain wall transition is driven by the change in local axial and lateral polarization direction across the domain wall. We have discovered new pair of triple points with the opposite/same sense of rotation at the junction of achiral and chiral domain walls. Finally, we unravel all the possible configurations of chiral and achiral domain walls in this system through different symmetry- breaking operations.  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 404, 180, 421]]<|/det|>
+## Results  
+
+<|ref|>text<|/ref|><|det|>[[113, 437, 884, 632]]<|/det|>
+Trilayer \((\mathrm{SrTiO}_3)_{16} / (\mathrm{PbTiO}_3)_{16} / (\mathrm{SrTiO}_3)_{16}\) (STO/PTO/STO) were grown on orthorhombic \(\mathrm{DyScO}_3\) (DSO) \([110]_{0}\) substrates with \(\mathrm{SrRuO}_3\) as a buffer layer using reflection high- energy electron diffraction (RHEED)- assisted pulsed- laser deposition (PLD) (Figure 1A and Supplementary Information). The polar vortex phase in this system is stabilized as a consequence of the interplay between depolarization energy at the PTO/STO interfaces, elastic energy from the tensile strain imposed by the DSO substrate, and the gradient energy in the ferroelectric \(^{17}\) .  
+
+<|ref|>text<|/ref|><|det|>[[113, 647, 880, 877]]<|/det|>
+A direct way to measure the polar textures in vortex topologies is through electron microscopy using atomic resolution images. We have used different types of STEM and TEM techniques to characterize the exact positions of the vortex centers. Figure 1a shows a low- magnification bright- field STEM image of the superlattice trilayer with an SRO buffer layer along the \([1\bar{1} 0]_{0}\) zone axis. The diffraction contrast in BF- STEM allows us to directly locate the vortex center as dark contrast; marked using red circles in Figure 1a. To precisely understand the polarization or displacement texture around each vortex center, we obtained the A- site
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[111, 85, 876, 670]]<|/det|>
+displacement vector maps at atomic resolution via gaussian fitting at A- sites (Methods, supplementary information). Figure 1b shows the High angle annular dark field (HAADF)- STEM image of trilayer STO/PTO/STO along \([1\bar{1} 0]_{0}\) zone axis where brighter regions are PTO and darker regions are STO. Overlaid yellow arrows show clockwise and counterclockwise rotating curls in the displacement of the A- cation in PTO/STO superlattices. The coexistence of the concomitant non- zero curl of polarization (red/blue contrast) with an alternating axial component of the polarization (perpendicular to the plane defined by the vortices) is the first symmetry- breaking operation that results in emergent chirality in an otherwise non- chiral system. The non- zero curl is larger in continuously rotating polarization textures such as polar vortices \(^{25,27}\) , merons \(^{30}\) , and skyrmions \(^{22}\) than in other polarization textures such as flux closure domains \(^{15,16}\) where the curl vanished in the central regions with \(180^{0}\) domain walls. Additionally, we observe that the cores of the polarization curls (indicated as blue/red contrast) are not located exactly at the center of the PTO layer, but follow a zig- zag type pattern, giving rise to a net in- plane polarization rotation along \([001]_{0}\) (lateral component) indicated as \(\mathrm{P}_{\mathrm{x}}\) in Figure 1b. This buckling, combined with a small difference in the size of the up and down domains, is the second symmetry- breaking operation that results in net chirality; in agreement with previous observations \(^{25}\) .
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[125, 95, 884, 400]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[115, 414, 883, 575]]<|/det|>
+<center>Figure 1. Chirality at different length scales. (a) Bright-Field STEM image showing the stack of 16 STO/16 PTO/16 STO with SRO buffer layer on DSO substrate. The red regions near the center of the PTO layer indicate the position of the vortex core approximately. (b) Vector mapping of the local displacements of A sites of highlighted region in (a) overlaid on HAADF-STEM image. The local red- and blue contrast at the center of the PTO layer indicates the local non-zero curl of displacement. The net lateral polarization resulting from vortex off-centering is indicated at the top. The dotted black line shows the center of the PTO layer. (c) Dark field TEM image along [110]o direction displaying long tubular vortex structure with domain walls shown as white dashed lines. </center>  
+
+<|ref|>text<|/ref|><|det|>[[113, 590, 874, 890]]<|/det|>
+The various permutations and combinations of atomic scale symmetry- breaking operations such as a non- zero curl of polarization together with the presence of an axial component, and the buckling of the vortices that yield a non- zero polarization component along [001]o, result in different types of domains walls at the mesoscale. We can visualize the mesoscale domain walls in these topological structures by imaging the vortices along the [110]o zone axis using weak beam dark field TEM (Figure 1c). We can observe the tubular nature of vortex textures by long bright and dark stripes regions in the image. Additionally, we observe different domain wall features (indicated as white dashed lines) cutting across vortex tubes. Overall, if we combine our observations from DF- TEM and HR- STEM, we observe a three-
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[112, 84, 879, 850]]<|/det|>
+dimensional structure in the PbTiO₃ layer sandwiched between SrTiO₃ layers where the polarization curls follow a tubular pattern (Figure 2a). Unfortunately, the images formed by weak beam dark field, HRSTEM, and BF- STEM are merely atomic projections and cannot give us an estimate of physical quantities such as polarization and chirality. On the other hand, 4D- STEM allows us to collect a diffraction pattern at each probe position, which can then be used to create precise maps of physical quantities. For the present experiment, we performed 4D- STEM imaging on a trilayer STO/PTO/STO along the [110]₀ zone axis (Figure 2a). We used a probe size of \(\sim 7 \text{Å}\) , larger than the STO/PTO unit cell dimensions ( \(\sim 4 \text{Å}\) ) to remove the atomic- resolution signal and to estimate the polarization at unit cell resolution. The 4D- STEM analysis was carried out in open source py4DSTEM analysis package ³¹. We define the [110]₀ direction as axial and [001]₀ direction as lateral. The rotation calibration was performed between the real space and diffraction space to determine the lateral and axial axis. Details are given in the supplementary information. For initial visualization of the polar textures, we created a virtual dark field image using the [2\(\bar{1}\)0]₀ disk (disk 3) as shown in Figure 2b- c. The polarization from the PTO layer can be determined qualitatively by subtracting opposite Friedel pair disks due to the violation of Friedel’s law ²⁷-²⁹. The polarization maps corresponding to regions delimited by rectangles in Figure 2b are shown in Figure 2d- e. We observe alternate longitudinal red and blue stripes representing positive and negative polarization in both the lateral ([001]₀) and axial [110]₀ directions with domain walls as seen from the weak beam dark field images marked with green and magenta lines in Figure 2d and with a black line in Figure 2e. We observe that the axial polarization magnitude is relatively smaller than the lateral polarization in agreement with the predictions from previous second principles calculations ²³,²⁵.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[120, 95, 880, 490]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[114, 509, 880, 654]]<|/det|>
+<center>Figure 2. Identification of polarization using 4D-STEM mapping. (a) Schematic showing the e-beam scanned in 4D STEM mode across the vortex sample in in-plane geometry. (b) Virtual dark field image of the vortex region obtained via integrating the intensity of disk 3 from the mean diffraction pattern in (c). Zoomed-in images of the (d) dash-dot region (e) solid line region showing lateral (P [001]o) and axial (P [110]o) polarization maps in vortices. \(\alpha , \beta ,\) and \(\gamma\) domain walls can be identified. \(\alpha\) domain wall (magenta curve) has an anti-parallel lateral (P [001]o) component, \(\beta\) (green curve) domain wall has an anti-parallel axial component (P [110]o), \(\gamma\) has both axial and lateral components antiparallel across the domain wall. </center>  
+
+<|ref|>text<|/ref|><|det|>[[113, 668, 880, 900]]<|/det|>
+We track the relative domain shift across the wall by the black dotted line as shown in Figure 2d- e. In Figure 2d, we observe a \(\alpha\) - domain wall (magenta line) where the lateral polarization shifts whereas the axial polarization remain the same, as shown in Figure 3. We also detect a \(\beta\) - domain wall (green line), where the axial polarization shifts, but the lateral polarization remains the same. Finally, in Figure 2e there is a third domain wall configuration i.e., a \(\gamma\) - domain wall (black line) as well where both the lateral and axial polarization shift. We verified this observation with average line profiles across the domain walls (Supplementary
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 88, 884, 214]]<|/det|>
+information, Figure S1). To detect whether a change of chirality occurs at these domain walls, a way to quantify the chirality is required. The order parameter that best captures the breakdown of chiral symmetry is the helicity H of the chiral field. In our case, the chiral field is the polarization, and for the helicity, we borrow the definition from fluid dynamics:  
+
+<|ref|>equation<|/ref|><|det|>[[172, 228, 575, 253]]<|/det|>
+\[\mathcal{H} = \int \vec{p}\cdot \left(\vec{\nabla}\times \vec{p}\right)d^{3}r, \quad (1)\]  
+
+<|ref|>text<|/ref|><|det|>[[113, 268, 875, 465]]<|/det|>
+where \(\vec{p}\) is the local value of polarization. Note that \(\mathcal{H}\) changes sign upon a mirror symmetry reflection \(^{32,33}\) . A nonzero helicity means chirality or lack of mirror symmetry of the polarization texture: right (left) handedness can be associated with positive (negative) values of \(\mathcal{H}\) . Assuming a vortex structure where the polarization lines in the plane defined by the vortices are closed, and that we can measure the axial and lateral components of the polarization at the topmost \(\mathrm{PbTiO_3}\) layer, then the previous equation can be estimated by  
+
+<|ref|>equation<|/ref|><|det|>[[172, 479, 575, 500]]<|/det|>
+\[\mathcal{H} = 2\cdot < p_{lateral} > < p_{axial} > \quad (2)\]  
+
+<|ref|>text<|/ref|><|det|>[[113, 514, 880, 744]]<|/det|>
+where \(p_{lateral}\) and \(p_{axial}\) are polarization along lateral and axial directions (Supplementary information, Figure S2). Using this equation we can understand the nature of domain walls found in Figure 2d- e. For the \(\alpha /\beta\) domain wall, only one of the lateral/axial polarization sign change across the domain wall. This causes a change in the overall sign of helicity, thus making them chiral domain walls. On the other hand, the \(\gamma\) - domain wall has both a lateral and axial polarization switch, which doesn't change the overall helicity of the system, thus making it an achiral domain wall.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[125, 90, 875, 288]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[113, 311, 884, 455]]<|/det|>
+<center>Figure 3. Identification of triple point topologies and chiral domain walls. (a) The helicity map formed from Figure 2b shows left and right-handed domains separated by \(\alpha\) (magenta) and \(\beta\) (green). Additionally, achiral domain walls (black line) also coexist. The resultant triple point topologies formed due to the co-existence of chiral and achiral domain walls are shown in encircled regions. The sense of rotation of these triple point topologies is indicated in the encircled region. (b-c) Possible pairs of triple points. (b) represents triple points with opposite sense of rotation with the point of inversion along \(\beta\) , \(\gamma\) , and \(\alpha\) (c) represents triple points with the same sense of rotation. The green ticks on the side show what has been observed in experiments. </center>  
+
+<|ref|>text<|/ref|><|det|>[[113, 476, 884, 884]]<|/det|>
+We can use the qualitative lateral and axial polarization data from 4D- STEM and create a map of the helicity over a large scale using the helicity equation as shown in Figure 3. The red and blue regions in the chirality maps indicate different signs of helicity in the system, making them left- handed and right- handed chiral domains separated by the \(\alpha\) or \(\beta\) domain walls. We find that most of these chiral and achiral domain walls were not visible in the virtual dark field images. Further, we also observe a unique triple point topology at the mesoscale whenever the two types of chiral domain walls meet an achiral domain wall as seen from black- encircled areas, thus forming a quasi- 1D defect in the network of chiral and achiral domain walls. These triple points tend to exist in pairs and exhibit a sense of rotation via the transition from \(\alpha\) to \(\beta\) to \(\gamma\) domain wall and vice- versa (Figure 3, S3- S4), similar to what has been observed previously in the trimerized domain walls in hexagonal manganates \(^{34}\) , vortex- antivortex phases in intercalated Vander- Waal ferromagnets \(^{6}\) , and ferroelectric vortex cores in BiFeO \(_{3}\) \(^{35}\) . The sense of rotation in
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[112, 85, 884, 570]]<|/det|>
+a triple point can be the same or opposite depending on the arrangement of \(\alpha , \beta\) or \(\gamma\) domain walls. Figure 3b,c illustrates this situation. Whenever a pair of domain walls \((\beta , \gamma\) or \(\alpha , \gamma\) or \(\alpha , \beta\) ) break inversion symmetry across \(\alpha , \beta\) or \(\gamma\) domains, we form triple point pairs with opposite sense of rotation. If the inversion symmetry across \(\alpha , \beta\) or \(\gamma\) domains is not broken, we form triple point pairs with the same sense of rotation. The origin of triple point pairs can be understood by the following hypothesis. Consider the example of the first triple point pair in Figure 3b. If this particular type of triple point has to be isolated, then \(\beta\) boundary would infinitely separate the positive and negative chirality regions. If \(\beta\) boundary is not infinite, then it has to meet somewhere an \(\alpha\) boundary to continue separating the positive and negative chirality regions. Now, if a \(\beta\) boundary (change in axial polarization) meets an \(\alpha\) boundary (change in lateral polarization), a \(\gamma\) boundary appears (change in both lateral and axial polarization). In such a scenario, we get another triple point in the vicinity of the first triple point thus explaining the origins of pairs for the majority of cases. \(\alpha / \beta\) boundary could be isolated only in very special circumstances when they are born/die at the surface or they are infinitely long.  
+
+<|ref|>image<|/ref|><|det|>[[124, 593, 770, 865]]<|/det|>
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[114, 88, 875, 232]]<|/det|>
+Figure 4. Types of chiral domain walls. (a) Four possible combinations of alternating clockwise/counterclockwise vortices are displaced, Top and bottom cartoons differ by the direction of the axial component of the polarization (red dot and blue cross). Left and right cartoons differ by the curl of the polarization. The chirality for each type of domain is indicated by a sketched hand. The possible domain walls between these configurations are marked as type \(\alpha /\alpha^{\prime}\) , \(\beta /\beta^{\prime}\) , and \(\gamma /\gamma^{\prime}\) . \(\alpha /\alpha^{\prime}\) and \(\beta /\beta^{\prime}\) domain walls change the chirality at the domain wall. \(\gamma /\gamma^{\prime}\) domains preserve the chirality. (b- d) Three- dimensional representation of three types of chiral domain walls observed by 4D- STEM measurements.  
+
+<|ref|>text<|/ref|><|det|>[[113, 253, 884, 696]]<|/det|>
+From a theoretical perspective, there are three symmetry- breaking operations for the formation of domain walls, 1) Change in the lateral polarization direction, 2) Change in axial polarization direction, and 3) Change in the net lateral polarization due to the vortex core shifting away from the center. If we consider all three factors, then we expect to have six types of domain walls as shown in Figure 4. The first pair of chiral domain walls, \(\alpha /\alpha^{\prime}\) , results from a combination of factors 1 and 3. The second chiral domain wall pair, \(\beta /\beta^{\prime}\) , results from a combination of factors 2 and 3. The third achiral domain wall pair results from all three factors. Unfortunately, it is challenging to measure the quantitative net lateral component in the vortices due to the very low sensitivity of electron scattering to sample changes along the beam direction. Due to this, \(\alpha /\alpha^{\prime}\) , \(\beta /\beta^{\prime}\) , and \(\gamma /\gamma^{\prime}\) are degenerate in this 4D- STEM and thus we observe only three types of domain walls. This has been consistent in multiple such 4D STEM as observed in Figure S3- S4. Future studies, such as depth sectioning or sample tilting experiments, may be able to probe this variation \(^{36}\) .  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 711, 207, 728]]<|/det|>
+## Discussion  
+
+<|ref|>text<|/ref|><|det|>[[114, 744, 880, 905]]<|/det|>
+We have unraveled the nanoscale three- dimensional domain wall network in topological polar vortices using quantitative 4D- STEM techniques. The polar vortex oxide superlattice has emergent chirality through different symmetry- breaking operations in the non- zero curl of polarization along with the alternate axial polarization component and vortex buckling- induced net- in plane rotation or lateral polarization component. The interplay of these symmetry- breaking
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 88, 879, 424]]<|/det|>
+operations results in the formation of two types of chiral and an achiral domain wall within tubular vortex topologies. Topology- driven domain wall existence in our work is unusual in comparison to other electrostatic wall conditions- driven domain walls in the ferroic materials \(^{16,34}\) . The finite nature of chiral and achiral domain walls results in the formation of unique triple points whenever these domain walls intersect. The most probable existence of these points is in pairs with the same/different handedness, similar to multiferroic materials such as barium hexaferrite \(^{37}\) and \(\mathrm{BiFeO_3}^{35}\) . To our best understanding, such an unconventional scenario has not been seen yet in improper ferroics literature. We hope that our studies could inspire future experiments to understand the electronic and magnetic transport at these triple points within the network of chiral and achiral domain walls in polar vortices oxide superlattices.  
+
+<|ref|>sub_title<|/ref|><|det|>[[116, 438, 314, 457]]<|/det|>
+## Materials and Methods  
+
+<|ref|>text<|/ref|><|det|>[[114, 480, 867, 605]]<|/det|>
+Synthesis: \([(PbTiO_3)_{16} / (SrTiO_3)_{16}]\) trilayer with \(\mathrm{SrRuO_3}\) buffer layer was synthesized on single- crystalline \(\mathrm{DyScO_3}\) (011) substrates via reflection high- energy electron diffraction (RHEED)- assisted pulsed- laser deposition (KrF laser). The \(\mathrm{PbTiO_3}\) and the \(\mathrm{SrTiO_3}\) layers were grown at \(610^{\circ}\mathrm{C}\) in \(100\mathrm{mTorr}\) oxygen pressure.  
+
+<|ref|>text<|/ref|><|det|>[[113, 626, 864, 857]]<|/det|>
+HR- STEM vector mapping: The vector mapping was performed via Gaussian fitting of A site atomic positions on the drift- corrected HR- STEM images \(^{38}\) . First, all the A- sites in the drift- corrected images were identified using “Atomap” atom finding tool \(^{39}\) . Once the atoms were identified, the atomic planes were divided into different zone axis such as along \([001]_0\) and \([001]_0\) . The deviation in local A- displacement was found by taking the difference between the local A site displacement and the corresponding average displacement in the local zone axis plane. The displacement vectors were further interpolated into a grid Cartesian grid and then
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[115, 89, 787, 144]]<|/det|>
+differentiated to obtain strain tensor maps. The infinitesimal rotation or the curl of the displacement of vortices was calculated using the following equation:  
+
+<|ref|>equation<|/ref|><|det|>[[422, 165, 568, 207]]<|/det|>
+\[\theta = \frac{1}{2}\left(\frac{\partial u}{\partial y} -\frac{\partial v}{\partial x}\right)\]  
+
+<|ref|>text<|/ref|><|det|>[[115, 229, 882, 284]]<|/det|>
+The color bar in the curl of displacement plot is plotted with respect to the mean intensities in the \(\mathrm{PbTiO_3}\) layer.  
+
+<|ref|>text<|/ref|><|det|>[[112, 300, 880, 888]]<|/det|>
+4D STEM analysis: All 4D STEM experiments were carried out on TEAM I microscope (aberration- corrected Thermo Fisher Scientific Titan 80- 300) using a Gatan K3 direct detection camera located at the end of a Gatan Continuum imaging filter. The microscope was operated at \(300\mathrm{kV}\) with a probe current of \(100\mathrm{pA}\) . The probe semi- angle used for the measurement was 2 mrad. Diffraction patterns were collected using a step size of \(1\mathrm{nm}\) with 514 by 399 scan positions. The K3 camera was used in full- frame electron counting mode with a binning of 4 pixels by 4 pixels and a camera length of \(1.05\mathrm{mm}\) . The exposure time for each diffraction pattern was \(47\mathrm{ms}\) . The 4D STEM analysis was carried out using the py4DSTEM modules. Briefly, rotation calibration was performed between the diffraction and image plane to identify the right orientation of the zone axis. For that process, the defocused image in the Ronchigram was compared to the focused scan image and the relative orientation of the two images was compared. Once the zone axis was identified, all the disks in the diffraction pattern at each probe position were fitted using the disk fitting function. The so- called polarization maps were generated by taking the normalized intensity difference between the opposite Friedel pair disks. Subsequently, the signal- to- noise in these polarization maps was improved by using a combination of low- pass and high- pass Gaussian filters. By using a high- pass Gaussian filter, we also minimized the dominating thickness contrast.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[115, 108, 216, 125]]<|/det|>
+## References:  
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+
+<|ref|>sub_title<|/ref|><|det|>[[116, 630, 272, 648]]<|/det|>
+## Acknowledgments  
+
+<|ref|>text<|/ref|><|det|>[[113, 663, 877, 893]]<|/det|>
+All the electron microscopy experiments were carried out at the National Center for Electron Microscopy (NCEM) located in the Molecular Foundry user facility at Lawrence Berkeley National Laboratory. Work at the Molecular Foundry was supported by the Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE- AC02- 05CH11231. S.S. and R.R. are supported by the DOE Quantum Materials Program. CO acknowledges support from a DOE Early Career Research Award. F.G.- O., P.G.- F., and J.J. acknowledge financial support from Grant No. PGC2018- 096955- B- C41 funded by
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 88, 881, 214]]<|/det|>
+MCIN/AEI/10.13039/501100011033 and by ERDF "A way of making Europe," by the European Union. F.G.- O. acknowledges financial support from Grant No. FPU18/04661 funded by MCIN/AEI/10.13039/501100011033. BHS was supported by the Toyota Research Institute. National Institute of Health Research UK.  
+
+<|ref|>text<|/ref|><|det|>[[172, 235, 877, 430]]<|/det|>
+Author contributions: S.S., R.R., and C.O. conceived the idea, and designed the experiments. S.S. analyzed the 4D STEM datasets, made the figures and wrote the initial draft of the manuscript. S.L. performed the 4D STEM experiments. B.H. provided inputs for the scripts of the 4D STEM analysis. F.G.O, P.G. F, and J.J helped in providing intellectual inputs regarding the origin of chiral domain walls and triple points. P.B. and P.E. participated in revising the manuscript. R.R. and C.O. supervised the project.  
+
+<|ref|>text<|/ref|><|det|>[[172, 480, 785, 500]]<|/det|>
+Competing interests: Authors declare that they have no competing interests.  
+
+<|ref|>text<|/ref|><|det|>[[173, 515, 794, 570]]<|/det|>
+Data and materials availability: All data are available in the main text or the supplementary materials.  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 586, 336, 604]]<|/det|>
+## Supplementary Materials.  
+
+<|ref|>text<|/ref|><|det|>[[115, 620, 694, 640]]<|/det|>
+All the supplementary materials is available in supplemental information.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[44, 42, 312, 70]]<|/det|>
+## Supplementary Files  
+
+<|ref|>text<|/ref|><|det|>[[44, 92, 765, 112]]<|/det|>
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+<|ref|>text<|/ref|><|det|>[[61, 130, 137, 149]]<|/det|>
+- Sl.pdf
+
+<--- Page Split --->

preprint/preprint__0179e628a853118df9da75d50a227d9cf3c9b00dfa8825f8efe5e29d0d1c5f4c/images_list.json

@@ -0,0 +1,92 @@
+[
+  {
+    "type": "image",
+    "img_path": "images/Figure_1.jpg",
+    "caption": "Fig. 1 | Concept of optical fiber sensing for LSB. (a) Schematic of a fiber optic sensor immersed in electrolyte for in-situ detection of sulfur concentration originating from the generated dissolved polysulfide and their transport activities (i.e., shuttle effect). (b) Backward-propagation guided modes inside fiber for sensing (Supplementary information Fig. S1). (c) Experimental spectra response to polysulfide. (d) The wavelength shifts of cladding mode resonance at \\(\\sim 1560 \\mathrm{nm}\\) to \\(100 \\mathrm{mM}\\) polysulfide \\(\\mathrm{Li}_2\\mathrm{S}_x\\) ( \\(x = 1, 2, 3, \\dots , 8\\) ), shaded in green; (e) to concentration variation of \\(\\mathrm{Li}_2\\mathrm{S}_4\\) and \\(\\mathrm{Li}_2\\mathrm{S}_8\\) from \\(0 \\mathrm{mM}\\) to \\(100 \\mathrm{mM}\\) ; (f) to same sulfur concentration of polysulfide \\(\\mathrm{Li}_2\\mathrm{S}_x\\) ( \\(x = 4, 5, 6, 7, 8\\) ).",
+    "footnote": [],
+    "bbox": [
+      [
+        163,
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+        838,
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+      ]
+    ],
+    "page_idx": 4
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_2.jpg",
+    "caption": "Fig. 2 | Decoding electrolyte sulfur concentration dynamic of LSB. (a) Operando measurement of LSB by TFBG and XRD at C/20 (left panel: polysulfide dissolution allowed (electrolyte of 1 M LiTFSI, 0.5 M LiNO₃ in DOL/DME (1:1, v/v)); right panel: polysulfide dissolution prohibited (electrolyte of LP30: 1 M LiPF₆ in EC/DMC) of sulfur and Super P carbon composite (60/40 wt.%) cathode. (b) Morphology (SEM) and content of elemental sulfur and Super P carbon (energy-dispersive X-ray spectroscopy, EDX) of the cathode at the end of charging. (c) The quantitative analysis of sulfur before cycling, end of first plateau of discharge and end of charge. (d) The recrystallized sulfur governed by comporptionation reactions and potential voltage. The shaded region in blue stands for 15 hours of rest (OCV mode) starting at the end of charging demonstrating that the re-crystallized sulfur (marked by green asterisk “\\*”) dissolves into the electrolyte in the form of soluble polysulfide through comporptionation reactions.",
+    "footnote": [],
+    "bbox": [
+      [
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+        95,
+        844,
+        650
+      ]
+    ],
+    "page_idx": 6
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_3.jpg",
+    "caption": "Fig. 3 | Decoding the disproportionation process and evolution. (a, b, c and d) The temporal voltage (black curve) and decoded electrolyte sulfur concentration (red curve) by GITT test (the capacity and optical spectra are given in supplementary Fig. S7). (a, b and c) Detailed view of electrolyte response to current pulse (kinetic process) and rest (thermodynamic process). (e) Transport flux of soluble sulfur based on current pulse (kinetics process, green triangle, \\(D_t = V_{BD} / (S \\times t)\\) and rest (thermodynamic process, blue square, \\(D_t = V_{DE} / (S \\times t)\\) , where \\(S\\) is across section area of electrolyte that sensor is immersed in, \\(t\\) is corresponding time. It indicates the slope of soluble sulfur consumption (negative) or formation (positive) in electrolyte, which is also plotted by arrows in bottom (arrow up: sulfur increasing, arrow down: sulfur decreasing). The normalized ratio (red sphere) represents the sulfur consumption of rest (thermodynamic process) by \\(Ratio = |D_t| / (|D_t| + |D_k|)\\) .",
+    "footnote": [],
+    "bbox": [
+      [
+        157,
+        173,
+        842,
+        682
+      ]
+    ],
+    "page_idx": 8
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_4.jpg",
+    "caption": "Fig. 4 | Operando monitoring over cycling and cycling rate. (a) Spectra contour of TFBG cladding mode resonances response (details are given in supplementary video). (b) Temporal voltage (grey line), decoded sulfur concentration (red line), and temperature of electrolyte (blue line) over the cycling. (c) Capacity variation of (b) upon time. (d) Soluble sulfur",
+    "footnote": [],
+    "bbox": [
+      [
+        192,
+        412,
+        808,
+        813
+      ]
+    ],
+    "page_idx": 10
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_5.jpg",
+    "caption": "Fig. 5 | Chemistry of LSB with outstanding polysulfide-trapping capability cathodes. (a) Operando measurement of LSB by TFBG and XRD at C/20 (left panel: nonpolar physical adsorption of polysulfide by KB carbon; right panel: polar adsorption of polysulfide by MOF-801(Zr)). (b) In-situ detection of polysulfide adsorption by MOF-801(Zr). (c) XRD spectra for MOF-801(Zr) before and after adsorption of \\(\\mathrm{Li}_2\\mathrm{S}_6\\) . (d) The temporal voltage (grey line), decoded sulfur concentration (red line) of cathode composite based on MOF-801(Zr), and decoded sulfur concentration (blue line) considering the cathode with KB substrate (same amount of active material) is used as a reference without showing the temporal voltage.",
+    "footnote": [],
+    "bbox": [
+      [
+        186,
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+    ],
+    "page_idx": 13
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_6.jpg",
+    "caption": "Fig. 6 | The correlation between electrochemical performance and \\(\\mathrm{Li}_2\\mathrm{S}\\) nucleation, sulfur crystallization. (a) Content of \\(\\mathrm{Li}_2\\mathrm{S}\\) nucleation (h1) and solid sulfur crystallization (h2). (b) Cycling performance of cathode composite at C/15 over 30 cycles. (c and d) The corresponding ratio of \\(\\mathrm{Li}_2\\mathrm{S}\\) nucleation (c) and solid sulfur crystallization (d). (e) Cycling rate performance of cathode composite. (f and g) The corresponding ratio of \\(\\mathrm{Li}_2\\mathrm{S}\\) nucleation (f) and solid sulfur crystallization (g). Note that all the cycling cell pursed in the presence of TFBG fiber and the \\(\\mathrm{Li}_2\\mathrm{S}\\) (h1) and solid sulfur (h2) is normalized by comparing the consumption sulfur in current cycle to that of sulfur fully dissolved in first cycle.",
+    "footnote": [],
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+  }
+]

preprint/preprint__0179e628a853118df9da75d50a227d9cf3c9b00dfa8825f8efe5e29d0d1c5f4c/preprint__0179e628a853118df9da75d50a227d9cf3c9b00dfa8825f8efe5e29d0d1c5f4c.mmd

@@ -0,0 +1,321 @@
+
+# Detangling electrolyte chemical dynamics and evolution in Li-S batteries by operando monitoring with optical resonance combs  
+
+Jean- Marie Tarascon ( Jean- marie.tarascon@college- de- france.fr) UMR 8260 « Chimie du Solide et de l'Energie », https://orcid.org/0000- 0002- 7059- 6845  
+
+Fu Liu College de France  
+
+Wenqing Lu École supérieure de physique et de chimie industrielles de la Ville de Paris  
+
+Jiaqiang Huang the Hong Kong University of Science and Technology (Guangzhou) https://orcid.org/0000- 0001- 8250- 228X  
+
+Vanessa Pimenta École supérieure de physique et de chimie industrielles de la Ville de Paris  
+
+Steven Boles NTNU - Norwegian University of Science and Technology https://orcid.org/0000- 0003- 1422- 5529  
+
+Rezan Demir- Çakan Gebze Technical University  
+
+Article  
+
+Keywords:  
+
+Posted Date: August 4th, 2023  
+
+DOI: https://doi.org/10.21203/rs.3.rs- 3192096/v1  
+
+License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+Additional Declarations: There is NO Competing Interest.  
+
+Version of Record: A version of this preprint was published at Nature Communications on November 14th, 2023. See the published version at https://doi.org/10.1038/s41467- 023- 43110- 8.
+
+<--- Page Split --->
+
+Detangling electrolyte chemical dynamics and evolution in Li- S batteries by operando monitoring with optical resonance combs  
+
+Fu Liu \(^{1,2}\) , Wenqing Lu \(^{3}\) , Jiaqiang Huang \(^{4}\) , Vanessa Pimenta \(^{3}\) , Steven Boles \(^{5}\) , Rezan  
+
+Demir- Cakan \(^{6,7*}\) & Jean- Marie Tarascon \(^{1,2,8*}\)  
+
+\(^{1}\) Collège de France, Chimie du Solide et de l'Energie—UMR 8260 CNRS, Paris, France. \(^{2}\) Réseau sur le Stockage Electrochimique de l'Energie (RS2E)—FR, CNRS 3459, Amiens, France. \(^{3}\) Institut des Matériaux Poreux de Paris (IMAP), ESPCI Paris, Ecole Normale Supérieure, CNRS, PSL University, Paris, France \(^{4}\) The Hong Kong University of Science and Technology (Guangzhou), Sustainable Energy and Environment Thrust, Nansha, Guangzhou, Guangdong 511400, P. R. China \(^{5}\) Department of Energy and Process Engineering, Faculty of Engineering, Norwegian University of Science and Technology (NTNU), Trondheim, Norway \(^{6}\) Gebze Technical University, Institute of Nanotechnology, Gebze, Kocaeli, 41400, Turkey \(^{7}\) Gebze Technical University, Department of Chemical Engineering, Kocaeli, 41400, Turkey \(^{8}\) Sorbonne Université—Université Pierre- et- Marie- Curie Paris (UPMC), Paris, France  
+
+Challenges in enabling next- generation rechargeable batteries with lower cost, higher energy density, and longer cycling life stem not only from combining appropriate materials, but from optimally using cell components given their respective evolutions. One- size- fits- all approaches to operational cycling and monitoring are limited in improving sustainability if they cannot utilize and capture essential chemical dynamics and states of electrodes and electrolytes. Herein we describe and show how the use of tilted fiber Bragg grating (TFBG) sensors to track, via the monitoring of both temperature and refractive index metrics, electrolyte- electrode coupled changes that fundamentally control lithium sulfur batteries. Through quantitative sensing of the sulfur concentration in the electrolyte, we demonstrate that the nucleation pathway and crystallization of \(\mathrm{Li}_2\mathrm{S}\) and sulfur governs the cycling performance. With this technique, a critical milestone is achieved, not only towards developing chemistry- wise cells (in terms of smart battery sensing leading to improved safety and health diagnostics), but further towards demonstrating that the coupling of sensing and cycling can revitalize known cell chemistries and break open new directions for their development.  
+
+## Introduction  
+
+Widescale utilization of renewable energy sources is essential to supplementing and perhaps replacing the carbon- based energy supply responsible for climate change. The recent success of electric vehicles made possible by lithium- ion battery technology, attributed to both improved reliability and cost reductions, demonstrates that new breakthrough chemistries may not be necessary for a 'green transition' if known electrochemical cell pairings can be mastered. Included among these chemistries are resurgent lithium sulfur batteries (LSB), which, in spite of their appeal in terms of theoretical specific energy ( \(\sim 2600 \mathrm{Wh / kg}\) ), are still not commercialized. This can be attributed to a number of unresolved challenges, including the insulating nature of sulfur and lithium sulfides, large volume expansion (80%) of the solid sulfur cathode during the formation of \(\mathrm{Li}_2\mathrm{S}\) , and shuttle effect caused by soluble polysulfide in electrolyte \(^{1,2}\) .
+
+<--- Page Split --->
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+Numerous characterization techniques have been deployed to clarify the underlying science of LSBs during operation, which have contributed significantly to a better understanding of the kinetics and thermodynamics of the dissolution/precipitation of polysulfides, whose critical role in LSBs has been known for nearly 50 years<sup>3</sup>. Since then, methods such as X- ray diffraction (XRD)<sup>4,5</sup>, electrochemical tests<sup>6- 8</sup>, and spectroscopic techniques<sup>9- 16</sup> have been used to provide valuable information regarding identification of polysulfide species and reaction kinetics. However, it is experimentally challenging to isolate the individual polysulfides due to the propensity of disproportionation, and these analytical techniques rely on special equipment and cell designs that cannot be directly deployed for long cycling periods. Recently, optical fiber sensors have attracted attention in battery sensing due to their low cost, compactness, remote sensing capabilities, and simple integration into batteries without interfering with their internal chemistry<sup>17</sup>. Among the fiber sensor family, the most commercialized Fiber Bragg grating (FBG) sensors have been well integrated inside Na (Li)- ion batteries for monitoring heat and pressure<sup>18</sup> or inside the solid- state batteries for tracking the stress dynamics<sup>19</sup>. Indeed, recently Ziyun et al. demonstrated that the cathode stress evolution of LSB can be in- situ monitored by FBG sensors for understanding the chemo- mechanics<sup>20</sup>. Nevertheless, testing polysulfides with FBGs is still limited, owing to the fact that the sensing signals are totally confined inside the fiber core and cannot sense the electrolyte surrounding the fiber surface.  
+
+In order to investigate the external medium of fiber, TFBGs (same structure as FBG without physical structure modification<sup>21</sup>, but rotating the grating plane to a specific angle) have been proposed to excite hundreds of discrete cladding mode resonances that are sensitive to the external medium refractive index perturbation via evanescent fields<sup>22</sup>, hence serving as an optical 'comb'. This has led to the development of high- performance sensors used in various areas, including biomedicine<sup>23</sup>, magnetic detection<sup>24</sup>, and gas monitoring<sup>25</sup>. Recently, TFBGs have been integrated into commercial batteries to detect chemical dynamics/state of electrolytes related to chemical evolution<sup>26</sup>. Interestingly, some TFBG- assisted surface plasmon resonance (TFBG- SPR) sensors with higher sensing sensitivity have also been developed for Zn- ion batteries to offer an alternative way of probing ion transport kinetics<sup>27</sup>. Overall, TFBG sensors provide new opportunities to deal with the challenge of battery sensing as they combine direct optical sensing of the environment, as well as physio- mechanical sensing of the environment via the confined optical modes.  
+
+Herein, TFBG sensors, enabling measurements with a wide array of parameters including refractive index, temperature, and strain, are proposed to operate track the chemical dynamics/states of the LSB via electrolyte sulfur concentration. We demonstrate that the capacity fading is strongly correlated with the dissolution/precipitation of polysulfides throughout the cycling and hence, with respect to cycling rates. By exploiting the kinetic and thermodynamic response of soluble sulfur in the electrolyte, the nonlinear transport flux clarifies the "invisible" disproportionation process together with their dynamic evolution. With this understanding, we show that altering the nucleation pathway of the crystalline Li<sub>2</sub>S and sulfur can be attributed to real improvements in cell cycling performance. Subsequently, it is noted that TFBGs have the ability to obtain key chemical- physical- thermal metrics in operando with notable time and spatial resolution that may extend beyond LSBs.
+
+<--- Page Split --->
+
+## Results  
+
+## Characteristics of TFBG sensing  
+
+Prior to in operando battery inspection, it is appropriate to first briefly visit the suitability of TFBG sensing for such chemistries, as related to fundamental principles of their operation. TFBGs, immersed in an electrolyte (Fig. 1a), were made in the core of the commercial single- mode fiber by ultraviolet pulse laser to induce periodically permanent refractive modulation. They obey a phase matching condition by enhancing the coupling between fundamental core mode and backward- propagation cladding modes22 (Fig. 1b):  
+
+\[\lambda = \left(n_{11}(\lambda) + n_{lm}(\lambda)\right)\lambda /\cos \theta \quad (1)\]  
+
+where \(\lambda\) is the cladding mode resonance wavelength, \(n_{11}(\lambda)\) is the effective index of core mode, and \(n_{lm}(\lambda)\) is the effective index of cladding mode with azimuthal order \(l\) and radial order \(m\) . \(\Lambda\) is the period of grating along the fiber axis, and \(\theta\) is the grating tilt angle. The experimental spectra are presented in Fig. 1c, where the core mode resonance (i.e., Bragg resonance) is located at the longest wavelength around \(1590 \mathrm{nm}\) (sensitive to temperature and strain \((T, \epsilon)\) )22. The cladding mode resonances guided by the fiber cladding (beside \(T, \epsilon\) , also sensitive to refractive index \((R)\) of the surrounding media) are shown on the left of Bragg resonances. The leaky modes are located at the region where there is a discontinuity in the cladding mode envelope and their amplitude, indicating the loss of total internal reflection at the point where the cladding mode effective index becomes equal to or smaller than the surrounding \(R\) . Therefore, with respect to soluble polysulfides which perturb electrolyte density, and hence the refractive- index, we focus on the high order guided cladding modes near the leaky mode region.
+
+<--- Page Split --->
+![](images/Figure_1.jpg)
+
+<center>Fig. 1 | Concept of optical fiber sensing for LSB. (a) Schematic of a fiber optic sensor immersed in electrolyte for in-situ detection of sulfur concentration originating from the generated dissolved polysulfide and their transport activities (i.e., shuttle effect). (b) Backward-propagation guided modes inside fiber for sensing (Supplementary information Fig. S1). (c) Experimental spectra response to polysulfide. (d) The wavelength shifts of cladding mode resonance at \(\sim 1560 \mathrm{nm}\) to \(100 \mathrm{mM}\) polysulfide \(\mathrm{Li}_2\mathrm{S}_x\) ( \(x = 1, 2, 3, \dots , 8\) ), shaded in green; (e) to concentration variation of \(\mathrm{Li}_2\mathrm{S}_4\) and \(\mathrm{Li}_2\mathrm{S}_8\) from \(0 \mathrm{mM}\) to \(100 \mathrm{mM}\) ; (f) to same sulfur concentration of polysulfide \(\mathrm{Li}_2\mathrm{S}_x\) ( \(x = 4, 5, 6, 7, 8\) ). </center>  
+
+To investigate the response of our TFBG to polysulfides, depicted in Fig. 1c, the TFBG was thoroughly immersed in a series of \(100 \mathrm{mM}\) polysulfide containing electrolytes in a modified Swagelok cell. Bearing this in mind, the Bragg resonance remains stable because any strain and temperature variation were eliminated during the measurements, indicating that the cladding mode wavelength shift is only related to refractive index variation. When the chain length of polysulfides is increased while keeping the polysulfide concentration the same, the guided modes on the left side of cladding mode at \(1560 \mathrm{nm}\) becomes leaky due to the increased refractive index. This is a result of the number sulfur atoms in solution becoming larger and perturbing the corresponding mode effective refractive index, while guided modes on its right side are linearly shifted to longer wavelength (Fig. 1d,e and Supplementary Fig. S2a,b). Noteworthy is the fact that the refractive index tested by TFBG sensor is an "average effect" of all the pertinent refractive indices of lithium polysulfide solutions. Following this,
+
+<--- Page Split --->
+
+dilution of the \(100~\mathrm{mM}\) polysulfide \(\mathrm{Li}_2\mathrm{S}_x\) ( \(x = 4\) , 5, 6, 7, 8) to an equivalent concentration of sulfur (Supplementary Fig. S2c) yields an equivalent optical effect, stemming from the refractive index of polysulfide solutions converging to the same density, (Fig. 1f and Supplementary Fig. S2d,e). Therefore, rather than recognizing the specific species inside the electrolyte, the TFBG sensor will distinctly reveal the electrolyte sulfur concentration evolution of LSB cell (so long as temperature is kept constant).  
+
+## Operando measurement of chemical dynamic state of LSB  
+
+Given the promising proof- of- concept of sulfur concentration measurement in electrolyte, we explored the capability of operando chemical dynamics/states testing of LSB cell by putting a \(2\mathrm{mm}\) thick, \(12.8\mathrm{mm}\) diameter polyether ether ketone (PEEK) ring ( \(1\mathrm{cm}\) long TFBG can go through) in the middle of the Swagelok to separate the cathode (sulfur and Super P carbon composite (60/40 wt. \(\%\) )) and lithium anode so that fiber sensor would not touch any of them (Supplementary Fig. S3). Filling the PEEK ring with electrolyte (500 μL, \(1\mathrm{M}\) LiTFSI, 0.5 M LiNO3 in DOL/DME (1:1, v/v)) where the sensor is immersed, the effect of ion concentration gradient of electrolyte including \(\mathrm{Li}^+\) , TFSI- and \(\mathrm{NO}_3^-\) in DOL and DME can be safely neglected. It should be noted that prior to LSB investigations, a control experiment was executed with lithium iron phosphate (LFP) as the cathode. Here it was found that the corresponding \(R / o\) of electrolyte variation is 20 times smaller than that in LSB in which dissolved polysulfide is formed (Supplementary Fig. S4). Therefore, the use of a TFBG can provide for the possibility of measuring sulfur concentration in the electrolyte during cell operation, and to a large extent, the measurement will be irrespective of the cell's state of charge or state of health.
+
+<--- Page Split --->
+![](images/Figure_2.jpg)
+
+<center>Fig. 2 | Decoding electrolyte sulfur concentration dynamic of LSB. (a) Operando measurement of LSB by TFBG and XRD at C/20 (left panel: polysulfide dissolution allowed (electrolyte of 1 M LiTFSI, 0.5 M LiNO₃ in DOL/DME (1:1, v/v)); right panel: polysulfide dissolution prohibited (electrolyte of LP30: 1 M LiPF₆ in EC/DMC) of sulfur and Super P carbon composite (60/40 wt.%) cathode. (b) Morphology (SEM) and content of elemental sulfur and Super P carbon (energy-dispersive X-ray spectroscopy, EDX) of the cathode at the end of charging. (c) The quantitative analysis of sulfur before cycling, end of first plateau of discharge and end of charge. (d) The recrystallized sulfur governed by comporptionation reactions and potential voltage. The shaded region in blue stands for 15 hours of rest (OCV mode) starting at the end of charging demonstrating that the re-crystallized sulfur (marked by green asterisk “\*”) dissolves into the electrolyte in the form of soluble polysulfide through comporptionation reactions. </center>
+
+<--- Page Split --->
+
+Based on the aforementioned concept, we measured the electrolyte sulfur concentration variation with a TFBG sensor while simultaneously deploying in operando XRD during C/20 cycling to track phase transitions of the composite electrode (Fig. 2a,c). At the upper voltage plateau around 2.4 V, the highest sulfur concentration in the electrolyte was monitored (left panel of Fig. 2a) and found to be accompanied by a decrease in the sulfur peak intensity (XRD pattern) resulting from a series of phase transformations, i.e., from solid sulfur to soluble intermediate polysulfides. On the other hand, when the carbonate- based electrolyte was used as a reference (i.e., LP30, right panel of Fig. 2a), no concentration gradient was observed in the electrolyte and remained nearly stable due to the fact that no soluble polysulfide intermediates were formed. Instead this resulted in the formation of insoluble and undetected products, since it is known that there is a nucleophilic reaction between sulfur radical and ethylene carbonate of LP30 to form thiocarbonate- like solid electrolyte interphase<sup>28,29</sup> (Supplementary Fig. S5). Turning to the lower voltage plateau around 2.1 V (Fig. 2a), the concentration of dissolved sulfur decreases as a result of the reduction of long- chain polysulfide into shorter chains, leading to insoluble Li<sub>2</sub>S compound in the cathode (Supplementary Fig. S6a) confirmed by XRD<sup>30,31</sup>. Upon charging, the sulfur concentration indicates reversible recovery consistent with the decay of Li<sub>2</sub>S peaks until complete disappearance at the voltage \(\sim 2.4 \text{V}\), where crystallization of sulfur starts and thus sulfur concentration in electrolyte drops again, even though the deposited solid sulfur in the cathode is featureless by XRD<sup>31</sup>. To confirm the sulfur at the end of charge, the cathode powder was recovered in the glovebox by washing and drying to remove any soluble polysulfide as well as remaining electrolyte salts (Fig.2b,c), confirming that \(1.4 \%\) sulfur was detected and its surface topography was unchanged (i.e., no formation of big crystalline particles)<sup>31</sup>. Furthermore, when setting the 15- hour open circuit voltage (OCV) after charging, the sulfur concentration increases and reaches a plateau within 9 hours (Fig.2c), whereas, on the other hand, no sulfur concentration changes were observed during rest periods applied at the end of discharge (Supplementary Fig. S6b). It is most likely explained by compropriotation reactions during the rest period when the recrystallized sulfur from the end of charging is transformed to soluble lower- order polysulfide via reacting with high- order polysulfide<sup>32</sup>. This is also supported by the beginning of 2 hours rest (first cycle before discharging) demonstrating relatively little variation of electrolyte since fresh electrolyte contains a minimum amount of high order polysulfide and the compropriotation reactions are thus not possible (Supplementary Fig. S6c). The crystalline sulfur at the end of charge is related to the cut- off potential that the sulfur recrystallization process disappears<sup>4</sup> (disappearance of sulfur concentration valley at the end of charge in Fig.2d) if setting the potential below 2.4 V (indicated that less sulfur suppresses the related compropriotation reactions, also detailed in Supplementary Fig. S6c). Altogether, the dynamic of sulfur concentration of electrolyte decoded by TFBG sensor supports the simplified chemical reaction process: during discharging the sulfur receives electrons and transfers them first to soluble Li<sub>2</sub>S at the high voltage plateau. This is followed by formation of insoluble Li<sub>2</sub>S at the low voltage plateau and vice versa for the charging process, indicating that the consumption rate of sulfur under the galvanostatic condition can be expressed as a ratio. According to the "linear" sulfur concentration variation rate by monitoring the slope (mM/h) on each plateau tested by sensor during the discharge and charging steps (Supplementary Fig. S4a) it was
+
+<--- Page Split --->
+
+observed that the ratio on the upper and lower plateaus at the discharge step is 3.9. This is similar to the value obtained during charging (0.9), suggesting that the rate of sulfur transformation to/from soluble polysulfide is \(\sim 4.3\) times faster than that to/from insoluble Li₂S and the respective polysulfides.  
+
+![](images/Figure_3.jpg)
+
+<center>Fig. 3 | Decoding the disproportionation process and evolution. (a, b, c and d) The temporal voltage (black curve) and decoded electrolyte sulfur concentration (red curve) by GITT test (the capacity and optical spectra are given in supplementary Fig. S7). (a, b and c) Detailed view of electrolyte response to current pulse (kinetic process) and rest (thermodynamic process). (e) Transport flux of soluble sulfur based on current pulse (kinetics process, green triangle, \(D_t = V_{BD} / (S \times t)\) and rest (thermodynamic process, blue square, \(D_t = V_{DE} / (S \times t)\) , where \(S\) is across section area of electrolyte that sensor is immersed in, \(t\) is corresponding time. It indicates the slope of soluble sulfur consumption (negative) or formation (positive) in electrolyte, which is also plotted by arrows in bottom (arrow up: sulfur increasing, arrow down: sulfur decreasing). The normalized ratio (red sphere) represents the sulfur consumption of rest (thermodynamic process) by \(Ratio = |D_t| / (|D_t| + |D_k|)\) . </center>
+
+<--- Page Split --->
+
+The disproportionation and association reactions of likely intermediates are mesmerizing, but despite an awareness of their existence, their mysterious dynamics make LSBs seemingly incomprehensible. Nevertheless, the real- time quantification of soluble sulfur afforded by TFBGs provides a convincing way to straighten their story when combined with GITT (Fig. 3). As depicted in Fig. 3d, the overall profile of sulfur concentration variation matches well with dissolution/precipitation of polysulfides and sulfur already confirmed in Fig.2, and we focus on the temporal response of electrolyte to the current pulse and respective rest period. According to Fig. 3a,b,c the "tiny" variation of sulfur concentration from A to B (moving from the rest to cycling mode) is the electrolyte instantaneous response to the leading edge of current pulse with strong electrical field gradient, originating from polysulfide redistribution driven by the sudden electrical field<sup>33</sup>. This effect trends in the opposite direction as polysulfide diffusion in the region from B to C, which relates to the current pulse (referred to herein as the kinetic process). The reader may note a discrepancy between voltage and concentration from C to D during rest, which is attributed to the "delay" between the electrochemical reaction at the electrode surface and the position of the sensor in the cell. Hence a small lag exists even if removing the current pulse. Regarding the OCV relaxation (3 hours rest period) and movement towards equilibrium (herein coined as the thermodynamic process) in the region from C to E, the sulfur concentration fluctuation is most likely explained by polysulfide disproportionation process (i.e. \(Li_2S_8 \leftrightarrow Li_2S_7 + 1/4S_8\) )<sup>34</sup> since the two best- remaining hypotheses, dissociation (i.e. \(Li_2S_8 \leftrightarrow Li^+ + LiS_7\) or \(Li_2S_8 \leftrightarrow 2LiS_7\) )<sup>34</sup> and non- uniform polysulfide distribution can be excluded: the dissociation of polysulfide including anions and radicals are rare while the neutral lithium polysulfide is dominant in electrolyte<sup>34</sup>; the polysulfide distribution reaches equilibrium within 1 min (time interval of spectra recording) that is nearly synchronous to electrochemistry (Fig. 2a), not matching the rest period situation with 3 hours of continuous soluble sulfur consumption or generation. After careful deliberation, we move forward with the idea that electrochemical and disproportionation processes can be extracted respectively by temporal response of electrolyte based on sulfur concentration decoded by TFBG sensor.  
+
+Encouraged by the results mentioned above, we have next attempted to build a quantitative relation between kinetic and thermodynamic processes through a primitive estimation of transport flux of sulfur in the electrolyte (Fig. 3e). In STAGE I, on the higher voltage plateau, solid sulfur was continuously consumed upon discharge to form long chain polysulfide, \(\mathrm{Li}_2\mathrm{S}_8\) (green triangle), together with the rapid disproportionation process \(Li_2S_8 \leftrightarrow Li_2S_6 + 1/4S_8\) <sup>35- 37</sup> leading to soluble sulfur consumption during relaxation (blue square). At the same time the normalized ratio (red sphere) between the rest and current pulse process nearly remains the same and fixed at 0.1 (shaded in light green color), meaning that there is a competition reaction between the soluble long chain polysulfide species formation and the \(\mathrm{S}_8\) - precipitation in the beginning of the discharge step. In STAGE II, regarding the first- to- second plateau transition whereby a voltage slope forms between 2.3 and 2.1 V, the shorter chain polysulfide \(\mathrm{Li}_2\mathrm{S}_4\) is expected to be generated<sup>38,39</sup>. This is accompanied by a reduction in the rate of formation of soluble sulfur in the electrolyte and hence, the sulfur concentration during resting keeps increasing while the generation of fresh long chain polysulfides winds down and the kinetic/thermodynamic ratio can reach 0.89. This indicates that polysulfide species formation via disproportionation is dominant due to fact that dissolved sulfur
+
+<--- Page Split --->
+
+continues to react with polysulfide present in the electrolyte during rest<sup>38,41</sup>. Undoubtedly an enriched concentration of sulfur in the electrolyte contributes significantly towards driving the formation of more polysulfides. In STAGE III where the lower voltage plateau marks the conversion between \(\mathrm{Li}_2\mathrm{S}_4\) to shorter chain \(\mathrm{Li}_2\mathrm{S}_2\) and \(\mathrm{Li}_2\mathrm{S}\) forms, the potential disproportionation process \(Li_2S_2 \leftrightarrow 1 / 3Li_2S_4 + 2 / 3Li_2S^{40,41}\) is highlighted from the middle of the second plateau, and raises the sulfur concentration to about a normalized ratio of 0.2 which follows until the end of the half- cycle. Upon charging (STAGE IV and STAGE V), it is evident that the process is not a fully reversible one, as seen with STAGE IV where \(\mathrm{Li}_2\mathrm{S}_4\) and \(\mathrm{Li}_2\mathrm{S}_6\) reappear and the push towards thermodynamic equilibrium necessitates disproportionation processes leading to consumption of sulfur during rest periods, but an overall concentration increase. In STAGE V, the sulfur concentration in electrolyte drops very sharply, caused by the recrystallization of sulfur during current pulsing, even though that is not visible in the operando XRD studies shown in Fig. 2a. Interestingly, the rise of soluble sulfur during these late- stage rest periods suggests nucleation and/or growth limitations of the recrystallized sulfur, which will be addressed here later. Altogether, the quantitative disproportionation process decoupled by the fiber sensor based on GITT provides meaningful details to understand micro- mechanisms of complicated kinetics processes.  
+
+![](images/Figure_4.jpg)
+
+<center>Fig. 4 | Operando monitoring over cycling and cycling rate. (a) Spectra contour of TFBG cladding mode resonances response (details are given in supplementary video). (b) Temporal voltage (grey line), decoded sulfur concentration (red line), and temperature of electrolyte (blue line) over the cycling. (c) Capacity variation of (b) upon time. (d) Soluble sulfur </center>
+
+<--- Page Split --->
+
+concentration dynamic related to cycling rate. (e) Soluble sulfur concentration dynamic related to cycling rate of first/second plateau. The concentration drop through the recrystallization of sulfur at the end of charge is marked by green asterisk \(^{**}\) . With Mode I, the cycling rate was set by upper plateau (C/20) and lower plateau (C/5); Mode II by upper plateau (C/5) and lower plateau (C/20) and Mode III by upper plateau (C/10) and lower plateau (C/10).  
+
+Inspired by aforementioned exploration of internal mechanisms of LSBs, we decide to further investigate the operando monitoring over cycling and cycling rates (Fig. 4a,b). Bearing in mind that the temperature (blue curve in Fig. 4b), decoded by Bragg resonance located at \(1589 \text{nm}^{26}\) , initially rises to \(25 \text{C}^\circ\) and keeps a constant afterward due to the shipping from glovebox to oven to reach thermal equilibrium. The periodic sulfur concentration evolution (red curve in Fig. 4b), decoded by the wavelength shift of cladding mode located at \(\sim 1559.5 \text{nm}\) in Fig. 4a, indicates the reversible dissolution/precipitation of polysulfides and sulfur. Noteworthy here is the feasibility of observing the amplitude of soluble sulfur variation (supplementary Fig. S8) that matches the cycling behavior associated with capacity fading owning to less and less \(\text{Li}_2\text{S}\) and solid sulfur crystallization over cycles (Fig.4c), which could be reasonably attributed to the high electrolyte to sulfur ratio (E/S ratio, \(>100 \mu \text{L} /\text{mg}\) ), thereby inducing stronger polysulfide shuttle effect with less sulfur utilization. Regarding a sulfur concentration response to the cycling rate depicted in Fig. 4d, a lower cycling rate (C/15) leads to the largest sulfur concentration change, strongly supporting the idea that the most soluble sulfur in electrolyte is transformed to solid species ( \(\text{Li}_2\text{S}\) ) when given adequate time for completion of the redox process. Accelerating the cycling to C/10, C/5 and C/3, partially soluble sulfur transformation is seen (i.e., background of sulfur concentration rises) together with less sulfur crystallization (i.e., the "dip" of soluble concentration at the end of charge).  
+
+Following the evidence thus far, it is then reasonable to conclude that regular galvanostatic operating conditions might not be perfect for LSBs, considering the various competing mechanisms explored above which are evident at different stages of charge and discharge. With this in mind, different modes of cycling protocols were also pursued to be able to tune the cycling performance. For instance, when setting a relatively low (high) cycling rate for the upper (lower) plateau depicted in Fig. 4e (Mode I), only half of the soluble sulfur inside electrolyte was transformed to \(\text{Li}_2\text{S}\) due to high cycling rate of lower plateau. On the upper voltage plateau the solid sulfur dissolution or recrystallization was \(100\%\) successful and which can be readily attributed to the low cycling rate in this voltage range. Further confirmation of the delicate cycling nature of LSBs was seen in Mode II as \(80\%\) of the soluble sulfur inside electrolyte was transformed to \(\text{Li}_2\text{S}\) due to long cycling in the lower plateau (C/20) but with less solid sulfur recrystallization at the end of charge (C/5). Overall, the quantitative relation of soluble sulfur evolution and cycling/cycling rate implies that the efficiency of solid sulfur and \(\text{Li}_2\text{S}\) formation governs the cycling performance and significant new progress might be made through cycling condition optimization alone.  
+
+## High performance based on novel functional cathode  
+
+Extensive work has been conducted over the years to improve the LSB performance through variety of means: the use of high- conductivity cathodes, strong polysulfide binding to
+
+<--- Page Split --->
+
+suppress the shuttling phenomenon, surface chemistry to control \(\mathrm{Li_2S}\) nucleation or dissolution, design of cathode architectures that are elastic to withstand volume expansion, and optimized electrolytes enabling high sulfur utilization<sup>42</sup>. Considering the strategy of trapping lithium polysulfides, it includes physical (spatial) entrapment by confining polysulfide in the pores of non- polar carbon materials<sup>43</sup> or designing a sulfur host material that exhibits stronger chemical interaction such as dipolar configurations based on polar surfaces<sup>44</sup>, metal- sulfur bonding<sup>45</sup> and surface chemistry for polysulfide grafting and catenation<sup>46</sup>, leading to long cycling without strong capacity fading. Nevertheless, the fundamental problem regardless of approach is all related to the polysulfides dissolving inside the electrolyte, thus a smart sensor that can reliably track the soluble sulfur in real time should be of particular value.  
+
+In this respect, in addition to the less porous Super P carbon discussed above, the Ketjen black (KB) carbon whose BET surface area \(>1200 \text{m}^2 /\text{g}\) is utilized as physical nonpolar sulfur confinement host (KB/S, 40/60 wt.%), and the electrolyte was simultaneously monitored by a TFBG sensor together with XRD to characterize the composite cathode phase transitions (left panel of Fig. 5a). After the melt- diffusion treatment process, part of sulfur penetrates into the nanostructure of KB (Supplementary Fig. S9); however, during cycling the nonpolar physical adsorption or confinement of polysulfide is very limited and massive soluble polysulfide is dissolved and detected. Surprisingly, the capability of \(\mathrm{Li_2S}\) crystallization is enhanced since more than 91 % soluble sulfur disappears and converts to solid short chain polysulfide at second plateau as indicated by the fiber sensor. Meanwhile, unlike linearly sulfur concentration change with super P carbon (BET surface area: \(62 \text{m}^2 /\text{g}\) ) substrate cell in Fig. 2a, KB containing cell becomes "nonlinear" with a higher rate, indicating that the nucleation rate of \(\mathrm{Li_2S}\) is increasing. It could be reasonably assigned by instantaneous nucleation that depletion of the nucleation site of KB occurs at a very early stage, following the nonlinear kinetics of the nucleation pathway: \(\mathcal{N} = \mathcal{N}_c[1 - \exp (- At)]\) where \(\mathcal{N}\) is the density of nuclei, \(\mathcal{N}_c\) is the density of available nucleation site, and \(A\) is the nucleation rate<sup>47</sup>. In contrast, considering less porous Super P carbon containing cell in Fig.2a with a lower nucleation rate, the initial density of nuclei increases linearly with time: \(\mathcal{N} = \mathcal{N}_c At\) (i.e. progressive nucleation)<sup>48,49</sup>. Moreover, the porous structure of KB also accelerates the dissolution and re- crystallization of sulfur, which results in a strong sulfur concentration saturation at the transient stage relating to semisolid \(\mathrm{Li_2S_4}\) generation (keeping a balance between electrochemical and disproportionation process) and enhanced soluble sulfur reduction to solid sulfur at the end of charging (Supplementary Fig. S10). The sulfur concentration dynamic responding to potential and cycling rate is consistent with the Super P substrate, detailed in Supplementary Fig. S11.
+
+<--- Page Split --->
+![](images/Figure_5.jpg)
+
+<center>Fig. 5 | Chemistry of LSB with outstanding polysulfide-trapping capability cathodes. (a) Operando measurement of LSB by TFBG and XRD at C/20 (left panel: nonpolar physical adsorption of polysulfide by KB carbon; right panel: polar adsorption of polysulfide by MOF-801(Zr)). (b) In-situ detection of polysulfide adsorption by MOF-801(Zr). (c) XRD spectra for MOF-801(Zr) before and after adsorption of \(\mathrm{Li}_2\mathrm{S}_6\) . (d) The temporal voltage (grey line), decoded sulfur concentration (red line) of cathode composite based on MOF-801(Zr), and decoded sulfur concentration (blue line) considering the cathode with KB substrate (same amount of active material) is used as a reference without showing the temporal voltage. </center>  
+
+To further extend the use of porous materials as host matrix for sulfur confinement, Metal- Organic Frameworks (MOFs) can be considered as efficient candidates for the selective adsorption of polysulfide species<sup>45,50</sup>. We have considered MOF- 801(Zr), a microporous zirconium fumarate with pores of about 5- 12 Å and a high specific surface area (1020 (±20) m<sup>2</sup>/g), to be considered as an efficient candidate for the adsorption of polysulfide species. Its 3D cubic structure is built- up from \(\mathrm{Zr_6O_4(OH)_4}\) oxo- clusters linked to fumarate ligands and exhibits abundant missing linkers defect sites and reactive terminal –OH groups. Due to the
+
+<--- Page Split --->
+
+Lewis acidic character of the Zr nodes and the reactivity of these terminal groups, the polysulfide species (soft Lewis bases) are expected to interact strongly with the framework<sup>51,52</sup>. Depicted in Fig. 5c, the \(0.5 \mathrm{ml} 100 \mathrm{mM}\) polysulfide \(\mathrm{Li}_2\mathrm{S}_x\) ( \(x = 4, 6, 8\) ) was monitored in real time by fiber sensor during adsorption, which is evidently finished within 1 hour ensuring efficient adsorption inside the cell. After fully adsorbing \(100 \mathrm{mM} \mathrm{Li}_2\mathrm{S}_6\) (Fig. 5d), the XRD pattern shows the appearance of new peaks in addition to the ones of MOF- 801(Zr), matching with the peaks of pure solid sulfur (Supplementary Fig. S12) consistent with the "yellow" color of the powder. This explains why the operando test starts with the peaks of sulfur (XRD pattern on the right panel of Fig. 5a). The subsequent electrochemical reaction is the same as the cathode substrate with KB including concentration saturation in the transition region between the first and the second plateau, nonlinear sulfur consumption rate with instantaneous nucleation pathway and enhanced sulfur crystallization at the end of charge. However, a key point and difference is that the sulfur concentration inside the electrolyte dramatically decreases by \(80.8\%\) with the same amount of active material due to the enhanced adsorption and localization of polysulfides through Lewis acid- base chemical interaction by MOF- 801(Zr) (Fig.5d and Support information, Fig. S13).  
+
+![](images/Figure_6.jpg)
+
+<center>Fig. 6 | The correlation between electrochemical performance and \(\mathrm{Li}_2\mathrm{S}\) nucleation, sulfur crystallization. (a) Content of \(\mathrm{Li}_2\mathrm{S}\) nucleation (h1) and solid sulfur crystallization (h2). (b) Cycling performance of cathode composite at C/15 over 30 cycles. (c and d) The corresponding ratio of \(\mathrm{Li}_2\mathrm{S}\) nucleation (c) and solid sulfur crystallization (d). (e) Cycling rate performance of cathode composite. (f and g) The corresponding ratio of \(\mathrm{Li}_2\mathrm{S}\) nucleation (f) and solid sulfur crystallization (g). Note that all the cycling cell pursed in the presence of TFBG fiber and the \(\mathrm{Li}_2\mathrm{S}\) (h1) and solid sulfur (h2) is normalized by comparing the consumption sulfur in current cycle to that of sulfur fully dissolved in first cycle. </center>
+
+<--- Page Split --->
+
+To evaluate the performance of cathode considering the three S cathode composites (with Super P, KB, MOF- 801(Zr)), we decided to compare their long cycling performance together with capabilities of \(\mathsf{Li}_2\mathsf{S}\) nucleation (h1) and sulfur crystallization (h2) (Fig. 6a). Depicted in Fig. 6b, the KB and MOF- 801(Zr) cell both experienced better capacity retention over 30 cycles at C/15 with a fade rate as \(0.99\%\) , \(0.88\%\) per cycles, respectively, while the super P cell is fading with \(4.7\%\) per cycles, which is consistent with the capability of crystallization of \(\mathsf{Li}_2\mathsf{S}\) and sulfur shown in Fig. 6c,d. Surprisingly, the KB and MOF- 801(Zr) cells have the similar efficiency of solid species crystallization (also supported by Fig. 6f) by taking the merit of high surface area, whereas the MOF- 801(Zr) exhibits the best capacity retention due to its strong chemical interaction for polysulfide which also well explains the capability to trap and recrystallize sulfur(Fig. 6g). Meanwhile, less sulfur utilization will be induced as well by intrinsic adsorption together with its low conductivity nature, leading to the lower capacity of MOF- 801(Zr) than that of KB cell over cycling. On the other hand, the Super P cell is fading fast because of high electrolyte volume with the E/S ratio over \(100\mu \mathrm{L} / \mathrm{mg}\) , presumably due to shuttle phenomenon enhancement. However, this problem does not affect the performance of KB and MOF- 801(Zr) cells, revealing that perhaps the most important parameter for cycling performance is related to the capability of crystallization of \(\mathsf{Li}_2\mathsf{S}\) and sulfur, which relies on a comprehensive balance with all involved factors such as high surface area, sulfur anion bonding and conductivity, in addition to cycling conditions.  
+
+## Discussions and conclusions  
+
+Herein, TFBG sensors with low cost, easy integration into batteries, and long cycling capabilities during cell operation were demonstrated for operando testing of electrolyte sulfur concentration evolution of the LSB, which reveals the correlated relationship between the capacity fading and dynamic of dissolution/precipitation of polysulfides over cycling and at different cycling rates. Meanwhile, the chemical kinetics and thermodynamic response of soluble polysulfide in electrolyte were decoded with GITT experiments, with the disproportionation dynamic process linked to the transport flux of soluble species. Moreover, the cycling performance is well improved by designing the composite cathodes via porous carbon as nonpolar physical sulfur confinement and MOF- 801(Zr) as polar adsorption of polysulfide, through a host- guest chemical interaction. These substrates indicate that the nucleation kinetics and growth of \(\mathsf{Li}_2\mathsf{S}\) are changing from progressive to an instantaneous pathway due to enhanced soluble sulfur consumption rate, ultimately leading to the improvement of crystallization capability of \(\mathsf{Li}_2\mathsf{S}\) and sulfur.  
+
+Despite the encouraging insights supported by TFBG technology, a limitation of our operando testing stems from the refractive index link, which as mentioned, is an "average" effect of all species inside electrolyte. Thus, somewhat complicated data inference is required with critical external inputs, as comparing to direct evidence and measurement of species by infrared fiber operando measurement<sup>53</sup> and Raman spectroscopy based on hollow- core optical fiber<sup>54</sup>. This could might be solved in future by machine learning algorithms to simplify analysis and hence lead to more efficient 'combing' of data. Second, the optical interrogator used here is both expensive and bulky (volume), so future efforts may warrant an optimized integrator system capable of capturing a sensing signal by energy (amplitude) instead of wavelength<sup>55</sup>, but intensity- based measurements may present other technical challenges.
+
+<--- Page Split --->
+
+Finally, the cycling based on Swagelok is achieved with high E/S ratio which enhances the shuttle effect of polysulfide, leading to acceleration of capacity fading. Therefore, long- term cycling configurations such as coin or 18650 cells integrated with fiber sensors might reveal other prominent performance- governing mechanisms. Nevertheless, TFBG sensors still provide fruitful details of the chemical dynamics of polysulfide as a diagnostic technique to monitor the state of health of cells in real- time.  
+
+Considering the specific properties of TFBG, including multi- resonance- peaks, high refractive index sensitivity, and ultrafast response, new opportunities of battery sensing can be envisioned, such that more than two gratings can be integrated inside the cell to map the sulfur gradient with more precision and accuracy, ultrathin solid electrolyte interphase (SEI) films could be characterized by sensitivity enhanced surface plasmon resonance based TFBG via surface and bulk refractive index discrimination<sup>56</sup>, the dynamic of electrons and phonon coupling inside cathode could be probed by ultrafast measurement through the pump- probe configuration of TFBG<sup>57</sup>. Overall, the non- disruptive diagnostic techniques based on the TFBG sensor allow us to monitor chemical- physical- thermal metrics in operando with notable time and spatial resolution. Therefore, with the use of these combs it becomes possible to detangle hidden high- value information such as states of charge, health estimations, and operational guidance along with non- electrochemical early- failure indicators, leading to increased straighter pathways to improving battery reliability, service life, and safety.  
+
+## Methods  
+
+## Materials and electrode preparation  
+
+## Synthesis of MOF-801(Zr)  
+
+10.86 mmol of \(\mathrm{ZrOCl_2\cdot 8H_2O}\) , 7.624 mmol of fumaric acid, 9 mL of formic acid and 40 mL of deionized water were mixed in the reactor<sup>58</sup>, following 5 h stirring when the solution becomes cloudy. The ultimate product was collected by centrifugation, abundantly washed with water and ethanol, and dried under vacuum.  
+
+Preparation of polysulfides solutions: the \(100~\mathrm{mM}\) lithium polysulfides solution \(\mathrm{Li_2S_x}\) ( \(x = 2\) , 3, \(\dots\) , 8) were prepared by mixing lithium sulfide (99.9 % \(\mathrm{Li_2S}\) , Sigma Aldrich) and sulfur (S, Sigma Aldrich) in stoichiometric ratio to organic electrolyte (1 M LiTFSI, 0.5 M LiNO<sub>3</sub> in DOL/DME (1:1, v/v)), and the solution was continuously stirred with additional heating process at \(55~\mathrm{C}^\circ\) for 4 days in argon filled glovebox.  
+
+Preparation of sulfur composite electrodes: sulfur and Super P conductive carbon (Ketjenblack carbon) with a ratio 60/40 wt% were mixed by hand- grinding, followed by a heat- treatment (160 \(^\circ \mathrm{C}\) during 8 hours under air). The 5 wt Poly(tetrafluoroethylene) dispersion (PTFE, Alfa Aesar) is mixed with the cathode composite, rolled to a film and punched into disks with sulfur loading around \(5.3\mathrm{mg / cm}^2\) , and dried under vacuum at \(80^\circ \mathrm{C}\) overnight. To make the cathode composite with MOF- 801(Zr), the thoroughly adsorption of \(100\mathrm{mM}\mathrm{Li}_2\mathrm{S}_6\) in DOL/DME (1:1, v/v) was achieved by MOF- 801(Zr) with a stoichiometric ratio of \(1\mathrm{mL} / 40\) mg, followed by washing the powder twice by DOL and tried in vacuum overnight. The sulfur contained MOF- 801(Zr) (40% sulfur) was mixed with Super P conductive carbon with 50/50 wt% by hand- grinding without heat- treatment.
+
+<--- Page Split --->
+
+## TFBG fabrication and sensing system.  
+
+Each \(10mm\) - long TFBG with \(7^{\circ}\) internal tilt angle was inscribed in hydrogen- loaded CORNING SMF- 28 fiber by laser irradiation based on phase- mask method<sup>22</sup>. Hydrogen loading of the fibers, enhancing their photosensitivity to ultraviolet light, was performed at room temperature and a pressure of 15.2 MPa for 14 days. The input light from KrF pulsed excimer laser (model PM- 848 from Light Machinery, Inc., emitting at 248 nm and 100 pulse/second) was cylindrically focused along the fiber axis with energy of \(\sim 40m\) over the grating region and also having passed through a 1078.4 nm period phase mask to produce a permanent periodic refractive index modulation in the core of the fiber. Rotating the fiber and phase mask, the tilt of grating fringes was obtained at an angle in the core as \(7^{\circ}\) .  
+
+## Computational details  
+
+The transmission spectra simulations were carried out based on three- layer cylindrical waveguide using analytical method<sup>57</sup>. First, the simulated spectra is calibrated by the experimental spectra in air; Second, the refractive index of electrolyte were obtained by increasing the simulation surrounding RI (third layer) manually to match the experimental spectra, which is 1.3858 at 1559 nm wavelength considering the dispersion (supplementary Fig. S2d,e), Finally, mode intensity profiles were simulated by a complex finite- difference vectoral simulation tool (FIMMWAVE, by Photon Design), consisting of a three- layer waveguide: \(8.2\mu m\) core diameter with refractive index 1.449311, \(125\mu m\) cladding diameter with refractive index 1.444078, \(80\mu m\) diameter medium of electrolyte (refractive index \(= 1.3858\) ).  
+
+## Integration of TFBG sensors into modified Swagelok  
+
+A ring made of PEEK (12.8 mm diameter, 2mm thick to fit 10 mm length fiber sensor) is fixed in the middle of 19 mm diameter Swagelok cell where fiber sensor can go through by drilling two holes. The Li metal foil (0.38 mm thickness, 14 mm diameter) is attached to one side of PEEK ring as anode, and on the other side of ring there is a steel grid to hold one Whatman separator beneath sulfur composite cathode. The cells were assembled in an argon- filled glovebox.  
+
+## Electrochemical measurements  
+
+The electrochemical performances of Swagelok cell were evaluated by BCS- 810 or MPG2 potentiostat (Biologic, France) at \(25^{\circ}C\) degree inside temperature- controlled oven (Memmert, \(\pm 0.1^{\circ}C\) ). The galvanostatic discharge- charge cycling was carried out with the voltage range of \(1.7V - 2.8V\) .  
+
+## Operando measurement by TFBG sensors and XRD  
+
+To achieve TFBG sensor operando measurement, the optical transmission spectra were recorded (1 min/spectra) by an optical integrator (CTP10, EXFO SOLUTIONS) with a resolution of \(1pm\) for wavelength ranging from 1500 nm to 1600 nm. Considering XRD operando measurement, it was performed on a D8 Advance diffractometer (Bruker) using a Cu Ka X- ray source \((\lambda_{\mathrm{cal}} = 1.54056\) A, \(\lambda_{\mathrm{cal}} = 1.54439\) A) and a LynxEye XE detector. The XRD pattern and electrochemical data are simultaneously recorded by a custom designed airtight cell with a beryllium window producing a full pattern every 20 min.
+
+<--- Page Split --->
+
+## Preparation of cycled electrode samples for SEM and EDX imaging  
+
+The samples, at the end of charging, were prepared by washing the cathode powder twice by DOL to remove any dissolved lithium polysulfide species and lithium salt, and dried in vacuum chamber overnight. Then the powder was coated with gold (plasma sputtering coater (GSL- 1100X- SPC- 12, MTI)) for SEM (FEI Magellan) equipped with an energy- dispersive X- ray spectroscopy detector (Oxford Instruments) performed under an acceleration voltage of 20 kV.  
+
+## Data availability  
+
+All relevant data are included in the paper and Supplementary Information. Extra data are available on reasonable request from the corresponding author.  
+
+## Reference  
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+37. Liu, J., Chen, H., Chen, W., Zhang, Y. & Zheng, Y. New insight into the "shuttle mechanism" of rechargeable lithium-sulfur batteries. ChemElectroChem 6, 2782-2787 (2019).  
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+38. Mikhaylik, Y. V. & Akridge, J. R. Polysulfide shuttle study in the Li/S battery system. J. Electrochem. Soc. 151, A1969-A1976 (2004).  
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+39. He, Q., Gorlin, Y., Patel, M. U. M., Gasteiger, H. A. & Lu, Y.-C. Unraveling the correlation between solvent properties and sulfur redox behavior in lithium-sulfur batteries. J. Electrochem. Soc. 165, A4027-A4033 (2018).  
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+40. Wujcik, K. H. et al. Fingerprinting lithium-sulfur battery reaction products by X-ray absorption spectroscopy. J. Electrochem. Soc. 161, A1100-A1106 (2014).  
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+41. Moy, D., Manivannan, A. & Narayanan, S. R. Direct measurement of polysulfide shuttle current: a window into understanding the performance of lithium-sulfur cells. J. Electrochem. Soc. 162, A1-A7 (2015).  
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+42. Pang, Q., Liang, X., Kwok, C. Y. & Nazar, L. F. Advances in lithium-sulfur batteries based on multifunctional cathodes and electrolytes. Nat. Energy 1, 16132 (2016).  
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+43. Ji, X., Lee, K. T. & Nazar, L. F. A highly ordered nanostructured carbon-sulphur cathode
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+45. Zheng, J. et al. Lewis acid-base interactions between polysulfide and metal organic framework in lithium sulfur batteries. Nano Lett. 14, 2345-2352 (2014).  
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+46. Liang, X., Hart, C., Pang, Q., Garsuch, A., Weiss, T. & Nazar, L. F. A highly efficient polysulfide mediator for lithium-sulfur batteries. Nat. Commun. 6, 5682 (2015).  
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+58. Dai, S., Nouar, F., Zhang, S., Tissot, A. & Serre, C. One-step room-temperature synthesis of metal (IV) carboxylate metal-organic frameworks. Angew. Chem. Int. Ed. 60, 4282-4288 (2021).
+
+<--- Page Split --->
+
+## Acknowledgements  
+
+AcknowledgementsJ.- M. Tarascon acknowledges the International Balzan Prize Foundation and the LABEX STOREXII for funding. F. Liu and J.- M. Tarascon acknowledge the European Project "Innovative physical/virtual sensor platform for battery cell" (INSTABAT) (European Union's Horizon 2020 research and innovation program under grant agreement No 955930). W. Lu acknowledges the support of the CSC scholarship (201906880002). R. Demir- Cakan is thankful to the French Embassy for the Visiting Researcher Fellowship (135694V). We thank Dr. J. Forero- Saboya for his assistance of scanning electron microscopy images. We thank Prof. J. Albert from Carleton University (Ottawa, Canada) for the fabrication of fiber sensors and assistance of simulation software. Finally, we gladly thank Dr. W. He, Dr. Y. Wang, Dr. X. Gao, Dr. B. Li, Dr. C. Gervillie- Mouravieff and Mr. C. Leau for extensive and valuable discussion and comments.  
+
+## Author Contributions  
+
+Author ContributionsF. Liu, J. Huang, R. Demir- Cakan and J.- M. Tarascon conceived the idea and designed the experiments. F. Liu performed the experiments. F. Liu, J. Huang, R. Demir- Cakan and J.- M. Tarascon performed the data analysis. W. Lu and V. Pimenta provided the MOF- 801(Zr). Finally, F. Liu, S. Boles, R. Demir- Cakan and J.- M. Tarascon wrote the paper with contributions from all authors.  
+
+## Conflicts of interest  
+
+The authors declare no competing financial interests  
+
+## Additional information  
+
+Supplementary information: the online version contains supplementary material available at  
+
+Correspondence and requests for materials should be addressed to R. Demir- Cakan or J.- M. Tarascon.
+
+<--- Page Split --->
+
+## Supplementary Files  
+
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+Supportinformation.pdf LiSbatterysensing.mp4
+
+<--- Page Split --->

preprint/preprint__0179e628a853118df9da75d50a227d9cf3c9b00dfa8825f8efe5e29d0d1c5f4c/preprint__0179e628a853118df9da75d50a227d9cf3c9b00dfa8825f8efe5e29d0d1c5f4c_det.mmd

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+<|ref|>title<|/ref|><|det|>[[44, 107, 912, 208]]<|/det|>
+# Detangling electrolyte chemical dynamics and evolution in Li-S batteries by operando monitoring with optical resonance combs  
+
+<|ref|>text<|/ref|><|det|>[[44, 228, 816, 270]]<|/det|>
+Jean- Marie Tarascon ( Jean- marie.tarascon@college- de- france.fr) UMR 8260 « Chimie du Solide et de l'Energie », https://orcid.org/0000- 0002- 7059- 6845  
+
+<|ref|>text<|/ref|><|det|>[[44, 276, 214, 316]]<|/det|>
+Fu Liu College de France  
+
+<|ref|>text<|/ref|><|det|>[[44, 323, 692, 364]]<|/det|>
+Wenqing Lu École supérieure de physique et de chimie industrielles de la Ville de Paris  
+
+<|ref|>text<|/ref|><|det|>[[44, 369, 950, 432]]<|/det|>
+Jiaqiang Huang the Hong Kong University of Science and Technology (Guangzhou) https://orcid.org/0000- 0001- 8250- 228X  
+
+<|ref|>text<|/ref|><|det|>[[44, 438, 692, 479]]<|/det|>
+Vanessa Pimenta École supérieure de physique et de chimie industrielles de la Ville de Paris  
+
+<|ref|>text<|/ref|><|det|>[[44, 484, 905, 526]]<|/det|>
+Steven Boles NTNU - Norwegian University of Science and Technology https://orcid.org/0000- 0003- 1422- 5529  
+
+<|ref|>text<|/ref|><|det|>[[44, 531, 291, 571]]<|/det|>
+Rezan Demir- Çakan Gebze Technical University  
+
+<|ref|>text<|/ref|><|det|>[[44, 613, 102, 630]]<|/det|>
+Article  
+
+<|ref|>text<|/ref|><|det|>[[44, 650, 137, 669]]<|/det|>
+Keywords:  
+
+<|ref|>text<|/ref|><|det|>[[44, 689, 310, 708]]<|/det|>
+Posted Date: August 4th, 2023  
+
+<|ref|>text<|/ref|><|det|>[[44, 726, 475, 746]]<|/det|>
+DOI: https://doi.org/10.21203/rs.3.rs- 3192096/v1  
+
+<|ref|>text<|/ref|><|det|>[[44, 764, 910, 807]]<|/det|>
+License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+<|ref|>text<|/ref|><|det|>[[44, 824, 530, 844]]<|/det|>
+Additional Declarations: There is NO Competing Interest.  
+
+<|ref|>text<|/ref|><|det|>[[42, 880, 958, 924]]<|/det|>
+Version of Record: A version of this preprint was published at Nature Communications on November 14th, 2023. See the published version at https://doi.org/10.1038/s41467- 023- 43110- 8.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[175, 85, 822, 120]]<|/det|>
+Detangling electrolyte chemical dynamics and evolution in Li- S batteries by operando monitoring with optical resonance combs  
+
+<|ref|>text<|/ref|><|det|>[[217, 124, 821, 143]]<|/det|>
+Fu Liu \(^{1,2}\) , Wenqing Lu \(^{3}\) , Jiaqiang Huang \(^{4}\) , Vanessa Pimenta \(^{3}\) , Steven Boles \(^{5}\) , Rezan  
+
+<|ref|>text<|/ref|><|det|>[[342, 152, 666, 170]]<|/det|>
+Demir- Cakan \(^{6,7*}\) & Jean- Marie Tarascon \(^{1,2,8*}\)  
+
+<|ref|>text<|/ref|><|det|>[[145, 177, 850, 380]]<|/det|>
+\(^{1}\) Collège de France, Chimie du Solide et de l'Energie—UMR 8260 CNRS, Paris, France. \(^{2}\) Réseau sur le Stockage Electrochimique de l'Energie (RS2E)—FR, CNRS 3459, Amiens, France. \(^{3}\) Institut des Matériaux Poreux de Paris (IMAP), ESPCI Paris, Ecole Normale Supérieure, CNRS, PSL University, Paris, France \(^{4}\) The Hong Kong University of Science and Technology (Guangzhou), Sustainable Energy and Environment Thrust, Nansha, Guangzhou, Guangdong 511400, P. R. China \(^{5}\) Department of Energy and Process Engineering, Faculty of Engineering, Norwegian University of Science and Technology (NTNU), Trondheim, Norway \(^{6}\) Gebze Technical University, Institute of Nanotechnology, Gebze, Kocaeli, 41400, Turkey \(^{7}\) Gebze Technical University, Department of Chemical Engineering, Kocaeli, 41400, Turkey \(^{8}\) Sorbonne Université—Université Pierre- et- Marie- Curie Paris (UPMC), Paris, France  
+
+<|ref|>text<|/ref|><|det|>[[147, 391, 852, 666]]<|/det|>
+Challenges in enabling next- generation rechargeable batteries with lower cost, higher energy density, and longer cycling life stem not only from combining appropriate materials, but from optimally using cell components given their respective evolutions. One- size- fits- all approaches to operational cycling and monitoring are limited in improving sustainability if they cannot utilize and capture essential chemical dynamics and states of electrodes and electrolytes. Herein we describe and show how the use of tilted fiber Bragg grating (TFBG) sensors to track, via the monitoring of both temperature and refractive index metrics, electrolyte- electrode coupled changes that fundamentally control lithium sulfur batteries. Through quantitative sensing of the sulfur concentration in the electrolyte, we demonstrate that the nucleation pathway and crystallization of \(\mathrm{Li}_2\mathrm{S}\) and sulfur governs the cycling performance. With this technique, a critical milestone is achieved, not only towards developing chemistry- wise cells (in terms of smart battery sensing leading to improved safety and health diagnostics), but further towards demonstrating that the coupling of sensing and cycling can revitalize known cell chemistries and break open new directions for their development.  
+
+<|ref|>sub_title<|/ref|><|det|>[[148, 678, 250, 693]]<|/det|>
+## Introduction  
+
+<|ref|>text<|/ref|><|det|>[[148, 705, 852, 907]]<|/det|>
+Widescale utilization of renewable energy sources is essential to supplementing and perhaps replacing the carbon- based energy supply responsible for climate change. The recent success of electric vehicles made possible by lithium- ion battery technology, attributed to both improved reliability and cost reductions, demonstrates that new breakthrough chemistries may not be necessary for a 'green transition' if known electrochemical cell pairings can be mastered. Included among these chemistries are resurgent lithium sulfur batteries (LSB), which, in spite of their appeal in terms of theoretical specific energy ( \(\sim 2600 \mathrm{Wh / kg}\) ), are still not commercialized. This can be attributed to a number of unresolved challenges, including the insulating nature of sulfur and lithium sulfides, large volume expansion (80%) of the solid sulfur cathode during the formation of \(\mathrm{Li}_2\mathrm{S}\) , and shuttle effect caused by soluble polysulfide in electrolyte \(^{1,2}\) .
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 85, 851, 436]]<|/det|>
+Numerous characterization techniques have been deployed to clarify the underlying science of LSBs during operation, which have contributed significantly to a better understanding of the kinetics and thermodynamics of the dissolution/precipitation of polysulfides, whose critical role in LSBs has been known for nearly 50 years<sup>3</sup>. Since then, methods such as X- ray diffraction (XRD)<sup>4,5</sup>, electrochemical tests<sup>6- 8</sup>, and spectroscopic techniques<sup>9- 16</sup> have been used to provide valuable information regarding identification of polysulfide species and reaction kinetics. However, it is experimentally challenging to isolate the individual polysulfides due to the propensity of disproportionation, and these analytical techniques rely on special equipment and cell designs that cannot be directly deployed for long cycling periods. Recently, optical fiber sensors have attracted attention in battery sensing due to their low cost, compactness, remote sensing capabilities, and simple integration into batteries without interfering with their internal chemistry<sup>17</sup>. Among the fiber sensor family, the most commercialized Fiber Bragg grating (FBG) sensors have been well integrated inside Na (Li)- ion batteries for monitoring heat and pressure<sup>18</sup> or inside the solid- state batteries for tracking the stress dynamics<sup>19</sup>. Indeed, recently Ziyun et al. demonstrated that the cathode stress evolution of LSB can be in- situ monitored by FBG sensors for understanding the chemo- mechanics<sup>20</sup>. Nevertheless, testing polysulfides with FBGs is still limited, owing to the fact that the sensing signals are totally confined inside the fiber core and cannot sense the electrolyte surrounding the fiber surface.  
+
+<|ref|>text<|/ref|><|det|>[[148, 446, 851, 686]]<|/det|>
+In order to investigate the external medium of fiber, TFBGs (same structure as FBG without physical structure modification<sup>21</sup>, but rotating the grating plane to a specific angle) have been proposed to excite hundreds of discrete cladding mode resonances that are sensitive to the external medium refractive index perturbation via evanescent fields<sup>22</sup>, hence serving as an optical 'comb'. This has led to the development of high- performance sensors used in various areas, including biomedicine<sup>23</sup>, magnetic detection<sup>24</sup>, and gas monitoring<sup>25</sup>. Recently, TFBGs have been integrated into commercial batteries to detect chemical dynamics/state of electrolytes related to chemical evolution<sup>26</sup>. Interestingly, some TFBG- assisted surface plasmon resonance (TFBG- SPR) sensors with higher sensing sensitivity have also been developed for Zn- ion batteries to offer an alternative way of probing ion transport kinetics<sup>27</sup>. Overall, TFBG sensors provide new opportunities to deal with the challenge of battery sensing as they combine direct optical sensing of the environment, as well as physio- mechanical sensing of the environment via the confined optical modes.  
+
+<|ref|>text<|/ref|><|det|>[[148, 696, 851, 899]]<|/det|>
+Herein, TFBG sensors, enabling measurements with a wide array of parameters including refractive index, temperature, and strain, are proposed to operate track the chemical dynamics/states of the LSB via electrolyte sulfur concentration. We demonstrate that the capacity fading is strongly correlated with the dissolution/precipitation of polysulfides throughout the cycling and hence, with respect to cycling rates. By exploiting the kinetic and thermodynamic response of soluble sulfur in the electrolyte, the nonlinear transport flux clarifies the "invisible" disproportionation process together with their dynamic evolution. With this understanding, we show that altering the nucleation pathway of the crystalline Li<sub>2</sub>S and sulfur can be attributed to real improvements in cell cycling performance. Subsequently, it is noted that TFBGs have the ability to obtain key chemical- physical- thermal metrics in operando with notable time and spatial resolution that may extend beyond LSBs.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[148, 86, 208, 100]]<|/det|>
+## Results  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 113, 400, 129]]<|/det|>
+## Characteristics of TFBG sensing  
+
+<|ref|>text<|/ref|><|det|>[[147, 140, 851, 250]]<|/det|>
+Prior to in operando battery inspection, it is appropriate to first briefly visit the suitability of TFBG sensing for such chemistries, as related to fundamental principles of their operation. TFBGs, immersed in an electrolyte (Fig. 1a), were made in the core of the commercial single- mode fiber by ultraviolet pulse laser to induce periodically permanent refractive modulation. They obey a phase matching condition by enhancing the coupling between fundamental core mode and backward- propagation cladding modes22 (Fig. 1b):  
+
+<|ref|>equation<|/ref|><|det|>[[357, 255, 828, 274]]<|/det|>
+\[\lambda = \left(n_{11}(\lambda) + n_{lm}(\lambda)\right)\lambda /\cos \theta \quad (1)\]  
+
+<|ref|>text<|/ref|><|det|>[[147, 280, 851, 520]]<|/det|>
+where \(\lambda\) is the cladding mode resonance wavelength, \(n_{11}(\lambda)\) is the effective index of core mode, and \(n_{lm}(\lambda)\) is the effective index of cladding mode with azimuthal order \(l\) and radial order \(m\) . \(\Lambda\) is the period of grating along the fiber axis, and \(\theta\) is the grating tilt angle. The experimental spectra are presented in Fig. 1c, where the core mode resonance (i.e., Bragg resonance) is located at the longest wavelength around \(1590 \mathrm{nm}\) (sensitive to temperature and strain \((T, \epsilon)\) )22. The cladding mode resonances guided by the fiber cladding (beside \(T, \epsilon\) , also sensitive to refractive index \((R)\) of the surrounding media) are shown on the left of Bragg resonances. The leaky modes are located at the region where there is a discontinuity in the cladding mode envelope and their amplitude, indicating the loss of total internal reflection at the point where the cladding mode effective index becomes equal to or smaller than the surrounding \(R\) . Therefore, with respect to soluble polysulfides which perturb electrolyte density, and hence the refractive- index, we focus on the high order guided cladding modes near the leaky mode region.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[163, 85, 838, 508]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[147, 520, 851, 668]]<|/det|>
+<center>Fig. 1 | Concept of optical fiber sensing for LSB. (a) Schematic of a fiber optic sensor immersed in electrolyte for in-situ detection of sulfur concentration originating from the generated dissolved polysulfide and their transport activities (i.e., shuttle effect). (b) Backward-propagation guided modes inside fiber for sensing (Supplementary information Fig. S1). (c) Experimental spectra response to polysulfide. (d) The wavelength shifts of cladding mode resonance at \(\sim 1560 \mathrm{nm}\) to \(100 \mathrm{mM}\) polysulfide \(\mathrm{Li}_2\mathrm{S}_x\) ( \(x = 1, 2, 3, \dots , 8\) ), shaded in green; (e) to concentration variation of \(\mathrm{Li}_2\mathrm{S}_4\) and \(\mathrm{Li}_2\mathrm{S}_8\) from \(0 \mathrm{mM}\) to \(100 \mathrm{mM}\) ; (f) to same sulfur concentration of polysulfide \(\mathrm{Li}_2\mathrm{S}_x\) ( \(x = 4, 5, 6, 7, 8\) ). </center>  
+
+<|ref|>text<|/ref|><|det|>[[147, 686, 851, 907]]<|/det|>
+To investigate the response of our TFBG to polysulfides, depicted in Fig. 1c, the TFBG was thoroughly immersed in a series of \(100 \mathrm{mM}\) polysulfide containing electrolytes in a modified Swagelok cell. Bearing this in mind, the Bragg resonance remains stable because any strain and temperature variation were eliminated during the measurements, indicating that the cladding mode wavelength shift is only related to refractive index variation. When the chain length of polysulfides is increased while keeping the polysulfide concentration the same, the guided modes on the left side of cladding mode at \(1560 \mathrm{nm}\) becomes leaky due to the increased refractive index. This is a result of the number sulfur atoms in solution becoming larger and perturbing the corresponding mode effective refractive index, while guided modes on its right side are linearly shifted to longer wavelength (Fig. 1d,e and Supplementary Fig. S2a,b). Noteworthy is the fact that the refractive index tested by TFBG sensor is an "average effect" of all the pertinent refractive indices of lithium polysulfide solutions. Following this,
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[148, 85, 851, 195]]<|/det|>
+dilution of the \(100~\mathrm{mM}\) polysulfide \(\mathrm{Li}_2\mathrm{S}_x\) ( \(x = 4\) , 5, 6, 7, 8) to an equivalent concentration of sulfur (Supplementary Fig. S2c) yields an equivalent optical effect, stemming from the refractive index of polysulfide solutions converging to the same density, (Fig. 1f and Supplementary Fig. S2d,e). Therefore, rather than recognizing the specific species inside the electrolyte, the TFBG sensor will distinctly reveal the electrolyte sulfur concentration evolution of LSB cell (so long as temperature is kept constant).  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 207, 614, 222]]<|/det|>
+## Operando measurement of chemical dynamic state of LSB  
+
+<|ref|>text<|/ref|><|det|>[[147, 233, 852, 492]]<|/det|>
+Given the promising proof- of- concept of sulfur concentration measurement in electrolyte, we explored the capability of operando chemical dynamics/states testing of LSB cell by putting a \(2\mathrm{mm}\) thick, \(12.8\mathrm{mm}\) diameter polyether ether ketone (PEEK) ring ( \(1\mathrm{cm}\) long TFBG can go through) in the middle of the Swagelok to separate the cathode (sulfur and Super P carbon composite (60/40 wt. \(\%\) )) and lithium anode so that fiber sensor would not touch any of them (Supplementary Fig. S3). Filling the PEEK ring with electrolyte (500 μL, \(1\mathrm{M}\) LiTFSI, 0.5 M LiNO3 in DOL/DME (1:1, v/v)) where the sensor is immersed, the effect of ion concentration gradient of electrolyte including \(\mathrm{Li}^+\) , TFSI- and \(\mathrm{NO}_3^-\) in DOL and DME can be safely neglected. It should be noted that prior to LSB investigations, a control experiment was executed with lithium iron phosphate (LFP) as the cathode. Here it was found that the corresponding \(R / o\) of electrolyte variation is 20 times smaller than that in LSB in which dissolved polysulfide is formed (Supplementary Fig. S4). Therefore, the use of a TFBG can provide for the possibility of measuring sulfur concentration in the electrolyte during cell operation, and to a large extent, the measurement will be irrespective of the cell's state of charge or state of health.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[170, 95, 844, 650]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[147, 668, 852, 890]]<|/det|>
+<center>Fig. 2 | Decoding electrolyte sulfur concentration dynamic of LSB. (a) Operando measurement of LSB by TFBG and XRD at C/20 (left panel: polysulfide dissolution allowed (electrolyte of 1 M LiTFSI, 0.5 M LiNO₃ in DOL/DME (1:1, v/v)); right panel: polysulfide dissolution prohibited (electrolyte of LP30: 1 M LiPF₆ in EC/DMC) of sulfur and Super P carbon composite (60/40 wt.%) cathode. (b) Morphology (SEM) and content of elemental sulfur and Super P carbon (energy-dispersive X-ray spectroscopy, EDX) of the cathode at the end of charging. (c) The quantitative analysis of sulfur before cycling, end of first plateau of discharge and end of charge. (d) The recrystallized sulfur governed by comporptionation reactions and potential voltage. The shaded region in blue stands for 15 hours of rest (OCV mode) starting at the end of charging demonstrating that the re-crystallized sulfur (marked by green asterisk “\*”) dissolves into the electrolyte in the form of soluble polysulfide through comporptionation reactions. </center>
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 78, 852, 900]]<|/det|>
+Based on the aforementioned concept, we measured the electrolyte sulfur concentration variation with a TFBG sensor while simultaneously deploying in operando XRD during C/20 cycling to track phase transitions of the composite electrode (Fig. 2a,c). At the upper voltage plateau around 2.4 V, the highest sulfur concentration in the electrolyte was monitored (left panel of Fig. 2a) and found to be accompanied by a decrease in the sulfur peak intensity (XRD pattern) resulting from a series of phase transformations, i.e., from solid sulfur to soluble intermediate polysulfides. On the other hand, when the carbonate- based electrolyte was used as a reference (i.e., LP30, right panel of Fig. 2a), no concentration gradient was observed in the electrolyte and remained nearly stable due to the fact that no soluble polysulfide intermediates were formed. Instead this resulted in the formation of insoluble and undetected products, since it is known that there is a nucleophilic reaction between sulfur radical and ethylene carbonate of LP30 to form thiocarbonate- like solid electrolyte interphase<sup>28,29</sup> (Supplementary Fig. S5). Turning to the lower voltage plateau around 2.1 V (Fig. 2a), the concentration of dissolved sulfur decreases as a result of the reduction of long- chain polysulfide into shorter chains, leading to insoluble Li<sub>2</sub>S compound in the cathode (Supplementary Fig. S6a) confirmed by XRD<sup>30,31</sup>. Upon charging, the sulfur concentration indicates reversible recovery consistent with the decay of Li<sub>2</sub>S peaks until complete disappearance at the voltage \(\sim 2.4 \text{V}\), where crystallization of sulfur starts and thus sulfur concentration in electrolyte drops again, even though the deposited solid sulfur in the cathode is featureless by XRD<sup>31</sup>. To confirm the sulfur at the end of charge, the cathode powder was recovered in the glovebox by washing and drying to remove any soluble polysulfide as well as remaining electrolyte salts (Fig.2b,c), confirming that \(1.4 \%\) sulfur was detected and its surface topography was unchanged (i.e., no formation of big crystalline particles)<sup>31</sup>. Furthermore, when setting the 15- hour open circuit voltage (OCV) after charging, the sulfur concentration increases and reaches a plateau within 9 hours (Fig.2c), whereas, on the other hand, no sulfur concentration changes were observed during rest periods applied at the end of discharge (Supplementary Fig. S6b). It is most likely explained by compropriotation reactions during the rest period when the recrystallized sulfur from the end of charging is transformed to soluble lower- order polysulfide via reacting with high- order polysulfide<sup>32</sup>. This is also supported by the beginning of 2 hours rest (first cycle before discharging) demonstrating relatively little variation of electrolyte since fresh electrolyte contains a minimum amount of high order polysulfide and the compropriotation reactions are thus not possible (Supplementary Fig. S6c). The crystalline sulfur at the end of charge is related to the cut- off potential that the sulfur recrystallization process disappears<sup>4</sup> (disappearance of sulfur concentration valley at the end of charge in Fig.2d) if setting the potential below 2.4 V (indicated that less sulfur suppresses the related compropriotation reactions, also detailed in Supplementary Fig. S6c). Altogether, the dynamic of sulfur concentration of electrolyte decoded by TFBG sensor supports the simplified chemical reaction process: during discharging the sulfur receives electrons and transfers them first to soluble Li<sub>2</sub>S at the high voltage plateau. This is followed by formation of insoluble Li<sub>2</sub>S at the low voltage plateau and vice versa for the charging process, indicating that the consumption rate of sulfur under the galvanostatic condition can be expressed as a ratio. According to the "linear" sulfur concentration variation rate by monitoring the slope (mM/h) on each plateau tested by sensor during the discharge and charging steps (Supplementary Fig. S4a) it was
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 85, 851, 156]]<|/det|>
+observed that the ratio on the upper and lower plateaus at the discharge step is 3.9. This is similar to the value obtained during charging (0.9), suggesting that the rate of sulfur transformation to/from soluble polysulfide is \(\sim 4.3\) times faster than that to/from insoluble Li₂S and the respective polysulfides.  
+
+<|ref|>image<|/ref|><|det|>[[157, 173, 842, 682]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[147, 696, 851, 900]]<|/det|>
+<center>Fig. 3 | Decoding the disproportionation process and evolution. (a, b, c and d) The temporal voltage (black curve) and decoded electrolyte sulfur concentration (red curve) by GITT test (the capacity and optical spectra are given in supplementary Fig. S7). (a, b and c) Detailed view of electrolyte response to current pulse (kinetic process) and rest (thermodynamic process). (e) Transport flux of soluble sulfur based on current pulse (kinetics process, green triangle, \(D_t = V_{BD} / (S \times t)\) and rest (thermodynamic process, blue square, \(D_t = V_{DE} / (S \times t)\) , where \(S\) is across section area of electrolyte that sensor is immersed in, \(t\) is corresponding time. It indicates the slope of soluble sulfur consumption (negative) or formation (positive) in electrolyte, which is also plotted by arrows in bottom (arrow up: sulfur increasing, arrow down: sulfur decreasing). The normalized ratio (red sphere) represents the sulfur consumption of rest (thermodynamic process) by \(Ratio = |D_t| / (|D_t| + |D_k|)\) . </center>
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 85, 852, 601]]<|/det|>
+The disproportionation and association reactions of likely intermediates are mesmerizing, but despite an awareness of their existence, their mysterious dynamics make LSBs seemingly incomprehensible. Nevertheless, the real- time quantification of soluble sulfur afforded by TFBGs provides a convincing way to straighten their story when combined with GITT (Fig. 3). As depicted in Fig. 3d, the overall profile of sulfur concentration variation matches well with dissolution/precipitation of polysulfides and sulfur already confirmed in Fig.2, and we focus on the temporal response of electrolyte to the current pulse and respective rest period. According to Fig. 3a,b,c the "tiny" variation of sulfur concentration from A to B (moving from the rest to cycling mode) is the electrolyte instantaneous response to the leading edge of current pulse with strong electrical field gradient, originating from polysulfide redistribution driven by the sudden electrical field<sup>33</sup>. This effect trends in the opposite direction as polysulfide diffusion in the region from B to C, which relates to the current pulse (referred to herein as the kinetic process). The reader may note a discrepancy between voltage and concentration from C to D during rest, which is attributed to the "delay" between the electrochemical reaction at the electrode surface and the position of the sensor in the cell. Hence a small lag exists even if removing the current pulse. Regarding the OCV relaxation (3 hours rest period) and movement towards equilibrium (herein coined as the thermodynamic process) in the region from C to E, the sulfur concentration fluctuation is most likely explained by polysulfide disproportionation process (i.e. \(Li_2S_8 \leftrightarrow Li_2S_7 + 1/4S_8\) )<sup>34</sup> since the two best- remaining hypotheses, dissociation (i.e. \(Li_2S_8 \leftrightarrow Li^+ + LiS_7\) or \(Li_2S_8 \leftrightarrow 2LiS_7\) )<sup>34</sup> and non- uniform polysulfide distribution can be excluded: the dissociation of polysulfide including anions and radicals are rare while the neutral lithium polysulfide is dominant in electrolyte<sup>34</sup>; the polysulfide distribution reaches equilibrium within 1 min (time interval of spectra recording) that is nearly synchronous to electrochemistry (Fig. 2a), not matching the rest period situation with 3 hours of continuous soluble sulfur consumption or generation. After careful deliberation, we move forward with the idea that electrochemical and disproportionation processes can be extracted respectively by temporal response of electrolyte based on sulfur concentration decoded by TFBG sensor.  
+
+<|ref|>text<|/ref|><|det|>[[148, 611, 852, 907]]<|/det|>
+Encouraged by the results mentioned above, we have next attempted to build a quantitative relation between kinetic and thermodynamic processes through a primitive estimation of transport flux of sulfur in the electrolyte (Fig. 3e). In STAGE I, on the higher voltage plateau, solid sulfur was continuously consumed upon discharge to form long chain polysulfide, \(\mathrm{Li}_2\mathrm{S}_8\) (green triangle), together with the rapid disproportionation process \(Li_2S_8 \leftrightarrow Li_2S_6 + 1/4S_8\) <sup>35- 37</sup> leading to soluble sulfur consumption during relaxation (blue square). At the same time the normalized ratio (red sphere) between the rest and current pulse process nearly remains the same and fixed at 0.1 (shaded in light green color), meaning that there is a competition reaction between the soluble long chain polysulfide species formation and the \(\mathrm{S}_8\) - precipitation in the beginning of the discharge step. In STAGE II, regarding the first- to- second plateau transition whereby a voltage slope forms between 2.3 and 2.1 V, the shorter chain polysulfide \(\mathrm{Li}_2\mathrm{S}_4\) is expected to be generated<sup>38,39</sup>. This is accompanied by a reduction in the rate of formation of soluble sulfur in the electrolyte and hence, the sulfur concentration during resting keeps increasing while the generation of fresh long chain polysulfides winds down and the kinetic/thermodynamic ratio can reach 0.89. This indicates that polysulfide species formation via disproportionation is dominant due to fact that dissolved sulfur
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 84, 852, 399]]<|/det|>
+continues to react with polysulfide present in the electrolyte during rest<sup>38,41</sup>. Undoubtedly an enriched concentration of sulfur in the electrolyte contributes significantly towards driving the formation of more polysulfides. In STAGE III where the lower voltage plateau marks the conversion between \(\mathrm{Li}_2\mathrm{S}_4\) to shorter chain \(\mathrm{Li}_2\mathrm{S}_2\) and \(\mathrm{Li}_2\mathrm{S}\) forms, the potential disproportionation process \(Li_2S_2 \leftrightarrow 1 / 3Li_2S_4 + 2 / 3Li_2S^{40,41}\) is highlighted from the middle of the second plateau, and raises the sulfur concentration to about a normalized ratio of 0.2 which follows until the end of the half- cycle. Upon charging (STAGE IV and STAGE V), it is evident that the process is not a fully reversible one, as seen with STAGE IV where \(\mathrm{Li}_2\mathrm{S}_4\) and \(\mathrm{Li}_2\mathrm{S}_6\) reappear and the push towards thermodynamic equilibrium necessitates disproportionation processes leading to consumption of sulfur during rest periods, but an overall concentration increase. In STAGE V, the sulfur concentration in electrolyte drops very sharply, caused by the recrystallization of sulfur during current pulsing, even though that is not visible in the operando XRD studies shown in Fig. 2a. Interestingly, the rise of soluble sulfur during these late- stage rest periods suggests nucleation and/or growth limitations of the recrystallized sulfur, which will be addressed here later. Altogether, the quantitative disproportionation process decoupled by the fiber sensor based on GITT provides meaningful details to understand micro- mechanisms of complicated kinetics processes.  
+
+<|ref|>image<|/ref|><|det|>[[192, 412, 808, 813]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[147, 826, 850, 899]]<|/det|>
+<center>Fig. 4 | Operando monitoring over cycling and cycling rate. (a) Spectra contour of TFBG cladding mode resonances response (details are given in supplementary video). (b) Temporal voltage (grey line), decoded sulfur concentration (red line), and temperature of electrolyte (blue line) over the cycling. (c) Capacity variation of (b) upon time. (d) Soluble sulfur </center>
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[148, 84, 851, 195]]<|/det|>
+concentration dynamic related to cycling rate. (e) Soluble sulfur concentration dynamic related to cycling rate of first/second plateau. The concentration drop through the recrystallization of sulfur at the end of charge is marked by green asterisk \(^{**}\) . With Mode I, the cycling rate was set by upper plateau (C/20) and lower plateau (C/5); Mode II by upper plateau (C/5) and lower plateau (C/20) and Mode III by upper plateau (C/10) and lower plateau (C/10).  
+
+<|ref|>text<|/ref|><|det|>[[148, 206, 852, 537]]<|/det|>
+Inspired by aforementioned exploration of internal mechanisms of LSBs, we decide to further investigate the operando monitoring over cycling and cycling rates (Fig. 4a,b). Bearing in mind that the temperature (blue curve in Fig. 4b), decoded by Bragg resonance located at \(1589 \text{nm}^{26}\) , initially rises to \(25 \text{C}^\circ\) and keeps a constant afterward due to the shipping from glovebox to oven to reach thermal equilibrium. The periodic sulfur concentration evolution (red curve in Fig. 4b), decoded by the wavelength shift of cladding mode located at \(\sim 1559.5 \text{nm}\) in Fig. 4a, indicates the reversible dissolution/precipitation of polysulfides and sulfur. Noteworthy here is the feasibility of observing the amplitude of soluble sulfur variation (supplementary Fig. S8) that matches the cycling behavior associated with capacity fading owning to less and less \(\text{Li}_2\text{S}\) and solid sulfur crystallization over cycles (Fig.4c), which could be reasonably attributed to the high electrolyte to sulfur ratio (E/S ratio, \(>100 \mu \text{L} /\text{mg}\) ), thereby inducing stronger polysulfide shuttle effect with less sulfur utilization. Regarding a sulfur concentration response to the cycling rate depicted in Fig. 4d, a lower cycling rate (C/15) leads to the largest sulfur concentration change, strongly supporting the idea that the most soluble sulfur in electrolyte is transformed to solid species ( \(\text{Li}_2\text{S}\) ) when given adequate time for completion of the redox process. Accelerating the cycling to C/10, C/5 and C/3, partially soluble sulfur transformation is seen (i.e., background of sulfur concentration rises) together with less sulfur crystallization (i.e., the "dip" of soluble concentration at the end of charge).  
+
+<|ref|>text<|/ref|><|det|>[[148, 548, 852, 825]]<|/det|>
+Following the evidence thus far, it is then reasonable to conclude that regular galvanostatic operating conditions might not be perfect for LSBs, considering the various competing mechanisms explored above which are evident at different stages of charge and discharge. With this in mind, different modes of cycling protocols were also pursued to be able to tune the cycling performance. For instance, when setting a relatively low (high) cycling rate for the upper (lower) plateau depicted in Fig. 4e (Mode I), only half of the soluble sulfur inside electrolyte was transformed to \(\text{Li}_2\text{S}\) due to high cycling rate of lower plateau. On the upper voltage plateau the solid sulfur dissolution or recrystallization was \(100\%\) successful and which can be readily attributed to the low cycling rate in this voltage range. Further confirmation of the delicate cycling nature of LSBs was seen in Mode II as \(80\%\) of the soluble sulfur inside electrolyte was transformed to \(\text{Li}_2\text{S}\) due to long cycling in the lower plateau (C/20) but with less solid sulfur recrystallization at the end of charge (C/5). Overall, the quantitative relation of soluble sulfur evolution and cycling/cycling rate implies that the efficiency of solid sulfur and \(\text{Li}_2\text{S}\) formation governs the cycling performance and significant new progress might be made through cycling condition optimization alone.  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 836, 579, 852]]<|/det|>
+## High performance based on novel functional cathode  
+
+<|ref|>text<|/ref|><|det|>[[149, 864, 850, 899]]<|/det|>
+Extensive work has been conducted over the years to improve the LSB performance through variety of means: the use of high- conductivity cathodes, strong polysulfide binding to
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 84, 851, 287]]<|/det|>
+suppress the shuttling phenomenon, surface chemistry to control \(\mathrm{Li_2S}\) nucleation or dissolution, design of cathode architectures that are elastic to withstand volume expansion, and optimized electrolytes enabling high sulfur utilization<sup>42</sup>. Considering the strategy of trapping lithium polysulfides, it includes physical (spatial) entrapment by confining polysulfide in the pores of non- polar carbon materials<sup>43</sup> or designing a sulfur host material that exhibits stronger chemical interaction such as dipolar configurations based on polar surfaces<sup>44</sup>, metal- sulfur bonding<sup>45</sup> and surface chemistry for polysulfide grafting and catenation<sup>46</sup>, leading to long cycling without strong capacity fading. Nevertheless, the fundamental problem regardless of approach is all related to the polysulfides dissolving inside the electrolyte, thus a smart sensor that can reliably track the soluble sulfur in real time should be of particular value.  
+
+<|ref|>text<|/ref|><|det|>[[147, 298, 851, 759]]<|/det|>
+In this respect, in addition to the less porous Super P carbon discussed above, the Ketjen black (KB) carbon whose BET surface area \(>1200 \text{m}^2 /\text{g}\) is utilized as physical nonpolar sulfur confinement host (KB/S, 40/60 wt.%), and the electrolyte was simultaneously monitored by a TFBG sensor together with XRD to characterize the composite cathode phase transitions (left panel of Fig. 5a). After the melt- diffusion treatment process, part of sulfur penetrates into the nanostructure of KB (Supplementary Fig. S9); however, during cycling the nonpolar physical adsorption or confinement of polysulfide is very limited and massive soluble polysulfide is dissolved and detected. Surprisingly, the capability of \(\mathrm{Li_2S}\) crystallization is enhanced since more than 91 % soluble sulfur disappears and converts to solid short chain polysulfide at second plateau as indicated by the fiber sensor. Meanwhile, unlike linearly sulfur concentration change with super P carbon (BET surface area: \(62 \text{m}^2 /\text{g}\) ) substrate cell in Fig. 2a, KB containing cell becomes "nonlinear" with a higher rate, indicating that the nucleation rate of \(\mathrm{Li_2S}\) is increasing. It could be reasonably assigned by instantaneous nucleation that depletion of the nucleation site of KB occurs at a very early stage, following the nonlinear kinetics of the nucleation pathway: \(\mathcal{N} = \mathcal{N}_c[1 - \exp (- At)]\) where \(\mathcal{N}\) is the density of nuclei, \(\mathcal{N}_c\) is the density of available nucleation site, and \(A\) is the nucleation rate<sup>47</sup>. In contrast, considering less porous Super P carbon containing cell in Fig.2a with a lower nucleation rate, the initial density of nuclei increases linearly with time: \(\mathcal{N} = \mathcal{N}_c At\) (i.e. progressive nucleation)<sup>48,49</sup>. Moreover, the porous structure of KB also accelerates the dissolution and re- crystallization of sulfur, which results in a strong sulfur concentration saturation at the transient stage relating to semisolid \(\mathrm{Li_2S_4}\) generation (keeping a balance between electrochemical and disproportionation process) and enhanced soluble sulfur reduction to solid sulfur at the end of charging (Supplementary Fig. S10). The sulfur concentration dynamic responding to potential and cycling rate is consistent with the Super P substrate, detailed in Supplementary Fig. S11.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[186, 90, 816, 595]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[147, 612, 850, 759]]<|/det|>
+<center>Fig. 5 | Chemistry of LSB with outstanding polysulfide-trapping capability cathodes. (a) Operando measurement of LSB by TFBG and XRD at C/20 (left panel: nonpolar physical adsorption of polysulfide by KB carbon; right panel: polar adsorption of polysulfide by MOF-801(Zr)). (b) In-situ detection of polysulfide adsorption by MOF-801(Zr). (c) XRD spectra for MOF-801(Zr) before and after adsorption of \(\mathrm{Li}_2\mathrm{S}_6\) . (d) The temporal voltage (grey line), decoded sulfur concentration (red line) of cathode composite based on MOF-801(Zr), and decoded sulfur concentration (blue line) considering the cathode with KB substrate (same amount of active material) is used as a reference without showing the temporal voltage. </center>  
+
+<|ref|>text<|/ref|><|det|>[[148, 770, 850, 898]]<|/det|>
+To further extend the use of porous materials as host matrix for sulfur confinement, Metal- Organic Frameworks (MOFs) can be considered as efficient candidates for the selective adsorption of polysulfide species<sup>45,50</sup>. We have considered MOF- 801(Zr), a microporous zirconium fumarate with pores of about 5- 12 Å and a high specific surface area (1020 (±20) m<sup>2</sup>/g), to be considered as an efficient candidate for the adsorption of polysulfide species. Its 3D cubic structure is built- up from \(\mathrm{Zr_6O_4(OH)_4}\) oxo- clusters linked to fumarate ligands and exhibits abundant missing linkers defect sites and reactive terminal –OH groups. Due to the
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 85, 857, 380]]<|/det|>
+Lewis acidic character of the Zr nodes and the reactivity of these terminal groups, the polysulfide species (soft Lewis bases) are expected to interact strongly with the framework<sup>51,52</sup>. Depicted in Fig. 5c, the \(0.5 \mathrm{ml} 100 \mathrm{mM}\) polysulfide \(\mathrm{Li}_2\mathrm{S}_x\) ( \(x = 4, 6, 8\) ) was monitored in real time by fiber sensor during adsorption, which is evidently finished within 1 hour ensuring efficient adsorption inside the cell. After fully adsorbing \(100 \mathrm{mM} \mathrm{Li}_2\mathrm{S}_6\) (Fig. 5d), the XRD pattern shows the appearance of new peaks in addition to the ones of MOF- 801(Zr), matching with the peaks of pure solid sulfur (Supplementary Fig. S12) consistent with the "yellow" color of the powder. This explains why the operando test starts with the peaks of sulfur (XRD pattern on the right panel of Fig. 5a). The subsequent electrochemical reaction is the same as the cathode substrate with KB including concentration saturation in the transition region between the first and the second plateau, nonlinear sulfur consumption rate with instantaneous nucleation pathway and enhanced sulfur crystallization at the end of charge. However, a key point and difference is that the sulfur concentration inside the electrolyte dramatically decreases by \(80.8\%\) with the same amount of active material due to the enhanced adsorption and localization of polysulfides through Lewis acid- base chemical interaction by MOF- 801(Zr) (Fig.5d and Support information, Fig. S13).  
+
+<|ref|>image<|/ref|><|det|>[[225, 390, 773, 747]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[147, 761, 851, 907]]<|/det|>
+<center>Fig. 6 | The correlation between electrochemical performance and \(\mathrm{Li}_2\mathrm{S}\) nucleation, sulfur crystallization. (a) Content of \(\mathrm{Li}_2\mathrm{S}\) nucleation (h1) and solid sulfur crystallization (h2). (b) Cycling performance of cathode composite at C/15 over 30 cycles. (c and d) The corresponding ratio of \(\mathrm{Li}_2\mathrm{S}\) nucleation (c) and solid sulfur crystallization (d). (e) Cycling rate performance of cathode composite. (f and g) The corresponding ratio of \(\mathrm{Li}_2\mathrm{S}\) nucleation (f) and solid sulfur crystallization (g). Note that all the cycling cell pursed in the presence of TFBG fiber and the \(\mathrm{Li}_2\mathrm{S}\) (h1) and solid sulfur (h2) is normalized by comparing the consumption sulfur in current cycle to that of sulfur fully dissolved in first cycle. </center>
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 85, 852, 435]]<|/det|>
+To evaluate the performance of cathode considering the three S cathode composites (with Super P, KB, MOF- 801(Zr)), we decided to compare their long cycling performance together with capabilities of \(\mathsf{Li}_2\mathsf{S}\) nucleation (h1) and sulfur crystallization (h2) (Fig. 6a). Depicted in Fig. 6b, the KB and MOF- 801(Zr) cell both experienced better capacity retention over 30 cycles at C/15 with a fade rate as \(0.99\%\) , \(0.88\%\) per cycles, respectively, while the super P cell is fading with \(4.7\%\) per cycles, which is consistent with the capability of crystallization of \(\mathsf{Li}_2\mathsf{S}\) and sulfur shown in Fig. 6c,d. Surprisingly, the KB and MOF- 801(Zr) cells have the similar efficiency of solid species crystallization (also supported by Fig. 6f) by taking the merit of high surface area, whereas the MOF- 801(Zr) exhibits the best capacity retention due to its strong chemical interaction for polysulfide which also well explains the capability to trap and recrystallize sulfur(Fig. 6g). Meanwhile, less sulfur utilization will be induced as well by intrinsic adsorption together with its low conductivity nature, leading to the lower capacity of MOF- 801(Zr) than that of KB cell over cycling. On the other hand, the Super P cell is fading fast because of high electrolyte volume with the E/S ratio over \(100\mu \mathrm{L} / \mathrm{mg}\) , presumably due to shuttle phenomenon enhancement. However, this problem does not affect the performance of KB and MOF- 801(Zr) cells, revealing that perhaps the most important parameter for cycling performance is related to the capability of crystallization of \(\mathsf{Li}_2\mathsf{S}\) and sulfur, which relies on a comprehensive balance with all involved factors such as high surface area, sulfur anion bonding and conductivity, in addition to cycling conditions.  
+
+<|ref|>sub_title<|/ref|><|det|>[[150, 447, 375, 462]]<|/det|>
+## Discussions and conclusions  
+
+<|ref|>text<|/ref|><|det|>[[148, 474, 852, 713]]<|/det|>
+Herein, TFBG sensors with low cost, easy integration into batteries, and long cycling capabilities during cell operation were demonstrated for operando testing of electrolyte sulfur concentration evolution of the LSB, which reveals the correlated relationship between the capacity fading and dynamic of dissolution/precipitation of polysulfides over cycling and at different cycling rates. Meanwhile, the chemical kinetics and thermodynamic response of soluble polysulfide in electrolyte were decoded with GITT experiments, with the disproportionation dynamic process linked to the transport flux of soluble species. Moreover, the cycling performance is well improved by designing the composite cathodes via porous carbon as nonpolar physical sulfur confinement and MOF- 801(Zr) as polar adsorption of polysulfide, through a host- guest chemical interaction. These substrates indicate that the nucleation kinetics and growth of \(\mathsf{Li}_2\mathsf{S}\) are changing from progressive to an instantaneous pathway due to enhanced soluble sulfur consumption rate, ultimately leading to the improvement of crystallization capability of \(\mathsf{Li}_2\mathsf{S}\) and sulfur.  
+
+<|ref|>text<|/ref|><|det|>[[148, 724, 852, 907]]<|/det|>
+Despite the encouraging insights supported by TFBG technology, a limitation of our operando testing stems from the refractive index link, which as mentioned, is an "average" effect of all species inside electrolyte. Thus, somewhat complicated data inference is required with critical external inputs, as comparing to direct evidence and measurement of species by infrared fiber operando measurement<sup>53</sup> and Raman spectroscopy based on hollow- core optical fiber<sup>54</sup>. This could might be solved in future by machine learning algorithms to simplify analysis and hence lead to more efficient 'combing' of data. Second, the optical interrogator used here is both expensive and bulky (volume), so future efforts may warrant an optimized integrator system capable of capturing a sensing signal by energy (amplitude) instead of wavelength<sup>55</sup>, but intensity- based measurements may present other technical challenges.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[148, 85, 851, 195]]<|/det|>
+Finally, the cycling based on Swagelok is achieved with high E/S ratio which enhances the shuttle effect of polysulfide, leading to acceleration of capacity fading. Therefore, long- term cycling configurations such as coin or 18650 cells integrated with fiber sensors might reveal other prominent performance- governing mechanisms. Nevertheless, TFBG sensors still provide fruitful details of the chemical dynamics of polysulfide as a diagnostic technique to monitor the state of health of cells in real- time.  
+
+<|ref|>text<|/ref|><|det|>[[148, 206, 851, 445]]<|/det|>
+Considering the specific properties of TFBG, including multi- resonance- peaks, high refractive index sensitivity, and ultrafast response, new opportunities of battery sensing can be envisioned, such that more than two gratings can be integrated inside the cell to map the sulfur gradient with more precision and accuracy, ultrathin solid electrolyte interphase (SEI) films could be characterized by sensitivity enhanced surface plasmon resonance based TFBG via surface and bulk refractive index discrimination<sup>56</sup>, the dynamic of electrons and phonon coupling inside cathode could be probed by ultrafast measurement through the pump- probe configuration of TFBG<sup>57</sup>. Overall, the non- disruptive diagnostic techniques based on the TFBG sensor allow us to monitor chemical- physical- thermal metrics in operando with notable time and spatial resolution. Therefore, with the use of these combs it becomes possible to detangle hidden high- value information such as states of charge, health estimations, and operational guidance along with non- electrochemical early- failure indicators, leading to increased straighter pathways to improving battery reliability, service life, and safety.  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 456, 222, 471]]<|/det|>
+## Methods  
+
+<|ref|>sub_title<|/ref|><|det|>[[150, 483, 438, 499]]<|/det|>
+## Materials and electrode preparation  
+
+<|ref|>sub_title<|/ref|><|det|>[[150, 507, 357, 523]]<|/det|>
+## Synthesis of MOF-801(Zr)  
+
+<|ref|>text<|/ref|><|det|>[[149, 534, 850, 606]]<|/det|>
+10.86 mmol of \(\mathrm{ZrOCl_2\cdot 8H_2O}\) , 7.624 mmol of fumaric acid, 9 mL of formic acid and 40 mL of deionized water were mixed in the reactor<sup>58</sup>, following 5 h stirring when the solution becomes cloudy. The ultimate product was collected by centrifugation, abundantly washed with water and ethanol, and dried under vacuum.  
+
+<|ref|>text<|/ref|><|det|>[[148, 617, 850, 708]]<|/det|>
+Preparation of polysulfides solutions: the \(100~\mathrm{mM}\) lithium polysulfides solution \(\mathrm{Li_2S_x}\) ( \(x = 2\) , 3, \(\dots\) , 8) were prepared by mixing lithium sulfide (99.9 % \(\mathrm{Li_2S}\) , Sigma Aldrich) and sulfur (S, Sigma Aldrich) in stoichiometric ratio to organic electrolyte (1 M LiTFSI, 0.5 M LiNO<sub>3</sub> in DOL/DME (1:1, v/v)), and the solution was continuously stirred with additional heating process at \(55~\mathrm{C}^\circ\) for 4 days in argon filled glovebox.  
+
+<|ref|>text<|/ref|><|det|>[[148, 720, 851, 903]]<|/det|>
+Preparation of sulfur composite electrodes: sulfur and Super P conductive carbon (Ketjenblack carbon) with a ratio 60/40 wt% were mixed by hand- grinding, followed by a heat- treatment (160 \(^\circ \mathrm{C}\) during 8 hours under air). The 5 wt Poly(tetrafluoroethylene) dispersion (PTFE, Alfa Aesar) is mixed with the cathode composite, rolled to a film and punched into disks with sulfur loading around \(5.3\mathrm{mg / cm}^2\) , and dried under vacuum at \(80^\circ \mathrm{C}\) overnight. To make the cathode composite with MOF- 801(Zr), the thoroughly adsorption of \(100\mathrm{mM}\mathrm{Li}_2\mathrm{S}_6\) in DOL/DME (1:1, v/v) was achieved by MOF- 801(Zr) with a stoichiometric ratio of \(1\mathrm{mL} / 40\) mg, followed by washing the powder twice by DOL and tried in vacuum overnight. The sulfur contained MOF- 801(Zr) (40% sulfur) was mixed with Super P conductive carbon with 50/50 wt% by hand- grinding without heat- treatment.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[150, 86, 446, 101]]<|/det|>
+## TFBG fabrication and sensing system.  
+
+<|ref|>text<|/ref|><|det|>[[148, 108, 851, 274]]<|/det|>
+Each \(10mm\) - long TFBG with \(7^{\circ}\) internal tilt angle was inscribed in hydrogen- loaded CORNING SMF- 28 fiber by laser irradiation based on phase- mask method<sup>22</sup>. Hydrogen loading of the fibers, enhancing their photosensitivity to ultraviolet light, was performed at room temperature and a pressure of 15.2 MPa for 14 days. The input light from KrF pulsed excimer laser (model PM- 848 from Light Machinery, Inc., emitting at 248 nm and 100 pulse/second) was cylindrically focused along the fiber axis with energy of \(\sim 40m\) over the grating region and also having passed through a 1078.4 nm period phase mask to produce a permanent periodic refractive index modulation in the core of the fiber. Rotating the fiber and phase mask, the tilt of grating fringes was obtained at an angle in the core as \(7^{\circ}\) .  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 282, 327, 298]]<|/det|>
+## Computational details  
+
+<|ref|>text<|/ref|><|det|>[[148, 306, 851, 489]]<|/det|>
+The transmission spectra simulations were carried out based on three- layer cylindrical waveguide using analytical method<sup>57</sup>. First, the simulated spectra is calibrated by the experimental spectra in air; Second, the refractive index of electrolyte were obtained by increasing the simulation surrounding RI (third layer) manually to match the experimental spectra, which is 1.3858 at 1559 nm wavelength considering the dispersion (supplementary Fig. S2d,e), Finally, mode intensity profiles were simulated by a complex finite- difference vectoral simulation tool (FIMMWAVE, by Photon Design), consisting of a three- layer waveguide: \(8.2\mu m\) core diameter with refractive index 1.449311, \(125\mu m\) cladding diameter with refractive index 1.444078, \(80\mu m\) diameter medium of electrolyte (refractive index \(= 1.3858\) ).  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 496, 568, 512]]<|/det|>
+## Integration of TFBG sensors into modified Swagelok  
+
+<|ref|>text<|/ref|><|det|>[[148, 520, 851, 629]]<|/det|>
+A ring made of PEEK (12.8 mm diameter, 2mm thick to fit 10 mm length fiber sensor) is fixed in the middle of 19 mm diameter Swagelok cell where fiber sensor can go through by drilling two holes. The Li metal foil (0.38 mm thickness, 14 mm diameter) is attached to one side of PEEK ring as anode, and on the other side of ring there is a steel grid to hold one Whatman separator beneath sulfur composite cathode. The cells were assembled in an argon- filled glovebox.  
+
+<|ref|>sub_title<|/ref|><|det|>[[150, 637, 398, 652]]<|/det|>
+## Electrochemical measurements  
+
+<|ref|>text<|/ref|><|det|>[[149, 660, 851, 732]]<|/det|>
+The electrochemical performances of Swagelok cell were evaluated by BCS- 810 or MPG2 potentiostat (Biologic, France) at \(25^{\circ}C\) degree inside temperature- controlled oven (Memmert, \(\pm 0.1^{\circ}C\) ). The galvanostatic discharge- charge cycling was carried out with the voltage range of \(1.7V - 2.8V\) .  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 740, 555, 756]]<|/det|>
+## Operando measurement by TFBG sensors and XRD  
+
+<|ref|>text<|/ref|><|det|>[[148, 764, 851, 891]]<|/det|>
+To achieve TFBG sensor operando measurement, the optical transmission spectra were recorded (1 min/spectra) by an optical integrator (CTP10, EXFO SOLUTIONS) with a resolution of \(1pm\) for wavelength ranging from 1500 nm to 1600 nm. Considering XRD operando measurement, it was performed on a D8 Advance diffractometer (Bruker) using a Cu Ka X- ray source \((\lambda_{\mathrm{cal}} = 1.54056\) A, \(\lambda_{\mathrm{cal}} = 1.54439\) A) and a LynxEye XE detector. The XRD pattern and electrochemical data are simultaneously recorded by a custom designed airtight cell with a beryllium window producing a full pattern every 20 min.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[149, 85, 682, 102]]<|/det|>
+## Preparation of cycled electrode samples for SEM and EDX imaging  
+
+<|ref|>text<|/ref|><|det|>[[148, 109, 851, 217]]<|/det|>
+The samples, at the end of charging, were prepared by washing the cathode powder twice by DOL to remove any dissolved lithium polysulfide species and lithium salt, and dried in vacuum chamber overnight. Then the powder was coated with gold (plasma sputtering coater (GSL- 1100X- SPC- 12, MTI)) for SEM (FEI Magellan) equipped with an energy- dispersive X- ray spectroscopy detector (Oxford Instruments) performed under an acceleration voltage of 20 kV.  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 230, 279, 246]]<|/det|>
+## Data availability  
+
+<|ref|>text<|/ref|><|det|>[[149, 257, 829, 293]]<|/det|>
+All relevant data are included in the paper and Supplementary Information. Extra data are available on reasonable request from the corresponding author.  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 304, 231, 320]]<|/det|>
+## Reference  
+
+<|ref|>text<|/ref|><|det|>[[145, 330, 852, 887]]<|/det|>
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+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[149, 86, 308, 101]]<|/det|>
+## Acknowledgements  
+
+<|ref|>text<|/ref|><|det|>[[148, 113, 852, 315]]<|/det|>
+AcknowledgementsJ.- M. Tarascon acknowledges the International Balzan Prize Foundation and the LABEX STOREXII for funding. F. Liu and J.- M. Tarascon acknowledge the European Project "Innovative physical/virtual sensor platform for battery cell" (INSTABAT) (European Union's Horizon 2020 research and innovation program under grant agreement No 955930). W. Lu acknowledges the support of the CSC scholarship (201906880002). R. Demir- Cakan is thankful to the French Embassy for the Visiting Researcher Fellowship (135694V). We thank Dr. J. Forero- Saboya for his assistance of scanning electron microscopy images. We thank Prof. J. Albert from Carleton University (Ottawa, Canada) for the fabrication of fiber sensors and assistance of simulation software. Finally, we gladly thank Dr. W. He, Dr. Y. Wang, Dr. X. Gao, Dr. B. Li, Dr. C. Gervillie- Mouravieff and Mr. C. Leau for extensive and valuable discussion and comments.  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 326, 321, 341]]<|/det|>
+## Author Contributions  
+
+<|ref|>text<|/ref|><|det|>[[148, 353, 851, 444]]<|/det|>
+Author ContributionsF. Liu, J. Huang, R. Demir- Cakan and J.- M. Tarascon conceived the idea and designed the experiments. F. Liu performed the experiments. F. Liu, J. Huang, R. Demir- Cakan and J.- M. Tarascon performed the data analysis. W. Lu and V. Pimenta provided the MOF- 801(Zr). Finally, F. Liu, S. Boles, R. Demir- Cakan and J.- M. Tarascon wrote the paper with contributions from all authors.  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 456, 307, 472]]<|/det|>
+## Conflicts of interest  
+
+<|ref|>text<|/ref|><|det|>[[149, 484, 548, 500]]<|/det|>
+The authors declare no competing financial interests  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 511, 334, 526]]<|/det|>
+## Additional information  
+
+<|ref|>text<|/ref|><|det|>[[148, 539, 850, 572]]<|/det|>
+Supplementary information: the online version contains supplementary material available at  
+
+<|ref|>text<|/ref|><|det|>[[148, 585, 848, 619]]<|/det|>
+Correspondence and requests for materials should be addressed to R. Demir- Cakan or J.- M. Tarascon.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[44, 43, 311, 70]]<|/det|>
+## Supplementary Files  
+
+<|ref|>text<|/ref|><|det|>[[44, 93, 765, 113]]<|/det|>
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+<|ref|>text<|/ref|><|det|>[[60, 131, 291, 177]]<|/det|>
+Supportinformation.pdf LiSbatterysensing.mp4
+
+<--- Page Split --->

preprint/preprint__018ea56f56282342b1a9e7b0cea3a8dc2890bbe74f3ff129238a91951bc7905d/images_list.json

@@ -0,0 +1,107 @@
+[
+  {
+    "type": "image",
+    "img_path": "images/Figure_1.jpg",
+    "caption": "Fig. 1 Multiple valence bands enable high \\(ZT\\) values in p-type PbSe. a Schmatic diagram of multi-bands (L, \\(\\Sigma\\) , \\(\\Lambda\\) ) involvement in transport. The Brillouin zone shows that the degeneracies at the L, \\(\\Sigma\\) , and \\(\\Lambda\\) points are 4, 12, and 8, respectively. b The activated third band \\(\\Lambda\\) enables higher \\(ZT\\) values compared with the single-band and two-band PbSe-based materials.",
+    "footnote": [],
+    "bbox": [
+      [
+        150,
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+        841,
+        333
+      ]
+    ],
+    "page_idx": 18
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_2.jpg",
+    "caption": "Fig. 2 Crystal structure and band gap. a Schematic crystal structure of Pb0.98Na0.02Se - \\(x\\%\\) AgInSe2 (LISS). b Powder XRD patterns of LISS. c Refined lattice constants of LISS. d Room-temperature infrared spectra for PbSe - \\(x\\%\\) AgInSe2 and Pb0.98Na0.02Se - \\(x\\%\\) AgInSe2.",
+    "footnote": [],
+    "bbox": [
+      [
+        155,
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+      ]
+    ],
+    "page_idx": 19
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_3.jpg",
+    "caption": "Fig. 3 Electrical properties as a function of temperature for \\(\\mathrm{Pb_{0.98}Na_{0.02}Se - x\\%}\\) AgInSe2 (LISS) compounds. a Electrical conductivity. b Seebeck coefficient. c Power factor. d Weighted mobility. The hollow circles in d represent the weighted mobility of single-band and two-band PbSe-based materials.",
+    "footnote": [],
+    "bbox": [
+      [
+        150,
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+        844,
+        500
+      ]
+    ],
+    "page_idx": 20
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_4.jpg",
+    "caption": "Fig. 4 Pisarenko plot and Hall carrier mobility. a Pisarenko plot of Seebeck coefficients as a function of Hall carrier concentration \\((n_{\\mathrm{H}})\\) for \\(\\mathrm{Pb_{0.98}Na_{0.02}Se - x\\%}\\) AgInSe2 (LISS). The solid black line is calculated assuming \\(m^{*} = 0.44m_{\\mathrm{e}}\\) and the purple line represents the result assuming \\(m^{*} = 0.81m_{\\mathrm{e}}\\) within the SPB model. The gray circles show the Pisarenko plot for Na-doped PbSe reported by Wang et al.38 b Hall carrier mobility \\((\\mu_{\\mathrm{H}})\\) versus Hall carrier concentration \\((n_{\\mathrm{H}})\\) at 303K.",
+    "footnote": [],
+    "bbox": [
+      [
+        150,
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+        300
+      ]
+    ],
+    "page_idx": 21
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_5.jpg",
+    "caption": "Fig. 5 Electronic band structure. a Electronic band structure of \\(\\mathrm{Pb_27Se_27}\\) (blue) and \\(\\mathrm{Pb_{25}AgInSe_{27}}\\) (red). b Electronic density of states (DOS) near the Fermi level for \\(\\mathrm{Pb_{27}Se_{27}}\\) (black), \\(\\mathrm{Pb_{26}AgSe_{27}}\\) (green), \\(\\mathrm{Pb_{26}InSe_{27}}\\) (blue) and \\(\\mathrm{Pb_{25}AgInSe_{27}}\\) (red), respectively. c Electronic band structure of \\(\\mathrm{Pb_{25}AgInSe_{27}}\\) at \\(300\\mathrm{K}\\) and \\(873\\mathrm{K}\\) , respectively. d Temperature-dependent infrared spectra of \\(\\mathrm{PbSe - 2\\%AgInSe_2}\\) . e The experimental (red) and theoretical (blue) bandgap \\((E_{\\mathrm{g}})\\) and the theoretical energy offset between VBM1 and VBM2 \\((\\Delta E_{1 - 2})\\) and between VBM1 and VBM3 \\((\\Delta E_{1 - 3})\\) as a function of temperature. f Temperature-dependent electronic DOS of \\(\\mathrm{Pb_{25}AgInSe_{27}}\\) near the VBM.",
+    "footnote": [],
+    "bbox": [
+      [
+        171,
+        88,
+        822,
+        670
+      ]
+    ],
+    "page_idx": 22
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_6.jpg",
+    "caption": "Fig. 6 Thermal transport properties and figure-of-merit \\(ZT\\) as a function of temperature for \\(\\mathrm{Pb_{0.98}Na_{0.02}Se - x\\%AgInSe_2}\\) (LISS) compounds. a Total thermal conductivity. b Lattice thermal conductivity. Inset shows the room-temperature lattice thermal conductivities departure from the theoretical line calculated by the Callaway model. c The average sound velocity \\(\\left(\\nu_{\\mathrm{avg}}\\right)\\) versus lattice thermal conductivity \\(\\left(\\kappa_{\\mathrm{L}}\\right)\\) for LISS compounds at room temperature. d Temperature-dependent \\(ZT\\) for LISS samples.",
+    "footnote": [],
+    "bbox": [
+      [
+        152,
+        88,
+        840,
+        500
+      ]
+    ],
+    "page_idx": 23
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_7.jpg",
+    "caption": "Fig. 7 Microstructures and local structure analysis for high-performance LISS sample. a Low magnification of bright-field TEM image for \\(\\mathrm{Pb_{0.98}Na_{0.02}Se}\\) - \\(2.05\\%\\) AgInSe2 sample. b The enlarged TEM pattern presents the nanoscale precipitates remarked by the arrows. c High resolution TEM (HRTEM) picture of a selected nanoprecipitate. d The corresponding selected area electron diffraction (SAED) pattern with cubic structure along [111]. e, f High angle annular dark field (HAADF) patterns for \\(\\mathrm{Pb_{0.98}Na_{0.02}Se}\\) - \\(2.05\\%\\) AgInSe2. g Experimental XANES spectra of In \\(K\\) -edge for \\(\\mathrm{Pb_{0.98}Na_{0.02}Se}\\) - \\(2\\%\\) AgInSe2 (red dots), and AgInSe2 (orange line), respectively. The blue line shows the theoretical XANES spectrum of In \\(K\\) -edge for In-doped PbSe assuming that In occupy the Pb site. The black line represents a linear combination fitting (LCF) result of In \\(K\\) -edge of \\(\\mathrm{Pb_{0.98}Na_{0.02}Se}\\) - \\(2\\%\\) AgInSe2 considering that the In \\(K\\) -edge of AgInSe2 and In-doped PbSe serves as standards. h Multiple scattering calculations of In \\(K\\) -edge XANES for In-doped PbSe with different atomic clusters. The inset shows the nearest-two shell of In atom when it occupies the Pb site in PbSe matrix. The \\(E_0\\) is the absorption edge energy of In \\(K\\) -edge of In foil.",
+    "footnote": [],
+    "bbox": [
+      [
+        149,
+        84,
+        848,
+        585
+      ]
+    ],
+    "page_idx": 24
+  }
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preprint/preprint__018ea56f56282342b1a9e7b0cea3a8dc2890bbe74f3ff129238a91951bc7905d/preprint__018ea56f56282342b1a9e7b0cea3a8dc2890bbe74f3ff129238a91951bc7905d.mmd

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+
+# Multiple valence bands convergence and strong phonon scattering lead to high thermoelectric performance in p-type PbSe  
+
+Yingcai Zhu  Beihang University  Dongyang Wang  Beihang University  Tao Hong  Beihang University  Lei Hu  Tokyo Institute of Technology  https://orcid.org/0000- 0002- 4647- 1604  Toshiaki Ina  Japan Synchrotron Radiation Research Institute  Shaoping Zhan  Beihang University  Bingchao Qin  Beihang University  Haonan Shi  Beihang University  Lizhong Su  Beihang University  Xiang Gao  Center for High Pressure Science and Technology Advanced Research  Li- Dong Zhao ( zhaolidong@buaa.edu.cn )  Beihang University  https://orcid.org/0000- 0003- 1247- 4345  
+
+## Article  
+
+# Keywords:  
+
+Posted Date: April 26th, 2022  
+
+DOI: https://doi.org/10.21203/rs.3.rs- 1575296/v1
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+License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+Version of Record: A version of this preprint was published at Nature Communications on July 19th, 2022. See the published version at https://doi.org/10.1038/s41467-022-31939-4.
+
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+# Multiple valence bands convergence and strong phonon scattering  
+
+# lead to high thermoelectric performance in p-type PbSe  
+
+Yingcai Zhu,1 Dongyang Wang,1 Tao Hong,1 Lei Hu,2 Toshiaki Ina,3 Shaoping, Zhan,1 Bingchao Qin,1 Haonan Shi,1 Lizhong Su,1 Xiang Gao,4 Li- Dong Zhao1\\*  
+
+1School of Materials Science and Engineering, Beihang University, Beijing 100191, China 2State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an 710049, China 3Research and Utilization Division, Japan Synchrotron Radiation Research Institute (JASRI/SPring- 8), Sayo, Hyogo, Japan 4Center for High Pressure Science and Technology Advanced Research (HPSTAR), Beijing, 100094, China  
+
+Thermoelectric generators enable the conversion of waste heat to electricity, which is an effective way to alleviate the global energy crisis. However, the inefficiency of thermoelectric materials is the main obstacle for realizing their widespread applications and thus developing materials with high thermoelectric performance is urgent. Here we show that multiple valence bands and strong phonon scattering can be realized simultaneously in p- type PbSe through the incorporation of AgInSe2. The multiple valleys enable large weighted mobility, indicating enhanced electrical properties. Local structure and microstructure analysis reveal that about 80 percent of Ag and In atoms form AgInSe2 as nano- scale precipitates, which result in strong phonon scattering and thus ultralow lattice thermal conductivity. Consequently, we achieve an exceptional \(ZT\) of \(\sim 2.1\) at 873 K in p- type PbSe. Our results demonstrate that a combination of band manipulation and microstructure engineering can be realized by tuning the composition. We expect our findings to be a general strategy for achieving high thermoelectric performance in bulk material, pushing the thermoelectric materials for realistic applications.
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+The depletion of fossil fuels and the deteriorating environment motivate the human beings to find sustainable and clean energy solutions. Thermoelectric devices can be used in energy harvesting from waste heat or be utilized in refrigeration, which is favorable for raising energy efficiency, attracting widespread attention from around the world. The efficiency of thermoelectric devices is largely determined by the figure of merit \(ZT\) of their constituent thermoelectric materials, \(ZT = \frac{S^2\sigma T}{\kappa_e + \kappa_L}\) , where \(S\) represents the Seebeck coefficient, \(\sigma\) is the electrical conductivity, \(\kappa_e\) is the electrical contribution to the thermal conductivity, \(\kappa_L\) is the lattice thermal conductivity, and \(T\) is the absolute temperature, respectively. However, decoupling the interdependence between electrical and thermal transport properties is a crucial but challenging issue for improving the thermoelectric performance of materials. To achieve good electrical properties, various strategies such as band convergence \(^{1 - 4}\) , band sharpening \(^{5}\) , band alignment \(^{6}\) , carrier mobility optimization \(^{7}\) and resonant states introduction \(^{8}\) were adopted. On the other hand, materials with disordered or complex crystal structure \(^{9,10}\) , giant anharmonicity \(^{11,12}\) , and lone pair electrons \(^{13}\) often exhibit intrinsic low lattice thermal conductivity, which are promising candidates for thermoelectric applications. Moreover, the lattice thermal conductivity can be largely suppressed by microstructural engineering, including nano-scale precipitates \(^{14,15}\) , dislocations \(^{16,17}\) , grain boundaries \(^{18}\) , and all- scale hierarchical architectures \(^{19 - 21}\) . Therefore, a synergistic combination of electronic band modulation and microstructural engineering is expected to achieve advanced thermoelectric materials.  
+
+PbTe has long been used for mid- temperature power generation, whereas the scarcity of element Te make it expensive for wide applications. PbSe is a perfect substitute for expensive PbTe due to the earth- abundant element Se. Hitherto, only limited studies show that the \(ZT\) of PbSe could reach \(1.7^{22 - 24}\) , motivating us to search strategies to improve the thermoelectric properties of PbSe. The weighted mobility \((\mu w = \mu (m^* /m_e)^{3 / 2})\) is a good descriptor for the inherent electrical performance of materials \(^{25}\) . Multiple degenerate electronic bands enable large density- of- states effective mass \(m^*\)
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+without obvious effect on the carrier mobility \((\mu)^{1}\) , facilitating the improvement of \(\mu \mathrm{w}\) . Indeed, the interplay of multiple bands enable large power factor or \(\mu \mathrm{w}\) and thus ultrahigh \(ZT^{26,27}\) . However, the two- band convergence is much difficult to realize due to the large energy offset between the valence band maximum (L) and the secondary valence band maximum \((\Sigma)\) in PbSe and to date only limited works can promote band convergence in it \(^{23,28,29}\) . It is more challenging to achieve multiple bands convergence in PbSe.  
+
+The lattice thermal conductivity is another important parameter for the thermoelectric performance indicated by the quality factor \(B\) ( \(B \propto \mu \mathrm{w} / \kappa \mathrm{L}\) ). The introduction of materials with low lattice thermal conductivity in MTe ( \(M = \mathrm{Pb}\) , Ge) matrixes was proved to be an effective method to manipulate their thermal transport properties \(^{30,31}\) . For example, the appearance of nanodots in AgPb \(_m\) SbTe \(_{2 + m}\) (LAST) system is considered as the origin of their low lattice thermal conductivity and thus the enhanced thermoelectric performance \(^{30}\) . Interestingly, the electrical properties of materials can also be optimized in similar way, such as in PbTe- AgInTe \(_2\) (LIST) \(^{32}\) and SnTe- AgInTe \(_2\) \(^{33}\) . These enhanced performances motivate us to search strategies for optimizing the \(\mu \mathrm{w}\) and \(\kappa \mathrm{L}\) simultaneously.  
+
+Beyond the two- band convergence between the L and \(\Sigma\) bands, we found that a third valence band \(\Lambda\) with a degeneracy \(N_{\mathrm{v}} = 8\) could be activated (Figure 1a) through the incorporation of AgInSe \(_2\) in the PbSe matrix doped with \(2\%\) Na (LISS). These three- band convergence tendency enables large weighted mobility. Additionally, local structure analysis by the x- ray absorption fine structure (XAFS) spectra indicates that more than \(80\%\) of Ag and In atoms form AgInSe \(_2\) in the system. Interestingly, AgInSe \(_2\) is also a good thermoelectric material with intrinsic ultralow lattice thermal conductivity \(^{34 - 36}\) . The tetragonal AgInSe \(_2\) is perfect inserted in the PbSe matrix as nano- scale precipitates revealed by the transmission electron microscopy (TEM), causing strong phonon scattering and hence resulting in ultralow lattice thermal conductivity. Therefore, a synergistic optimization of \(\mu \mathrm{w}\) and \(\kappa \mathrm{L}\) is realized. As a consequence, an
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+exceptional high \(ZT \sim 2.1\) is achieved at 873 K, which is much better than the single- band and two- band activated p- type PbSe- based materials<sup>28,29</sup> (Figure 1b).  
+
+## Results  
+
+Crystal structure. The LISS compounds crystallize in cubic structure (Space group, \(Fm - 3m\) ), which is reflected by the x- ray diffraction (XRD) measurements that the XRD patterns can be indexed on the basis of cubic PbSe and no secondary phase is observed within the instrumental detection limit (Figure 2a, 2b). The diffraction peaks tend to shift to higher angles with increment of AgInSe<sub>2</sub>. Therefore, the lattice parameter \((a)\) slightly decreases with increasing AgInSe<sub>2</sub> content (Figure 2c), which may be attributed to the smaller atomic radius of Ag, and In compared with that of Pb. This phenomenon also demonstrates that the AgInSe<sub>2</sub> is incorporated in the Pb<sub>0.98</sub>Na<sub>0.02</sub>Se matrix.  
+
+Electronic transport properties. The continuous decrease of the electrical conductivity with increasing temperature indicates a degenerate semiconducting property for LISS samples (Figure 3a). Additionally, the electrical conductivity is suppressed significantly after the introduction of AgInSe<sub>2</sub>. The electrical conductivity of Pb<sub>0.98</sub>Na<sub>0.02</sub>Se is as large as 3848 S/cm at room temperature, which decline to 774 S/cm for Pb<sub>0.98</sub>Na<sub>0.02</sub>Se- 2.15% AgInSe<sub>2</sub> sample. To uncover this behavior, room temperature Hall measurements were performed. Obviously, the carrier concentration is reduced largely with increasing AgInSe<sub>2</sub> (Figure S1), explaining the depressed electrical conductivity. The reduction of carrier concentration may be due to the formation of In<sub>Pb</sub> defects. These In<sub>Pb</sub> defects are shallow donors in PbSe<sup>37</sup>, which will counteract with holes.  
+
+The Seebeck coefficient increases with elevated temperature for all samples and no saturate peak appears (Figure 2b), demonstrating that no obvious bipolar effect occur at high temperatures. The Seebeck coefficient is largely enhanced over the whole temperature range with the increment of AgInSe<sub>2</sub>. Typically, the Seebeck coefficient of Pb<sub>0.98</sub>Na<sub>0.02</sub>Se is only 19.2 μV/K at room temperature, whereas a much larger Seebeck
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+coefficient value of \(116\mu \mathrm{V / K}\) is achieved for \(\mathrm{Pb_{0.98}Na_{0.02}Se - 2.15\% AgInSe_2}\) sample. This dramatically promoted Seebeck coefficients will facilitate the enhancement of power factor. Indeed, the \(PF\) have an apparent improvement especially at the 300 - 600 K temperature range for all doped samples (Figure 3c). The room temperature \(PF\) value of \(\mathrm{Pb_{0.98}Na_{0.02}Se}\) is only \(\sim 1.4\mu \mathrm{Wcm^{- 1}K^{- 2}}\) . In sharp contrast, the room temperature \(PF\) increases to \(\sim 11.1\mu \mathrm{Wcm^{- 1}K^{- 2}}\) when \(x = 2.1\) and this value is continuously improved to \(\sim 15.6\mu \mathrm{Wcm^{- 1}K^{- 2}}\) at 423 K (Figure 3c).  
+
+To understand the nature of the improvement of Seebeck coefficient, the relationship of Seebeck coefficient as a function of carrier concentration (Pisarenko curve) was compared at room temperature (Figure 4a). Generally, the Seebeck coefficient increases with decreasing carrier concentration. However, the Seebeck coefficient is largely departure from the theoretical values estimated by the single parabolic band (SPB) model, which indicate that a complex electronic band should be involved in the electrical transport properties. Accordingly, the effective mass \((m^*)\) of LISS is largely increased from \(0.44m_{\mathrm{e}}\) to \(0.81m_{\mathrm{e}}\) with the introduction of \(\mathrm{AgInSe_2}\) (Figure 4a). In contrast, the effective mass of Na- doped PbSe is only \(\sim 0.28m_{\mathrm{e}}\) (Figure 4a). The Hall carrier mobility increases with doping and a maximum value of \(\sim 125\mathrm{cm}^2\mathrm{V}^{- 1}\mathrm{s}^{- 1}\) is obtained when \(\mathrm{x} = 2\) (Figure 4b), which is largely due to the depressed carrier concentration. Consequently, the weighted mobility \((\mu \mathrm{w})\) of LISS compounds is largely enhanced especially at the 300- 600K temperature range, which is higher than that of single- band and two- band PbSe- based materials (Figure 3d).  
+
+DFT calculations were also conducted to understand the origin of the enhanced Seebeck coefficients. We observed significant change of the electronic band structure with the incorporation of \(\mathrm{AgInSe_2}\) in PbSe matrix (Figure 5a). The bandgap is enlarged upon doping, which will depress the bipolar effect and facilitate the enhancement of Seebeck coefficient. These calculations are well in accordance with our experimental results. The experimental bandgap is \(\sim 0.24\mathrm{eV}\) for the pristine PbSe, while the bandgap increases obviously with the incorporation of \(\mathrm{AgInSe_2}\) and a large bandgap \(\sim 0.33\mathrm{eV}\)
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+is achieved for the PbSe - \(2\%\) AgInSe \(_2\) sample (Figure 2d). Interestingly, the bandgap is further enlarged to \(\sim 0.38 \mathrm{eV}\) with Na doping (Figure 2d). In addition, the L band is flattened. The sharp peaks reflected in the density of states (DOS) for valence band also reveal the band flattening character (Figure 5b). Simultaneously, the \(\Sigma\) band is elevated and hence the energy offset \((\Delta E_{1 - 2})\) between L and \(\Sigma\) band is shortened. Surprisingly, a third valence band at the \(\Lambda\) point is activated and it remains at the same energy level compared with the \(\Sigma\) band (Figure 5a). These multiple valence bands enable large effective mass without significant affect the carrier mobility, which is the origin of enhanced Seebeck coefficient and the weighted mobility \((\mu \mathrm{w})\) . The electronic band structures of Ag and In doped PbSe were also calculated (Figure S2a, S2b). The Ag- doping and In- doping reflect p- type and n- type doping effect, respectively, which are consistent with previous experimental results \(^{38,39}\) . Additionally, In- doping has a more important effect on decreasing energy offset \((\Delta E_{1 - 2})\) compared with the Ag- doping (Figure S2c), while Ag- doping plays a major role in enlarging the bandgap (Figure S2d). Both Ag and In atoms play an important role in band manipulation.  
+
+Using the lattice parameters extracted from the temperature- dependent synchrotron radiation x- ray diffraction (SR- XRD) patterns (Figure S3), we calculated the band structures as a function of temperature (Figure 5c, Figure S4). Clearly, the bandgap increases with rising temperature, which is also verified experimentally (Figure 5d). However, the theoretical bandgap is smaller than the experimental result (Figure 5e), which may be attribute to the neglect of the effect of thermal disorder on the bandgap in our calculations. Moreover, the energy offset \((\Delta E_{1 - 2})\) between L and \(\Sigma\) bands decreases with increasing temperature (Figure 5e). Interestingly, the energy offset \((\Delta E_{1 - 3})\) between L and \(\Lambda\) also shows a decline tendency with rising temperature and its value is even smaller than \(\Delta E_{1 - 2}\) in the whole temperature range (Figure 5e). The convergence tendency and the involvement of the third valence band is also reflected in the DOS corresponding to the valence band increases with increasing temperature (Figure 5f). This convergence behavior is experimentally verified via Hall measurements, in which a maximum Hall coefficient \((R_{\mathrm{H}})\) is observed (Figure S5a) and it is a sign of band
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+convergence of the multi- valence bands \(^{20,23}\) . Consequently, the effective mass \((m^{*})\) of \(\mathrm{Pb_{0.98}Na_{0.02}Se - 2.05\%AgInSe_2}\) increases from \(0.73m_{\mathrm{e}}\) to \(2.16m_{\mathrm{e}}\) with rising temperature, which is much higher than the \(m^{*}\) of single Na- doped \(\mathrm{PbSe^{40}}\) (Figure S5b).  
+
+Thermal transport properties and the figure- of- merit \(ZT\) . Thermal conductivity is another important property for thermoelectric performance. The total thermal conductivity \((\kappa_{\mathrm{tot}})\) decreases significantly with increasing \(\mathrm{AgInSe_2}\) (Figure 6a). The \(\kappa_{\mathrm{tot}}\) is a composite of lattice thermal conductivity \((\kappa_{\mathrm{L}})\) and electronic contributions to the thermal conductivity \((\kappa_{\mathrm{e}})\) . The \(\kappa_{\mathrm{e}}\) was calculated by the Wiedemann- Franz relation, \(\kappa_{\mathrm{e}} = L\sigma T\) , where \(L\) is estimated by SPB model (Figure S6a). The \(\kappa_{\mathrm{e}}\) decreases remarkably with doping due to largely depressed electrical conductivity (Figure S6b). Furthermore, the \(\kappa_{\mathrm{L}}\) is obtained by subtracting the electronic contribution from the total thermal conductivity (Figure 6b). Similarly, the \(\kappa_{\mathrm{L}}\) is largely suppressed with doping and the room- temperature \(\kappa_{\mathrm{L}}\) values are much lower than the theoretical estimation by the Callaway model (Figure 6b, inset). In addition, the \(\kappa_{\mathrm{L}}\) decreases with rising temperature and a clear departure from \(T^{- 1}\) relation is observed, demonstrating that the strong phonon scattering occurs. To further uncover the mechanism of the reduction of \(\kappa_{\mathrm{L}}\) , sound velocities were measured for all the samples at room temperature (Table S1). Interestingly, the average sound velocity \((v_{\mathrm{avg}})\) slightly increases after doping (Figure 6c). The deduced Grüneisen parameters of LISS are almost unchanged. The lattice thermal conductivity can be expressed as \(\kappa_{\mathrm{L}} = \frac{1}{3} Cv_{\mathrm{avg}}^{2}\tau\) based on the simple kinetic theory \(^{41}\) , where \(C\) is the specific heat, \(\tau\) is the phonon lifetime. Here, the \(v_{\mathrm{avg}}\) increases upon doping and thus the reduction of lattice thermal conductivity should be derived from the decrease of phonon lifetime. In another word, enhanced phonon scattering is the main origin of the largely suppressed lattice thermal conductivity.  
+
+Thanks to the complex band structure behavior and strong phonon scattering with the introduction of \(\mathrm{AgInSe_2}\) , the figure- of- merit \(ZT\) is largely enhanced in the whole temperature range and a maximum \(ZT\) value of \(\sim 2.1\) is achieved at 873K for
+
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+\(\mathrm{Pb_{0.98}Na_{0.02}Se - 2.05\% AgInSe_2}\) sample (Figure 6d). Considering the correction of heat capacity, we can also obtain a large \(ZT\) of \(\sim 1.9\) at 873K (Figure 6d, Figure S7). The high thermoelectric properties of LISS samples are reproducible (Figure S8).  
+
+Microstructure and local structure analysis. The TEM images of LISS sample display that abundant nanosized precipitates are embedded in PbSe matrix (Figure 7a, 7b). In addition, strip- like dislocations are also observed (circled regions in Figure 7a). Both nanoscale precipitates and multi- scale dislocations are effective phonon scattering centers. The annular dark- field (ADF) STEM image and the EDS elemental mappings exhibit obvious Ag- rich and In- rich patterns for the precipitates (Figure S9). Accordingly, the elemental distributions of Se and Na are relatively homogeneous in the entire area, whereas Pb- poor regions are observed within the precipitates (Figure S9). A more clearer microstructural features of the precipitate is revealed by performing HRTEM (Figure 7c). The corresponding SAED pattern indicate a main cubic structure along [111], whereas the precipitates shows a different crystal structure from the PbSe matrix as another series of diffraction spots are exhibited which can be indexed to AgInSe₂ (Figure 7d). The HAADF patterns show that the tetragonal AgInSe₂ is perfectly inserted in the cubic PbSe matrix as a nine- atom grid (Figure 7e). We can also observe lattice dislocation in the HAADF (Figure 7f). The lattice mismatch induced by precipitates and dislocations will introduce large strain fluctuations and thus enhance the phonon scattering \(^{42}\) . Therefore, the lattice thermal conductivity of LISS was largely reduced to its amorphous limit of 0.31 Wm⁻¹K⁻¹ at 873 K arising from the strong phonon scattering.  
+
+Understanding the atomic occupation of doping elements in the lattice allow us elucidate their role on manipulating the thermal or electrical transport properties. The x- ray absorption fine structure (XAFS) spectroscopy is a powerful tool to investigate the local structure in materials \(^{43 - 48}\) . Here, we performed XAFS measurements for PbSe, AgInSe₂ and LISS, respectively. The x- ray absorption near- edge structure (XANES) of Se K- edge and Pb L₃- edge did not show any change after the introduction of AgInSe₂
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+in PbSe (Figure S10), demonstrating a steady PbSe cubic matrix, which is well consistent with the XRD patterns. There are four main features in the XANES of In \(K\) - edge of LISS (Figure 7g), in which 1, 3, 4 features can well reflected in the In \(K\) - edge of AgInSe \(_2\) , indicating that most of In atoms may form AgInSe \(_2\) in the system. Furthermore, we calculated the XANES spectra of In \(K\) - edge for the In- doped PbSe assuming that In occupy the Pb site, in which the peak B is well corresponding with the feature 2 of In \(K\) - edge of LISS. Moreover, after adding the second shell (twelve Pb atoms) as shown by the 19- atoms cluster calculation (Figure 7h), the feature B is well reflected and the calculation is almost convergent. Therefore, the origin of feature 2 is mainly arising from the multiple scattering of the photoelectrons by the second shell of Pb atoms in the PbSe matrix. The linear combination fitting (LCF) was used to evaluate the atomic occupied ratio of In atoms in each standards (Figure 7g). Similar analysis was performed for the XANES of Ag \(K\) - edge (Figure S11). The LCF fitting results indicate that more than \(80\%\) of Ag and In atoms form AgInSe \(_2\) in the system (Table S2), causing strong phonon scattering.  
+
+## Discussion  
+
+In summary, a combined effect of three activated valence bands and strong phonon scattering is realized via introducing AgInSe \(_2\) in Pb \(_{0.98}\) Na \(_{0.02}\) Se matrix. These multiple valence bands convergence enable the enhancement of thermoelectric power factor at low temperature region and maintain at a high level at elevated temperature. Interestingly, local structure studies by XANES reveal that most of Ag or In atoms form AgInSe \(_2\) secondary phase. The numerous nanoscale AgInSe \(_2\) precipitates and multiscale dislocations observed in the TEM will cause strong phonon scattering. Therefore, the lattice thermal conductivity is largely depressed. As a consequence, a distinguished figure- of- merit \(ZT\) of \(\sim 2.1\) is achieved at 873K, which is among the best bulk thermoelectric materials. This work proves that multiple valence bands could be activated in p- type PbSe and highlights the strong phonon scattering effect through the introduction of secondary phase with ultralow thermal conductivity, which guide a new route to achieve excellent thermoelectric performance in bulk materials. The
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+quantitative atomic occupation of doping elements provides a new perspective to understand their role on manipulating transport properties. We expect more advanced thermoelectric materials can be achieved by employing this strategy.  
+
+## Methods  
+
+Synthesis. High- purity starting materials, Pb (99.999%), Se (99.999%), Na (99.9%), Ag (99.99%), In (99.99 %) were weighted in stoichiometric ratio (Pb0.98Na0.02Se - x% AgInSe2) and loaded in carbon coating silica tubes under an N2- filled glove box. The silica tubes were sealed under vacuum and then slowly heated to 1423 K in 24 h, soaked at this temperature for 10 h and followed by furnace cooling down to room temperature. The obtained ingots were grounded into powders and then densified at 873 K for 6 minutes with a pressure of 50 MPa using spark plasma sintering (SPS- 211Lx). Finally, highly dense bulk samples ( \(>97\%\) of theoretical density) were obtained.  
+
+Thermoelectric property measurements. The bulk samples were cut into rectangular solids ( \(3 \times 3 \times 10 \mathrm{mm}^3\) ) and square pieces ( \(10 \times 10 \times 1 \mathrm{mm}^3\) ) for electrical and thermal transport properties measurements, respectively. The Seebeck coefficients and electrical conductivities were measured using the Ulvac Riko ZEM- 3 instrument. We calculated the thermal conductivity using the equation of \(\kappa_{tot} = D \cdot C_p \cdot \rho\) , where the thermal diffusivity \((D)\) was determined using a laser flash method by the Netzsch LFA- 457 facility, the heat capacity \((C_p)\) was estimated using both the Dulong- Petit law and an empirical equation \((C_p / k_B \mathrm{atom}^{- 1} = 3.07 + 4.7 \times 10^{- 4} (T / \mathrm{K} - 300))^{29}\) , and the density is calculated by the dimensions and mass of the samples. The combined uncertainty of all measurements for determining the \(ZT\) is less than \(20\%\) .  
+
+Characterizations. Room- temperature powder x- ray diffraction measurements were conducted using a D/MAX 2500 PC system with Cu \(\mathrm{K}_{\alpha}\) radiation. High- temperature synchrotron radiation x- ray diffraction (SR- XRD) patterns were performed for \(\mathrm{Pb}_{0.98}\mathrm{Na}_{0.02}\mathrm{Se} - 2\% \mathrm{AgInSe}_2\) at the BL14B1 beamline of Shanghai synchrotron radiation facility (SSRF). The wavelength of the x- ray is \(0.6887 \mathrm{\AA}\) . The sample was heated from \(300 \mathrm{K}\) to \(875 \mathrm{K}\) at a rate of \(1.5 \mathrm{K min}^{- 1}\) . The bandgap was measured using the Shimadezu
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+Model UV- 3600 Plus instrument and was estimated by the Kubelka- Munk equation. The Hall coefficient \((R_{\mathrm{H}})\) was conducted by the Van der Pauw method using the Lake Shore 8400 Series. Scanning transmission electron microscopy (STEM) and transmission electron microscopy (TEM) were performed using a JEOL ARM200F equipped with cold field emission gun and ASCOR probe corrector. More details can be found in the Supporting Information.  
+
+First- principles calculations. Density functional theory (DFT) calculations were performed using the projector- augmented wave (PAW) method<sup>49</sup>, as implemented in the Vienna Ab initio Simulation Package (VASP)<sup>50,51</sup>. We utilized the revised Perdew- Burke- Ernzerhof (PBE)<sup>52</sup> generalized gradient approximation (GGA) to estimate the exchange- correlation interactions. A cutoff energy was set to 450 eV for the plane- wave expansion of the electron density and the Monkhorst- Pack \(k\) - point sampling 0.1 Å<sup>- 1</sup> was used within all the calculations. The atomic positions were fully relaxed when the maximum residual ionic force and total energy difference are converged within 0.01 eV Å<sup>- 1</sup> and 10<sup>- 7</sup> eV, respectively. Several 3×3×3 supercells were constructed (Pb<sub>27</sub>Se<sub>27</sub>, Pb<sub>26</sub>AgSe<sub>27</sub>, Pb<sub>26</sub>InSe<sub>27</sub>, Pb<sub>25</sub>AgInSe<sub>27</sub>), avoiding the defect- defect interaction. The occupations of Ag or/and In atoms in the supercells were relaxed in our calculations. The temperature- dependent electronic band structures were performed using the experimental lattice parameters at elevated temperatures deriving from the SR- XRD data.  
+
+X- ray absorption fine structure (XAFS) spectroscopy measurements. The XAFS experiments were performed at BL01B1 beamline of Spring- 8 in Japan. The electron energy of the storage ring is 8.0 GeV with a top- up filling of 99.5 mA accumulated current during the experiment. The Si (311) double- crystal monochromator was used for tuning the monochromatic beam. We measured the XAFS of Ag \(K\) - edge and In \(K\) - edge for AgInSe<sub>2</sub> in transmission mode. The XAFS measurements of Se \(K\) - edge, and Pb \(L_{3}\) - edge for PbSe and Pb<sub>0.98</sub>Na<sub>0.02</sub>Se - 2% AgInSe<sub>2</sub> were conducted in transmission mode, while the measurements of Ag \(K\) - edge and In \(K\) - edge for Pb<sub>0.98</sub>Na<sub>0.02</sub>Se - 2% AgInSe<sub>2</sub> were performed in fluorescence mode using 19- element Ge solid- state detector (SSD). All experimental XAFS spectra were preprocessed using the IFFEFIT package<sup>53</sup>.
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+XAFS calculation and analysis. The x- ray absorption near edge structure (XANES) calculations of Ag \(K\) - edge for Ag- doped PbSe and In \(K\) - edge for In- doped PbSe were performed based on the full multiple scattering (FMS) theory using FEFF9 program \(^{54,55}\) . We use self- consistent field (SCF) method to estimate the atomic scattering potential. To investigate the doping site of Ag or In in PbSe, we simply replace the central Pb absorber with Ag or In atom while maintaining the coordinates. To achieve good convergence, the cluster radius for SCF and FMS was fixed as 8 and 10 Angstrom, respectively. Linear combination fittings (LCF) of In \(K\) - edge for \(\mathrm{Pb_{0.98}Na_{0.02}Se}\) - \(2\%\) AgInSe \(_2\) was performed using the Athena software assuming that the XANES of In \(K\) - edge of AgInSe \(_2\) and In- doped PbSe as the standards. A similar LCF analysis was applied for the XANES of Ag \(K\) - edge of \(\mathrm{Pb_{0.98}Na_{0.02}Se}\) - \(2\%\) AgInSe \(_2\) . Since we cannot ensure that the Ag or In atoms totally occupy the Pb site in PbSe matrix without formation of impurity phases, we thus used the calculated XANES as one of standards in the LCF analysis.  
+
+## References  
+
+1 Pei, Y. et al. Convergence of electronic bands for high performance bulk thermoelectrics. Nature 473, 66- 69 (2011).  2 Tang, Y. et al. Convergence of multi- valley bands as the electronic origin of high thermoelectric performance in CoSb \(_3\) skutterudites. Nat. Mater. 14, 1223- 1228 (2015).  3 Hu, L. et al. High thermoelectric performance enabled by convergence of nested conduction bands in \(\mathrm{Pb_7Bi_4Se_{13}}\) with low thermal conductivity. Nat. Commun. 12, 4793 (2021).  4 Chang, C. et al. 3D charge and 2D phonon transports leading to high out- of- plane zT in n- type SnSe crystals. Science 360, 778- 783 (2018).  5 Xiao, Y. et al. Band Sharpening and Band Alignment Enable High Quality Factor to Enhance Thermoelectric Performance in n- Type PbS. J. Am. Chem. Soc. 142, 4051- 4060 (2020).  6 Zhao, L.- D., Dravid, V. P. & Kanatzidis, M. G. The panoscopic approach to high performance thermoelectrics. Energy Environ. Sci. 7, 251- 268 (2014).  7 Qin, B. C. & Zhao, L. D. Carriers: the less, the faster. Mat. Lab 1, 220004 (2022).  8 Zhang, Q. et al. Enhancement of Thermoelectric Figure- of- Merit by Resonant States of Aluminium Doping in Lead Selenide. Energy Environ. Sci. 5(1), 5246- 5251 (2012).  9 Snyder, G. J. & Toberer, E. S. Complex thermoelectric materials. Nat. Mater. 7,
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+29 Wang, H., Gibbs, Z. M., Takagiwa, Y. & Snyder, G. J. Tuning bands of PbSe for better thermoelectric efficiency. Energy Environ. Sci. 7, 804- 811 (2014). 30 Hsu, K. F., Loo, S., Guo, F., Chen, W., Dyck, J. S., Uher, C., Hogan, T., Polychroniadis, E. K., Kanatzidis, M. G. Cubic AgPbmSbTe2+m Bulk Thermoelectric Materials with High Figure of Merit. Science, 818- 821 (2004). 31 Plachkova, S. K. Thermoelectric Figure of Merit of the System (GeTe)1-x- (AgSbTe2)x. Phys. Stat. Sol. (a) 83, 349 (1984). 32 Xiao, Y. et al. Amphoteric Indium Enables Carrier Engineering to Enhance the Power Factor and Thermoelectric Performance in n- Type AgnPb100 InnTe 100+2n (LIST). Adv. Energy Mater. 9, 1900414 (2019). 33 Banik, A., Shenoy, U. S., Saha, S., Waghmare, U. V. & Biswas, K. High Power Factor and Enhanced Thermoelectric Performance of SnTe- AgInTe2: Synergistic Effect of Resonance Level and Valence Band Convergence. J. Am. Chem. Soc. 138, 13068- 13075 (2016). 34 Zhu, Y. et al. Synergistically optimizing carrier concentration and decreasing sound velocity in n- type AgInSe2 Thermoelectrics. Chem. Mater. 31, 8182- 8190 (2019). 35 Zhu, Y. et al. Physical insights on the low lattice thermal conductivity of AgInSe2. Mater. Today Phys. 19, 100428 (2021). 36 Qiu, P. et al. Intrinsically high thermoelectric performance in AgInSe2 n- type diamond- like compounds Adv. Sci. 5, 1700727 (2018). 37 Wrasse, E. O., Baierle, R. J., Fazzio, A. & Schmidt, T. M. First- principles study of group III impurity doped PbSe: Bulk and nanowire. Phys. Rev. B 87 (2013). 38 Wang, S. et al. Exploring the doping effects of Ag in p- type PbSe compounds with enhanced thermoelectric performance. J. Phys. D: Appl. Phys. 44, 475304 (2011). 39 Androulakis, J., Lee, Y., Todorov, I., Chung, D.- Y. & Kanatzidis, M. High- temperature thermoelectric properties of n- type PbSe doped with Ga, In, and Pb. Phys. Rev. B 83 (2011). 40 Wang, H., Pei, Y., LaLonde, A. D. & Snyder, G. J. Heavily doped p- type PbSe with high thermoelectric performance: an alternative for PbTe. Adv. Mater. 23, 1366- 1370 (2011). 41 Christensen, M. et al. Avoided crossing of rattler modes in thermoelectric materials. Nat. Mater. 7, 811- 815 (2008). 42 Wu, H. J. et al. Broad temperature plateau for thermoelectric figure of merit ZT>2 in phase- separated PbTe0.7S0.3. Nat. Commun. 5, 4515 (2014). 43 Xu, W., Liu, Y., Marcelli, A., Shang, P. P. & Liu, W. S. The complexity of thermoelectric materials: why we need powerful and brilliant synchrotron radiation sources? Mater. Today Phys. 6, 68- 82 (2018). 44 Liu, Y. et al. Synergistically Optimizing Electrical and Thermal Transport Properties of BiCuSeO via a Dual- Doping Approach. Adv. Energy Mater. 6, 1502423 (2016). 45 Hu, L. et al. Localized Symmetry Breaking for Tuning Thermal Expansion in
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+<--- Page Split --->
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+ScF3 Nanoscale Frameworks. J. Am. Chem. Soc. 140, 4477- 4480 (2018). 46 Keiber, T., Bridges, F. & Sales, B. C. Lead is not off center in PbTe: the importance of r- space phase information in extended X- ray absorption fine structure spectroscopy. Phys. Rev. Lett. 111 (2013). 47 Zhu, Y. C. et al. Lattice dynamics and thermal conductivity in \(\mathrm{Cu_2Zn_{1 - x}Co_xSnSe_4}\) . Inorg. Chem. 57, 6051- 6056 (2018). 48 Xu, W. et al. Nanoscale heterogeneity in thermoelectrics: the occurrence of phase separation in Fe- doped \(\mathrm{Ca_3Co_4O_9}\) . Phys. Chem. Chem. Phys. 18, 14580- 14587 (2016). 49 Blöchl, P. E. Projector augmented- wave method. Phys. Rev. B 50, 17953- 17979 (1994). 50 Kresse, G. & Furthmuller, J. Efficient iterative schemes for ab initio total- energy calculations using a plane- wave basis set. Phys. Rev. B 54, 11169 (1996). 51 Kresse, G. & Furthmuller, J. Efficiency of ab- initio total energy calculations for metals and semiconductors using a plane- wave basis set. Comput. Mat. Sci. 6, 15- 50 (1996). 52 Perdew, J. P. et al. Restoring the density- gradient expansion for exchange in solids and surfaces. Phys. Rev. Lett. 100, 136406 (2008). 53 Ravel, B., Newville, M. ATHENA, ARTEMIS, HEPHAESTUS: data analysis for X- ray absorption spectroscopy using IFEFFIT. J. Synchrotron Rad. 12, 537- 541 (2005). 54 J.J. Rehr, R. C. A. Modern Theory of XAFS. Rev. Mod. Phys. 72, 621 (2000). 55 Rehr, J. J., Kas, J. J., Vila, F. D., Prange, M. P. & Jorissen, K. Parameter- free calculations of X- ray spectra with FEFF9. Phys. Chem. Chem. Phys. 12, 5503- 5513 (2010).
+
+<--- Page Split --->
+
+## Acknowledgements  
+
+AcknowledgementsThe authors thank BL14B1 at Shanghai synchrotron radiation facility (SSRF) for the SR- XRD measurements. We thank BL01B1 at Spring- 8 for the XAFS experiments (Proposal Number: 2021B1109). This work was supported by National Natural Science Foundation of China (51772012, 52002042 and 52002011), National Key Research and Development Program of China (2018YFA0702100 and 2018YFB0703600), National Postdoctoral Program for Innovative Talents (BX20200028), the Beijing Natural Science Foundation (JQ18004), and 111 Project (B17002). L.- D.Z. appreciates the support of the high performance computing (HPC) resources at Beihang University, the National Science Fund for Distinguished Young Scholars (51925101), and center for High Pressure Science and Technology Advanced Research (HPSTAR) for SEM and TEM measurements.  
+
+## Authors contributions  
+
+Y.Z. and L.- D.Z. prepared the samples, carried out the experiments, analyzed the results and wrote the paper. T.H. and X.G. performed the microscopy experiments. D.Y.W. carried out the DFT calculations. Y.Z., L.H. and T.I. conducted the XAFS measurements and analyzed the data. S.Z., B.Q., H.S. and L.S. performed the SR- XRD experiments. All authors coedited the manuscript.  
+
+## Competing interests  
+
+The authors declare no competing interests.  
+
+## Additional information  
+
+Correspondence and requests for materials should be addressed to L.- D. Zhao.
+
+<--- Page Split --->
+![](images/Figure_1.jpg)
+
+<center>Fig. 1 Multiple valence bands enable high \(ZT\) values in p-type PbSe. a Schmatic diagram of multi-bands (L, \(\Sigma\) , \(\Lambda\) ) involvement in transport. The Brillouin zone shows that the degeneracies at the L, \(\Sigma\) , and \(\Lambda\) points are 4, 12, and 8, respectively. b The activated third band \(\Lambda\) enables higher \(ZT\) values compared with the single-band and two-band PbSe-based materials. </center>
+
+<--- Page Split --->
+![](images/Figure_2.jpg)
+
+<center>Fig. 2 Crystal structure and band gap. a Schematic crystal structure of Pb0.98Na0.02Se - \(x\%\) AgInSe2 (LISS). b Powder XRD patterns of LISS. c Refined lattice constants of LISS. d Room-temperature infrared spectra for PbSe - \(x\%\) AgInSe2 and Pb0.98Na0.02Se - \(x\%\) AgInSe2. </center>
+
+<--- Page Split --->
+![](images/Figure_3.jpg)
+
+<center>Fig. 3 Electrical properties as a function of temperature for \(\mathrm{Pb_{0.98}Na_{0.02}Se - x\%}\) AgInSe2 (LISS) compounds. a Electrical conductivity. b Seebeck coefficient. c Power factor. d Weighted mobility. The hollow circles in d represent the weighted mobility of single-band and two-band PbSe-based materials. </center>
+
+<--- Page Split --->
+![](images/Figure_4.jpg)
+
+<center>Fig. 4 Pisarenko plot and Hall carrier mobility. a Pisarenko plot of Seebeck coefficients as a function of Hall carrier concentration \((n_{\mathrm{H}})\) for \(\mathrm{Pb_{0.98}Na_{0.02}Se - x\%}\) AgInSe2 (LISS). The solid black line is calculated assuming \(m^{*} = 0.44m_{\mathrm{e}}\) and the purple line represents the result assuming \(m^{*} = 0.81m_{\mathrm{e}}\) within the SPB model. The gray circles show the Pisarenko plot for Na-doped PbSe reported by Wang et al.38 b Hall carrier mobility \((\mu_{\mathrm{H}})\) versus Hall carrier concentration \((n_{\mathrm{H}})\) at 303K. </center>
+
+<--- Page Split --->
+![](images/Figure_5.jpg)
+
+<center>Fig. 5 Electronic band structure. a Electronic band structure of \(\mathrm{Pb_27Se_27}\) (blue) and \(\mathrm{Pb_{25}AgInSe_{27}}\) (red). b Electronic density of states (DOS) near the Fermi level for \(\mathrm{Pb_{27}Se_{27}}\) (black), \(\mathrm{Pb_{26}AgSe_{27}}\) (green), \(\mathrm{Pb_{26}InSe_{27}}\) (blue) and \(\mathrm{Pb_{25}AgInSe_{27}}\) (red), respectively. c Electronic band structure of \(\mathrm{Pb_{25}AgInSe_{27}}\) at \(300\mathrm{K}\) and \(873\mathrm{K}\) , respectively. d Temperature-dependent infrared spectra of \(\mathrm{PbSe - 2\%AgInSe_2}\) . e The experimental (red) and theoretical (blue) bandgap \((E_{\mathrm{g}})\) and the theoretical energy offset between VBM1 and VBM2 \((\Delta E_{1 - 2})\) and between VBM1 and VBM3 \((\Delta E_{1 - 3})\) as a function of temperature. f Temperature-dependent electronic DOS of \(\mathrm{Pb_{25}AgInSe_{27}}\) near the VBM. </center>
+
+<--- Page Split --->
+![](images/Figure_6.jpg)
+
+<center>Fig. 6 Thermal transport properties and figure-of-merit \(ZT\) as a function of temperature for \(\mathrm{Pb_{0.98}Na_{0.02}Se - x\%AgInSe_2}\) (LISS) compounds. a Total thermal conductivity. b Lattice thermal conductivity. Inset shows the room-temperature lattice thermal conductivities departure from the theoretical line calculated by the Callaway model. c The average sound velocity \(\left(\nu_{\mathrm{avg}}\right)\) versus lattice thermal conductivity \(\left(\kappa_{\mathrm{L}}\right)\) for LISS compounds at room temperature. d Temperature-dependent \(ZT\) for LISS samples. </center>
+
+<--- Page Split --->
+![](images/Figure_7.jpg)
+
+<center>Fig. 7 Microstructures and local structure analysis for high-performance LISS sample. a Low magnification of bright-field TEM image for \(\mathrm{Pb_{0.98}Na_{0.02}Se}\) - \(2.05\%\) AgInSe2 sample. b The enlarged TEM pattern presents the nanoscale precipitates remarked by the arrows. c High resolution TEM (HRTEM) picture of a selected nanoprecipitate. d The corresponding selected area electron diffraction (SAED) pattern with cubic structure along [111]. e, f High angle annular dark field (HAADF) patterns for \(\mathrm{Pb_{0.98}Na_{0.02}Se}\) - \(2.05\%\) AgInSe2. g Experimental XANES spectra of In \(K\) -edge for \(\mathrm{Pb_{0.98}Na_{0.02}Se}\) - \(2\%\) AgInSe2 (red dots), and AgInSe2 (orange line), respectively. The blue line shows the theoretical XANES spectrum of In \(K\) -edge for In-doped PbSe assuming that In occupy the Pb site. The black line represents a linear combination fitting (LCF) result of In \(K\) -edge of \(\mathrm{Pb_{0.98}Na_{0.02}Se}\) - \(2\%\) AgInSe2 considering that the In \(K\) -edge of AgInSe2 and In-doped PbSe serves as standards. h Multiple scattering calculations of In \(K\) -edge XANES for In-doped PbSe with different atomic clusters. The inset shows the nearest-two shell of In atom when it occupies the Pb site in PbSe matrix. The \(E_0\) is the absorption edge energy of In \(K\) -edge of In foil. </center>
+
+<--- Page Split --->
+
+## Supplementary Files  
+
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+SupplementaryInformation.docx
+
+<--- Page Split --->

preprint/preprint__018ea56f56282342b1a9e7b0cea3a8dc2890bbe74f3ff129238a91951bc7905d/preprint__018ea56f56282342b1a9e7b0cea3a8dc2890bbe74f3ff129238a91951bc7905d_det.mmd

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+<|ref|>title<|/ref|><|det|>[[44, 108, 883, 209]]<|/det|>
+# Multiple valence bands convergence and strong phonon scattering lead to high thermoelectric performance in p-type PbSe  
+
+<|ref|>text<|/ref|><|det|>[[42, 230, 675, 740]]<|/det|>
+Yingcai Zhu  Beihang University  Dongyang Wang  Beihang University  Tao Hong  Beihang University  Lei Hu  Tokyo Institute of Technology  https://orcid.org/0000- 0002- 4647- 1604  Toshiaki Ina  Japan Synchrotron Radiation Research Institute  Shaoping Zhan  Beihang University  Bingchao Qin  Beihang University  Haonan Shi  Beihang University  Lizhong Su  Beihang University  Xiang Gao  Center for High Pressure Science and Technology Advanced Research  Li- Dong Zhao ( zhaolidong@buaa.edu.cn )  Beihang University  https://orcid.org/0000- 0003- 1247- 4345  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 777, 101, 794]]<|/det|>
+## Article  
+
+<|ref|>title<|/ref|><|det|>[[44, 814, 135, 832]]<|/det|>
+# Keywords:  
+
+<|ref|>text<|/ref|><|det|>[[44, 852, 300, 870]]<|/det|>
+Posted Date: April 26th, 2022  
+
+<|ref|>text<|/ref|><|det|>[[44, 890, 473, 908]]<|/det|>
+DOI: https://doi.org/10.21203/rs.3.rs- 1575296/v1
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[42, 44, 907, 87]]<|/det|>
+License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+<|ref|>text<|/ref|><|det|>[[42, 123, 905, 167]]<|/det|>
+Version of Record: A version of this preprint was published at Nature Communications on July 19th, 2022. See the published version at https://doi.org/10.1038/s41467-022-31939-4.
+
+<--- Page Split --->
+<|ref|>title<|/ref|><|det|>[[163, 93, 833, 114]]<|/det|>
+# Multiple valence bands convergence and strong phonon scattering  
+
+<|ref|>title<|/ref|><|det|>[[216, 130, 779, 151]]<|/det|>
+# lead to high thermoelectric performance in p-type PbSe  
+
+<|ref|>text<|/ref|><|det|>[[148, 157, 850, 196]]<|/det|>
+Yingcai Zhu,1 Dongyang Wang,1 Tao Hong,1 Lei Hu,2 Toshiaki Ina,3 Shaoping, Zhan,1 Bingchao Qin,1 Haonan Shi,1 Lizhong Su,1 Xiang Gao,4 Li- Dong Zhao1\\*  
+
+<|ref|>text<|/ref|><|det|>[[147, 215, 853, 362]]<|/det|>
+1School of Materials Science and Engineering, Beihang University, Beijing 100191, China 2State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an 710049, China 3Research and Utilization Division, Japan Synchrotron Radiation Research Institute (JASRI/SPring- 8), Sayo, Hyogo, Japan 4Center for High Pressure Science and Technology Advanced Research (HPSTAR), Beijing, 100094, China  
+
+<|ref|>text<|/ref|><|det|>[[147, 393, 853, 803]]<|/det|>
+Thermoelectric generators enable the conversion of waste heat to electricity, which is an effective way to alleviate the global energy crisis. However, the inefficiency of thermoelectric materials is the main obstacle for realizing their widespread applications and thus developing materials with high thermoelectric performance is urgent. Here we show that multiple valence bands and strong phonon scattering can be realized simultaneously in p- type PbSe through the incorporation of AgInSe2. The multiple valleys enable large weighted mobility, indicating enhanced electrical properties. Local structure and microstructure analysis reveal that about 80 percent of Ag and In atoms form AgInSe2 as nano- scale precipitates, which result in strong phonon scattering and thus ultralow lattice thermal conductivity. Consequently, we achieve an exceptional \(ZT\) of \(\sim 2.1\) at 873 K in p- type PbSe. Our results demonstrate that a combination of band manipulation and microstructure engineering can be realized by tuning the composition. We expect our findings to be a general strategy for achieving high thermoelectric performance in bulk material, pushing the thermoelectric materials for realistic applications.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[146, 85, 855, 677]]<|/det|>
+The depletion of fossil fuels and the deteriorating environment motivate the human beings to find sustainable and clean energy solutions. Thermoelectric devices can be used in energy harvesting from waste heat or be utilized in refrigeration, which is favorable for raising energy efficiency, attracting widespread attention from around the world. The efficiency of thermoelectric devices is largely determined by the figure of merit \(ZT\) of their constituent thermoelectric materials, \(ZT = \frac{S^2\sigma T}{\kappa_e + \kappa_L}\) , where \(S\) represents the Seebeck coefficient, \(\sigma\) is the electrical conductivity, \(\kappa_e\) is the electrical contribution to the thermal conductivity, \(\kappa_L\) is the lattice thermal conductivity, and \(T\) is the absolute temperature, respectively. However, decoupling the interdependence between electrical and thermal transport properties is a crucial but challenging issue for improving the thermoelectric performance of materials. To achieve good electrical properties, various strategies such as band convergence \(^{1 - 4}\) , band sharpening \(^{5}\) , band alignment \(^{6}\) , carrier mobility optimization \(^{7}\) and resonant states introduction \(^{8}\) were adopted. On the other hand, materials with disordered or complex crystal structure \(^{9,10}\) , giant anharmonicity \(^{11,12}\) , and lone pair electrons \(^{13}\) often exhibit intrinsic low lattice thermal conductivity, which are promising candidates for thermoelectric applications. Moreover, the lattice thermal conductivity can be largely suppressed by microstructural engineering, including nano-scale precipitates \(^{14,15}\) , dislocations \(^{16,17}\) , grain boundaries \(^{18}\) , and all- scale hierarchical architectures \(^{19 - 21}\) . Therefore, a synergistic combination of electronic band modulation and microstructural engineering is expected to achieve advanced thermoelectric materials.  
+
+<|ref|>text<|/ref|><|det|>[[147, 709, 852, 896]]<|/det|>
+PbTe has long been used for mid- temperature power generation, whereas the scarcity of element Te make it expensive for wide applications. PbSe is a perfect substitute for expensive PbTe due to the earth- abundant element Se. Hitherto, only limited studies show that the \(ZT\) of PbSe could reach \(1.7^{22 - 24}\) , motivating us to search strategies to improve the thermoelectric properties of PbSe. The weighted mobility \((\mu w = \mu (m^* /m_e)^{3 / 2})\) is a good descriptor for the inherent electrical performance of materials \(^{25}\) . Multiple degenerate electronic bands enable large density- of- states effective mass \(m^*\)
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 88, 852, 275]]<|/det|>
+without obvious effect on the carrier mobility \((\mu)^{1}\) , facilitating the improvement of \(\mu \mathrm{w}\) . Indeed, the interplay of multiple bands enable large power factor or \(\mu \mathrm{w}\) and thus ultrahigh \(ZT^{26,27}\) . However, the two- band convergence is much difficult to realize due to the large energy offset between the valence band maximum (L) and the secondary valence band maximum \((\Sigma)\) in PbSe and to date only limited works can promote band convergence in it \(^{23,28,29}\) . It is more challenging to achieve multiple bands convergence in PbSe.  
+
+<|ref|>text<|/ref|><|det|>[[147, 311, 853, 581]]<|/det|>
+The lattice thermal conductivity is another important parameter for the thermoelectric performance indicated by the quality factor \(B\) ( \(B \propto \mu \mathrm{w} / \kappa \mathrm{L}\) ). The introduction of materials with low lattice thermal conductivity in MTe ( \(M = \mathrm{Pb}\) , Ge) matrixes was proved to be an effective method to manipulate their thermal transport properties \(^{30,31}\) . For example, the appearance of nanodots in AgPb \(_m\) SbTe \(_{2 + m}\) (LAST) system is considered as the origin of their low lattice thermal conductivity and thus the enhanced thermoelectric performance \(^{30}\) . Interestingly, the electrical properties of materials can also be optimized in similar way, such as in PbTe- AgInTe \(_2\) (LIST) \(^{32}\) and SnTe- AgInTe \(_2\) \(^{33}\) . These enhanced performances motivate us to search strategies for optimizing the \(\mu \mathrm{w}\) and \(\kappa \mathrm{L}\) simultaneously.  
+
+<|ref|>text<|/ref|><|det|>[[147, 616, 852, 914]]<|/det|>
+Beyond the two- band convergence between the L and \(\Sigma\) bands, we found that a third valence band \(\Lambda\) with a degeneracy \(N_{\mathrm{v}} = 8\) could be activated (Figure 1a) through the incorporation of AgInSe \(_2\) in the PbSe matrix doped with \(2\%\) Na (LISS). These three- band convergence tendency enables large weighted mobility. Additionally, local structure analysis by the x- ray absorption fine structure (XAFS) spectra indicates that more than \(80\%\) of Ag and In atoms form AgInSe \(_2\) in the system. Interestingly, AgInSe \(_2\) is also a good thermoelectric material with intrinsic ultralow lattice thermal conductivity \(^{34 - 36}\) . The tetragonal AgInSe \(_2\) is perfect inserted in the PbSe matrix as nano- scale precipitates revealed by the transmission electron microscopy (TEM), causing strong phonon scattering and hence resulting in ultralow lattice thermal conductivity. Therefore, a synergistic optimization of \(\mu \mathrm{w}\) and \(\kappa \mathrm{L}\) is realized. As a consequence, an
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[148, 88, 850, 136]]<|/det|>
+exceptional high \(ZT \sim 2.1\) is achieved at 873 K, which is much better than the single- band and two- band activated p- type PbSe- based materials<sup>28,29</sup> (Figure 1b).  
+
+<|ref|>sub_title<|/ref|><|det|>[[148, 174, 215, 190]]<|/det|>
+## Results  
+
+<|ref|>text<|/ref|><|det|>[[147, 200, 853, 415]]<|/det|>
+Crystal structure. The LISS compounds crystallize in cubic structure (Space group, \(Fm - 3m\) ), which is reflected by the x- ray diffraction (XRD) measurements that the XRD patterns can be indexed on the basis of cubic PbSe and no secondary phase is observed within the instrumental detection limit (Figure 2a, 2b). The diffraction peaks tend to shift to higher angles with increment of AgInSe<sub>2</sub>. Therefore, the lattice parameter \((a)\) slightly decreases with increasing AgInSe<sub>2</sub> content (Figure 2c), which may be attributed to the smaller atomic radius of Ag, and In compared with that of Pb. This phenomenon also demonstrates that the AgInSe<sub>2</sub> is incorporated in the Pb<sub>0.98</sub>Na<sub>0.02</sub>Se matrix.  
+
+<|ref|>text<|/ref|><|det|>[[147, 449, 853, 746]]<|/det|>
+Electronic transport properties. The continuous decrease of the electrical conductivity with increasing temperature indicates a degenerate semiconducting property for LISS samples (Figure 3a). Additionally, the electrical conductivity is suppressed significantly after the introduction of AgInSe<sub>2</sub>. The electrical conductivity of Pb<sub>0.98</sub>Na<sub>0.02</sub>Se is as large as 3848 S/cm at room temperature, which decline to 774 S/cm for Pb<sub>0.98</sub>Na<sub>0.02</sub>Se- 2.15% AgInSe<sub>2</sub> sample. To uncover this behavior, room temperature Hall measurements were performed. Obviously, the carrier concentration is reduced largely with increasing AgInSe<sub>2</sub> (Figure S1), explaining the depressed electrical conductivity. The reduction of carrier concentration may be due to the formation of In<sub>Pb</sub> defects. These In<sub>Pb</sub> defects are shallow donors in PbSe<sup>37</sup>, which will counteract with holes.  
+
+<|ref|>text<|/ref|><|det|>[[147, 784, 853, 914]]<|/det|>
+The Seebeck coefficient increases with elevated temperature for all samples and no saturate peak appears (Figure 2b), demonstrating that no obvious bipolar effect occur at high temperatures. The Seebeck coefficient is largely enhanced over the whole temperature range with the increment of AgInSe<sub>2</sub>. Typically, the Seebeck coefficient of Pb<sub>0.98</sub>Na<sub>0.02</sub>Se is only 19.2 μV/K at room temperature, whereas a much larger Seebeck
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 88, 853, 275]]<|/det|>
+coefficient value of \(116\mu \mathrm{V / K}\) is achieved for \(\mathrm{Pb_{0.98}Na_{0.02}Se - 2.15\% AgInSe_2}\) sample. This dramatically promoted Seebeck coefficients will facilitate the enhancement of power factor. Indeed, the \(PF\) have an apparent improvement especially at the 300 - 600 K temperature range for all doped samples (Figure 3c). The room temperature \(PF\) value of \(\mathrm{Pb_{0.98}Na_{0.02}Se}\) is only \(\sim 1.4\mu \mathrm{Wcm^{- 1}K^{- 2}}\) . In sharp contrast, the room temperature \(PF\) increases to \(\sim 11.1\mu \mathrm{Wcm^{- 1}K^{- 2}}\) when \(x = 2.1\) and this value is continuously improved to \(\sim 15.6\mu \mathrm{Wcm^{- 1}K^{- 2}}\) at 423 K (Figure 3c).  
+
+<|ref|>text<|/ref|><|det|>[[147, 310, 853, 692]]<|/det|>
+To understand the nature of the improvement of Seebeck coefficient, the relationship of Seebeck coefficient as a function of carrier concentration (Pisarenko curve) was compared at room temperature (Figure 4a). Generally, the Seebeck coefficient increases with decreasing carrier concentration. However, the Seebeck coefficient is largely departure from the theoretical values estimated by the single parabolic band (SPB) model, which indicate that a complex electronic band should be involved in the electrical transport properties. Accordingly, the effective mass \((m^*)\) of LISS is largely increased from \(0.44m_{\mathrm{e}}\) to \(0.81m_{\mathrm{e}}\) with the introduction of \(\mathrm{AgInSe_2}\) (Figure 4a). In contrast, the effective mass of Na- doped PbSe is only \(\sim 0.28m_{\mathrm{e}}\) (Figure 4a). The Hall carrier mobility increases with doping and a maximum value of \(\sim 125\mathrm{cm}^2\mathrm{V}^{- 1}\mathrm{s}^{- 1}\) is obtained when \(\mathrm{x} = 2\) (Figure 4b), which is largely due to the depressed carrier concentration. Consequently, the weighted mobility \((\mu \mathrm{w})\) of LISS compounds is largely enhanced especially at the 300- 600K temperature range, which is higher than that of single- band and two- band PbSe- based materials (Figure 3d).  
+
+<|ref|>text<|/ref|><|det|>[[147, 728, 853, 914]]<|/det|>
+DFT calculations were also conducted to understand the origin of the enhanced Seebeck coefficients. We observed significant change of the electronic band structure with the incorporation of \(\mathrm{AgInSe_2}\) in PbSe matrix (Figure 5a). The bandgap is enlarged upon doping, which will depress the bipolar effect and facilitate the enhancement of Seebeck coefficient. These calculations are well in accordance with our experimental results. The experimental bandgap is \(\sim 0.24\mathrm{eV}\) for the pristine PbSe, while the bandgap increases obviously with the incorporation of \(\mathrm{AgInSe_2}\) and a large bandgap \(\sim 0.33\mathrm{eV}\)
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 88, 854, 500]]<|/det|>
+is achieved for the PbSe - \(2\%\) AgInSe \(_2\) sample (Figure 2d). Interestingly, the bandgap is further enlarged to \(\sim 0.38 \mathrm{eV}\) with Na doping (Figure 2d). In addition, the L band is flattened. The sharp peaks reflected in the density of states (DOS) for valence band also reveal the band flattening character (Figure 5b). Simultaneously, the \(\Sigma\) band is elevated and hence the energy offset \((\Delta E_{1 - 2})\) between L and \(\Sigma\) band is shortened. Surprisingly, a third valence band at the \(\Lambda\) point is activated and it remains at the same energy level compared with the \(\Sigma\) band (Figure 5a). These multiple valence bands enable large effective mass without significant affect the carrier mobility, which is the origin of enhanced Seebeck coefficient and the weighted mobility \((\mu \mathrm{w})\) . The electronic band structures of Ag and In doped PbSe were also calculated (Figure S2a, S2b). The Ag- doping and In- doping reflect p- type and n- type doping effect, respectively, which are consistent with previous experimental results \(^{38,39}\) . Additionally, In- doping has a more important effect on decreasing energy offset \((\Delta E_{1 - 2})\) compared with the Ag- doping (Figure S2c), while Ag- doping plays a major role in enlarging the bandgap (Figure S2d). Both Ag and In atoms play an important role in band manipulation.  
+
+<|ref|>text<|/ref|><|det|>[[147, 534, 853, 914]]<|/det|>
+Using the lattice parameters extracted from the temperature- dependent synchrotron radiation x- ray diffraction (SR- XRD) patterns (Figure S3), we calculated the band structures as a function of temperature (Figure 5c, Figure S4). Clearly, the bandgap increases with rising temperature, which is also verified experimentally (Figure 5d). However, the theoretical bandgap is smaller than the experimental result (Figure 5e), which may be attribute to the neglect of the effect of thermal disorder on the bandgap in our calculations. Moreover, the energy offset \((\Delta E_{1 - 2})\) between L and \(\Sigma\) bands decreases with increasing temperature (Figure 5e). Interestingly, the energy offset \((\Delta E_{1 - 3})\) between L and \(\Lambda\) also shows a decline tendency with rising temperature and its value is even smaller than \(\Delta E_{1 - 2}\) in the whole temperature range (Figure 5e). The convergence tendency and the involvement of the third valence band is also reflected in the DOS corresponding to the valence band increases with increasing temperature (Figure 5f). This convergence behavior is experimentally verified via Hall measurements, in which a maximum Hall coefficient \((R_{\mathrm{H}})\) is observed (Figure S5a) and it is a sign of band
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 87, 852, 164]]<|/det|>
+convergence of the multi- valence bands \(^{20,23}\) . Consequently, the effective mass \((m^{*})\) of \(\mathrm{Pb_{0.98}Na_{0.02}Se - 2.05\%AgInSe_2}\) increases from \(0.73m_{\mathrm{e}}\) to \(2.16m_{\mathrm{e}}\) with rising temperature, which is much higher than the \(m^{*}\) of single Na- doped \(\mathrm{PbSe^{40}}\) (Figure S5b).  
+
+<|ref|>text<|/ref|><|det|>[[146, 199, 852, 789]]<|/det|>
+Thermal transport properties and the figure- of- merit \(ZT\) . Thermal conductivity is another important property for thermoelectric performance. The total thermal conductivity \((\kappa_{\mathrm{tot}})\) decreases significantly with increasing \(\mathrm{AgInSe_2}\) (Figure 6a). The \(\kappa_{\mathrm{tot}}\) is a composite of lattice thermal conductivity \((\kappa_{\mathrm{L}})\) and electronic contributions to the thermal conductivity \((\kappa_{\mathrm{e}})\) . The \(\kappa_{\mathrm{e}}\) was calculated by the Wiedemann- Franz relation, \(\kappa_{\mathrm{e}} = L\sigma T\) , where \(L\) is estimated by SPB model (Figure S6a). The \(\kappa_{\mathrm{e}}\) decreases remarkably with doping due to largely depressed electrical conductivity (Figure S6b). Furthermore, the \(\kappa_{\mathrm{L}}\) is obtained by subtracting the electronic contribution from the total thermal conductivity (Figure 6b). Similarly, the \(\kappa_{\mathrm{L}}\) is largely suppressed with doping and the room- temperature \(\kappa_{\mathrm{L}}\) values are much lower than the theoretical estimation by the Callaway model (Figure 6b, inset). In addition, the \(\kappa_{\mathrm{L}}\) decreases with rising temperature and a clear departure from \(T^{- 1}\) relation is observed, demonstrating that the strong phonon scattering occurs. To further uncover the mechanism of the reduction of \(\kappa_{\mathrm{L}}\) , sound velocities were measured for all the samples at room temperature (Table S1). Interestingly, the average sound velocity \((v_{\mathrm{avg}})\) slightly increases after doping (Figure 6c). The deduced Grüneisen parameters of LISS are almost unchanged. The lattice thermal conductivity can be expressed as \(\kappa_{\mathrm{L}} = \frac{1}{3} Cv_{\mathrm{avg}}^{2}\tau\) based on the simple kinetic theory \(^{41}\) , where \(C\) is the specific heat, \(\tau\) is the phonon lifetime. Here, the \(v_{\mathrm{avg}}\) increases upon doping and thus the reduction of lattice thermal conductivity should be derived from the decrease of phonon lifetime. In another word, enhanced phonon scattering is the main origin of the largely suppressed lattice thermal conductivity.  
+
+<|ref|>text<|/ref|><|det|>[[147, 821, 851, 895]]<|/det|>
+Thanks to the complex band structure behavior and strong phonon scattering with the introduction of \(\mathrm{AgInSe_2}\) , the figure- of- merit \(ZT\) is largely enhanced in the whole temperature range and a maximum \(ZT\) value of \(\sim 2.1\) is achieved at 873K for
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 88, 852, 165]]<|/det|>
+\(\mathrm{Pb_{0.98}Na_{0.02}Se - 2.05\% AgInSe_2}\) sample (Figure 6d). Considering the correction of heat capacity, we can also obtain a large \(ZT\) of \(\sim 1.9\) at 873K (Figure 6d, Figure S7). The high thermoelectric properties of LISS samples are reproducible (Figure S8).  
+
+<|ref|>text<|/ref|><|det|>[[147, 199, 853, 721]]<|/det|>
+Microstructure and local structure analysis. The TEM images of LISS sample display that abundant nanosized precipitates are embedded in PbSe matrix (Figure 7a, 7b). In addition, strip- like dislocations are also observed (circled regions in Figure 7a). Both nanoscale precipitates and multi- scale dislocations are effective phonon scattering centers. The annular dark- field (ADF) STEM image and the EDS elemental mappings exhibit obvious Ag- rich and In- rich patterns for the precipitates (Figure S9). Accordingly, the elemental distributions of Se and Na are relatively homogeneous in the entire area, whereas Pb- poor regions are observed within the precipitates (Figure S9). A more clearer microstructural features of the precipitate is revealed by performing HRTEM (Figure 7c). The corresponding SAED pattern indicate a main cubic structure along [111], whereas the precipitates shows a different crystal structure from the PbSe matrix as another series of diffraction spots are exhibited which can be indexed to AgInSe₂ (Figure 7d). The HAADF patterns show that the tetragonal AgInSe₂ is perfectly inserted in the cubic PbSe matrix as a nine- atom grid (Figure 7e). We can also observe lattice dislocation in the HAADF (Figure 7f). The lattice mismatch induced by precipitates and dislocations will introduce large strain fluctuations and thus enhance the phonon scattering \(^{42}\) . Therefore, the lattice thermal conductivity of LISS was largely reduced to its amorphous limit of 0.31 Wm⁻¹K⁻¹ at 873 K arising from the strong phonon scattering.  
+
+<|ref|>text<|/ref|><|det|>[[147, 755, 853, 914]]<|/det|>
+Understanding the atomic occupation of doping elements in the lattice allow us elucidate their role on manipulating the thermal or electrical transport properties. The x- ray absorption fine structure (XAFS) spectroscopy is a powerful tool to investigate the local structure in materials \(^{43 - 48}\) . Here, we performed XAFS measurements for PbSe, AgInSe₂ and LISS, respectively. The x- ray absorption near- edge structure (XANES) of Se K- edge and Pb L₃- edge did not show any change after the introduction of AgInSe₂
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 88, 853, 499]]<|/det|>
+in PbSe (Figure S10), demonstrating a steady PbSe cubic matrix, which is well consistent with the XRD patterns. There are four main features in the XANES of In \(K\) - edge of LISS (Figure 7g), in which 1, 3, 4 features can well reflected in the In \(K\) - edge of AgInSe \(_2\) , indicating that most of In atoms may form AgInSe \(_2\) in the system. Furthermore, we calculated the XANES spectra of In \(K\) - edge for the In- doped PbSe assuming that In occupy the Pb site, in which the peak B is well corresponding with the feature 2 of In \(K\) - edge of LISS. Moreover, after adding the second shell (twelve Pb atoms) as shown by the 19- atoms cluster calculation (Figure 7h), the feature B is well reflected and the calculation is almost convergent. Therefore, the origin of feature 2 is mainly arising from the multiple scattering of the photoelectrons by the second shell of Pb atoms in the PbSe matrix. The linear combination fitting (LCF) was used to evaluate the atomic occupied ratio of In atoms in each standards (Figure 7g). Similar analysis was performed for the XANES of Ag \(K\) - edge (Figure S11). The LCF fitting results indicate that more than \(80\%\) of Ag and In atoms form AgInSe \(_2\) in the system (Table S2), causing strong phonon scattering.  
+
+<|ref|>sub_title<|/ref|><|det|>[[148, 535, 242, 551]]<|/det|>
+## Discussion  
+
+<|ref|>text<|/ref|><|det|>[[147, 560, 853, 914]]<|/det|>
+In summary, a combined effect of three activated valence bands and strong phonon scattering is realized via introducing AgInSe \(_2\) in Pb \(_{0.98}\) Na \(_{0.02}\) Se matrix. These multiple valence bands convergence enable the enhancement of thermoelectric power factor at low temperature region and maintain at a high level at elevated temperature. Interestingly, local structure studies by XANES reveal that most of Ag or In atoms form AgInSe \(_2\) secondary phase. The numerous nanoscale AgInSe \(_2\) precipitates and multiscale dislocations observed in the TEM will cause strong phonon scattering. Therefore, the lattice thermal conductivity is largely depressed. As a consequence, a distinguished figure- of- merit \(ZT\) of \(\sim 2.1\) is achieved at 873K, which is among the best bulk thermoelectric materials. This work proves that multiple valence bands could be activated in p- type PbSe and highlights the strong phonon scattering effect through the introduction of secondary phase with ultralow thermal conductivity, which guide a new route to achieve excellent thermoelectric performance in bulk materials. The
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[148, 89, 851, 164]]<|/det|>
+quantitative atomic occupation of doping elements provides a new perspective to understand their role on manipulating transport properties. We expect more advanced thermoelectric materials can be achieved by employing this strategy.  
+
+<|ref|>sub_title<|/ref|><|det|>[[148, 201, 226, 217]]<|/det|>
+## Methods  
+
+<|ref|>text<|/ref|><|det|>[[147, 228, 852, 442]]<|/det|>
+Synthesis. High- purity starting materials, Pb (99.999%), Se (99.999%), Na (99.9%), Ag (99.99%), In (99.99 %) were weighted in stoichiometric ratio (Pb0.98Na0.02Se - x% AgInSe2) and loaded in carbon coating silica tubes under an N2- filled glove box. The silica tubes were sealed under vacuum and then slowly heated to 1423 K in 24 h, soaked at this temperature for 10 h and followed by furnace cooling down to room temperature. The obtained ingots were grounded into powders and then densified at 873 K for 6 minutes with a pressure of 50 MPa using spark plasma sintering (SPS- 211Lx). Finally, highly dense bulk samples ( \(>97\%\) of theoretical density) were obtained.  
+
+<|ref|>text<|/ref|><|det|>[[147, 450, 852, 737]]<|/det|>
+Thermoelectric property measurements. The bulk samples were cut into rectangular solids ( \(3 \times 3 \times 10 \mathrm{mm}^3\) ) and square pieces ( \(10 \times 10 \times 1 \mathrm{mm}^3\) ) for electrical and thermal transport properties measurements, respectively. The Seebeck coefficients and electrical conductivities were measured using the Ulvac Riko ZEM- 3 instrument. We calculated the thermal conductivity using the equation of \(\kappa_{tot} = D \cdot C_p \cdot \rho\) , where the thermal diffusivity \((D)\) was determined using a laser flash method by the Netzsch LFA- 457 facility, the heat capacity \((C_p)\) was estimated using both the Dulong- Petit law and an empirical equation \((C_p / k_B \mathrm{atom}^{- 1} = 3.07 + 4.7 \times 10^{- 4} (T / \mathrm{K} - 300))^{29}\) , and the density is calculated by the dimensions and mass of the samples. The combined uncertainty of all measurements for determining the \(ZT\) is less than \(20\%\) .  
+
+<|ref|>text<|/ref|><|det|>[[147, 747, 852, 905]]<|/det|>
+Characterizations. Room- temperature powder x- ray diffraction measurements were conducted using a D/MAX 2500 PC system with Cu \(\mathrm{K}_{\alpha}\) radiation. High- temperature synchrotron radiation x- ray diffraction (SR- XRD) patterns were performed for \(\mathrm{Pb}_{0.98}\mathrm{Na}_{0.02}\mathrm{Se} - 2\% \mathrm{AgInSe}_2\) at the BL14B1 beamline of Shanghai synchrotron radiation facility (SSRF). The wavelength of the x- ray is \(0.6887 \mathrm{\AA}\) . The sample was heated from \(300 \mathrm{K}\) to \(875 \mathrm{K}\) at a rate of \(1.5 \mathrm{K min}^{- 1}\) . The bandgap was measured using the Shimadezu
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 88, 852, 247]]<|/det|>
+Model UV- 3600 Plus instrument and was estimated by the Kubelka- Munk equation. The Hall coefficient \((R_{\mathrm{H}})\) was conducted by the Van der Pauw method using the Lake Shore 8400 Series. Scanning transmission electron microscopy (STEM) and transmission electron microscopy (TEM) were performed using a JEOL ARM200F equipped with cold field emission gun and ASCOR probe corrector. More details can be found in the Supporting Information.  
+
+<|ref|>text<|/ref|><|det|>[[147, 255, 853, 636]]<|/det|>
+First- principles calculations. Density functional theory (DFT) calculations were performed using the projector- augmented wave (PAW) method<sup>49</sup>, as implemented in the Vienna Ab initio Simulation Package (VASP)<sup>50,51</sup>. We utilized the revised Perdew- Burke- Ernzerhof (PBE)<sup>52</sup> generalized gradient approximation (GGA) to estimate the exchange- correlation interactions. A cutoff energy was set to 450 eV for the plane- wave expansion of the electron density and the Monkhorst- Pack \(k\) - point sampling 0.1 Å<sup>- 1</sup> was used within all the calculations. The atomic positions were fully relaxed when the maximum residual ionic force and total energy difference are converged within 0.01 eV Å<sup>- 1</sup> and 10<sup>- 7</sup> eV, respectively. Several 3×3×3 supercells were constructed (Pb<sub>27</sub>Se<sub>27</sub>, Pb<sub>26</sub>AgSe<sub>27</sub>, Pb<sub>26</sub>InSe<sub>27</sub>, Pb<sub>25</sub>AgInSe<sub>27</sub>), avoiding the defect- defect interaction. The occupations of Ag or/and In atoms in the supercells were relaxed in our calculations. The temperature- dependent electronic band structures were performed using the experimental lattice parameters at elevated temperatures deriving from the SR- XRD data.  
+
+<|ref|>text<|/ref|><|det|>[[147, 644, 853, 916]]<|/det|>
+X- ray absorption fine structure (XAFS) spectroscopy measurements. The XAFS experiments were performed at BL01B1 beamline of Spring- 8 in Japan. The electron energy of the storage ring is 8.0 GeV with a top- up filling of 99.5 mA accumulated current during the experiment. The Si (311) double- crystal monochromator was used for tuning the monochromatic beam. We measured the XAFS of Ag \(K\) - edge and In \(K\) - edge for AgInSe<sub>2</sub> in transmission mode. The XAFS measurements of Se \(K\) - edge, and Pb \(L_{3}\) - edge for PbSe and Pb<sub>0.98</sub>Na<sub>0.02</sub>Se - 2% AgInSe<sub>2</sub> were conducted in transmission mode, while the measurements of Ag \(K\) - edge and In \(K\) - edge for Pb<sub>0.98</sub>Na<sub>0.02</sub>Se - 2% AgInSe<sub>2</sub> were performed in fluorescence mode using 19- element Ge solid- state detector (SSD). All experimental XAFS spectra were preprocessed using the IFFEFIT package<sup>53</sup>.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 88, 853, 470]]<|/det|>
+XAFS calculation and analysis. The x- ray absorption near edge structure (XANES) calculations of Ag \(K\) - edge for Ag- doped PbSe and In \(K\) - edge for In- doped PbSe were performed based on the full multiple scattering (FMS) theory using FEFF9 program \(^{54,55}\) . We use self- consistent field (SCF) method to estimate the atomic scattering potential. To investigate the doping site of Ag or In in PbSe, we simply replace the central Pb absorber with Ag or In atom while maintaining the coordinates. To achieve good convergence, the cluster radius for SCF and FMS was fixed as 8 and 10 Angstrom, respectively. Linear combination fittings (LCF) of In \(K\) - edge for \(\mathrm{Pb_{0.98}Na_{0.02}Se}\) - \(2\%\) AgInSe \(_2\) was performed using the Athena software assuming that the XANES of In \(K\) - edge of AgInSe \(_2\) and In- doped PbSe as the standards. A similar LCF analysis was applied for the XANES of Ag \(K\) - edge of \(\mathrm{Pb_{0.98}Na_{0.02}Se}\) - \(2\%\) AgInSe \(_2\) . Since we cannot ensure that the Ag or In atoms totally occupy the Pb site in PbSe matrix without formation of impurity phases, we thus used the calculated XANES as one of standards in the LCF analysis.  
+
+<|ref|>sub_title<|/ref|><|det|>[[148, 507, 245, 523]]<|/det|>
+## References  
+
+<|ref|>text<|/ref|><|det|>[[145, 529, 852, 902]]<|/det|>
+1 Pei, Y. et al. Convergence of electronic bands for high performance bulk thermoelectrics. Nature 473, 66- 69 (2011).  2 Tang, Y. et al. Convergence of multi- valley bands as the electronic origin of high thermoelectric performance in CoSb \(_3\) skutterudites. Nat. Mater. 14, 1223- 1228 (2015).  3 Hu, L. et al. High thermoelectric performance enabled by convergence of nested conduction bands in \(\mathrm{Pb_7Bi_4Se_{13}}\) with low thermal conductivity. Nat. Commun. 12, 4793 (2021).  4 Chang, C. et al. 3D charge and 2D phonon transports leading to high out- of- plane zT in n- type SnSe crystals. Science 360, 778- 783 (2018).  5 Xiao, Y. et al. Band Sharpening and Band Alignment Enable High Quality Factor to Enhance Thermoelectric Performance in n- Type PbS. J. Am. Chem. Soc. 142, 4051- 4060 (2020).  6 Zhao, L.- D., Dravid, V. P. & Kanatzidis, M. G. The panoscopic approach to high performance thermoelectrics. Energy Environ. Sci. 7, 251- 268 (2014).  7 Qin, B. C. & Zhao, L. D. Carriers: the less, the faster. Mat. Lab 1, 220004 (2022).  8 Zhang, Q. et al. Enhancement of Thermoelectric Figure- of- Merit by Resonant States of Aluminium Doping in Lead Selenide. Energy Environ. Sci. 5(1), 5246- 5251 (2012).  9 Snyder, G. J. & Toberer, E. S. Complex thermoelectric materials. Nat. Mater. 7,
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+105- 114 (2008).10 Liu, H. et al. Copper ion liquid- like thermoelectrics. Nat. Mater. 11, 422- 425 (2012).11 Zhao, L. D. et al. Ultralow thermal conductivity and high thermoelectric figure of merit in SnSe crystals. Nature 508, 373- 377 (2014).12 Delaire, O. et al. Giant anharmonic phonon scattering in PbTe. Nat. Mater. 10, 614- 619 (2011).13 Skoug, E. J. & Morelli, D. T. Role of Lone- Pair Electrons in Producing Minimum Thermal Conductivity in Nitrogen- Group Chalcogenide Compounds. Phys. Rev. Lett. 107, 235901 (2011).14 Pei, Y., Lensch- Falk, J., Toberer, E. S., Medlin, D. L. & Snyder, G. J. High thermoelectric performance in PbTe due to large nanoscale \(\mathrm{Ag_2Te}\) precipitates and La doping. Adv. Funct. Mater. 21, 241- 249 (2011).15 Hong, T. et al. Band convergence and nanostructure modulations lead to high thermoelectric performance in \(\mathrm{SnPb_{0.04}Te - y\%AgSbTe_2}\) . Mater. Today Phys. 21, 100505 (2021).16 Kim, S. I. et al. Dense dislocation arrays embedded in grain boundaries for high- performance bulk thermoelectrics. Science 348, 109- 114 (2015).17 Chen, Z. et al. Lattice Dislocations Enhancing Thermoelectric PbTe in Addition to Band Convergence. Adv. Mater. 29 (2017).18 Meng, X. F. et al. Grain Boundary Engineering for Achieving High Thermoelectric Performance in n- Type Skutterudites. Adv. Energy Mater. 7, 1602582 (2017).19 Biswas, K. et al. High- performance bulk thermoelectrics with all- scale hierarchical architectures. Nature 489, 414- 418 (2012).20 Zhao, L. D. et al. All- scale hierarchical thermoelectrics: MgTe in PbTe facilitates valence band convergence and suppresses bipolar thermal transport for high performance. Energy Environ. Sci. 6, 3346 (2013).21 Wang, S. et al. Hierarchical structures lead to high thermoelectric performance in \(\mathrm{Cu_{m + n}Pb_{100}Sb_{m}Te_{100}Se_{2m}}\) (CLAST). Energy Environ. Sci. 14, 451- 461 (2021).22 Jiang, B. et al. Entropy engineering promotes thermoelectric performance in p- type chalcogenides. Nat. Commun. 12, 3234 (2021).23 Tan, G. et al. All- Scale Hierarchically Structured p- Type PbSe Alloys with High Thermoelectric Performance Enabled by Improved Band Degeneracy. J. Am. Chem. Soc. 141, 4480- 4486 (2019).24 Jiang, B. et al. High- entropy- stabilized chalcogenides with high thermoelectric performance. Science 371, 830- 834 (2021).25 Snyder, G. J. et al. Weighted Mobility. Adv. Mater. 32, e2001537 (2020).26 He, W. et al. High thermoelectric performance in low- cost \(\mathrm{SnS_{0.91}Se_{0.09}}\) crystals. Science 365, 1418- 1424 (2019).27 Qin, B. et al. Power generation and thermoelectric cooling enabled by momentum and energy multiband alignments. Science 373, 556- 561 (2021).28 Hodges, J. M. et al. Chemical Insights into PbSe- x%HgSe: High Power Factor and Improved Thermoelectric Performance by Alloying with Discordant Atoms.
+
+<--- Page Split --->
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+29 Wang, H., Gibbs, Z. M., Takagiwa, Y. & Snyder, G. J. Tuning bands of PbSe for better thermoelectric efficiency. Energy Environ. Sci. 7, 804- 811 (2014). 30 Hsu, K. F., Loo, S., Guo, F., Chen, W., Dyck, J. S., Uher, C., Hogan, T., Polychroniadis, E. K., Kanatzidis, M. G. Cubic AgPbmSbTe2+m Bulk Thermoelectric Materials with High Figure of Merit. Science, 818- 821 (2004). 31 Plachkova, S. K. Thermoelectric Figure of Merit of the System (GeTe)1-x- (AgSbTe2)x. Phys. Stat. Sol. (a) 83, 349 (1984). 32 Xiao, Y. et al. Amphoteric Indium Enables Carrier Engineering to Enhance the Power Factor and Thermoelectric Performance in n- Type AgnPb100 InnTe 100+2n (LIST). Adv. Energy Mater. 9, 1900414 (2019). 33 Banik, A., Shenoy, U. S., Saha, S., Waghmare, U. V. & Biswas, K. High Power Factor and Enhanced Thermoelectric Performance of SnTe- AgInTe2: Synergistic Effect of Resonance Level and Valence Band Convergence. J. Am. Chem. Soc. 138, 13068- 13075 (2016). 34 Zhu, Y. et al. Synergistically optimizing carrier concentration and decreasing sound velocity in n- type AgInSe2 Thermoelectrics. Chem. Mater. 31, 8182- 8190 (2019). 35 Zhu, Y. et al. Physical insights on the low lattice thermal conductivity of AgInSe2. Mater. Today Phys. 19, 100428 (2021). 36 Qiu, P. et al. Intrinsically high thermoelectric performance in AgInSe2 n- type diamond- like compounds Adv. Sci. 5, 1700727 (2018). 37 Wrasse, E. O., Baierle, R. J., Fazzio, A. & Schmidt, T. M. First- principles study of group III impurity doped PbSe: Bulk and nanowire. Phys. Rev. B 87 (2013). 38 Wang, S. et al. Exploring the doping effects of Ag in p- type PbSe compounds with enhanced thermoelectric performance. J. Phys. D: Appl. Phys. 44, 475304 (2011). 39 Androulakis, J., Lee, Y., Todorov, I., Chung, D.- Y. & Kanatzidis, M. High- temperature thermoelectric properties of n- type PbSe doped with Ga, In, and Pb. Phys. Rev. B 83 (2011). 40 Wang, H., Pei, Y., LaLonde, A. D. & Snyder, G. J. Heavily doped p- type PbSe with high thermoelectric performance: an alternative for PbTe. Adv. Mater. 23, 1366- 1370 (2011). 41 Christensen, M. et al. Avoided crossing of rattler modes in thermoelectric materials. Nat. Mater. 7, 811- 815 (2008). 42 Wu, H. J. et al. Broad temperature plateau for thermoelectric figure of merit ZT>2 in phase- separated PbTe0.7S0.3. Nat. Commun. 5, 4515 (2014). 43 Xu, W., Liu, Y., Marcelli, A., Shang, P. P. & Liu, W. S. The complexity of thermoelectric materials: why we need powerful and brilliant synchrotron radiation sources? Mater. Today Phys. 6, 68- 82 (2018). 44 Liu, Y. et al. Synergistically Optimizing Electrical and Thermal Transport Properties of BiCuSeO via a Dual- Doping Approach. Adv. Energy Mater. 6, 1502423 (2016). 45 Hu, L. et al. Localized Symmetry Breaking for Tuning Thermal Expansion in
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+ScF3 Nanoscale Frameworks. J. Am. Chem. Soc. 140, 4477- 4480 (2018). 46 Keiber, T., Bridges, F. & Sales, B. C. Lead is not off center in PbTe: the importance of r- space phase information in extended X- ray absorption fine structure spectroscopy. Phys. Rev. Lett. 111 (2013). 47 Zhu, Y. C. et al. Lattice dynamics and thermal conductivity in \(\mathrm{Cu_2Zn_{1 - x}Co_xSnSe_4}\) . Inorg. Chem. 57, 6051- 6056 (2018). 48 Xu, W. et al. Nanoscale heterogeneity in thermoelectrics: the occurrence of phase separation in Fe- doped \(\mathrm{Ca_3Co_4O_9}\) . Phys. Chem. Chem. Phys. 18, 14580- 14587 (2016). 49 Blöchl, P. E. Projector augmented- wave method. Phys. Rev. B 50, 17953- 17979 (1994). 50 Kresse, G. & Furthmuller, J. Efficient iterative schemes for ab initio total- energy calculations using a plane- wave basis set. Phys. Rev. B 54, 11169 (1996). 51 Kresse, G. & Furthmuller, J. Efficiency of ab- initio total energy calculations for metals and semiconductors using a plane- wave basis set. Comput. Mat. Sci. 6, 15- 50 (1996). 52 Perdew, J. P. et al. Restoring the density- gradient expansion for exchange in solids and surfaces. Phys. Rev. Lett. 100, 136406 (2008). 53 Ravel, B., Newville, M. ATHENA, ARTEMIS, HEPHAESTUS: data analysis for X- ray absorption spectroscopy using IFEFFIT. J. Synchrotron Rad. 12, 537- 541 (2005). 54 J.J. Rehr, R. C. A. Modern Theory of XAFS. Rev. Mod. Phys. 72, 621 (2000). 55 Rehr, J. J., Kas, J. J., Vila, F. D., Prange, M. P. & Jorissen, K. Parameter- free calculations of X- ray spectra with FEFF9. Phys. Chem. Chem. Phys. 12, 5503- 5513 (2010).
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[149, 91, 318, 107]]<|/det|>
+## Acknowledgements  
+
+<|ref|>text<|/ref|><|det|>[[147, 117, 853, 412]]<|/det|>
+AcknowledgementsThe authors thank BL14B1 at Shanghai synchrotron radiation facility (SSRF) for the SR- XRD measurements. We thank BL01B1 at Spring- 8 for the XAFS experiments (Proposal Number: 2021B1109). This work was supported by National Natural Science Foundation of China (51772012, 52002042 and 52002011), National Key Research and Development Program of China (2018YFA0702100 and 2018YFB0703600), National Postdoctoral Program for Innovative Talents (BX20200028), the Beijing Natural Science Foundation (JQ18004), and 111 Project (B17002). L.- D.Z. appreciates the support of the high performance computing (HPC) resources at Beihang University, the National Science Fund for Distinguished Young Scholars (51925101), and center for High Pressure Science and Technology Advanced Research (HPSTAR) for SEM and TEM measurements.  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 450, 343, 467]]<|/det|>
+## Authors contributions  
+
+<|ref|>text<|/ref|><|det|>[[147, 477, 852, 608]]<|/det|>
+Y.Z. and L.- D.Z. prepared the samples, carried out the experiments, analyzed the results and wrote the paper. T.H. and X.G. performed the microscopy experiments. D.Y.W. carried out the DFT calculations. Y.Z., L.H. and T.I. conducted the XAFS measurements and analyzed the data. S.Z., B.Q., H.S. and L.S. performed the SR- XRD experiments. All authors coedited the manuscript.  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 647, 323, 664]]<|/det|>
+## Competing interests  
+
+<|ref|>text<|/ref|><|det|>[[149, 674, 503, 691]]<|/det|>
+The authors declare no competing interests.  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 729, 351, 745]]<|/det|>
+## Additional information  
+
+<|ref|>text<|/ref|><|det|>[[147, 756, 782, 774]]<|/det|>
+Correspondence and requests for materials should be addressed to L.- D. Zhao.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[150, 88, 841, 333]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[147, 344, 850, 436]]<|/det|>
+<center>Fig. 1 Multiple valence bands enable high \(ZT\) values in p-type PbSe. a Schmatic diagram of multi-bands (L, \(\Sigma\) , \(\Lambda\) ) involvement in transport. The Brillouin zone shows that the degeneracies at the L, \(\Sigma\) , and \(\Lambda\) points are 4, 12, and 8, respectively. b The activated third band \(\Lambda\) enables higher \(ZT\) values compared with the single-band and two-band PbSe-based materials. </center>
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[155, 95, 844, 515]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[147, 530, 852, 603]]<|/det|>
+<center>Fig. 2 Crystal structure and band gap. a Schematic crystal structure of Pb0.98Na0.02Se - \(x\%\) AgInSe2 (LISS). b Powder XRD patterns of LISS. c Refined lattice constants of LISS. d Room-temperature infrared spectra for PbSe - \(x\%\) AgInSe2 and Pb0.98Na0.02Se - \(x\%\) AgInSe2. </center>
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[150, 90, 844, 500]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[147, 511, 851, 585]]<|/det|>
+<center>Fig. 3 Electrical properties as a function of temperature for \(\mathrm{Pb_{0.98}Na_{0.02}Se - x\%}\) AgInSe2 (LISS) compounds. a Electrical conductivity. b Seebeck coefficient. c Power factor. d Weighted mobility. The hollow circles in d represent the weighted mobility of single-band and two-band PbSe-based materials. </center>
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[150, 90, 847, 300]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[147, 308, 850, 418]]<|/det|>
+<center>Fig. 4 Pisarenko plot and Hall carrier mobility. a Pisarenko plot of Seebeck coefficients as a function of Hall carrier concentration \((n_{\mathrm{H}})\) for \(\mathrm{Pb_{0.98}Na_{0.02}Se - x\%}\) AgInSe2 (LISS). The solid black line is calculated assuming \(m^{*} = 0.44m_{\mathrm{e}}\) and the purple line represents the result assuming \(m^{*} = 0.81m_{\mathrm{e}}\) within the SPB model. The gray circles show the Pisarenko plot for Na-doped PbSe reported by Wang et al.38 b Hall carrier mobility \((\mu_{\mathrm{H}})\) versus Hall carrier concentration \((n_{\mathrm{H}})\) at 303K. </center>
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[171, 88, 822, 670]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[147, 678, 850, 844]]<|/det|>
+<center>Fig. 5 Electronic band structure. a Electronic band structure of \(\mathrm{Pb_27Se_27}\) (blue) and \(\mathrm{Pb_{25}AgInSe_{27}}\) (red). b Electronic density of states (DOS) near the Fermi level for \(\mathrm{Pb_{27}Se_{27}}\) (black), \(\mathrm{Pb_{26}AgSe_{27}}\) (green), \(\mathrm{Pb_{26}InSe_{27}}\) (blue) and \(\mathrm{Pb_{25}AgInSe_{27}}\) (red), respectively. c Electronic band structure of \(\mathrm{Pb_{25}AgInSe_{27}}\) at \(300\mathrm{K}\) and \(873\mathrm{K}\) , respectively. d Temperature-dependent infrared spectra of \(\mathrm{PbSe - 2\%AgInSe_2}\) . e The experimental (red) and theoretical (blue) bandgap \((E_{\mathrm{g}})\) and the theoretical energy offset between VBM1 and VBM2 \((\Delta E_{1 - 2})\) and between VBM1 and VBM3 \((\Delta E_{1 - 3})\) as a function of temperature. f Temperature-dependent electronic DOS of \(\mathrm{Pb_{25}AgInSe_{27}}\) near the VBM. </center>
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[152, 88, 840, 500]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[147, 510, 851, 621]]<|/det|>
+<center>Fig. 6 Thermal transport properties and figure-of-merit \(ZT\) as a function of temperature for \(\mathrm{Pb_{0.98}Na_{0.02}Se - x\%AgInSe_2}\) (LISS) compounds. a Total thermal conductivity. b Lattice thermal conductivity. Inset shows the room-temperature lattice thermal conductivities departure from the theoretical line calculated by the Callaway model. c The average sound velocity \(\left(\nu_{\mathrm{avg}}\right)\) versus lattice thermal conductivity \(\left(\kappa_{\mathrm{L}}\right)\) for LISS compounds at room temperature. d Temperature-dependent \(ZT\) for LISS samples. </center>
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[149, 84, 848, 585]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[147, 587, 850, 864]]<|/det|>
+<center>Fig. 7 Microstructures and local structure analysis for high-performance LISS sample. a Low magnification of bright-field TEM image for \(\mathrm{Pb_{0.98}Na_{0.02}Se}\) - \(2.05\%\) AgInSe2 sample. b The enlarged TEM pattern presents the nanoscale precipitates remarked by the arrows. c High resolution TEM (HRTEM) picture of a selected nanoprecipitate. d The corresponding selected area electron diffraction (SAED) pattern with cubic structure along [111]. e, f High angle annular dark field (HAADF) patterns for \(\mathrm{Pb_{0.98}Na_{0.02}Se}\) - \(2.05\%\) AgInSe2. g Experimental XANES spectra of In \(K\) -edge for \(\mathrm{Pb_{0.98}Na_{0.02}Se}\) - \(2\%\) AgInSe2 (red dots), and AgInSe2 (orange line), respectively. The blue line shows the theoretical XANES spectrum of In \(K\) -edge for In-doped PbSe assuming that In occupy the Pb site. The black line represents a linear combination fitting (LCF) result of In \(K\) -edge of \(\mathrm{Pb_{0.98}Na_{0.02}Se}\) - \(2\%\) AgInSe2 considering that the In \(K\) -edge of AgInSe2 and In-doped PbSe serves as standards. h Multiple scattering calculations of In \(K\) -edge XANES for In-doped PbSe with different atomic clusters. The inset shows the nearest-two shell of In atom when it occupies the Pb site in PbSe matrix. The \(E_0\) is the absorption edge energy of In \(K\) -edge of In foil. </center>
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[44, 42, 311, 70]]<|/det|>
+## Supplementary Files  
+
+<|ref|>text<|/ref|><|det|>[[44, 93, 765, 113]]<|/det|>
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+<|ref|>text<|/ref|><|det|>[[61, 131, 366, 150]]<|/det|>
+SupplementaryInformation.docx
+
+<--- Page Split --->

preprint/preprint__019a1322a4a18120bcf8546c95c7927691c4f4ceee9e4ba840a419b80a2ebe10/images_list.json

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+  {
+    "type": "image",
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+    "caption": "Figure 1. MR-EILLS model. MR-EILLS model aims to infer the causal relationships between one or multiple exposures and one outcome, integrating multiple GWAS summary datasets from heterogeneous populations. There are different pleiotropic effects and IV strengths for the same IVs in heterogeneous populations. The objective function of MR-EILLS model considers the both correlated and uncorrelated pleiotropy and remove the invalid IVs. Figure (A-C) are the results of motivating simulation. Figure (A) shows the point plot for absolute value of \\(\\hat{\\epsilon}_{j}^{(\\ell)}\\) in different populations, and larger point means the larger value of \\(\\hat{\\epsilon}_{j}^{(\\ell)}\\) . As the pleiotropic effect larger, the \\(\\hat{\\epsilon}_{j}^{(\\ell)}\\) is larger, thus the first part of MR-EILLS model minimize pleiotropic effect between different populations. Figure (B) shows the correlation between \\(\\hat{\\epsilon}_{j}^{(\\ell)}\\) and \\(\\hat{\\theta}_{p^{\\prime}}^{(\\ell)}\\) , which representing the correlated pleiotropic effect or the violation of InSIDE assumption. As the correlated pleiotropic effect increasing, this correlation is larger. This corresponding to the second part of MR-EILLS, the empirical focused linear invariance regularizer, which discourages selecting exposures with strong correlation between \\(\\hat{\\theta}_{p^{\\prime}}^{(\\ell)}\\) and \\(\\epsilon_{j}^{(\\ell)}\\) in some populations because this correlation would distort the causal effect estimation. Figure (C) shows the ridge plot of \\(\\sum_{n\\in \\mathbb{J}}\\hat{\\epsilon}_{j}^{(\\ell)}| + \\sum_{p\\in \\mathbb{P}}\\sum_{n\\in \\mathbb{J}}\\hat{\\theta}_{p^{\\prime}}^{(\\ell)}\\epsilon_{j}^{(\\ell)}|\\) when there are different proportion of invalid IVs. When there are invalid IVs, the ridge plot has two peaks, while the ridge plot has only one peak when there is no invalid IV. The corresponding abscission value at the lowest point between the two peaks is the optimal \\(\\lambda\\) . The third part of MR-EILLS model is removing the invalid IVs by \\(\\lambda\\) .",
+    "footnote": [],
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+  {
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+    "caption": "Figure 1. MR-EILLS model. MR-EILLS model aims to infer the causal relationships between one or multiple exposures and one outcome, integrating multiple GWAS summary datasets from heterogeneous populations. There are different pleiotropic effects and IV strengths for the same IVs in heterogeneous populations. The objective function of MR-EILLS model considers the both correlated and uncorrelated pleiotropy and remove the invalid IVs. Figure (A-C) are the results of motivating simulation. Figure (A) shows the point plot for absolute value of \\(\\hat{\\epsilon}_{j}^{(\\ell)}\\) in different populations, and larger point means the larger value of \\(\\hat{\\epsilon}_{j}^{(\\ell)}\\) . As the pleiotropic effect larger, the \\(\\hat{\\epsilon}_{j}^{(\\ell)}\\) is larger, thus the first part of MR-EILLS model minimize pleiotropic effect between different populations. Figure (B) shows the correlation between \\(\\hat{\\epsilon}_{j}^{(\\ell)}\\) and \\(\\hat{\\theta}_{p^{\\prime}}^{(\\ell)}\\) , which representing the correlated pleiotropic effect or the violation of InSIDE assumption. As the correlated pleiotropic effect increasing, this correlation is larger. This corresponding to the second part of MR-EILLS, the empirical focused linear invariance regularizer, which discourages selecting exposures with strong correlation between \\(\\hat{\\theta}_{p^{\\prime}}^{(\\ell)}\\) and \\(\\epsilon_{j}^{(\\ell)}\\) in some populations because this correlation would distort the causal effect estimation. Figure (C) shows the ridge plot of \\(\\sum_{n\\in \\mathbb{J}}\\hat{\\epsilon}_{j}^{(\\ell)}| + \\sum_{p\\in \\mathbb{P}}\\sum_{n\\in \\mathbb{J}}\\hat{\\theta}_{p^{\\prime}}^{(\\ell)}\\epsilon_{j}^{(\\ell)}|\\) when there are different proportion of invalid IVs. When there are invalid IVs, the ridge plot has two peaks, while the ridge plot has only one peak when there is no invalid IV. The corresponding abscission value at the lowest point between the two peaks is the optimal \\(\\lambda\\) . The third part of MR-EILLS model is removing the invalid IVs by \\(\\lambda\\) .",
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preprint/preprint__019a1322a4a18120bcf8546c95c7927691c4f4ceee9e4ba840a419b80a2ebe10/preprint__019a1322a4a18120bcf8546c95c7927691c4f4ceee9e4ba840a419b80a2ebe10.mmd

@@ -0,0 +1,375 @@
+
+# Invariance-based Mendelian Randomization Method Integrating Multiple Heterogeneous GWAS Summary Datasets  
+
+Xiaohua Zhou  
+
+azhou@bicmr.pku.edu.cn  
+
+Beijing International Center for Mathematical Research, Peking University  Lei Hou  
+
+Peking University  
+
+Hao Chen  Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University  
+
+## Article  
+
+Keywords: univariate mendelian randomization, multivariate mendelian randomization, GWAS summary datasets, heterogeneous populations, multiple ancestries  
+
+Posted Date: December 17th, 2024  
+
+DOI: https://doi.org/10.21203/rs.3.rs- 5602368/v1  
+
+License: © © This work is licensed under a Creative Commons Attribution 4.0 International License.  Read Full License  
+
+Additional Declarations: There is NO Competing Interest.  
+
+Version of Record: A version of this preprint was published at Nature Communications on August 18th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 62823- 6.
+
+<--- Page Split --->
+
+# Invariance-based Mendelian Randomization Method Integrating Multiple Heterogeneous GWAS Summary Datasets  
+
+3 Lei Hou \(^{1}\) , Hao Chen \(^{4}\) , Xiao- Hua Zhou \(^{1,2,3*}\)  
+
+# 4 Author affiliations:  
+
+5 1. Beijing International Center for Mathematical Research, Peking University, 6 Beijing, P.R.China, 100871 7 2. Department of Biostatistics, Peking University, Beijing, P.R.China, 100871 8 3. Chongqing Big Data Research Institute, Peking University, Chongqing, P.R.China, 9 401333 10 4. Department of biostatistics, School of Public Health, Cheeloo College of Medicine, 11 Shandong University, Shandong, Beijing, P.R.China, 250000  
+
+# \*Corresponding author:  
+
+14 Xiao- Hua Zhou, 15 E- mail: azhou@bicmr.pku.edu.cn, 16 Telephone: +86 18910208518, 17 Address: Peking University, No.5 Yiheyuan Road Haidian District, Beijing, P.R.China
+
+<--- Page Split --->
+
+## Abstract  
+
+Various geographical landscapes, diverse lifestyles and genetic structures may lead the heterogeneity among the GWAS summary datasets from distinct populations, especially different ethnic groups. This increases the difficulty in inferring global causal relationships from exposures on the outcome when integrating multiple GWAS summary datasets. We proposed a mendelian randomization (MR) method called MR- EILLS, which leverages the Environment Invariant Linear Least Squares (EILLS) to deduce the global causal relationship that invariant in all heterogeneous populations. The MR- EILLS model works in both univariate and multivariate scenarios, and allows the invalid instrumental variables (IVs) violating exchangeability and exclusion restriction assumptions. In addition, MR- EILLS shows the unbiased causal effect estimations of one or multiple exposures on the outcome, whether there are valid or invalid IVs. Comparing with traditional MR and meta methods, MR- EILLS demonstrates the highest estimation accuracy, the most stable type I error rates, and the highest statistical power. Finally, MR- EILLS is applied to explore the independent causal relationships between 11 blood cells and lung function, using GWAS summary statistics from five ancestries (African, East Asian, South Asian, Hispanics Latinos and European). The results cover most of the expected causal links which have biological interpretations and several new links supported by previous observational literatures.  
+
+Keywords: univariate mendelian randomization, multivariate mendelian randomization, GWAS summary datasets, heterogeneous populations, multiple ancestries
+
+<--- Page Split --->
+
+## Introduction  
+
+In recent years, with the rising number of Genome- Wide Association Study (GWAS) investigations, there has been a notable increase in the public availability and utilization of GWAS summary data by researchers [1- 2]. This inclusive dataset encompasses information from diverse populations and ethnic backgrounds [3- 6], a development that researchers find valuable, thus making it a current focal point of research interest. Owing to a range of influences including geographical landscapes and varied lifestyles, genetic structures exhibit significant diversity among distinct populations [7- 8], also we called population stratification, potentially leading to heterogeneity in GWAS summary data across different ethnic groups, such as those of European, Asian, and American descent. Mendelian randomization (MR) [2, 9] is a methodology that relies on the utilization of publicly available GWAS summary data for causal inference. It uses genetic variants as instrumental variables (IVs) to infer the causal effect of one or multiple exposures on an outcome, that is, univariable or multivariable MR [10- 11], respectively. A valid IV must satisfy the flowing three assumptions: relevance, exchangeability and exclusion restriction [9]. When we consider heterogeneous populations, one valid IV in a population may be an invalid IV in another population due to various genetic structures. For example, \(G_{1}\) is a valid IV in population I, it may be correlated with the confounder \(U\) between exposure and outcome in the population II, while \(U\) is not the confounder in the population I. In this case, \(G_{1}\) violates the exchangeability in population II. In addition, \(G_{1}\) may be correlated (linkage disequilibrium (LD)) [12] with another SNP \(G_{2}\) which directly affect the outcome in the population II, but \(G_{1}\) is independent with \(G_{2}\) in the population I. In this case, \(G_{1}\) violates the exclusion restriction in population II and this is due to the LD references in different populations are different. This complexity amplifies the difficulty of deducing the global causal relationship by integrating multiple heterogeneous GWAS summary datasets.  
+
+One straightforward idea to infer global causal relationships using MR is that, first conduct MR analysis separately using valid IVs in different populations and obtain the causal effect estimations in each population, then combine all estimations by meta- analysis [13- 14]. Even there may be invalid IVs in the first step, lots of MR methods [15- 18] are proposed to remove the influence of invalid IVs on the causal effect estimation.
+
+<--- Page Split --->
+
+However, the accuracy of meta-analysis results depends on the robustness of different MR methods, while these MR methods require different assumptions [15- 18], which may be difficult to satisfy or even cannot be tested. This may induce the inconsistent causal effect estimation in different populations, and bring difficulties for inferring global causal relationships (see section Application). Another idea is that first conduct GWAS meta- analysis for heterogeneous populations, then select valid IVs to infer causal relationship using MR. The difficulty for this strategy is that only a short number of independent SNPs (no LD) can be selected because the LD reference panels in different populations are different [8,19]. These two strategies are both two- step process, and bring the doubled statistical errors, which yields the lower accuracy of causal effect estimation. Besides, meta- analysis is a statistical technique used to combine and analyze results from multiple studies [20], if one result is inaccurate, the results of meta- analysis is also incorrect. It is not a causal method in itself and does not necessarily provide causal evidence that holds true in every population included in the analysis. Therefore, following we proposed a one- step method which integrating all information but not only MR results in each population, and provide the causal evidence that holds true (also called invariant) in each population.  
+
+In this paper, we provide a MR method called MR- EILLS, which utilizes the Environment Invariant Linear Least Squares (EILLS) [21] to integrating multiple heterogeneous GWAS summary datasets, then infer global causal relationship. The MR- EILLS model works in both univariate and multivariate scenarios, and allows the invalid IVs violating exchangeability and exclusion restriction assumptions. In addition, MR- EILLS shows the unbiased causal effect estimation of one or multiple exposures on the outcome, whether there are valid or invalid IVs. Comparing with traditional MR and Meta methods, MR- EILLS demonstrates the highest estimation accuracy, the most stable type I error rates, and higher statistical power. Finally, MR- EILLS is applied to explore the independent causal relationships between 11 blood cells and 4 lung function indexes, using GWAS summary statistics from 5 ancestries: African, East Asian, South Asian, Hispanics Latinos and European.  
+
+## Results  
+
+## Method overview  
+
+[please insert the Figure 1 here]
+
+<--- Page Split --->
+
+MR- EILLS model integrating the GWAS summary statistics from multiple heterogeneous populations, and find the causal exposures which have invariant effects with outcome in heterogeneous populations. GWAS summary statistics in \(E\) heterogeneous populations include \(G_{j} - X\) association \(\hat{\theta}_{p,j}^{(e)}\) and its standard error \(\sigma_{G_jX_p}^{(e)^2}\) , as well as \(G_{j} - Y\) association \(\hat{\Gamma}_{y,j}^{(e)}\) and its standard error \(\sigma_{y,j}^{(e)^2}\) for \(E = e\) . We assume that the causal effects of causal exposures on \(Y\) is invariant in different populations, that is \(\beta_{0p}^{(1)} = \beta_{0p}^{(2)} = \ldots = \beta_{0p}^{(E)} = \beta_{0p}^{*}\) for \(p\in P^{*}\) , while the genetic associations between SNPs and exposures/outcome/confounders may be different, and confounders between exposures and the outcome are also different. MR- EILLS model (Figure 1) aims to explore the global causal effect estimation by minimizing the following objective function  
+
+\[\begin{array}{rl} & {\mathcal{Q}(\beta_{0p}^{*};\hat{\theta}_{p,j}^{(e)},\hat{\Gamma}_{y,j}^{(e)},\sigma_{y,j}^{(e)^2})}\\ & {= \sum_{e\in E}w^{(e)}\mathrm{E}_{j\in S^{*}}[|w_{j}^{(e)}\hat{\mathcal{E}}_{j}^{(e)}|^2 ] + \gamma \sum_{p\in P}\sum_{e\in E}w^{(e)}|\mathrm{E}_{j\in S^{*}}[\hat{\theta}_{p,j}^{(e)}\cdot w_{j}^{(e)}\hat{\mathcal{E}}_{j}^{(e)}]|^2} \end{array} \quad (1)\]  
+
+where \(w_{j}^{(e)}\) is the weight of IV \(G_{j}\) on the causal effect estimation in population \(E = e\) , and \(w^{(e)}\) is the weight of population \(E = e\) on the global causal effect estimation. The first part of objective function (1) is the empirical \(L_{2}\) loss, which is the multiple populations version of objective function (6) in one population (see Method section), and \(\hat{\mathcal{E}}_{j}^{(e)} = \hat{\Gamma}_{y,j}^{(e)} - \sum_{p}\hat{\theta}_{p,j}^{(e)}\beta_{0p}^{(e)}\) also denotes the pleiotropic effect. Motivating simulation (Figure 1A, Figure S1A) demonstrates that as the pleiotropic effect (no matter correlated or uncorrelated) increasing, the absolute value of \(\hat{\mathcal{E}}_{j}^{(e)}\) is larger. The second part of objective function (1) is the empirical focused linear invariance regularizer, which discourages selecting exposures with strong correlation between \(\theta_{p,j}^{(e)}\) and \(\epsilon_{j}^{(e)}\) in some populations because this correlation would distort the causal effect estimation (see Method section). Motivating simulation (Figure 1B, Figure S1B) demonstrates that as correlated pleiotropic effect increasing, the correlation between \(\hat{\mathcal{E}}_{j}^{(e)}\) and \(\hat{\theta}_{p,j}^{(e)}\) is larger, and this means the violation of the InSIDE assumption [18] is more severe. \(\gamma > 0\) is the hyper parameter. In addition, we add the restriction  
+
+\[S^{*} = \{j:\sum_{e\in E}|\epsilon_{j}^{(e)}| + \sum_{p\in P}\sum_{e\in E}|\hat{\theta}_{p,j}^{(e)}\epsilon_{j}^{(e)}|< \lambda \} \quad (2)\]
+
+<--- Page Split --->
+
+to select the valid IVs. The first part in equation (2) represents the total pleiotropic effect for \(j - th\) IV, and the second part in equation (2) represents the correlation between \(\theta_{p,j}^{(e)}\) and \(\epsilon_{j}^{(e)}\) for \(j - th\) IV. \(\lambda >0\) is the hyper parameter controlling the strictness of filtering IVs. When there are invalid IVs, the ridge plot of \(\sum_{e\in E}\left|\epsilon_{j}^{(e)}\right| + \sum_{p\in P}\sum_{e\in E}\left|\hat{\theta}_{p,j}^{(e)}\epsilon_{j}^{(e)}\right|\) has at least two peaks (Figure 1C, Method section), while the ridge plot has only one peak when there is no invalid IV. The corresponding abscission value at the lowest point between the two peaks is the optimal \(\lambda\) . Thus equation (2) removes the invalid IVs with pleiotropic effects are larger than \(\lambda\) .  
+
+## Simulation  
+
+We generated the GWAS summary statistics of \(E\) heterogeneous populations with different edges' effects, IV strength and pleiotropy in the cases of UVMR and MVMR, respectively. And we compared MR- EILLS with six published methods includes IVW, MR- Egger, MR- Lasso, MR- Median, MR- cML and MR- BMA, and all of them had the UVMR and MVMR version except MR- BMA. For these MR methods, we consider two strategies: metaMR: first meta all the GWAS summary statistics of multiple datasets for each variable then conduct the MR analysis; mrMeta: first conduct the MR analysis in multiple datasets separately then meta all the MR results. Meta methods include the random- effect and fixed- effect meta- analysis.  
+
+[please insert the Figure 2 here]  
+
+For UVMR, in case (a), when there is correlated and uncorrelated pleiotropy (30% invalid IVs), MR- EILLS and MR- cML with metaMR show the unbiased causal effect estimation, while other methods are biased (Figure 2). MR- EILLS exhibits the higher accuracy, more stable type I error rates when causal effect is 0, and higher statistical power when causal effect isn't zero, than MR- cML with metaMR. When the proportion of invalid IVs is 80%, causal effect estimation using all MR methods including MR- cML are all biased, while MR- EILLS shows the unbiased causal effect estimation. MR- EILLS also exhibits the stable type I error rate when causal effect is 0 and statistical power is above 90% when the number of IVs is 300 in the case of causal effect is not zero. For the case (b), simulation results are similar as that in case (c). For the case (c), when there is no pleiotropy, all the methods show the unbiased causal effect estimation, stable type I error rate when causal effect is zero and high statistical power when causal effect isn't zero. Simulation results are shown in Figure S2- S21.
+
+<--- Page Split --->
+
+[please insert the Figure 3 here]  
+
+[please insert the Figure 4 here]  
+
+For MVMR, Figure 3 shows the causal effect estimation when there are 8 exposures, and \(30\%\) IVs have correlated or uncorrelated pleiotropy (case(a)). MR- EILLS shows unbiased causal effect estimations for all exposures, while other methods show the biased causal effect estimation, and MR- cML with metaMR also exhibits sightly biased causal effect estimations for some exposures. MR- EILLS also shows the highest accuracy among all methods. Figure 4 shows the type I error rate when causal effect is zero and statistical power when causal effect isn't zero. MR- EILLS shows the highest statistical power when causal effect isn't zero, and the most stable type I error rate while it is slightly lower than 0.05 for several exposures, but this phenomenon disappears when the number of populations is larger, e.g. \(E = 8\) (Figure S2- S3). When \(P = 3\) , the results of simulation are similar as above. When the proportion of invalid IVs is \(80\%\) , causal effect estimation using all MR methods are biased, while MR- EILLS shows the unbiased causal effect estimation. MR- EILLS also exhibits the stable type I error rate when causal effect is 0 and statistical power is above \(90\%\) when the number of IVs is 300 in the case of causal effect is not zero. For the case (b), simulation results are similar as that in case (c). For the case (c), when there is no pleiotropy, all the methods show the unbiased causal effect estimation, stable type I error rate when causal effect is zero and high statistical power when causal effect isn't zero. Simulation results are shown in Figure S22- S43. When \(P = 15\) , we calculate the mean of F1 score, recall and precision for each method in Figure 5. MR- EILLS shows the highest F1 score, recall and precision among all methods.  
+
+[please insert the Figure 5 here]  
+
+We also demonstrate the heterogeneity of causal effect estimations among different populations. The summary of \(I^2\) for all simulation are shown in Table S1- S3. We randomly select one simulation and demonstrate its causal effects' estimation for each MR methods and each dataset in Figure S31, S37 and S43, which show the forest plot of causal effect estimation in different populations for different methods. The \(I^2\) in case (a) is higher than case (c), that is, the pleiotropy improve the heterogeneity between populations. The causal effect estimation in different populations show the inconsistent causal effect estimation.
+
+<--- Page Split --->
+
+## Application  
+
+We explore the causal relationships between total 11 blood cells (5 red blood cells: hemoglobin concentration (HGB), hematocrit (HCT), mean corpuscular hemoglobin (MCH), mean corpuscular volume (MCV), mean corpuscular hemoglobin concentration (MCHC); 5 white blood cells: white blood cell count (WBC), neutrophil count (Neutro), monocyte count (Mono), basophil count (Baso), eosinophil counts (Eosin); 1 platelets: platelet count (PLT)) and 4 lung function indexes (forced expiratory volume (FEV), vital capacity (FVC), FEV/FVC ratio, peak expiratory flow (PEF)) using GWAS summary statistics from 5 ancestries: African, East Asian, South Asian, Hispanics Latinos and European. Details for GWAS summary statistics were shown in Method section and Table S4.  
+
+Firstly, we conducted traditional MR analysis in 5 ancestries, respectively, and performed the heterogeneous analysis for each MR method. Results were shown in Figure 6A. We found that there were large heterogeneities ( \(I^2 > 0.75\) ) for a number of blood cells in 5 ancestries. Then we conducted MR- EILLS analysis to explore independent causal effect from 11 blood cells on each lung function index. We plot ridge plots for each outcome in 5 ancestries and results were shown in Figure S44. Based on the ridge plot, we set the \(\lambda\) for MR- EILLS (Table S5). Results of MR- EILLS revealed that 3 blood cells (2 white blood cells: WBC and Neutro; 1 red blood cells: HGB) were causally associated with FEV; 1 blood cell (white blood cells: WBC) was causally associated with FVC; 3 blood cells (1 platelets: PLT; 2 red blood cells: HGB and HCT) was causally associated with FEV/FVC; 1 blood cell (red blood cells: HGB) was causally associated with PEF.  
+
+[please insert the Figure 6 here]  
+
+We found that the higher counts of some white blood cells, red blood cells or platelets would independently reduce the levels of lung function. For FEV, higher counts of WBC, Neutro and HGB would causally induce the lower level of FEV (WBC: \(\mathrm{beta} = - 0.14\) , \(95\% \mathrm{CI}\) : [- 0.24, - 0.04]; Neutro: \(\mathrm{beta} = - 0.17\) , \(95\% \mathrm{CI}\) : [- 0.24, - 0.04]; HGB: \(\mathrm{beta} = - 0.29\) , \(95\% \mathrm{CI}\) : [- 0.54, - 0.03]). The counts of Neutro and HCT were negatively associated with the level of FVC (Neutro: \(\mathrm{beta} = - 0.09\) , \(95\% \mathrm{CI}\) : [- 0.18, - 0.01]; HCT: \(\mathrm{beta} = - 0.06\) , \(95\% \mathrm{CI}\) : [- 0.13, - 0.002]). Besides, elevation in the levels of PLT and Neutro were associated with a decreased FEV/FVC ratio (PLT: \(\mathrm{beta} = - 0.26\) , \(95\% \mathrm{CI}\) : [- 0.49, - 0.02]; Neutro: \(\mathrm{beta} = - 0.16\) , \(95\% \mathrm{CI}\) : [- 0.30, - 0.02]). Higher concentrations of MCH
+
+<--- Page Split --->
+
+might result in a lower PEF level (beta=- 0.08, 95%CI: [- 0.16, - 0.004]). James et al. validated that an increased WBC count has been associated with lower levels of lung function and provided the biological explanations [22]. A 15- year longitudinal study demonstrated that higher blood neutrophil concentrations was associated with accelerated FEV decline [23]. The inverse relations between FEV, FVC and red blood cell counts were also supported by observational studies [24- 25]. A prospectively Longitudinal analyses revealed that higher baseline neutrophil count predicted lower serially obtained FVC [26]. A retrospective study found that there is a strong correlation between PLT and FEV/FVC ratio [27]. The results cover most of the expected causal links which have biological interpretations and several new links supported by previous observational literatures. Details of results were shown in Table S6- S11.  
+
+## Discussion  
+
+In this paper, we proposed a MR method MR- EILLS, which works in both univariable and multivariable framework, and it outputted the global causal effect estimation of multiple heterogeneous populations using only GWAS summary statistics. Results of simulation exhibited the superior performance of MR- EILLS and its application in exploring causal relationships from 11 blood cells to lung function covered most of the expected causal links.  
+
+MR- EILLS integrates the GWAS summary datasets from heterogeneous populations, and for each population, GWAS summary datasets for exposure and outcome can be from either the same individuals or the different but heterogeneous individuals. Actually, this assumption is the same as that in traditional two- sample MR analysis, which require two homogeneous but non- overlap samples. MR- EILLS assumes that the GWAS summary datasets for each population are from homogeneous but non- overlap samples. In the application, we assume that the individuals in each ancestry are homogeneous, and the genetic diversity in different ancestries lead the heterogeneous among ancestries (different IV strength and pleiotropy).  
+
+MR- EILLS allows different IV set in different populations. However, the strategy for metaMR, that is, first conduct GWAS meta- analysis then perform MR analysis, require the SNPs that are independent (no LD) in all populations, this reduces a large number of IVs, although GWAS meta- analysis helps researchers obtain more significant SNPs with \(P< 5\times 10^{- 8}\) . Besides, only a few MR methods allow the SNP set
+
+<--- Page Split --->
+
+with large LD. MR- EILLS solved this tricky issue and it only requires that IV set in each population are independent without LD.  
+
+MR- EILLS model has two hyper- parameters, which need researchers to set appropriate value to estimate causal effects of exposures on the outcome. For \(\gamma\) , we recommend \(\gamma > 0.4\) in UVMR, and \(\gamma > 0\) in MVMR. The larger \(\gamma\) , the stronger the role of empirical focused linear invariance regularizer. For \(\lambda\) , we suggest the researchers plot the ridge plot to find the optimal value. In model (2), we keep the SNP, for which the pleiotropic effect in all populations is lower than \(\lambda\) . When the scales of different populations are different, the model (2) can be modified as the following model (2- 1)  
+
+\[S^{*} = \{j:\left|\epsilon_{j}^{(e)}\right| + \sum_{p\in P}\left|\hat{\theta}_{p,j}^{(e)}\epsilon_{j}^{(e)}\right|< \lambda_{e}\text{for any} e\} . \quad (2-1)\]  
+
+The researchers can set different \(\lambda_{e}\) for different populations. For example, in our applications, we set different \(\lambda_{e}\) for five ancestries, respectively, and five ridge plots are plotted for each outcome. MR- EILLS works if and only if there are at least \(J \geq P\) valid IVs in the IV set and this assumption is less strict than the plurality assumption [17], which requires the valid IVs form the largest group of IVs sharing the same causal parameter value.  
+
+There are several limitations for MR- EILLS. The first is that MR- EILLS doesn't work in the high- dimensional case yet. One future key research direction is to extend MR- EILLS to high- dimensional exposure scenarios, especially for the high- dimensional- omics biomarkers, for this, correlated IVs is also an important issue to be solved. Another point is that inappropriate settings of hyper parameters may induce the incorrect inference of causal relationships between exposures and outcome. It is important to choose the appropriate for hyper parameters, especially for \(\lambda\) . The value of \(\lambda\) determined that whether the invalid IVs are removed, and if \(\lambda\) is too large, the causal effect estimation would be distorted. If \(\lambda\) is too small, the number of remaining IVs is small, thus in the future it is necessary to extend MR- EILLS to correlated IVs scenarios.  
+
+In conclusion, we proposed a MR method MR- EILLS, which integrate multiple heterogeneous GWAS summary datasets to infer the global causal relationships between exposures and outcome. This study has important guiding significance for the discovery of new disease- related factors. We look forward to offering constructive
+
+<--- Page Split --->
+
+suggestions for disease diagnosis and applying our method beyond the scope considered here.  
+
+## Methods  
+
+## MR-EILLS model: MR integrating multiple heterogeneous populations  
+
+For one population, assume \(P\) exposures \(X_{p}, p \in \{1, \ldots , P\}\) and one outcome \(Y\) . The \(J\) independent IVs \(G_{j}, j \in \{1, \ldots , J\}\) satisfy the following three assumptions:  
+
+A1. \(G_{j}\) is associated with at least one of \(P\) exposures;  
+
+A2. \(G_{j}\) is not associated with the confounder between \(P\) exposures and the outcome;  
+
+A3. \(G_{j}\) affects the outcome only through exposures.  
+
+Then the MR model based on the individual data is:  
+
+\[\begin{array}{l}{{U=\sum_{j}\omega_{j}G_{j}+\epsilon_{X_{U}}}}\\ {{X_{p}=\sum_{j}\alpha_{p j}G_{j}+\sum_{X_{k}\in p a(X_{p})}\beta_{X_{k}X_{p}}X_{k}+\beta_{1p}U+\epsilon_{X_{p}}}}\\ {{Y=\sum_{j}\gamma_{j}G_{j}+\sum_{p}\beta_{0p}X_{p}+\beta_{2}U+\epsilon_{Y}}}\end{array} \quad (3)\]  
+
+where \(\epsilon_{X_{U}}, \epsilon_{X_{p}}, \epsilon_{Y} \sim N(0,1)\) . \(\gamma_{j}\) represents the uncorrelated pleiotropic effect and \(\omega_{j}\) represents the correlated pleiotropy. \(\beta_{0p}\) denote the causal effect of \(X_{p}\) on \(Y\) . We call the exposures with \(\beta_{0p} \neq 0\) are the causal exposures, which we want to discover, while the exposures with \(\beta_{0p} = 0\) are the spurious exposures, which are not the true cause of outcome. We define the set of causal exposures is \(\{X_{p}\} , p \in P^{*} \subseteq \{1, \ldots , P\}\) . When \(P = 1\) , above model is called UVMR, while when \(P > 1\) , it is called MVMR. To simplify the expression, our model below uniformly uses \(P\) exposures, both applicable to UVMR and MVMR.  
+
+GWAS summary statistics including \(G_{j} - X_{p}\) association \(\hat{\theta}_{p,j}\) and its variance \(\sigma_{p,j}^{2}\) , as well as \(G_{j} - Y\) association \(\hat{\Gamma}_{y,j}\) and its variance \(\sigma_{y,j}^{2}\) . Based on model (3), we have  
+
+\[\begin{array}{l}{\theta_{p,j} = \alpha_{p,j} + \omega_{j}\beta_{1p} + \sum_{X_{k}\in p a(X_{p})}\theta_{k,j}\beta_{X_{k}X_{p}}}\\ {\Gamma_{y,j} = \omega_{j}\beta_{2} + \gamma_{j} + \sum_{p}\theta_{p,j}\beta_{0p}} \end{array} \quad (4)\]
+
+<--- Page Split --->
+
+When \(G_{j}\) is a valid IV (no pleiotropy), that is \(\gamma_{j} = \omega_{j} = 0\) , then \(\epsilon_{j} = \Gamma_{y,j} - \sum_{p}\theta_{p,j}\beta_{0p}\) is zero and it is dependent with \(\theta_{p,j}\) . For \(j\in \{1,\dots,J\}\) , we can identify \(\beta_{0p}\) \((p\in \{1,\dots,P\})\) by the system of linear equations \(\Gamma_{y,j} = \sum_{p}\theta_{p,j}\beta_{0p}\) if and only if \(J\geq P\) . The causal effects of exposures on the outcome \(\beta_{0p}\) can be estimated by weighted version of ordinary least squares (OLS), that is, the IVW regression  
+
+\[\hat{\Gamma}_{y,j} = \sum_{p}\hat{\theta}_{p,j}\beta_{0p} + \zeta_{j},\zeta_{j}\sim N(0,\sigma_{y,j}^{2}), \quad (5)\]  
+
+which set the intercept is zero. This model minimizes the empirical \(L_{2}\) loss objective function  
+
+\[\begin{array}{rl} & Q(\beta_{0p};\hat{\theta}_{p,j},\hat{\Gamma}_{y,j},\sigma_{y,j}^{2})\\ & = \mathrm{E}[|w_{j}\epsilon_{j}|^{2}]\\ & = \mathrm{E}[|w_{j}(\hat{\Gamma}_{y,j} - \sum_{p}\hat{\theta}_{p,j}\beta_{0p})|^{2}] \end{array} \quad (6)\]  
+
+where \(w_{j}\) represents the weight of IV \(G_{j}\) on the casual effect estimation. If \(G_{j}\) have uncorrelated pleiotropy \((\gamma_{j}\neq 0)\) , that is, \(G_{j}\) is causally associated with \(Y\) not through any \(X_{p}\) , then the \(\epsilon_{j} = \gamma_{j}\) is no more equal to zero, and it represents the uncorrelated pleiotropic effect. MR- Egger regression [18] is proposed to solved this problem by allowing the intercept term in model (5), and the intercept represent the pleiotropic effect. MR- Egger regression requires the InSIDE assumption, which means the pleiotropic effect is independent with \(\theta_{p,j}\) . If \(G_{j}\) have correlated pleiotropy \((\omega_{j}\neq 0)\) , that is, \(G_{j}\) is causally associated with the unmeasured confounding \(U\) between \(X_{p}\) and \(Y\) , then pleiotropic effect \(\epsilon_{j} = \omega_{j}\beta_{2} + \gamma_{j}\) is not independent with \(\theta_{p,j}\) because of the common term \(\omega_{j}\) . This is the violation of the InSIDE assumption. Equation (5- 6) and MR- Egger require that \(\epsilon_{j}\) is independent with \(\theta_{p,j}\) because the correlation between intercept term and independent variables would distort the causal effect estimation. Results of motivating simulation for correlated and uncorrelated pleiotropy are shown in Figure S1.  
+
+When there are \(E\) heterogeneous populations, GWAS summary statistics include \(\hat{\theta}_{p,j}^{(e)}\) , \(\sigma_{G_jX_p}^{(e)2}\) , \(\hat{\Gamma}_{y,j}^{(e)}\) and \(\sigma_{y,j}^{(e)2}\) for \(E = e\) . We define \(\epsilon_{j}^{(e)} = \Gamma_{y,j}^{(e)} - \sum_{p}\theta_{p,j}^{(e)}\beta_{0p}^{(e)}\) and \(\hat{\epsilon}_{j}^{(e)} = \hat{\Gamma}_{y,j}^{(e)} - \sum_{p}\hat{\theta}_{p,j}^{(e)}\beta_{0p}^{(e)}\) in the version of multiple populations. We use superscript \((e)\)
+
+<--- Page Split --->
+
+to denote the \(e\) - th population. We assume that the pleiotropic effect, IV strength and the relationships among exposures are different in heterogeneous populations, while the causal effects of causal exposures on \(Y\) is invariant, that is \(\beta_{0p}^{(1)} = \beta_{0p}^{(2)} = \ldots = \beta_{0p}^{(E)} = \beta_{0p}^{*}\) for \(p \in P^{*}\) , this assumption called the structure assumption [21]. These assumptions are rational because the IV satisfying A1- A3 only control the unmeasured confounders between \(X_{p}\) and \(Y\) , while other unmeasured confounders between IV and exposure, or between IV and outcome, or between exposures, are not controlled, and these unmeasured confounders also the reason for heterogeneity between populations.  
+
+Note that one valid IV in one population may be the invalid IV in the other heterogeneous populations. On the other hand, an IV may be associated with the exposures in all heterogeneous populations, while it may have different uncorrelated or correlated pleiotropy in the different populations. This leads to inconsistent independence relationships between \(\theta_{p,j}^{(e)}\) and \(\epsilon_{j}^{(e)}\) across different populations and inconsistent causal effect estimation of exposures on the outcome in different heterogeneous populations. Therefore, we leverage the Environment Invariant Linear Least Squares (EILLS) [21], the multiple heterogeneous populations version of OLS, to construct the MR- EILLS model. MR- EILLS model integrating the GWAS summary statistics from multiple heterogeneous populations, and find the causal exposures which have invariant effects with outcome in heterogeneous populations. MR- EILLS model aims to minimize the following objective function  
+
+\[\begin{array}{rl} & {\mathcal{Q}(\beta_{0p}^{*};\hat{\theta}_{p,j}^{(e)},\hat{\Gamma}_{y,j}^{(e)},\sigma_{y,j}^{(e)2})}\\ & {= \sum_{e\in E}w^{(e)}\mathrm{E}_{j\in S^{*}}\{\mid w_{j}^{(e)}\hat{\xi}_{j}^{(e)}\mid^{2}\} +\gamma \sum_{e\in E}w^{(e)}\sum_{p\in P}\mid \mathrm{E}_{j\in S^{*}}[\hat{\theta}_{p,j}^{(e)}\cdot w_{j}^{(e)}\hat{\xi}_{j}^{(e)}]\mid^{2}} \end{array} \quad (1)\]  
+
+where  
+
+\[w_{j}^{(e)} = \frac{\sigma_{y,j}^{(e) - 2}}{\sum_{j\in S^{*}}\sigma_{y,j}^{(e) - 2}}\mathrm{and}w^{(e)} = \frac{N_{e}}{N} \quad (7)\]  
+
+\(w_{j}^{(e)}\) is the weight of IV \(G_{j}\) on the casual effect estimation in population \(E = e\) , and \(w^{(e)}\) is the weight of population \(E = e\) on the final casual effect estimation. The first part of objective function (1) is the empirical \(L_{2}\) loss, which is the multiple populations version of objective function (6) in one population. The second part of objective function (1) is the empirical focused linear invariance regularizer, which discourages
+
+<--- Page Split --->
+
+selecting exposures with strong correlation between \(\theta_{p,j}^{(e)}\) and \(\epsilon_{j}^{(e)}\) in some populations because this will distort the causal effect estimation. \(\gamma >0\) is the hyper parameter. In addition, we add the restriction  
+
+\[S^{*} = \{j:\sum_{e\in E}\left|\epsilon_{j}^{(e)}\right| + \sum_{p\in P}\sum_{e\in E}\left|\hat{\theta}_{p,j}^{(e)}\epsilon_{j}^{(e)}\right|< \lambda \} \quad (2)\]  
+
+to select the valid IVs. The first part in equation (2) represents the uncorrelated pleiotropic effect for \(j - th\) IV, and the second part in equation (2) represents the correlated pleiotropic effect for \(j - th\) IV. \(\lambda >0\) is the hyper parameter controlling the strictness of filtering IVs. Equation (2) removes the invalid IVs with pleiotropic effect above \(\lambda\) .  
+
+The causal effects \(\beta_{0p}^{*}\) can be identified under the assumption [21] that there are at least \(P\) valid IVs in the IV set, that is \(J\geq P\) . We use the a limited- memory modification of the BFGS quasi- Newton method [28] to find the optimal solution \(\beta_{0p}^{*}\) of objective function (1) under the restriction of equation (2). The confidence interval is estimated by Bootstrap method.  
+
+## Simulation  
+
+We generate the GWAS summary statistics of \(E\) heterogeneous populations by following process:  
+
+\[\theta_{p,j}^{(e)} = \alpha_{p,j}^{(e)} + \omega_{j}^{(e)}\beta_{1p}^{(e)} + \sum_{X_{k}\in pa(X_{p})}\theta_{k,j}^{(e)}\beta_{X_{k}X_{p}}^{(e)} + \xi_{p,j}^{(e)}\] \[\Gamma_{y,j}^{(e)} = \omega_{j}^{(e)}\beta_{2}^{(e)} + \gamma_{j}^{(e)} + \sum_{p}\theta_{p,j}^{(e)}\beta_{0p}^{(e)} + \xi_{y,j}^{(e)}\]  
+
+Totally \(P\) exposures, the causal exposures are the top \(30\%\) of all exposures (e.g. \(P = 8\) , floor( \(P\times 30\% = 2\) , then the top two ( \(X_{1}\) and \(X_{2}\) ) are the causal exposures). The effect of causal exposure on \(Y\) ( \(\beta_{0p}^{(e)}\) , \(p\in P^{*}\) ) is 0.2 for MVMR ( \(P > 1\) ) and 0.1 for UVMR ( \(P = 1\) ), and the effect of other spurious exposure on \(Y\) ( \(\beta_{0p}^{(e)}\) , \(p\notin P^{*}\) ) is 0. \(\beta_{X_{k}X_{p}}^{(e)}\sim U(- 1,1)\) for the effect of edge \(X_{k}\to X_{p}\) . We set IV strength \(\alpha_{p,j}^{(e)}\sim U(0.05,0.2)\) for \(E = e\) and \(X_{p}\) ; \(\xi_{p,j}^{(e)}\sim N(0,\sigma_{p,j}^{(e)2})\) for \(E = e\) and \(X_{p}\) , \(\sigma_{p,j}^{(e)2}\sim U(0.01,0.05)\) for \(X_{p}\) ; \(\xi_{y,j}^{(e)}\sim N(0,\sigma_{y,j}^{(e)2})\) for \(E = e\) , \(\sigma_{y,j}^{(e)2}\sim U(0.05,0.1)\) and different variances represent different sample sizes; \(\beta_{1p}^{(e)}\sim U(0.5,0.8)\) for \(X_{p}\) ; \(\beta_{2}^{(e)}\sim U(0.5,0.8)\) . We consider three scenarios:
+
+<--- Page Split --->
+
+(a) No pleiotropy;  
+
+(b) uncorrelated pleiotropy effect \(\gamma_{j}^{(e)} \sim U(0,0.5)\) .  
+
+(c) uncorrelated and correlated pleiotropy effect, \(\gamma_{j}^{(e)} \sim U(0,0.5)\) and \(\omega_{j}^{(e)} \sim U(0,0.5)\) .  
+
+The parameters of edges' effects, IV strength and pleiotropy are random select from uniform distribution, thus they are different in different datasets and these represent the heterogeneous datasets. We vary the number of populations are \(E = 3\) or 8; the number of IVs is 100 or 300; the number of exposures is \(P = 1, 3, 8\) or 15, which include the cases of univariable and multivariable MR.  
+
+We conduct 200 repeated simulations to evaluate the performance of MR- EILLS. We also compare six methods includes IVW, MR- Egger, MR- Lasso, MR- Median, MR- cML and MR- BMA. For \(P = 1\) , we compare five methods in the UVMR version except MR- BMA; for \(P = 3, 8\) and 15, we compare all six methods in the MVMR version. For these MR methods, we consider two strategies: (1) first meta all the GWAS summary statistics of \(E\) datasets for each variable then conduct the MR analysis; (2) first conduct the MR analysis in \(E\) datasets separately then meta all the MR results. Meta methods include the random- effect and fixed- effect meta- analysis.  
+
+We evaluate the performance of all methods by box- violin plot for causal effect estimation, histogram for type I error when causal effect is zero and statistical power when causal effect isn't zero. Besides, we calculate the \(I^{2}\) statistics in each simulation, to evaluate the heterogeneity of causal effect estimation among different datasets, for each MR method. We plot the violin plot of \(I^{2}\) statistics for the estimations of each variable, and we random select six simulations to demonstrate the quartiles of estimation, then plot the forest plot of estimations for each method and each variable. For \(P = 15\) , we calculate the mean of F1 score, recall and precision for each method. Recall (i.e. power, or sensitivity) measures how many relationships a method can recover from the true causal relationships, whereas precision (i.e., 1- FDR) measures how many correct relationships are recovered in the inferred relationships. The F1 score is a combined index of recall and precision.  
+
+## Setting of hyper parameters \(\gamma\) and \(\lambda\)  
+
+We recommend that the practitioners determine the value of \(\lambda\) by plotting a ridge plot. The abscissa is the value of \(\sum_{e \in E} | e_{j}^{(e)} | + \sum_{p \in P} \sum_{e \in E} | \hat{\theta}_{p,j}^{(e)} e_{j}^{(e)} |\) for each IV in equation (8). We plot the ridge plot in simulations in Figure S45- S49. These plots demonstrates
+
+<--- Page Split --->
+
+that when there is no pleiotropy, the figure has only one peak, and the \(\lambda\) just takes the value of the abscission after the first peak. When there is pleiotropy, the figure has two peaks, and the corresponding abscission value at the lowest point between the two peaks is the optimal \(\lambda\) . We provide the function of ridge plot in R package MREILLS.  
+
+In addition, we evaluate the root mean square error (RMSE) of causal effect estimation using a grid search: \(\gamma\) ranges from 0.1 to 200 and \(\lambda\) ranges from 0.1 to 1. Results are shown in the Figure S50- 58. We conclude the ranges of hyper parameters when RMSE<0.1 in Table S12. For UVMR, we recommend \(\gamma >0.4\) . When \(\gamma >0.4\) , the RMSE is less than 0.1, especially for the case of correlated and uncorrelated pleiotropy, while in other cases, RMSE is less than 0.05. For MVMR, \(\gamma >0\) is recommended. Comparing with all valid IVs, invalid IVs increased the RMSE of causal effect estimation, no matter correlated or uncorrelated pleiotropy. Therefore, \(\gamma\) is loosely valued, especially when \(P > 1\) . The larger \(\gamma\) , the stronger the role of empirical focused linear invariance regularizer.  
+
+## Application  
+
+We explored the causal effect of 11 blood cells on 4 lung function indexes using GWAS summary statistics from 5 ancestries: African, East Asian, South Asian, Hispanics Latinos and European. GWAS summary statistics for blood cells were extracted from Chen et al. [29], which conducted trans- ethnic and ancestry- specific GWAS in 746,667 individuals from 5 global populations (15,171, 151,807, 8,189, 9,368 and 563,947 individuals for 5 ancestries, respectively). GWAS summary statistics for lung function were extracted from Shrine et al. [30], which conducted trans- ethnic GWAS analysis in 49 cohorts from 5 populations (8,590, 85,279, 4,270, 14,668 and 475,645 individuals for 5 ancestries, respectively). Details are shown in Table S4.  
+
+Firstly, we select IVs for MR analysis. For MR- EILLS and mrMeta analysis, we separately select SNPs with \(P< 5\times 10^{- 8}\) and clump the LD with \(r^2 >0.01\) in each population (Table S9). For metaMR analysis, we select SNPs with \(P< 5\times 10^{- 8}\) in each population then clump the union set of above SNPs with \(r^2 >0.01\) (Table S10). Then we extract the summary statistics for IVs and conduct the MR- EILLS, mrMeta and metaMR analysis. We also calculate the \(I^2\) statistics to evaluate the heterogeneity of causal effect estimation among different populations, for each MR method. For MR
+
+<--- Page Split --->
+
+467 EILLS, we plot the ridge plot in each population, and set \(\gamma = 0.5\) . The setting of \(\lambda\) are shown in the Table S5.
+
+<--- Page Split --->
+
+## Acknowledgements  
+
+None.  
+
+## Author Contributions  
+
+LH and ZX conceived the study. LH contributed to theoretical derivation with assistance from ZX. LH and HC contributed to the data simulation and application. LH and ZX wrote the manuscript with input from all other authors. All authors reviewed and approved the final manuscript.  
+
+## Competing Interests statement  
+
+The authors declare no competing interests.  
+
+## Data and code availability  
+
+GWAS summary statistics for blood cells are publicly available at http://www.mhi- humangenetics.org/en/resources/. The GWAS summary data for lung function are publicly available at GWAS catalog. All the analysis in our article were implemented by R software (version 4.3.2). R packages used in our analysis include TwoSampleMR, MendelianRandomization, and ggplot2. MREILLS model can be implemented by R package https://github.com/hhoulei/ MREILLS. All the codes for simulation are uploaded in https://github.com/hhoulei/MREILLS_Simul.  
+
+## Ethics approval and consent to participate  
+
+The data used in our study was all publicly available and obtained written informed consent from all participants.  
+
+## Source of Funding  
+
+This work was supported by the National Natural Science Foundation of China (Grant 82404378, T2341018), China Postdoctoral Science Foundation (Grant GZB20230011, 2024M750115, 2024T170014).
+
+<--- Page Split --->
+
+## Reference  
+
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+572 Progression and Mortality in Two Independent Systemic Sclerosis Cohorts. Arthritis care & 573 research, 75(3), 648- 656. https://doi.org/10.1002/acr.24880 574 [27]. Ulasli, S. S., Ozyurek, B. A., Yilmaz, E. B., & Ulubay, G. (2012). Mean platelet volume 575 as an inflammatory marker in acute exacerbation of chronic obstructive pulmonary disease. 576 Polskie Archiwum Medycyny Wewnetrznej, 122(6), 284- 290. 577 [28]. Eisen, M., Mokhtari, A., & Ribeiro, A. (2017). Decentralized quasi- Newton methods. 578 IEEE Transactions on Signal Processing, 65(10), 2613- 2628. 579 [29]. Chen, M. H., Raffield, L. M., Mousas, A., Sakaue, S., Huffman, J. E., Moscati, A., Trivedi, 580 B., Jiang, T., Akbari, P., Vuckovic, D., Bao, E. L., Zhong, X., Manansala, R., Laplante, V., Chen, 581 M., Lo, K. S., Qian, H., Lareau, C. A., Beaudoin, M., Hunt, K. A., ... Lettre, G. (2020). Trans- 582 ethnic and Ancestry- Specific Blood- Cell Genetics in 746,667 Individuals from 5 Global 583 Populations. Cell, 182(5), 1198- 1213. e14. 584 [30]. Shrine, N., Izquierdo, A. G., Chen, J., Packer, R., Hall, R. J., Guyatt, A. L., Batini, C., 585 Thompson, R. J., Pavuluri, C., Malik, V., Hobbs, B. D., Moll, M., Kim, W., Tal- Singer, R., 586 Bakke, P., Fawcett, K. A., John, C., Coley, K., Piga, N. N., Pozarickij, A., ... Tobin, M. D. 587 (2023). Multi- ancestry genome- wide association analyses improve resolution of genes and 588 pathways influencing lung function and chronic obstructive pulmonary disease risk. Nature 589 genetics, 55(3), 410- 422. 590
+
+<--- Page Split --->
+
+## Figure Legends  
+
+Figure 1. MR- EILLS model. MR- EILLS model aims to infer the causal relationships between one or multiple exposures and one outcome, integrating multiple GWAS summary datasets from heterogeneous populations. There are different pleiotropic effects and IV strengths for the same IVs in heterogeneous populations. The objective function of MR- EILLS model considers the both correlated and uncorrelated pleiotropy and remove the invalid IVs. Figure (A- C) are the results of motivating simulation. Figure (A) shows the point plot for absolute value of \(\hat{\mathcal{E}}_j^{(e)}\) in different populations, and larger point means the larger value of \(|\hat{\mathcal{E}}_j^{(e)}|\) . As the pleiotropic effect larger, the \(|\hat{\mathcal{E}}_j^{(e)}|\) is larger, thus the first part of MR- EILLS model minimize pleiotropic effect between different populations. Figure (B) shows the correlation between \(\hat{\mathcal{E}}_j^{(e)}\) and \(\hat{\theta}_{p,j}^{(e)}\) , which representing the correlated pleiotropic effect or the violation of InSIDE assumption. As the correlated pleiotropic effect increasing, this correlation is larger. This corresponding to the second part of MR- EILLS, the empirical focused linear invariance regularizer, which discourages selecting exposures with strong correlation between \(\theta_{p,j}^{(e)}\) and \(\mathcal{E}_j^{(e)}\) in some populations because this correlation would distort the causal effect estimation. Figure (C) shows the ridge plot of \(\sum_{e\in E}|\mathcal{E}_j^{(e)}| + \sum_{p\in P}\sum_{e\in E}|\hat{\theta}_{p,j}^{(e)}\mathcal{E}_j^{(e)}|\) when there are different proportion of invalid IVs. When there are invalid IVs, the ridge plot has two peaks, while the ridge plot has only one peak when there is no invalid IV. The corresponding abscission value at the lowest point between the two peaks is the optimal \(\lambda\) . The third part of MR- EILLS model is removing the invalid IVs by \(\lambda\) .  
+
+Figure 2. Simulation results when \(P = 1\) (UVMR). (A- B) Results of causal effect estimation and type I error rate when the causal effect is zero. (C- D) Results of causal effect estimation and type I error rate when the causal effect is 0.1. The number of IVs is 100 and the proportion of invalid IVs is \(30\%\) . The number of populations is \(E = 3\) .  
+
+Figure 3. Simulation results of causal effect estimation when \(P = 8\) (MVMR). The number of IVs is 100 and the proportion of invalid IVs is \(30\%\) . The number of populations is \(E = 3\) .  
+
+Figure 4. Simulation results of type I error rate for spurious exposures and statistical power for causal exposures when \(P = 8\) (MVMR). The number of IVs is 100 and the proportion of invalid IVs is \(30\%\) . The number of populations is \(E = 3\) .
+
+<--- Page Split --->
+
+Figure 5. Simulation results of F1 score, precision and recall when \(P = 15\) (MVMR). The number of IVs is 100 and the proportion of invalid IVs is \(30\%\) . The number of populations is \(E = 3\) .  
+
+Figure 6. Results in application. (A) the heterogeneity among different populations; (B) the causal effect estimations of 11 blood cells on lung function.
+
+<--- Page Split --->
+
+## Figures  
+
+![](images/Figure_1.jpg)
+  
+
+Summary statistics:  
+
+\(G - X_{p}\) association \(\hat{\theta}_{p^{\prime}}^{(\ell)}\) \(\sigma_{p^{\prime}}^{(\ell)}\) \(G - Y\) association \(\hat{\Gamma}_{p^{\prime}}^{(\ell)}\) \(\sigma_{p^{\prime}}^{(\ell)}\)  
+
+MR- EILLS Model:  
+
+![](images/Figure_1.jpg)
+
+<center>Figure 1. MR-EILLS model. MR-EILLS model aims to infer the causal relationships between one or multiple exposures and one outcome, integrating multiple GWAS summary datasets from heterogeneous populations. There are different pleiotropic effects and IV strengths for the same IVs in heterogeneous populations. The objective function of MR-EILLS model considers the both correlated and uncorrelated pleiotropy and remove the invalid IVs. Figure (A-C) are the results of motivating simulation. Figure (A) shows the point plot for absolute value of \(\hat{\epsilon}_{j}^{(\ell)}\) in different populations, and larger point means the larger value of \(\hat{\epsilon}_{j}^{(\ell)}\) . As the pleiotropic effect larger, the \(\hat{\epsilon}_{j}^{(\ell)}\) is larger, thus the first part of MR-EILLS model minimize pleiotropic effect between different populations. Figure (B) shows the correlation between \(\hat{\epsilon}_{j}^{(\ell)}\) and \(\hat{\theta}_{p^{\prime}}^{(\ell)}\) , which representing the correlated pleiotropic effect or the violation of InSIDE assumption. As the correlated pleiotropic effect increasing, this correlation is larger. This corresponding to the second part of MR-EILLS, the empirical focused linear invariance regularizer, which discourages selecting exposures with strong correlation between \(\hat{\theta}_{p^{\prime}}^{(\ell)}\) and \(\epsilon_{j}^{(\ell)}\) in some populations because this correlation would distort the causal effect estimation. Figure (C) shows the ridge plot of \(\sum_{n\in \mathbb{J}}\hat{\epsilon}_{j}^{(\ell)}| + \sum_{p\in \mathbb{P}}\sum_{n\in \mathbb{J}}\hat{\theta}_{p^{\prime}}^{(\ell)}\epsilon_{j}^{(\ell)}|\) when there are different proportion of invalid IVs. When there are invalid IVs, the ridge plot has two peaks, while the ridge plot has only one peak when there is no invalid IV. The corresponding abscission value at the lowest point between the two peaks is the optimal \(\lambda\) . The third part of MR-EILLS model is removing the invalid IVs by \(\lambda\) . </center>  
+
+Question: How to infer global causal relationships by integrating multiple heterogeneous GWAS summary datasets?  
+
+![](images/Figure_1.jpg)
+
+<center>Figure 1. MR-EILLS model. MR-EILLS model aims to infer the causal relationships between one or multiple exposures and one outcome, integrating multiple GWAS summary datasets from heterogeneous populations. There are different pleiotropic effects and IV strengths for the same IVs in heterogeneous populations. The objective function of MR-EILLS model considers the both correlated and uncorrelated pleiotropy and remove the invalid IVs. Figure (A-C) are the results of motivating simulation. Figure (A) shows the point plot for absolute value of \(\hat{\epsilon}_{j}^{(\ell)}\) in different populations, and larger point means the larger value of \(\hat{\epsilon}_{j}^{(\ell)}\) . As the pleiotropic effect larger, the \(\hat{\epsilon}_{j}^{(\ell)}\) is larger, thus the first part of MR-EILLS model minimize pleiotropic effect between different populations. Figure (B) shows the correlation between \(\hat{\epsilon}_{j}^{(\ell)}\) and \(\hat{\theta}_{p^{\prime}}^{(\ell)}\) , which representing the correlated pleiotropic effect or the violation of InSIDE assumption. As the correlated pleiotropic effect increasing, this correlation is larger. This corresponding to the second part of MR-EILLS, the empirical focused linear invariance regularizer, which discourages selecting exposures with strong correlation between \(\hat{\theta}_{p^{\prime}}^{(\ell)}\) and \(\epsilon_{j}^{(\ell)}\) in some populations because this correlation would distort the causal effect estimation. Figure (C) shows the ridge plot of \(\sum_{n\in \mathbb{J}}\hat{\epsilon}_{j}^{(\ell)}| + \sum_{p\in \mathbb{P}}\sum_{n\in \mathbb{J}}\hat{\theta}_{p^{\prime}}^{(\ell)}\epsilon_{j}^{(\ell)}|\) when there are different proportion of invalid IVs. When there are invalid IVs, the ridge plot has two peaks, while the ridge plot has only one peak when there is no invalid IV. The corresponding abscission value at the lowest point between the two peaks is the optimal \(\lambda\) . The third part of MR-EILLS model is removing the invalid IVs by \(\lambda\) . </center>  
+
+## Figure 1  
+
+See image above for figure legend.
+
+<--- Page Split --->
+![](images/Figure_3.jpg)
+
+<center>Figure 2 </center>  
+
+Simulation results when \(\mathrm{P} = 1\) (UVMR). (A- B) Results of causal effect estimation and type I error rate when the causal effect is zero. (C- D) Results of causal effect estimation and type I error rate when the causal effect is 0.1. The number of IVs is 100 and the proportion of invalid IVs is \(30\%\) . The number of populations is \(\mathrm{E} = 3\) .
+
+<--- Page Split --->
+![](images/Figure_4.jpg)
+
+<center>Figure 3 </center>  
+
+Simulation results of causal effect estimation when \(\mathbf{P} = \mathbf{8}\) (MVMR). The number of IVs is 100 and the proportion of invalid IVs is \(30\%\) . The number of populations is \(\mathrm{E} = 3\) .
+
+<--- Page Split --->
+![](images/Figure_5.jpg)
+
+<center>Figure 4 </center>  
+
+Simulation results of type I error rate for spurious exposures and statistical power for causal exposures when \(\mathbf{P} = \mathbf{8}\) (MVMR). The number of IVs is 100 and the proportion of invalid IVs is \(30\%\) . The number of populations is \(\mathrm{E} = 3\) .
+
+<--- Page Split --->
+![](images/Figure_6.jpg)
+
+<center>Figure 5 </center>  
+
+Simulation results of F1 score, precision and recall when \(\mathbf{P} = 15\) (MVMR). The number of IVs is 100 and the proportion of invalid IVs is \(30\%\) . The number of populations is \(\mathrm{E} = 3\) .  
+
+![PLACEHOLDER_28_1]
+
+<center>Figure 6 </center>  
+
+Results in application.(A) the heterogeneity among different populations; (B) the causal effect estimations of 11 blood cells on lung function.  
+
+## Supplementary Files  
+
+This is a list of supplementary files associated with this preprint. Click to download.
+
+<--- Page Split --->
+
+SupplementaryTable.xlsxSupplementaryMaterials1208.docx
+
+<--- Page Split --->

preprint/preprint__019a1322a4a18120bcf8546c95c7927691c4f4ceee9e4ba840a419b80a2ebe10/preprint__019a1322a4a18120bcf8546c95c7927691c4f4ceee9e4ba840a419b80a2ebe10_det.mmd

@@ -0,0 +1,492 @@
+<|ref|>title<|/ref|><|det|>[[44, 106, 950, 208]]<|/det|>
+# Invariance-based Mendelian Randomization Method Integrating Multiple Heterogeneous GWAS Summary Datasets  
+
+<|ref|>text<|/ref|><|det|>[[44, 229, 150, 247]]<|/det|>
+Xiaohua Zhou  
+
+<|ref|>text<|/ref|><|det|>[[54, 256, 300, 273]]<|/det|>
+azhou@bicmr.pku.edu.cn  
+
+<|ref|>text<|/ref|><|det|>[[50, 302, 730, 343]]<|/det|>
+Beijing International Center for Mathematical Research, Peking University  Lei Hou  
+
+<|ref|>text<|/ref|><|det|>[[52, 348, 208, 366]]<|/det|>
+Peking University  
+
+<|ref|>text<|/ref|><|det|>[[44, 373, 770, 415]]<|/det|>
+Hao Chen  Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 456, 103, 473]]<|/det|>
+## Article  
+
+<|ref|>text<|/ref|><|det|>[[44, 493, 940, 536]]<|/det|>
+Keywords: univariate mendelian randomization, multivariate mendelian randomization, GWAS summary datasets, heterogeneous populations, multiple ancestries  
+
+<|ref|>text<|/ref|><|det|>[[44, 553, 350, 572]]<|/det|>
+Posted Date: December 17th, 2024  
+
+<|ref|>text<|/ref|><|det|>[[44, 591, 475, 611]]<|/det|>
+DOI: https://doi.org/10.21203/rs.3.rs- 5602368/v1  
+
+<|ref|>text<|/ref|><|det|>[[44, 629, 916, 672]]<|/det|>
+License: © © This work is licensed under a Creative Commons Attribution 4.0 International License.  Read Full License  
+
+<|ref|>text<|/ref|><|det|>[[44, 690, 535, 710]]<|/det|>
+Additional Declarations: There is NO Competing Interest.  
+
+<|ref|>text<|/ref|><|det|>[[42, 745, 936, 789]]<|/det|>
+Version of Record: A version of this preprint was published at Nature Communications on August 18th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 62823- 6.
+
+<--- Page Split --->
+<|ref|>title<|/ref|><|det|>[[117, 98, 828, 149]]<|/det|>
+# Invariance-based Mendelian Randomization Method Integrating Multiple Heterogeneous GWAS Summary Datasets  
+
+<|ref|>text<|/ref|><|det|>[[108, 172, 495, 192]]<|/det|>
+3 Lei Hou \(^{1}\) , Hao Chen \(^{4}\) , Xiao- Hua Zhou \(^{1,2,3*}\)  
+
+<|ref|>title<|/ref|><|det|>[[108, 220, 316, 237]]<|/det|>
+# 4 Author affiliations:  
+
+<|ref|>text<|/ref|><|det|>[[108, 243, 852, 412]]<|/det|>
+5 1. Beijing International Center for Mathematical Research, Peking University, 6 Beijing, P.R.China, 100871 7 2. Department of Biostatistics, Peking University, Beijing, P.R.China, 100871 8 3. Chongqing Big Data Research Institute, Peking University, Chongqing, P.R.China, 9 401333 10 4. Department of biostatistics, School of Public Health, Cheeloo College of Medicine, 11 Shandong University, Shandong, Beijing, P.R.China, 250000  
+
+<|ref|>title<|/ref|><|det|>[[148, 460, 360, 478]]<|/det|>
+# \*Corresponding author:  
+
+<|ref|>text<|/ref|><|det|>[[108, 485, 850, 640]]<|/det|>
+14 Xiao- Hua Zhou, 15 E- mail: azhou@bicmr.pku.edu.cn, 16 Telephone: +86 18910208518, 17 Address: Peking University, No.5 Yiheyuan Road Haidian District, Beijing, P.R.China
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[148, 85, 240, 103]]<|/det|>
+## Abstract  
+
+<|ref|>text<|/ref|><|det|>[[144, 123, 852, 563]]<|/det|>
+Various geographical landscapes, diverse lifestyles and genetic structures may lead the heterogeneity among the GWAS summary datasets from distinct populations, especially different ethnic groups. This increases the difficulty in inferring global causal relationships from exposures on the outcome when integrating multiple GWAS summary datasets. We proposed a mendelian randomization (MR) method called MR- EILLS, which leverages the Environment Invariant Linear Least Squares (EILLS) to deduce the global causal relationship that invariant in all heterogeneous populations. The MR- EILLS model works in both univariate and multivariate scenarios, and allows the invalid instrumental variables (IVs) violating exchangeability and exclusion restriction assumptions. In addition, MR- EILLS shows the unbiased causal effect estimations of one or multiple exposures on the outcome, whether there are valid or invalid IVs. Comparing with traditional MR and meta methods, MR- EILLS demonstrates the highest estimation accuracy, the most stable type I error rates, and the highest statistical power. Finally, MR- EILLS is applied to explore the independent causal relationships between 11 blood cells and lung function, using GWAS summary statistics from five ancestries (African, East Asian, South Asian, Hispanics Latinos and European). The results cover most of the expected causal links which have biological interpretations and several new links supported by previous observational literatures.  
+
+<|ref|>text<|/ref|><|det|>[[147, 590, 850, 656]]<|/det|>
+Keywords: univariate mendelian randomization, multivariate mendelian randomization, GWAS summary datasets, heterogeneous populations, multiple ancestries
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[147, 85, 279, 103]]<|/det|>
+## Introduction  
+
+<|ref|>text<|/ref|><|det|>[[144, 120, 852, 789]]<|/det|>
+In recent years, with the rising number of Genome- Wide Association Study (GWAS) investigations, there has been a notable increase in the public availability and utilization of GWAS summary data by researchers [1- 2]. This inclusive dataset encompasses information from diverse populations and ethnic backgrounds [3- 6], a development that researchers find valuable, thus making it a current focal point of research interest. Owing to a range of influences including geographical landscapes and varied lifestyles, genetic structures exhibit significant diversity among distinct populations [7- 8], also we called population stratification, potentially leading to heterogeneity in GWAS summary data across different ethnic groups, such as those of European, Asian, and American descent. Mendelian randomization (MR) [2, 9] is a methodology that relies on the utilization of publicly available GWAS summary data for causal inference. It uses genetic variants as instrumental variables (IVs) to infer the causal effect of one or multiple exposures on an outcome, that is, univariable or multivariable MR [10- 11], respectively. A valid IV must satisfy the flowing three assumptions: relevance, exchangeability and exclusion restriction [9]. When we consider heterogeneous populations, one valid IV in a population may be an invalid IV in another population due to various genetic structures. For example, \(G_{1}\) is a valid IV in population I, it may be correlated with the confounder \(U\) between exposure and outcome in the population II, while \(U\) is not the confounder in the population I. In this case, \(G_{1}\) violates the exchangeability in population II. In addition, \(G_{1}\) may be correlated (linkage disequilibrium (LD)) [12] with another SNP \(G_{2}\) which directly affect the outcome in the population II, but \(G_{1}\) is independent with \(G_{2}\) in the population I. In this case, \(G_{1}\) violates the exclusion restriction in population II and this is due to the LD references in different populations are different. This complexity amplifies the difficulty of deducing the global causal relationship by integrating multiple heterogeneous GWAS summary datasets.  
+
+<|ref|>text<|/ref|><|det|>[[147, 791, 851, 907]]<|/det|>
+One straightforward idea to infer global causal relationships using MR is that, first conduct MR analysis separately using valid IVs in different populations and obtain the causal effect estimations in each population, then combine all estimations by meta- analysis [13- 14]. Even there may be invalid IVs in the first step, lots of MR methods [15- 18] are proposed to remove the influence of invalid IVs on the causal effect estimation.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[144, 82, 852, 496]]<|/det|>
+However, the accuracy of meta-analysis results depends on the robustness of different MR methods, while these MR methods require different assumptions [15- 18], which may be difficult to satisfy or even cannot be tested. This may induce the inconsistent causal effect estimation in different populations, and bring difficulties for inferring global causal relationships (see section Application). Another idea is that first conduct GWAS meta- analysis for heterogeneous populations, then select valid IVs to infer causal relationship using MR. The difficulty for this strategy is that only a short number of independent SNPs (no LD) can be selected because the LD reference panels in different populations are different [8,19]. These two strategies are both two- step process, and bring the doubled statistical errors, which yields the lower accuracy of causal effect estimation. Besides, meta- analysis is a statistical technique used to combine and analyze results from multiple studies [20], if one result is inaccurate, the results of meta- analysis is also incorrect. It is not a causal method in itself and does not necessarily provide causal evidence that holds true in every population included in the analysis. Therefore, following we proposed a one- step method which integrating all information but not only MR results in each population, and provide the causal evidence that holds true (also called invariant) in each population.  
+
+<|ref|>text<|/ref|><|det|>[[144, 502, 853, 791]]<|/det|>
+In this paper, we provide a MR method called MR- EILLS, which utilizes the Environment Invariant Linear Least Squares (EILLS) [21] to integrating multiple heterogeneous GWAS summary datasets, then infer global causal relationship. The MR- EILLS model works in both univariate and multivariate scenarios, and allows the invalid IVs violating exchangeability and exclusion restriction assumptions. In addition, MR- EILLS shows the unbiased causal effect estimation of one or multiple exposures on the outcome, whether there are valid or invalid IVs. Comparing with traditional MR and Meta methods, MR- EILLS demonstrates the highest estimation accuracy, the most stable type I error rates, and higher statistical power. Finally, MR- EILLS is applied to explore the independent causal relationships between 11 blood cells and 4 lung function indexes, using GWAS summary statistics from 5 ancestries: African, East Asian, South Asian, Hispanics Latinos and European.  
+
+<|ref|>sub_title<|/ref|><|det|>[[148, 810, 225, 828]]<|/det|>
+## Results  
+
+<|ref|>sub_title<|/ref|><|det|>[[148, 850, 293, 866]]<|/det|>
+## Method overview  
+
+<|ref|>text<|/ref|><|det|>[[368, 874, 628, 891]]<|/det|>
+[please insert the Figure 1 here]
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[144, 82, 852, 375]]<|/det|>
+MR- EILLS model integrating the GWAS summary statistics from multiple heterogeneous populations, and find the causal exposures which have invariant effects with outcome in heterogeneous populations. GWAS summary statistics in \(E\) heterogeneous populations include \(G_{j} - X\) association \(\hat{\theta}_{p,j}^{(e)}\) and its standard error \(\sigma_{G_jX_p}^{(e)^2}\) , as well as \(G_{j} - Y\) association \(\hat{\Gamma}_{y,j}^{(e)}\) and its standard error \(\sigma_{y,j}^{(e)^2}\) for \(E = e\) . We assume that the causal effects of causal exposures on \(Y\) is invariant in different populations, that is \(\beta_{0p}^{(1)} = \beta_{0p}^{(2)} = \ldots = \beta_{0p}^{(E)} = \beta_{0p}^{*}\) for \(p\in P^{*}\) , while the genetic associations between SNPs and exposures/outcome/confounders may be different, and confounders between exposures and the outcome are also different. MR- EILLS model (Figure 1) aims to explore the global causal effect estimation by minimizing the following objective function  
+
+<|ref|>equation<|/ref|><|det|>[[245, 380, 848, 450]]<|/det|>
+\[\begin{array}{rl} & {\mathcal{Q}(\beta_{0p}^{*};\hat{\theta}_{p,j}^{(e)},\hat{\Gamma}_{y,j}^{(e)},\sigma_{y,j}^{(e)^2})}\\ & {= \sum_{e\in E}w^{(e)}\mathrm{E}_{j\in S^{*}}[|w_{j}^{(e)}\hat{\mathcal{E}}_{j}^{(e)}|^2 ] + \gamma \sum_{p\in P}\sum_{e\in E}w^{(e)}|\mathrm{E}_{j\in S^{*}}[\hat{\theta}_{p,j}^{(e)}\cdot w_{j}^{(e)}\hat{\mathcal{E}}_{j}^{(e)}]|^2} \end{array} \quad (1)\]  
+
+<|ref|>text<|/ref|><|det|>[[144, 458, 852, 850]]<|/det|>
+where \(w_{j}^{(e)}\) is the weight of IV \(G_{j}\) on the causal effect estimation in population \(E = e\) , and \(w^{(e)}\) is the weight of population \(E = e\) on the global causal effect estimation. The first part of objective function (1) is the empirical \(L_{2}\) loss, which is the multiple populations version of objective function (6) in one population (see Method section), and \(\hat{\mathcal{E}}_{j}^{(e)} = \hat{\Gamma}_{y,j}^{(e)} - \sum_{p}\hat{\theta}_{p,j}^{(e)}\beta_{0p}^{(e)}\) also denotes the pleiotropic effect. Motivating simulation (Figure 1A, Figure S1A) demonstrates that as the pleiotropic effect (no matter correlated or uncorrelated) increasing, the absolute value of \(\hat{\mathcal{E}}_{j}^{(e)}\) is larger. The second part of objective function (1) is the empirical focused linear invariance regularizer, which discourages selecting exposures with strong correlation between \(\theta_{p,j}^{(e)}\) and \(\epsilon_{j}^{(e)}\) in some populations because this correlation would distort the causal effect estimation (see Method section). Motivating simulation (Figure 1B, Figure S1B) demonstrates that as correlated pleiotropic effect increasing, the correlation between \(\hat{\mathcal{E}}_{j}^{(e)}\) and \(\hat{\theta}_{p,j}^{(e)}\) is larger, and this means the violation of the InSIDE assumption [18] is more severe. \(\gamma > 0\) is the hyper parameter. In addition, we add the restriction  
+
+<|ref|>equation<|/ref|><|det|>[[309, 860, 848, 896]]<|/det|>
+\[S^{*} = \{j:\sum_{e\in E}|\epsilon_{j}^{(e)}| + \sum_{p\in P}\sum_{e\in E}|\hat{\theta}_{p,j}^{(e)}\epsilon_{j}^{(e)}|< \lambda \} \quad (2)\]
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 83, 852, 298]]<|/det|>
+to select the valid IVs. The first part in equation (2) represents the total pleiotropic effect for \(j - th\) IV, and the second part in equation (2) represents the correlation between \(\theta_{p,j}^{(e)}\) and \(\epsilon_{j}^{(e)}\) for \(j - th\) IV. \(\lambda >0\) is the hyper parameter controlling the strictness of filtering IVs. When there are invalid IVs, the ridge plot of \(\sum_{e\in E}\left|\epsilon_{j}^{(e)}\right| + \sum_{p\in P}\sum_{e\in E}\left|\hat{\theta}_{p,j}^{(e)}\epsilon_{j}^{(e)}\right|\) has at least two peaks (Figure 1C, Method section), while the ridge plot has only one peak when there is no invalid IV. The corresponding abscission value at the lowest point between the two peaks is the optimal \(\lambda\) . Thus equation (2) removes the invalid IVs with pleiotropic effects are larger than \(\lambda\) .  
+
+<|ref|>sub_title<|/ref|><|det|>[[148, 315, 244, 331]]<|/det|>
+## Simulation  
+
+<|ref|>text<|/ref|><|det|>[[147, 339, 852, 554]]<|/det|>
+We generated the GWAS summary statistics of \(E\) heterogeneous populations with different edges' effects, IV strength and pleiotropy in the cases of UVMR and MVMR, respectively. And we compared MR- EILLS with six published methods includes IVW, MR- Egger, MR- Lasso, MR- Median, MR- cML and MR- BMA, and all of them had the UVMR and MVMR version except MR- BMA. For these MR methods, we consider two strategies: metaMR: first meta all the GWAS summary statistics of multiple datasets for each variable then conduct the MR analysis; mrMeta: first conduct the MR analysis in multiple datasets separately then meta all the MR results. Meta methods include the random- effect and fixed- effect meta- analysis.  
+
+<|ref|>text<|/ref|><|det|>[[368, 560, 629, 578]]<|/det|>
+[please insert the Figure 2 here]  
+
+<|ref|>text<|/ref|><|det|>[[147, 584, 852, 899]]<|/det|>
+For UVMR, in case (a), when there is correlated and uncorrelated pleiotropy (30% invalid IVs), MR- EILLS and MR- cML with metaMR show the unbiased causal effect estimation, while other methods are biased (Figure 2). MR- EILLS exhibits the higher accuracy, more stable type I error rates when causal effect is 0, and higher statistical power when causal effect isn't zero, than MR- cML with metaMR. When the proportion of invalid IVs is 80%, causal effect estimation using all MR methods including MR- cML are all biased, while MR- EILLS shows the unbiased causal effect estimation. MR- EILLS also exhibits the stable type I error rate when causal effect is 0 and statistical power is above 90% when the number of IVs is 300 in the case of causal effect is not zero. For the case (b), simulation results are similar as that in case (c). For the case (c), when there is no pleiotropy, all the methods show the unbiased causal effect estimation, stable type I error rate when causal effect is zero and high statistical power when causal effect isn't zero. Simulation results are shown in Figure S2- S21.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[366, 84, 629, 101]]<|/det|>
+[please insert the Figure 3 here]  
+
+<|ref|>text<|/ref|><|det|>[[368, 108, 629, 126]]<|/det|>
+[please insert the Figure 4 here]  
+
+<|ref|>text<|/ref|><|det|>[[145, 131, 852, 644]]<|/det|>
+For MVMR, Figure 3 shows the causal effect estimation when there are 8 exposures, and \(30\%\) IVs have correlated or uncorrelated pleiotropy (case(a)). MR- EILLS shows unbiased causal effect estimations for all exposures, while other methods show the biased causal effect estimation, and MR- cML with metaMR also exhibits sightly biased causal effect estimations for some exposures. MR- EILLS also shows the highest accuracy among all methods. Figure 4 shows the type I error rate when causal effect is zero and statistical power when causal effect isn't zero. MR- EILLS shows the highest statistical power when causal effect isn't zero, and the most stable type I error rate while it is slightly lower than 0.05 for several exposures, but this phenomenon disappears when the number of populations is larger, e.g. \(E = 8\) (Figure S2- S3). When \(P = 3\) , the results of simulation are similar as above. When the proportion of invalid IVs is \(80\%\) , causal effect estimation using all MR methods are biased, while MR- EILLS shows the unbiased causal effect estimation. MR- EILLS also exhibits the stable type I error rate when causal effect is 0 and statistical power is above \(90\%\) when the number of IVs is 300 in the case of causal effect is not zero. For the case (b), simulation results are similar as that in case (c). For the case (c), when there is no pleiotropy, all the methods show the unbiased causal effect estimation, stable type I error rate when causal effect is zero and high statistical power when causal effect isn't zero. Simulation results are shown in Figure S22- S43. When \(P = 15\) , we calculate the mean of F1 score, recall and precision for each method in Figure 5. MR- EILLS shows the highest F1 score, recall and precision among all methods.  
+
+<|ref|>text<|/ref|><|det|>[[368, 650, 629, 668]]<|/det|>
+[please insert the Figure 5 here]  
+
+<|ref|>text<|/ref|><|det|>[[145, 675, 852, 870]]<|/det|>
+We also demonstrate the heterogeneity of causal effect estimations among different populations. The summary of \(I^2\) for all simulation are shown in Table S1- S3. We randomly select one simulation and demonstrate its causal effects' estimation for each MR methods and each dataset in Figure S31, S37 and S43, which show the forest plot of causal effect estimation in different populations for different methods. The \(I^2\) in case (a) is higher than case (c), that is, the pleiotropy improve the heterogeneity between populations. The causal effect estimation in different populations show the inconsistent causal effect estimation.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[147, 85, 248, 101]]<|/det|>
+## Application  
+
+<|ref|>text<|/ref|><|det|>[[145, 108, 852, 348]]<|/det|>
+We explore the causal relationships between total 11 blood cells (5 red blood cells: hemoglobin concentration (HGB), hematocrit (HCT), mean corpuscular hemoglobin (MCH), mean corpuscular volume (MCV), mean corpuscular hemoglobin concentration (MCHC); 5 white blood cells: white blood cell count (WBC), neutrophil count (Neutro), monocyte count (Mono), basophil count (Baso), eosinophil counts (Eosin); 1 platelets: platelet count (PLT)) and 4 lung function indexes (forced expiratory volume (FEV), vital capacity (FVC), FEV/FVC ratio, peak expiratory flow (PEF)) using GWAS summary statistics from 5 ancestries: African, East Asian, South Asian, Hispanics Latinos and European. Details for GWAS summary statistics were shown in Method section and Table S4.  
+
+<|ref|>text<|/ref|><|det|>[[145, 354, 852, 644]]<|/det|>
+Firstly, we conducted traditional MR analysis in 5 ancestries, respectively, and performed the heterogeneous analysis for each MR method. Results were shown in Figure 6A. We found that there were large heterogeneities ( \(I^2 > 0.75\) ) for a number of blood cells in 5 ancestries. Then we conducted MR- EILLS analysis to explore independent causal effect from 11 blood cells on each lung function index. We plot ridge plots for each outcome in 5 ancestries and results were shown in Figure S44. Based on the ridge plot, we set the \(\lambda\) for MR- EILLS (Table S5). Results of MR- EILLS revealed that 3 blood cells (2 white blood cells: WBC and Neutro; 1 red blood cells: HGB) were causally associated with FEV; 1 blood cell (white blood cells: WBC) was causally associated with FVC; 3 blood cells (1 platelets: PLT; 2 red blood cells: HGB and HCT) was causally associated with FEV/FVC; 1 blood cell (red blood cells: HGB) was causally associated with PEF.  
+
+<|ref|>text<|/ref|><|det|>[[369, 652, 629, 670]]<|/det|>
+[please insert the Figure 6 here]  
+
+<|ref|>text<|/ref|><|det|>[[145, 676, 852, 892]]<|/det|>
+We found that the higher counts of some white blood cells, red blood cells or platelets would independently reduce the levels of lung function. For FEV, higher counts of WBC, Neutro and HGB would causally induce the lower level of FEV (WBC: \(\mathrm{beta} = - 0.14\) , \(95\% \mathrm{CI}\) : [- 0.24, - 0.04]; Neutro: \(\mathrm{beta} = - 0.17\) , \(95\% \mathrm{CI}\) : [- 0.24, - 0.04]; HGB: \(\mathrm{beta} = - 0.29\) , \(95\% \mathrm{CI}\) : [- 0.54, - 0.03]). The counts of Neutro and HCT were negatively associated with the level of FVC (Neutro: \(\mathrm{beta} = - 0.09\) , \(95\% \mathrm{CI}\) : [- 0.18, - 0.01]; HCT: \(\mathrm{beta} = - 0.06\) , \(95\% \mathrm{CI}\) : [- 0.13, - 0.002]). Besides, elevation in the levels of PLT and Neutro were associated with a decreased FEV/FVC ratio (PLT: \(\mathrm{beta} = - 0.26\) , \(95\% \mathrm{CI}\) : [- 0.49, - 0.02]; Neutro: \(\mathrm{beta} = - 0.16\) , \(95\% \mathrm{CI}\) : [- 0.30, - 0.02]). Higher concentrations of MCH
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[144, 83, 852, 348]]<|/det|>
+might result in a lower PEF level (beta=- 0.08, 95%CI: [- 0.16, - 0.004]). James et al. validated that an increased WBC count has been associated with lower levels of lung function and provided the biological explanations [22]. A 15- year longitudinal study demonstrated that higher blood neutrophil concentrations was associated with accelerated FEV decline [23]. The inverse relations between FEV, FVC and red blood cell counts were also supported by observational studies [24- 25]. A prospectively Longitudinal analyses revealed that higher baseline neutrophil count predicted lower serially obtained FVC [26]. A retrospective study found that there is a strong correlation between PLT and FEV/FVC ratio [27]. The results cover most of the expected causal links which have biological interpretations and several new links supported by previous observational literatures. Details of results were shown in Table S6- S11.  
+
+<|ref|>sub_title<|/ref|><|det|>[[148, 367, 256, 386]]<|/det|>
+## Discussion  
+
+<|ref|>text<|/ref|><|det|>[[147, 406, 852, 547]]<|/det|>
+In this paper, we proposed a MR method MR- EILLS, which works in both univariable and multivariable framework, and it outputted the global causal effect estimation of multiple heterogeneous populations using only GWAS summary statistics. Results of simulation exhibited the superior performance of MR- EILLS and its application in exploring causal relationships from 11 blood cells to lung function covered most of the expected causal links.  
+
+<|ref|>text<|/ref|><|det|>[[147, 553, 852, 769]]<|/det|>
+MR- EILLS integrates the GWAS summary datasets from heterogeneous populations, and for each population, GWAS summary datasets for exposure and outcome can be from either the same individuals or the different but heterogeneous individuals. Actually, this assumption is the same as that in traditional two- sample MR analysis, which require two homogeneous but non- overlap samples. MR- EILLS assumes that the GWAS summary datasets for each population are from homogeneous but non- overlap samples. In the application, we assume that the individuals in each ancestry are homogeneous, and the genetic diversity in different ancestries lead the heterogeneous among ancestries (different IV strength and pleiotropy).  
+
+<|ref|>text<|/ref|><|det|>[[147, 775, 852, 895]]<|/det|>
+MR- EILLS allows different IV set in different populations. However, the strategy for metaMR, that is, first conduct GWAS meta- analysis then perform MR analysis, require the SNPs that are independent (no LD) in all populations, this reduces a large number of IVs, although GWAS meta- analysis helps researchers obtain more significant SNPs with \(P< 5\times 10^{- 8}\) . Besides, only a few MR methods allow the SNP set
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 83, 850, 127]]<|/det|>
+with large LD. MR- EILLS solved this tricky issue and it only requires that IV set in each population are independent without LD.  
+
+<|ref|>text<|/ref|><|det|>[[147, 133, 852, 333]]<|/det|>
+MR- EILLS model has two hyper- parameters, which need researchers to set appropriate value to estimate causal effects of exposures on the outcome. For \(\gamma\) , we recommend \(\gamma > 0.4\) in UVMR, and \(\gamma > 0\) in MVMR. The larger \(\gamma\) , the stronger the role of empirical focused linear invariance regularizer. For \(\lambda\) , we suggest the researchers plot the ridge plot to find the optimal value. In model (2), we keep the SNP, for which the pleiotropic effect in all populations is lower than \(\lambda\) . When the scales of different populations are different, the model (2) can be modified as the following model (2- 1)  
+
+<|ref|>equation<|/ref|><|det|>[[312, 345, 848, 380]]<|/det|>
+\[S^{*} = \{j:\left|\epsilon_{j}^{(e)}\right| + \sum_{p\in P}\left|\hat{\theta}_{p,j}^{(e)}\epsilon_{j}^{(e)}\right|< \lambda_{e}\text{for any} e\} . \quad (2-1)\]  
+
+<|ref|>text<|/ref|><|det|>[[147, 388, 852, 540]]<|/det|>
+The researchers can set different \(\lambda_{e}\) for different populations. For example, in our applications, we set different \(\lambda_{e}\) for five ancestries, respectively, and five ridge plots are plotted for each outcome. MR- EILLS works if and only if there are at least \(J \geq P\) valid IVs in the IV set and this assumption is less strict than the plurality assumption [17], which requires the valid IVs form the largest group of IVs sharing the same causal parameter value.  
+
+<|ref|>text<|/ref|><|det|>[[147, 546, 852, 812]]<|/det|>
+There are several limitations for MR- EILLS. The first is that MR- EILLS doesn't work in the high- dimensional case yet. One future key research direction is to extend MR- EILLS to high- dimensional exposure scenarios, especially for the high- dimensional- omics biomarkers, for this, correlated IVs is also an important issue to be solved. Another point is that inappropriate settings of hyper parameters may induce the incorrect inference of causal relationships between exposures and outcome. It is important to choose the appropriate for hyper parameters, especially for \(\lambda\) . The value of \(\lambda\) determined that whether the invalid IVs are removed, and if \(\lambda\) is too large, the causal effect estimation would be distorted. If \(\lambda\) is too small, the number of remaining IVs is small, thus in the future it is necessary to extend MR- EILLS to correlated IVs scenarios.  
+
+<|ref|>text<|/ref|><|det|>[[147, 818, 852, 911]]<|/det|>
+In conclusion, we proposed a MR method MR- EILLS, which integrate multiple heterogeneous GWAS summary datasets to infer the global causal relationships between exposures and outcome. This study has important guiding significance for the discovery of new disease- related factors. We look forward to offering constructive
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 83, 850, 127]]<|/det|>
+suggestions for disease diagnosis and applying our method beyond the scope considered here.  
+
+<|ref|>sub_title<|/ref|><|det|>[[147, 145, 240, 164]]<|/det|>
+## Methods  
+
+<|ref|>sub_title<|/ref|><|det|>[[147, 184, 745, 204]]<|/det|>
+## MR-EILLS model: MR integrating multiple heterogeneous populations  
+
+<|ref|>text<|/ref|><|det|>[[147, 211, 847, 261]]<|/det|>
+For one population, assume \(P\) exposures \(X_{p}, p \in \{1, \ldots , P\}\) and one outcome \(Y\) . The \(J\) independent IVs \(G_{j}, j \in \{1, \ldots , J\}\) satisfy the following three assumptions:  
+
+<|ref|>text<|/ref|><|det|>[[187, 273, 631, 293]]<|/det|>
+A1. \(G_{j}\) is associated with at least one of \(P\) exposures;  
+
+<|ref|>text<|/ref|><|det|>[[147, 305, 850, 353]]<|/det|>
+A2. \(G_{j}\) is not associated with the confounder between \(P\) exposures and the outcome;  
+
+<|ref|>text<|/ref|><|det|>[[187, 361, 615, 380]]<|/det|>
+A3. \(G_{j}\) affects the outcome only through exposures.  
+
+<|ref|>text<|/ref|><|det|>[[147, 390, 570, 409]]<|/det|>
+Then the MR model based on the individual data is:  
+
+<|ref|>equation<|/ref|><|det|>[[315, 415, 848, 500]]<|/det|>
+\[\begin{array}{l}{{U=\sum_{j}\omega_{j}G_{j}+\epsilon_{X_{U}}}}\\ {{X_{p}=\sum_{j}\alpha_{p j}G_{j}+\sum_{X_{k}\in p a(X_{p})}\beta_{X_{k}X_{p}}X_{k}+\beta_{1p}U+\epsilon_{X_{p}}}}\\ {{Y=\sum_{j}\gamma_{j}G_{j}+\sum_{p}\beta_{0p}X_{p}+\beta_{2}U+\epsilon_{Y}}}\end{array} \quad (3)\]  
+
+<|ref|>text<|/ref|><|det|>[[147, 508, 851, 732]]<|/det|>
+where \(\epsilon_{X_{U}}, \epsilon_{X_{p}}, \epsilon_{Y} \sim N(0,1)\) . \(\gamma_{j}\) represents the uncorrelated pleiotropic effect and \(\omega_{j}\) represents the correlated pleiotropy. \(\beta_{0p}\) denote the causal effect of \(X_{p}\) on \(Y\) . We call the exposures with \(\beta_{0p} \neq 0\) are the causal exposures, which we want to discover, while the exposures with \(\beta_{0p} = 0\) are the spurious exposures, which are not the true cause of outcome. We define the set of causal exposures is \(\{X_{p}\} , p \in P^{*} \subseteq \{1, \ldots , P\}\) . When \(P = 1\) , above model is called UVMR, while when \(P > 1\) , it is called MVMR. To simplify the expression, our model below uniformly uses \(P\) exposures, both applicable to UVMR and MVMR.  
+
+<|ref|>text<|/ref|><|det|>[[147, 741, 850, 823]]<|/det|>
+GWAS summary statistics including \(G_{j} - X_{p}\) association \(\hat{\theta}_{p,j}\) and its variance \(\sigma_{p,j}^{2}\) , as well as \(G_{j} - Y\) association \(\hat{\Gamma}_{y,j}\) and its variance \(\sigma_{y,j}^{2}\) . Based on model (3), we have  
+
+<|ref|>equation<|/ref|><|det|>[[310, 830, 848, 888]]<|/det|>
+\[\begin{array}{l}{\theta_{p,j} = \alpha_{p,j} + \omega_{j}\beta_{1p} + \sum_{X_{k}\in p a(X_{p})}\theta_{k,j}\beta_{X_{k}X_{p}}}\\ {\Gamma_{y,j} = \omega_{j}\beta_{2} + \gamma_{j} + \sum_{p}\theta_{p,j}\beta_{0p}} \end{array} \quad (4)\]
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[145, 83, 850, 230]]<|/det|>
+When \(G_{j}\) is a valid IV (no pleiotropy), that is \(\gamma_{j} = \omega_{j} = 0\) , then \(\epsilon_{j} = \Gamma_{y,j} - \sum_{p}\theta_{p,j}\beta_{0p}\) is zero and it is dependent with \(\theta_{p,j}\) . For \(j\in \{1,\dots,J\}\) , we can identify \(\beta_{0p}\) \((p\in \{1,\dots,P\})\) by the system of linear equations \(\Gamma_{y,j} = \sum_{p}\theta_{p,j}\beta_{0p}\) if and only if \(J\geq P\) . The causal effects of exposures on the outcome \(\beta_{0p}\) can be estimated by weighted version of ordinary least squares (OLS), that is, the IVW regression  
+
+<|ref|>equation<|/ref|><|det|>[[312, 234, 848, 264]]<|/det|>
+\[\hat{\Gamma}_{y,j} = \sum_{p}\hat{\theta}_{p,j}\beta_{0p} + \zeta_{j},\zeta_{j}\sim N(0,\sigma_{y,j}^{2}), \quad (5)\]  
+
+<|ref|>text<|/ref|><|det|>[[145, 273, 850, 321]]<|/det|>
+which set the intercept is zero. This model minimizes the empirical \(L_{2}\) loss objective function  
+
+<|ref|>equation<|/ref|><|det|>[[320, 328, 848, 412]]<|/det|>
+\[\begin{array}{rl} & Q(\beta_{0p};\hat{\theta}_{p,j},\hat{\Gamma}_{y,j},\sigma_{y,j}^{2})\\ & = \mathrm{E}[|w_{j}\epsilon_{j}|^{2}]\\ & = \mathrm{E}[|w_{j}(\hat{\Gamma}_{y,j} - \sum_{p}\hat{\theta}_{p,j}\beta_{0p})|^{2}] \end{array} \quad (6)\]  
+
+<|ref|>text<|/ref|><|det|>[[145, 419, 852, 815]]<|/det|>
+where \(w_{j}\) represents the weight of IV \(G_{j}\) on the casual effect estimation. If \(G_{j}\) have uncorrelated pleiotropy \((\gamma_{j}\neq 0)\) , that is, \(G_{j}\) is causally associated with \(Y\) not through any \(X_{p}\) , then the \(\epsilon_{j} = \gamma_{j}\) is no more equal to zero, and it represents the uncorrelated pleiotropic effect. MR- Egger regression [18] is proposed to solved this problem by allowing the intercept term in model (5), and the intercept represent the pleiotropic effect. MR- Egger regression requires the InSIDE assumption, which means the pleiotropic effect is independent with \(\theta_{p,j}\) . If \(G_{j}\) have correlated pleiotropy \((\omega_{j}\neq 0)\) , that is, \(G_{j}\) is causally associated with the unmeasured confounding \(U\) between \(X_{p}\) and \(Y\) , then pleiotropic effect \(\epsilon_{j} = \omega_{j}\beta_{2} + \gamma_{j}\) is not independent with \(\theta_{p,j}\) because of the common term \(\omega_{j}\) . This is the violation of the InSIDE assumption. Equation (5- 6) and MR- Egger require that \(\epsilon_{j}\) is independent with \(\theta_{p,j}\) because the correlation between intercept term and independent variables would distort the causal effect estimation. Results of motivating simulation for correlated and uncorrelated pleiotropy are shown in Figure S1.  
+
+<|ref|>text<|/ref|><|det|>[[147, 819, 850, 905]]<|/det|>
+When there are \(E\) heterogeneous populations, GWAS summary statistics include \(\hat{\theta}_{p,j}^{(e)}\) , \(\sigma_{G_jX_p}^{(e)2}\) , \(\hat{\Gamma}_{y,j}^{(e)}\) and \(\sigma_{y,j}^{(e)2}\) for \(E = e\) . We define \(\epsilon_{j}^{(e)} = \Gamma_{y,j}^{(e)} - \sum_{p}\theta_{p,j}^{(e)}\beta_{0p}^{(e)}\) and \(\hat{\epsilon}_{j}^{(e)} = \hat{\Gamma}_{y,j}^{(e)} - \sum_{p}\hat{\theta}_{p,j}^{(e)}\beta_{0p}^{(e)}\) in the version of multiple populations. We use superscript \((e)\)
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[144, 82, 852, 292]]<|/det|>
+to denote the \(e\) - th population. We assume that the pleiotropic effect, IV strength and the relationships among exposures are different in heterogeneous populations, while the causal effects of causal exposures on \(Y\) is invariant, that is \(\beta_{0p}^{(1)} = \beta_{0p}^{(2)} = \ldots = \beta_{0p}^{(E)} = \beta_{0p}^{*}\) for \(p \in P^{*}\) , this assumption called the structure assumption [21]. These assumptions are rational because the IV satisfying A1- A3 only control the unmeasured confounders between \(X_{p}\) and \(Y\) , while other unmeasured confounders between IV and exposure, or between IV and outcome, or between exposures, are not controlled, and these unmeasured confounders also the reason for heterogeneity between populations.  
+
+<|ref|>text<|/ref|><|det|>[[144, 297, 852, 594]]<|/det|>
+Note that one valid IV in one population may be the invalid IV in the other heterogeneous populations. On the other hand, an IV may be associated with the exposures in all heterogeneous populations, while it may have different uncorrelated or correlated pleiotropy in the different populations. This leads to inconsistent independence relationships between \(\theta_{p,j}^{(e)}\) and \(\epsilon_{j}^{(e)}\) across different populations and inconsistent causal effect estimation of exposures on the outcome in different heterogeneous populations. Therefore, we leverage the Environment Invariant Linear Least Squares (EILLS) [21], the multiple heterogeneous populations version of OLS, to construct the MR- EILLS model. MR- EILLS model integrating the GWAS summary statistics from multiple heterogeneous populations, and find the causal exposures which have invariant effects with outcome in heterogeneous populations. MR- EILLS model aims to minimize the following objective function  
+
+<|ref|>equation<|/ref|><|det|>[[248, 599, 848, 666]]<|/det|>
+\[\begin{array}{rl} & {\mathcal{Q}(\beta_{0p}^{*};\hat{\theta}_{p,j}^{(e)},\hat{\Gamma}_{y,j}^{(e)},\sigma_{y,j}^{(e)2})}\\ & {= \sum_{e\in E}w^{(e)}\mathrm{E}_{j\in S^{*}}\{\mid w_{j}^{(e)}\hat{\xi}_{j}^{(e)}\mid^{2}\} +\gamma \sum_{e\in E}w^{(e)}\sum_{p\in P}\mid \mathrm{E}_{j\in S^{*}}[\hat{\theta}_{p,j}^{(e)}\cdot w_{j}^{(e)}\hat{\xi}_{j}^{(e)}]\mid^{2}} \end{array} \quad (1)\]  
+
+<|ref|>text<|/ref|><|det|>[[145, 697, 202, 712]]<|/det|>
+where  
+
+<|ref|>equation<|/ref|><|det|>[[350, 718, 848, 770]]<|/det|>
+\[w_{j}^{(e)} = \frac{\sigma_{y,j}^{(e) - 2}}{\sum_{j\in S^{*}}\sigma_{y,j}^{(e) - 2}}\mathrm{and}w^{(e)} = \frac{N_{e}}{N} \quad (7)\]  
+
+<|ref|>text<|/ref|><|det|>[[144, 777, 852, 914]]<|/det|>
+\(w_{j}^{(e)}\) is the weight of IV \(G_{j}\) on the casual effect estimation in population \(E = e\) , and \(w^{(e)}\) is the weight of population \(E = e\) on the final casual effect estimation. The first part of objective function (1) is the empirical \(L_{2}\) loss, which is the multiple populations version of objective function (6) in one population. The second part of objective function (1) is the empirical focused linear invariance regularizer, which discourages
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[146, 84, 850, 160]]<|/det|>
+selecting exposures with strong correlation between \(\theta_{p,j}^{(e)}\) and \(\epsilon_{j}^{(e)}\) in some populations because this will distort the causal effect estimation. \(\gamma >0\) is the hyper parameter. In addition, we add the restriction  
+
+<|ref|>equation<|/ref|><|det|>[[339, 172, 848, 208]]<|/det|>
+\[S^{*} = \{j:\sum_{e\in E}\left|\epsilon_{j}^{(e)}\right| + \sum_{p\in P}\sum_{e\in E}\left|\hat{\theta}_{p,j}^{(e)}\epsilon_{j}^{(e)}\right|< \lambda \} \quad (2)\]  
+
+<|ref|>text<|/ref|><|det|>[[146, 216, 851, 341]]<|/det|>
+to select the valid IVs. The first part in equation (2) represents the uncorrelated pleiotropic effect for \(j - th\) IV, and the second part in equation (2) represents the correlated pleiotropic effect for \(j - th\) IV. \(\lambda >0\) is the hyper parameter controlling the strictness of filtering IVs. Equation (2) removes the invalid IVs with pleiotropic effect above \(\lambda\) .  
+
+<|ref|>text<|/ref|><|det|>[[146, 348, 851, 477]]<|/det|>
+The causal effects \(\beta_{0p}^{*}\) can be identified under the assumption [21] that there are at least \(P\) valid IVs in the IV set, that is \(J\geq P\) . We use the a limited- memory modification of the BFGS quasi- Newton method [28] to find the optimal solution \(\beta_{0p}^{*}\) of objective function (1) under the restriction of equation (2). The confidence interval is estimated by Bootstrap method.  
+
+<|ref|>sub_title<|/ref|><|det|>[[147, 494, 245, 510]]<|/det|>
+## Simulation  
+
+<|ref|>text<|/ref|><|det|>[[147, 517, 850, 561]]<|/det|>
+We generate the GWAS summary statistics of \(E\) heterogeneous populations by following process:  
+
+<|ref|>equation<|/ref|><|det|>[[301, 567, 695, 622]]<|/det|>
+\[\theta_{p,j}^{(e)} = \alpha_{p,j}^{(e)} + \omega_{j}^{(e)}\beta_{1p}^{(e)} + \sum_{X_{k}\in pa(X_{p})}\theta_{k,j}^{(e)}\beta_{X_{k}X_{p}}^{(e)} + \xi_{p,j}^{(e)}\] \[\Gamma_{y,j}^{(e)} = \omega_{j}^{(e)}\beta_{2}^{(e)} + \gamma_{j}^{(e)} + \sum_{p}\theta_{p,j}^{(e)}\beta_{0p}^{(e)} + \xi_{y,j}^{(e)}\]  
+
+<|ref|>text<|/ref|><|det|>[[146, 630, 851, 900]]<|/det|>
+Totally \(P\) exposures, the causal exposures are the top \(30\%\) of all exposures (e.g. \(P = 8\) , floor( \(P\times 30\% = 2\) , then the top two ( \(X_{1}\) and \(X_{2}\) ) are the causal exposures). The effect of causal exposure on \(Y\) ( \(\beta_{0p}^{(e)}\) , \(p\in P^{*}\) ) is 0.2 for MVMR ( \(P > 1\) ) and 0.1 for UVMR ( \(P = 1\) ), and the effect of other spurious exposure on \(Y\) ( \(\beta_{0p}^{(e)}\) , \(p\notin P^{*}\) ) is 0. \(\beta_{X_{k}X_{p}}^{(e)}\sim U(- 1,1)\) for the effect of edge \(X_{k}\to X_{p}\) . We set IV strength \(\alpha_{p,j}^{(e)}\sim U(0.05,0.2)\) for \(E = e\) and \(X_{p}\) ; \(\xi_{p,j}^{(e)}\sim N(0,\sigma_{p,j}^{(e)2})\) for \(E = e\) and \(X_{p}\) , \(\sigma_{p,j}^{(e)2}\sim U(0.01,0.05)\) for \(X_{p}\) ; \(\xi_{y,j}^{(e)}\sim N(0,\sigma_{y,j}^{(e)2})\) for \(E = e\) , \(\sigma_{y,j}^{(e)2}\sim U(0.05,0.1)\) and different variances represent different sample sizes; \(\beta_{1p}^{(e)}\sim U(0.5,0.8)\) for \(X_{p}\) ; \(\beta_{2}^{(e)}\sim U(0.5,0.8)\) . We consider three scenarios:
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[145, 85, 300, 102]]<|/det|>
+(a) No pleiotropy;  
+
+<|ref|>text<|/ref|><|det|>[[145, 110, 555, 131]]<|/det|>
+(b) uncorrelated pleiotropy effect \(\gamma_{j}^{(e)} \sim U(0,0.5)\) .  
+
+<|ref|>text<|/ref|><|det|>[[145, 140, 848, 163]]<|/det|>
+(c) uncorrelated and correlated pleiotropy effect, \(\gamma_{j}^{(e)} \sim U(0,0.5)\) and \(\omega_{j}^{(e)} \sim U(0,0.5)\) .  
+
+<|ref|>text<|/ref|><|det|>[[145, 171, 852, 290]]<|/det|>
+The parameters of edges' effects, IV strength and pleiotropy are random select from uniform distribution, thus they are different in different datasets and these represent the heterogeneous datasets. We vary the number of populations are \(E = 3\) or 8; the number of IVs is 100 or 300; the number of exposures is \(P = 1, 3, 8\) or 15, which include the cases of univariable and multivariable MR.  
+
+<|ref|>text<|/ref|><|det|>[[145, 296, 852, 488]]<|/det|>
+We conduct 200 repeated simulations to evaluate the performance of MR- EILLS. We also compare six methods includes IVW, MR- Egger, MR- Lasso, MR- Median, MR- cML and MR- BMA. For \(P = 1\) , we compare five methods in the UVMR version except MR- BMA; for \(P = 3, 8\) and 15, we compare all six methods in the MVMR version. For these MR methods, we consider two strategies: (1) first meta all the GWAS summary statistics of \(E\) datasets for each variable then conduct the MR analysis; (2) first conduct the MR analysis in \(E\) datasets separately then meta all the MR results. Meta methods include the random- effect and fixed- effect meta- analysis.  
+
+<|ref|>text<|/ref|><|det|>[[145, 494, 852, 788]]<|/det|>
+We evaluate the performance of all methods by box- violin plot for causal effect estimation, histogram for type I error when causal effect is zero and statistical power when causal effect isn't zero. Besides, we calculate the \(I^{2}\) statistics in each simulation, to evaluate the heterogeneity of causal effect estimation among different datasets, for each MR method. We plot the violin plot of \(I^{2}\) statistics for the estimations of each variable, and we random select six simulations to demonstrate the quartiles of estimation, then plot the forest plot of estimations for each method and each variable. For \(P = 15\) , we calculate the mean of F1 score, recall and precision for each method. Recall (i.e. power, or sensitivity) measures how many relationships a method can recover from the true causal relationships, whereas precision (i.e., 1- FDR) measures how many correct relationships are recovered in the inferred relationships. The F1 score is a combined index of recall and precision.  
+
+<|ref|>sub_title<|/ref|><|det|>[[147, 805, 460, 824]]<|/det|>
+## Setting of hyper parameters \(\gamma\) and \(\lambda\)  
+
+<|ref|>text<|/ref|><|det|>[[145, 832, 850, 912]]<|/det|>
+We recommend that the practitioners determine the value of \(\lambda\) by plotting a ridge plot. The abscissa is the value of \(\sum_{e \in E} | e_{j}^{(e)} | + \sum_{p \in P} \sum_{e \in E} | \hat{\theta}_{p,j}^{(e)} e_{j}^{(e)} |\) for each IV in equation (8). We plot the ridge plot in simulations in Figure S45- S49. These plots demonstrates
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[145, 84, 851, 179]]<|/det|>
+that when there is no pleiotropy, the figure has only one peak, and the \(\lambda\) just takes the value of the abscission after the first peak. When there is pleiotropy, the figure has two peaks, and the corresponding abscission value at the lowest point between the two peaks is the optimal \(\lambda\) . We provide the function of ridge plot in R package MREILLS.  
+
+<|ref|>text<|/ref|><|det|>[[145, 185, 852, 444]]<|/det|>
+In addition, we evaluate the root mean square error (RMSE) of causal effect estimation using a grid search: \(\gamma\) ranges from 0.1 to 200 and \(\lambda\) ranges from 0.1 to 1. Results are shown in the Figure S50- 58. We conclude the ranges of hyper parameters when RMSE<0.1 in Table S12. For UVMR, we recommend \(\gamma >0.4\) . When \(\gamma >0.4\) , the RMSE is less than 0.1, especially for the case of correlated and uncorrelated pleiotropy, while in other cases, RMSE is less than 0.05. For MVMR, \(\gamma >0\) is recommended. Comparing with all valid IVs, invalid IVs increased the RMSE of causal effect estimation, no matter correlated or uncorrelated pleiotropy. Therefore, \(\gamma\) is loosely valued, especially when \(P > 1\) . The larger \(\gamma\) , the stronger the role of empirical focused linear invariance regularizer.  
+
+<|ref|>sub_title<|/ref|><|det|>[[147, 460, 248, 477]]<|/det|>
+## Application  
+
+<|ref|>text<|/ref|><|det|>[[145, 483, 852, 696]]<|/det|>
+We explored the causal effect of 11 blood cells on 4 lung function indexes using GWAS summary statistics from 5 ancestries: African, East Asian, South Asian, Hispanics Latinos and European. GWAS summary statistics for blood cells were extracted from Chen et al. [29], which conducted trans- ethnic and ancestry- specific GWAS in 746,667 individuals from 5 global populations (15,171, 151,807, 8,189, 9,368 and 563,947 individuals for 5 ancestries, respectively). GWAS summary statistics for lung function were extracted from Shrine et al. [30], which conducted trans- ethnic GWAS analysis in 49 cohorts from 5 populations (8,590, 85,279, 4,270, 14,668 and 475,645 individuals for 5 ancestries, respectively). Details are shown in Table S4.  
+
+<|ref|>text<|/ref|><|det|>[[145, 703, 852, 881]]<|/det|>
+Firstly, we select IVs for MR analysis. For MR- EILLS and mrMeta analysis, we separately select SNPs with \(P< 5\times 10^{- 8}\) and clump the LD with \(r^2 >0.01\) in each population (Table S9). For metaMR analysis, we select SNPs with \(P< 5\times 10^{- 8}\) in each population then clump the union set of above SNPs with \(r^2 >0.01\) (Table S10). Then we extract the summary statistics for IVs and conduct the MR- EILLS, mrMeta and metaMR analysis. We also calculate the \(I^2\) statistics to evaluate the heterogeneity of causal effect estimation among different populations, for each MR method. For MR
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[87, 84, 850, 131]]<|/det|>
+467 EILLS, we plot the ridge plot in each population, and set \(\gamma = 0.5\) . The setting of \(\lambda\) are shown in the Table S5.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[148, 85, 347, 104]]<|/det|>
+## Acknowledgements  
+
+<|ref|>text<|/ref|><|det|>[[148, 125, 200, 141]]<|/det|>
+None.  
+
+<|ref|>sub_title<|/ref|><|det|>[[148, 162, 373, 181]]<|/det|>
+## Author Contributions  
+
+<|ref|>text<|/ref|><|det|>[[147, 201, 822, 293]]<|/det|>
+LH and ZX conceived the study. LH contributed to theoretical derivation with assistance from ZX. LH and HC contributed to the data simulation and application. LH and ZX wrote the manuscript with input from all other authors. All authors reviewed and approved the final manuscript.  
+
+<|ref|>sub_title<|/ref|><|det|>[[148, 313, 460, 333]]<|/det|>
+## Competing Interests statement  
+
+<|ref|>text<|/ref|><|det|>[[148, 352, 503, 370]]<|/det|>
+The authors declare no competing interests.  
+
+<|ref|>sub_title<|/ref|><|det|>[[148, 390, 413, 410]]<|/det|>
+## Data and code availability  
+
+<|ref|>text<|/ref|><|det|>[[147, 428, 838, 595]]<|/det|>
+GWAS summary statistics for blood cells are publicly available at http://www.mhi- humangenetics.org/en/resources/. The GWAS summary data for lung function are publicly available at GWAS catalog. All the analysis in our article were implemented by R software (version 4.3.2). R packages used in our analysis include TwoSampleMR, MendelianRandomization, and ggplot2. MREILLS model can be implemented by R package https://github.com/hhoulei/ MREILLS. All the codes for simulation are uploaded in https://github.com/hhoulei/MREILLS_Simul.  
+
+<|ref|>sub_title<|/ref|><|det|>[[147, 614, 576, 634]]<|/det|>
+## Ethics approval and consent to participate  
+
+<|ref|>text<|/ref|><|det|>[[147, 653, 825, 696]]<|/det|>
+The data used in our study was all publicly available and obtained written informed consent from all participants.  
+
+<|ref|>sub_title<|/ref|><|det|>[[148, 716, 338, 736]]<|/det|>
+## Source of Funding  
+
+<|ref|>text<|/ref|><|det|>[[147, 755, 851, 823]]<|/det|>
+This work was supported by the National Natural Science Foundation of China (Grant 82404378, T2341018), China Postdoctoral Science Foundation (Grant GZB20230011, 2024M750115, 2024T170014).
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[144, 84, 252, 103]]<|/det|>
+## Reference  
+
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+[1]. Zhu, Z., Zhang, F., Hu, H., Bakshi, A., Robinson, M. R., Powell, J. E., Montgomery, G. W., Goddard, M. E., Wray, N. R., Visscher, P. M., & Yang, J. (2016). Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nature genetics, 48(5), 481–487. [2]. Emdin, C. A., Khera, A. V., & Kathiresan, S. (2017). Mendelian randomization. Jama, 318(19), 5521925-1926. [3]. Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., ... & Marchini, J. (2018). The UK Biobank resource with deep phenotype and genomic data. Nature, 562(7726), 203-209. [4]. Kubo, M. (2017). BioBank Japan project: epidemiological study. Journal of epidemiology, 27(3Suppl), S1. [5]. Zhao, H., Rasheed, H., Nöst, T. H., Cho, Y., Liu, Y., Bhatta, L., ... & Zheng, J. (2022). Proteome-wide Mendelian randomization in global biobank meta-analysis reveals multi-ancestry drug targets for common diseases. Cell Genomics, 2(11).[6]. Feng, Y. A., Chen, C. Y., Chen, T. T., Kuo, P. H., Hsu, Y. H., Yang, H. I., Chen, W. J., Su, M. W., Chu, H. W., Shen, C. Y., Ge, T., Huang, H., & Lin, Y. F. (2022). Taiwan Biobank: A rich biomedical research database of the Taiwanese population. Cell genomics, 2(11), 100197. [7]. Lewis, A. C., Molina, S. J., Appelbaum, P. S., Dauda, B., Di Rienzo, A., Fuentes, A., ... & Allen, D. S. (2022). Getting genetic ancestry right for science and society. Science, 376(6590), 250-252. [8]. Petrovski, S., & Goldstein, D. B. (2016). Unequal representation of genetic variation across ancestry groups creates healthcare inequality in the application of precision medicine. Genome biology, 17, 1-3. [9]. Sanderson, E., Glymour, M. M., Holmes, M. V., Kang, H., Morrison, J., Munafò, M. R., ... & Davey Smith, G. (2022). Mendelian randomization. Nature Reviews Methods Primers, 2(1), 6. [10]. Sanderson, E., Davey Smith, G., Windmeijer, F., & Bowden, J. (2019). An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. International journal of epidemiology, 48(3), 713-727. [11]. Burgess, S., & Thompson, S. G. (2015). Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. American journal of epidemiology, 181(4), 251-260. https://doi.org/10.1093/aje/kwu283[12]. Slatkin M. (2008). Linkage disequilibrium--understanding the evolutionary past and mapping the medical future. Nature reviews. Genetics, 9(6), 477-485. [13]. Fang, A., Zhao, Y., Yang, P., Zhang, X., & Giovannucci, E. L. (2024). Vitamin D and human health: evidence from Mendelian randomization studies. European journal of epidemiology, 39(5), 467-490. https://doi.org/10.1007/s10654-023-01075-4
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[90, 75, 855, 905]]<|/det|>
+[14]. Bowden, J., & Holmes, M. V. (2019). Meta-analysis and Mendelian randomization: A review. Research synthesis methods, 10(4), 486-496. https://doi.org/10.1002/jrsm.1346[15]. Bowden J, Davey Smith G, Haycock PC, et al. Consistent estimation in mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol, 2016, 40(4):304-314. [16]. Hartwig FP, Davey Smith G, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol 2017;46:1985-98. [17]. Burgess S, Zuber V, Gkatzionis A, Foley CN. Modal-based estimation via heterogeneity- penalized weighting: model averaging for consistent and efficient estimation in Mendelian randomization when a plurality of candidate instruments are valid. Int J Epidemiol. 2018 Aug 1;47(4):1242-1254. [18]. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015 Apr;44(2):512-25. [19]. Ellegren, H., & Galtier, N. (2016). Determinants of genetic diversity. Nature reviews. Genetics, 17(7), 422-433. [20]. Stroup, D. F., Berlin, J. A., Morton, S. C., Olkin, I., Williamson, G. D., Rennie, D., Moher, D., Becker, B. J., Sipe, T. A., & Thacker, S. B. (2000). Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA, 283(15), 2008-2012. https://doi.org/10.1001/jama.283.15.2008[21]. Fan, J., Fang, C., Gu, Y., & Zhang, T. (2023). Environment invariant linear least squares. arXiv preprint arXiv:2303.03092. [22]. James, A. L., Knuiman, M. W., Divitini, M. L., Musk, A. W., Ryan, G., & Bartholomew, H. C. (1999). Associations between white blood cell count, lung function, respiratory illness and mortality: the Busselton Health Study. The European respiratory journal, 13(5), 1115-1119. [23]. Zeig-Owens, R., Singh, A., Aldrich, T. K., Hall, C. B., Schwartz, T., Webber, M. P., Cohen, H. W., Kelly, K. J., Nolan, A., Prezant, D. J., & Weiden, M. D. (2018). Blood Leukocyte Concentrations, FEV1 Decline, and Airflow Limitation. A 15-Year Longitudinal Study of World Trade Center-exposed Firefighters. Annals of the American Thoracic Society, 15(2), 173-183. [24]. Grant, B. J., Kudalkar, D. P., Muti, P., McCann, S. E., Trevisan, M., Freudenheim, J. L., & Schünemann, H. J. (2003). Relation between lung function and RBC distribution width in a population-based study. Chest, 124(2), 494-500. [25]. Huang, Y., Wang, J., Shen, J., Ma, J., Miao, X., Ding, K., Jiang, B., Hu, B., Fu, F., Huang, L., Cao, M., & Zhang, X. (2021). Relationship of Red Cell Index with the Severity of Chronic Obstructive Pulmonary Disease. International journal of chronic obstructive pulmonary disease, 16, 825-834. https://doi.org/10.2147/COPD.S292666[26]. Wareing, N., Mohan, V., Taherian, R., Volkmann, E. R., Lyons, M. A., Wilhalme, H., Roth, M. D., Estrada-Y-Martin, R. M., Skaug, B., Mayes, M. D., Tashkin, D. P., & Assassi, S. (2023). Blood Neutrophil Count and Neutrophil-to-Lymphocyte Ratio for Prediction of Disease
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[85, 82, 852, 459]]<|/det|>
+572 Progression and Mortality in Two Independent Systemic Sclerosis Cohorts. Arthritis care & 573 research, 75(3), 648- 656. https://doi.org/10.1002/acr.24880 574 [27]. Ulasli, S. S., Ozyurek, B. A., Yilmaz, E. B., & Ulubay, G. (2012). Mean platelet volume 575 as an inflammatory marker in acute exacerbation of chronic obstructive pulmonary disease. 576 Polskie Archiwum Medycyny Wewnetrznej, 122(6), 284- 290. 577 [28]. Eisen, M., Mokhtari, A., & Ribeiro, A. (2017). Decentralized quasi- Newton methods. 578 IEEE Transactions on Signal Processing, 65(10), 2613- 2628. 579 [29]. Chen, M. H., Raffield, L. M., Mousas, A., Sakaue, S., Huffman, J. E., Moscati, A., Trivedi, 580 B., Jiang, T., Akbari, P., Vuckovic, D., Bao, E. L., Zhong, X., Manansala, R., Laplante, V., Chen, 581 M., Lo, K. S., Qian, H., Lareau, C. A., Beaudoin, M., Hunt, K. A., ... Lettre, G. (2020). Trans- 582 ethnic and Ancestry- Specific Blood- Cell Genetics in 746,667 Individuals from 5 Global 583 Populations. Cell, 182(5), 1198- 1213. e14. 584 [30]. Shrine, N., Izquierdo, A. G., Chen, J., Packer, R., Hall, R. J., Guyatt, A. L., Batini, C., 585 Thompson, R. J., Pavuluri, C., Malik, V., Hobbs, B. D., Moll, M., Kim, W., Tal- Singer, R., 586 Bakke, P., Fawcett, K. A., John, C., Coley, K., Piga, N. N., Pozarickij, A., ... Tobin, M. D. 587 (2023). Multi- ancestry genome- wide association analyses improve resolution of genes and 588 pathways influencing lung function and chronic obstructive pulmonary disease risk. Nature 589 genetics, 55(3), 410- 422. 590
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[147, 85, 308, 105]]<|/det|>
+## Figure Legends  
+
+<|ref|>text<|/ref|><|det|>[[145, 123, 852, 655]]<|/det|>
+Figure 1. MR- EILLS model. MR- EILLS model aims to infer the causal relationships between one or multiple exposures and one outcome, integrating multiple GWAS summary datasets from heterogeneous populations. There are different pleiotropic effects and IV strengths for the same IVs in heterogeneous populations. The objective function of MR- EILLS model considers the both correlated and uncorrelated pleiotropy and remove the invalid IVs. Figure (A- C) are the results of motivating simulation. Figure (A) shows the point plot for absolute value of \(\hat{\mathcal{E}}_j^{(e)}\) in different populations, and larger point means the larger value of \(|\hat{\mathcal{E}}_j^{(e)}|\) . As the pleiotropic effect larger, the \(|\hat{\mathcal{E}}_j^{(e)}|\) is larger, thus the first part of MR- EILLS model minimize pleiotropic effect between different populations. Figure (B) shows the correlation between \(\hat{\mathcal{E}}_j^{(e)}\) and \(\hat{\theta}_{p,j}^{(e)}\) , which representing the correlated pleiotropic effect or the violation of InSIDE assumption. As the correlated pleiotropic effect increasing, this correlation is larger. This corresponding to the second part of MR- EILLS, the empirical focused linear invariance regularizer, which discourages selecting exposures with strong correlation between \(\theta_{p,j}^{(e)}\) and \(\mathcal{E}_j^{(e)}\) in some populations because this correlation would distort the causal effect estimation. Figure (C) shows the ridge plot of \(\sum_{e\in E}|\mathcal{E}_j^{(e)}| + \sum_{p\in P}\sum_{e\in E}|\hat{\theta}_{p,j}^{(e)}\mathcal{E}_j^{(e)}|\) when there are different proportion of invalid IVs. When there are invalid IVs, the ridge plot has two peaks, while the ridge plot has only one peak when there is no invalid IV. The corresponding abscission value at the lowest point between the two peaks is the optimal \(\lambda\) . The third part of MR- EILLS model is removing the invalid IVs by \(\lambda\) .  
+
+<|ref|>text<|/ref|><|det|>[[145, 657, 852, 770]]<|/det|>
+Figure 2. Simulation results when \(P = 1\) (UVMR). (A- B) Results of causal effect estimation and type I error rate when the causal effect is zero. (C- D) Results of causal effect estimation and type I error rate when the causal effect is 0.1. The number of IVs is 100 and the proportion of invalid IVs is \(30\%\) . The number of populations is \(E = 3\) .  
+
+<|ref|>text<|/ref|><|det|>[[145, 756, 852, 825]]<|/det|>
+Figure 3. Simulation results of causal effect estimation when \(P = 8\) (MVMR). The number of IVs is 100 and the proportion of invalid IVs is \(30\%\) . The number of populations is \(E = 3\) .  
+
+<|ref|>text<|/ref|><|det|>[[145, 831, 852, 902]]<|/det|>
+Figure 4. Simulation results of type I error rate for spurious exposures and statistical power for causal exposures when \(P = 8\) (MVMR). The number of IVs is 100 and the proportion of invalid IVs is \(30\%\) . The number of populations is \(E = 3\) .
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[145, 83, 854, 152]]<|/det|>
+Figure 5. Simulation results of F1 score, precision and recall when \(P = 15\) (MVMR). The number of IVs is 100 and the proportion of invalid IVs is \(30\%\) . The number of populations is \(E = 3\) .  
+
+<|ref|>text<|/ref|><|det|>[[145, 158, 850, 202]]<|/det|>
+Figure 6. Results in application. (A) the heterogeneity among different populations; (B) the causal effect estimations of 11 blood cells on lung function.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[45, 43, 143, 68]]<|/det|>
+## Figures  
+
+<|ref|>image<|/ref|><|det|>[[45, 92, 536, 235]]<|/det|>  
+
+<|ref|>text<|/ref|><|det|>[[45, 250, 130, 259]]<|/det|>
+Summary statistics:  
+
+<|ref|>text<|/ref|><|det|>[[48, 260, 161, 281]]<|/det|>
+\(G - X_{p}\) association \(\hat{\theta}_{p^{\prime}}^{(\ell)}\) \(\sigma_{p^{\prime}}^{(\ell)}\) \(G - Y\) association \(\hat{\Gamma}_{p^{\prime}}^{(\ell)}\) \(\sigma_{p^{\prime}}^{(\ell)}\)  
+
+<|ref|>text<|/ref|><|det|>[[48, 291, 130, 300]]<|/det|>
+MR- EILLS Model:  
+
+<|ref|>image<|/ref|><|det|>[[50, 303, 530, 530]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[68, 551, 483, 810]]<|/det|>
+<center>Figure 1. MR-EILLS model. MR-EILLS model aims to infer the causal relationships between one or multiple exposures and one outcome, integrating multiple GWAS summary datasets from heterogeneous populations. There are different pleiotropic effects and IV strengths for the same IVs in heterogeneous populations. The objective function of MR-EILLS model considers the both correlated and uncorrelated pleiotropy and remove the invalid IVs. Figure (A-C) are the results of motivating simulation. Figure (A) shows the point plot for absolute value of \(\hat{\epsilon}_{j}^{(\ell)}\) in different populations, and larger point means the larger value of \(\hat{\epsilon}_{j}^{(\ell)}\) . As the pleiotropic effect larger, the \(\hat{\epsilon}_{j}^{(\ell)}\) is larger, thus the first part of MR-EILLS model minimize pleiotropic effect between different populations. Figure (B) shows the correlation between \(\hat{\epsilon}_{j}^{(\ell)}\) and \(\hat{\theta}_{p^{\prime}}^{(\ell)}\) , which representing the correlated pleiotropic effect or the violation of InSIDE assumption. As the correlated pleiotropic effect increasing, this correlation is larger. This corresponding to the second part of MR-EILLS, the empirical focused linear invariance regularizer, which discourages selecting exposures with strong correlation between \(\hat{\theta}_{p^{\prime}}^{(\ell)}\) and \(\epsilon_{j}^{(\ell)}\) in some populations because this correlation would distort the causal effect estimation. Figure (C) shows the ridge plot of \(\sum_{n\in \mathbb{J}}\hat{\epsilon}_{j}^{(\ell)}| + \sum_{p\in \mathbb{P}}\sum_{n\in \mathbb{J}}\hat{\theta}_{p^{\prime}}^{(\ell)}\epsilon_{j}^{(\ell)}|\) when there are different proportion of invalid IVs. When there are invalid IVs, the ridge plot has two peaks, while the ridge plot has only one peak when there is no invalid IV. The corresponding abscission value at the lowest point between the two peaks is the optimal \(\lambda\) . The third part of MR-EILLS model is removing the invalid IVs by \(\lambda\) . </center>  
+
+<|ref|>text<|/ref|><|det|>[[360, 250, 535, 281]]<|/det|>
+Question: How to infer global causal relationships by integrating multiple heterogeneous GWAS summary datasets?  
+
+<|ref|>image<|/ref|><|det|>[[50, 303, 530, 530]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[48, 551, 483, 810]]<|/det|>
+<center>Figure 1. MR-EILLS model. MR-EILLS model aims to infer the causal relationships between one or multiple exposures and one outcome, integrating multiple GWAS summary datasets from heterogeneous populations. There are different pleiotropic effects and IV strengths for the same IVs in heterogeneous populations. The objective function of MR-EILLS model considers the both correlated and uncorrelated pleiotropy and remove the invalid IVs. Figure (A-C) are the results of motivating simulation. Figure (A) shows the point plot for absolute value of \(\hat{\epsilon}_{j}^{(\ell)}\) in different populations, and larger point means the larger value of \(\hat{\epsilon}_{j}^{(\ell)}\) . As the pleiotropic effect larger, the \(\hat{\epsilon}_{j}^{(\ell)}\) is larger, thus the first part of MR-EILLS model minimize pleiotropic effect between different populations. Figure (B) shows the correlation between \(\hat{\epsilon}_{j}^{(\ell)}\) and \(\hat{\theta}_{p^{\prime}}^{(\ell)}\) , which representing the correlated pleiotropic effect or the violation of InSIDE assumption. As the correlated pleiotropic effect increasing, this correlation is larger. This corresponding to the second part of MR-EILLS, the empirical focused linear invariance regularizer, which discourages selecting exposures with strong correlation between \(\hat{\theta}_{p^{\prime}}^{(\ell)}\) and \(\epsilon_{j}^{(\ell)}\) in some populations because this correlation would distort the causal effect estimation. Figure (C) shows the ridge plot of \(\sum_{n\in \mathbb{J}}\hat{\epsilon}_{j}^{(\ell)}| + \sum_{p\in \mathbb{P}}\sum_{n\in \mathbb{J}}\hat{\theta}_{p^{\prime}}^{(\ell)}\epsilon_{j}^{(\ell)}|\) when there are different proportion of invalid IVs. When there are invalid IVs, the ridge plot has two peaks, while the ridge plot has only one peak when there is no invalid IV. The corresponding abscission value at the lowest point between the two peaks is the optimal \(\lambda\) . The third part of MR-EILLS model is removing the invalid IVs by \(\lambda\) . </center>  
+
+<|ref|>sub_title<|/ref|><|det|>[[45, 850, 113, 867]]<|/det|>
+## Figure 1  
+
+<|ref|>text<|/ref|><|det|>[[45, 892, 345, 910]]<|/det|>
+See image above for figure legend.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[40, 45, 949, 352]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[42, 372, 117, 392]]<|/det|>
+<center>Figure 2 </center>  
+
+<|ref|>text<|/ref|><|det|>[[41, 414, 928, 504]]<|/det|>
+Simulation results when \(\mathrm{P} = 1\) (UVMR). (A- B) Results of causal effect estimation and type I error rate when the causal effect is zero. (C- D) Results of causal effect estimation and type I error rate when the causal effect is 0.1. The number of IVs is 100 and the proportion of invalid IVs is \(30\%\) . The number of populations is \(\mathrm{E} = 3\) .
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[44, 45, 866, 789]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[44, 802, 116, 821]]<|/det|>
+<center>Figure 3 </center>  
+
+<|ref|>text<|/ref|><|det|>[[42, 842, 907, 888]]<|/det|>
+Simulation results of causal effect estimation when \(\mathbf{P} = \mathbf{8}\) (MVMR). The number of IVs is 100 and the proportion of invalid IVs is \(30\%\) . The number of populations is \(\mathrm{E} = 3\) .
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[44, 45, 870, 789]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[44, 802, 117, 820]]<|/det|>
+<center>Figure 4 </center>  
+
+<|ref|>text<|/ref|><|det|>[[42, 843, 940, 909]]<|/det|>
+Simulation results of type I error rate for spurious exposures and statistical power for causal exposures when \(\mathbf{P} = \mathbf{8}\) (MVMR). The number of IVs is 100 and the proportion of invalid IVs is \(30\%\) . The number of populations is \(\mathrm{E} = 3\) .
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[44, 48, 950, 280]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[44, 301, 118, 320]]<|/det|>
+<center>Figure 5 </center>  
+
+<|ref|>text<|/ref|><|det|>[[42, 342, 930, 386]]<|/det|>
+Simulation results of F1 score, precision and recall when \(\mathbf{P} = 15\) (MVMR). The number of IVs is 100 and the proportion of invalid IVs is \(30\%\) . The number of populations is \(\mathrm{E} = 3\) .  
+
+<|ref|>image<|/ref|><|det|>[[45, 406, 950, 742]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[44, 765, 116, 784]]<|/det|>
+<center>Figure 6 </center>  
+
+<|ref|>text<|/ref|><|det|>[[42, 806, 858, 849]]<|/det|>
+Results in application.(A) the heterogeneity among different populations; (B) the causal effect estimations of 11 blood cells on lung function.  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 873, 312, 900]]<|/det|>
+## Supplementary Files  
+
+<|ref|>text<|/ref|><|det|>[[44, 923, 768, 942]]<|/det|>
+This is a list of supplementary files associated with this preprint. Click to download.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[60, 46, 390, 92]]<|/det|>
+SupplementaryTable.xlsxSupplementaryMaterials1208.docx
+
+<--- Page Split --->

preprint/preprint__01cb486613d82fb6bd28494662843f5e750475d21b9066890284fd7c62002182/images_list.json

@@ -0,0 +1,152 @@
+[
+  {
+    "type": "image",
+    "img_path": "images/Figure_1.jpg",
+    "caption": "Figure 1: Ternary complex of SMARCA2 and VHL/ElonginC/ElonginB (VCB) induced by ACBI1 shows structural similarities with PROTAC 1 and PROTAC 2: a Overall perspective of SMARCA2 Isoform 2 (green) and VCB (grey) induced by degrader molecule ACBI1 (bright orange). b ACBI1-induced interface contacts between SMARCA2 and VCB. The proteins are shown in space-filling, the colors are as in a, annotated residues are among those that make the highest number of contacts (see c). c A contact map for the interface of the crystal structure. The circle size reflects the number of atoms (including hydrogen atoms) participating in interactions. d Superposition of 6HAY (purple), 6HAX (salmon), 7S4E (green) by aligning VHL (grey) shows varied conformations of the warheads of the three degraders PROTAC 1, PROTAC 2, or ACBI1 (up to 1.7 Å) resulting in alterations of SMARCA2 within the ternary complex.",
+    "footnote": [],
+    "bbox": [
+      [
+        195,
+        184,
+        808,
+        579
+      ]
+    ],
+    "page_idx": 9
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_2.jpg",
+    "caption": "Figure 2: ACBI1-induced ternary complex formation of SMC2:VCB leads to protection of specific sites:a-d, SMARCA2 isoform 2(a), VHL(b), Elongin C(c), and Elongin B(d) monitored for hydrogen-deuterium exchange over time. The difference plots of each protein in the binary and ternary states are generated by subtracting the deuterium exchange of like peptides of the APO or binary from the binary or ternary states (defined as Binary \\(\\Delta\\) APO and Ternary \\(\\Delta\\) Binary), respectively. Regions that exchange significantly less than the comparative state are depicted in blue (negative), whereas regions that exchange significantly more appear in red (positive). The resultant difference plots of the binary (e), or ternary complex (f) were mapped onto the structure 7S4E. The experiments were repeated on 2 separate days. All raw relative uptake plots of the deuterium exchange for each state and experiment can be found in the supplementary material (Supplementary Figures 38–88).",
+    "footnote": [],
+    "bbox": [
+      [
+        240,
+        120,
+        757,
+        660
+      ]
+    ],
+    "page_idx": 12
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_3.jpg",
+    "caption": "Figure 3: Comparison of WESTPA simulations with and without data from HDX-MS experiments. I-RMSD probability densities are shown for ternary complexes with a warhead RMSD \\(< 2 \\text{Å}\\) and \\(> 30\\) contacts between protected residues. Structures sampled by WES+HDX (red) have almost exclusively an I-RMSD value \\(< 2 \\text{Å}\\) , whereas those from WE simulations without any information from HDX (blue) are widely distributed.",
+    "footnote": [],
+    "bbox": [
+      [
+        150,
+        364,
+        852,
+        672
+      ]
+    ],
+    "page_idx": 17
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_4.jpg",
+    "caption": "Figure 4: Illustration of the representative prediction produced by REVO simulation and its comparison to the co-crystallized structure (PDB ID: 6HAX) (a) predicted ternary structure with I-RMSD=1.1 Å; (b) detail of the binding interface; (c) contact maps for the interfaces of co-crystallized and predicted structures. The circle size reflects the number of atoms (including hydrogens) participating in interactions; (d) structurally aligned prediction (green) and co-crystallized structure (pink) with a detailed PROTAC 2 comparison shown.",
+    "footnote": [],
+    "bbox": [
+      [
+        130,
+        150,
+        888,
+        730
+      ]
+    ],
+    "page_idx": 18
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_5.jpg",
+    "caption": "Figure 5: Assessing ternary complex formation. (a) The minimum I-RMSD over time during the simulation for the REVO simulations of the PROTAC2 system. Each green line indicates one replica and the black line is the average between all runs. The blue line indicates the I-RMSD for a vanilla molecular dynamics simulation. (b) A scatter plot of the free energy vs the I-RMSD of each of the 500 clusters from the PROTAC 2 simulations. The circles are colored by w-RMSD. (c) The predicted binding rates for PROTAC 1 system (purple) and the ACBI1 system (green). The black line is the experimental on rate determined via SPR.",
+    "footnote": [],
+    "bbox": [
+      [
+        315,
+        110,
+        662,
+        744
+      ]
+    ],
+    "page_idx": 19
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_6.jpg",
+    "caption": "Figure 6: Comparing the bound ensembles determined by docking and REVO simulations with information from HDX-MS for the PDB ID 6HAX ternary complex. The REVO bound ensemble is defined as structures below a warhead RMSD of 2 Å and more than 30 contacts between the target and ligase interface. The docking bound basin is defined as the top-100 structures as determined by Rosetta-scoring. (a) Probability density function distributions of I-RMSD values for the bound ensembles. (b) The percent of structures in the predicted bound ensembles below specific I-RMSD thresholds (2 Å, 2.5 Å, and 3 Å).",
+    "footnote": [],
+    "bbox": [
+      [
+        250,
+        145,
+        723,
+        712
+      ]
+    ],
+    "page_idx": 22
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_7.jpg",
+    "caption": "Figure 7: SAXS profiles and structural ensembles of SMC2-isoform1/isoform2:ACBI1:VCB complexes. (a) Comparison of theoretical and experimental SAXS profiles, SAXS intensity vs. \\(q\\) . (b) The histograms of \\(\\mathrm{R}_g\\) of SMC2-isoform1:ACBI1:VCB (red) and SMC2-isoform2:ACBI1:VCB (blue) complexes calculated from HREMD simulations. The inverted red and blue triangles are the \\(\\mathrm{R}_g\\) values of starting structures of SMC2-isoform1/isoform2:ACBI1:VCB from homology model and crystallography respectively.",
+    "footnote": [],
+    "bbox": [
+      [
+        220,
+        101,
+        770,
+        777
+      ]
+    ],
+    "page_idx": 24
+  },
+  {
+    "type": "image",
+    "img_path": "images/Supplementary_Figure_15.jpg",
+    "caption": "Figure 8: Most populated structures of SMARCA2 bound to VHL with different degrader molecules, identified by dimension reduction and clustering of HREMD simulation data. (a-d) Colors of VHL and SMARCA2 represent HDX-MS protection in the presence of the degrader molecules relative to the situation in the absence of the degrader. The second ranked structures of c PROTAC 2 and d isoform 1 SMARCA2 are displayed and support our HDX-MS data, whereas the top three structures are included in Supplementary Figure 15. Elongin B and Elongin C are also included in panel d. e The top structures of ternary complexes are compared after aligning VHL to illustrate conformational differences among top structures of ternary complexes.",
+    "footnote": [],
+    "bbox": [
+      [
+        137,
+        103,
+        870,
+        728
+      ]
+    ],
+    "page_idx": 25
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_9.jpg",
+    "caption": "Figure 9: a Conformational free energy landscape as a function of the first two tICA features of SMARCA2-PROTAC2-VHL ternary complex inferred from a Markov state model (MSM). The ensemble of bound states from REVO simulations is shown as blue points; the crystal structure (PDB ID 6HAX) is shown as a red X. In this projection, states II and V are close to state I. b Network diagram of the coarse-grained MSM calculated using a lag time of 50 ns, with the stationary probabilities associated with each state indicated. c Mean first-passage times to transition (MFPT) from one state in the MSM to another. Numbers indicate predicted MFPTs in \\(\\mu \\mathrm{s}\\) . d-e Comparison of the crystal structure (gray) with the lowest free energy state (cyan) and a metastable state (orange) predicted by the MSM. Arrows indicate a change of orientation relative to d.",
+    "footnote": [],
+    "bbox": [
+      [
+        313,
+        116,
+        685,
+        666
+      ]
+    ],
+    "page_idx": 26
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_10.jpg",
+    "caption": "Figure 10: Degrader dependent SMARCA2 Lys densities in the CRL-VHL ubiquitination zone. a. Active form of CRL-VHL with bound SMARCA2 and E2-ubiquitin with open CRL conformation. b. same as a with a closed conformation of CRL generated by meta-eABF simulations. c. Distance of Lys residues (side-chain nitrogen atom) from SMARCA2 to the C-terminus glycine C atom of ubiquitin for three different degraders. d. Density of Lys residues in 3D space near ubiquitination zone of CRL-VHL.",
+    "footnote": [],
+    "bbox": [
+      [
+        197,
+        110,
+        820,
+        773
+      ]
+    ],
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+  }
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preprint/preprint__01cfd239b63d0b5b3bd8fba2f46c4523116e4ea7471e771fa2f189c14b26428e/images_list.json

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+    "type": "image",
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+    "caption": "Fig. 1: Scalable production of UFPFs. a Schematic of the good solvent exchange strategy to prepare UFPFs in a modified wet spinning protocol. In the case of poor solvent exchange (light orange region, upper panel), PANi molecules are rapidly solidified into thick gels and protofibres with rough crystallized particles. In the case of good solvent exchange (light blue region, lower panel), the formed gels with low viscosity occur an impressive gel extension and are slenderized into ultrafine fibres. b Schematic of the modified wet spinning process. c Scanning electron microscope (SEM) image of the marked region in b, showing the sharp necking behavior of gel PANi fibres. The close observation to region 1 (d), region 2 (e), and region 3 (f) in the marked zone of c, illustrating the sharply necking process of PANi gels. g Photograph of a 5.4-kilometres-long UFPF collected in two hours. Scale bars: c 20 μm, d 2 μm, e 10 μm, g 150 mm.",
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+    "caption": "Fig. 2: Mechanism and mechanical properties of UFPPs. a SEM images of the PAni fibres produced in different solvating species. Specifically, the upper four panels showing the fibres prepared from poor solvents, and the lower two panels showing the fibres fabricated from good solvents. b Raman spectra of PAni fibres after placing in air for four weeks. c The diffusivity from PAni dispersions (in m-cresol) to various solvating species. d The viscosity of PAni gels formed in various solvating species. e Mechanics simulation results of extension behaviors of PAni gel fibres at different interfacial pressure. f Typical tensile stress-strain curves of UFPPs. g Ashby plot comparing the mechanical strength of UFPPs to previously reported CPFs. Scale bars in a: Water, Ethanol, EA, Acetone \\(20\\mu \\mathrm{m}\\) (left) \\(10\\mu \\mathrm{m}\\) (right); NMP \\(20\\mu \\mathrm{m}\\) (left) \\(5\\mu \\mathrm{m}\\) (right), DMF \\(20\\mu \\mathrm{m}\\) (left) \\(2\\mu \\mathrm{m}\\) (right).",
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+    "img_path": "images/Figure_3.jpg",
+    "caption": "Fig. 3: Energy and charge storage capacities of UFPFs. a Schematic of a micro capacitor constructed using two UFPF electrodes on a substrate. b Cyclic voltammetry curves with the increasing scan rates from 10 to 20, 50, 80 and \\(100\\mathrm{mV}\\mathrm{s}^{-1}\\) . c Galvanostatic charge/discharge curves at various current densities increasing from 0.32 to 0.63, 1.59 and \\(3.18\\mathrm{mA}\\mathrm{cm}^{-2}\\) . d The area capacitance of UFPFs comparing to previous reported electrodes. e Cycle galvanostatic charge/discharge curves during 120 cycles between 0 and \\(0.6\\mathrm{V}\\) at \\(1.59\\mathrm{mA}\\mathrm{cm}^{-2}\\) . f. The relationship between current and voltage at a slow rate of \\(10\\mathrm{mV}\\mathrm{s}^{-1}\\) . g The charge storage capacity of UFPFs comparing to other charge storage materials.",
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+    "img_path": "images/Figure_4.jpg",
+    "caption": "Fig. 4: Demonstration and characterization of all-solid organic electrochemical transistor based on UFPFs. a Schematic of the all-solid OECT composed of three polymer layers, one silver wire as the gate electrode, and one UFPF as the drain-source channel. b Cross-section SEM image and schematic of OECT. The yellow break lines direct the charge flow along the fibre chains (green solid lines). c Transmittance of the OECT in the region of visible light. A typical output curve (d), transfer curve (e), and power consumption in operation (f) of OECT. Scale bars: b 20 μm.",
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+    "img_path": "images/Figure_5.jpg",
+    "caption": "Fig. 5: Electrical response of the all-solid OECT to mechanical deformations. a Schematic of the mechanism explaining the response to the action of external pressure. b Relative drain-source change \\((\\Delta \\mathrm{I}_{\\mathrm{DS}} / \\mathrm{I}_{\\mathrm{DS0}})\\) and sensitivity as a function of pressure. c Response time of the all-solid OECT when pressing (rising edge) and releasing (falling part) under the instantaneous pressure of 17.8 KPa. d Cyclic response at three different pressure levels (0.92, 6.8, and 22.2 KPa). e, Schematic of the working principle of the response to friction. f Cyclic response at three different frictions (1.84, 4.69, and 5.55 KPa). g An enlarged curve of the marked part in (f). h Cyclic response at different friction speeds from 4, 6, 8, 10, 15, to \\(20\\mathrm{mm}\\mathrm{s}^{-1}\\) .",
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preprint/preprint__01cfd239b63d0b5b3bd8fba2f46c4523116e4ea7471e771fa2f189c14b26428e/preprint__01cfd239b63d0b5b3bd8fba2f46c4523116e4ea7471e771fa2f189c14b26428e.mmd

@@ -0,0 +1,258 @@
+
+# Scalable production of ultrafine polyaniline fibres for tactile organic electrochemical transistors  
+
+Bo Fang Hong Kong Polytechnic University  
+
+Jianmin Yan Hong Kong Polytechnic University  
+
+Dan Chang Zhejiang University  
+
+Jinli Piao Hong Kong Polytechnic University  
+
+Kit Ming Ma Hong Kong Polytechnic University  
+
+Qiao Du Hong Kong University of Science and Technology  
+
+Ping Gao Hong Kong University of Science and Technology  
+
+Yang Chai Hong Kong Polytechnic University https://orcid.org/0000- 0002- 8943- 0861  
+
+Xiaoming Tao ( \(\boxed{\times}\) xiao- ming.tao@polyu.edu.hk) Hong Kong Polytechnic University https://orcid.org/0000- 0002- 2406- 0695  
+
+## Article  
+
+# Keywords:  
+
+Posted Date: December 8th, 2021  
+
+DOI: https://doi.org/10.21203/rs.3.rs- 1126903/v1  
+
+License: \(\circledcirc\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+Version of Record: A version of this preprint was published at Nature Communications on April 19th, 2022. See the published version at https://doi.org/10.1038/s41467- 022- 29773- 9.
+
+<--- Page Split --->
+
+# Scalable production of ultrafine polyaniline fibres for tactile organic electrochemical transistors  
+
+Bo Fang<sup>1,2</sup>, Jianmin Yan<sup>1,3</sup>, Dan Chang<sup>4</sup>, Jinli Piao<sup>1,2</sup>, Kit Ming Ma<sup>1,2</sup>, Qiao Gu<sup>5</sup>, Ping Gao<sup>5</sup>, Yang Chai<sup>1,3\*</sup> Xiaoming Tao<sup>1,2\*</sup>  
+
+<sup>1</sup>Research Institute for Intelligent Wearable Systems, Hong Kong Polytechnic University, Hong Kong, 999077 China  
+
+<sup>2</sup>Institute of Textiles and Clothing, Hong Kong Polytechnic University, Hong Kong, 999077 China  
+
+<sup>3</sup>Department of Applied Physics, Hong Kong Polytechnic University, Hong Kong, 999077 China  
+
+<sup>4</sup>Department of Polymer Science and Engineering, Zhejiang University, Hangzhou, 310027 China  
+
+<sup>5</sup>Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong, 999077 China  
+
+Email: ychai@polyu.edu.hk; xiao- ming.tao@polyu.edu.hk  
+
+The development of continuous conducting polymer fibres is essential for applications ranging from advanced fibrous devices to frontier fabric electronics. The use of continuous conducting polymer fibres requires a small diameter to maximize their electroactive surfaces, microstructural orientations, and mechanical strengths. However, regularly used wet spinning techniques have rarely achieved this goal due primarily to the insufficient slenderization of rapidly solidified conducting polymer molecules in poor solvents. Here we report a good solvent exchange strategy to wet spin the ultrafine polyaniline fibres at the large scale. The slow diffusion between good solvents distinctly decreases the viscosity of gel protofibers, which undergo an impressive drawing ratio. The continuously collected polyaniline fibres have a previously unattained diameter below \(5 \mu \mathrm{m}\) , high energy and charge storage capacities, and favorable mechanical performance. We demonstrated an ultrathin all- solid organic electrochemical transistor based
+
+<--- Page Split --->
+
+on ultrafine polyaniline fibres, which substantially amplified microampere drain- source electrical signals with less one volt driving voltage and effectively operated as a tactile sensor detecting pressure and friction forces at different levels. The aggressive electronical and electrochemical merits of ultrafine polyaniline fibres and their great potentials to prepare on industrial scale offer new opportunities for high- performance soft electronics and large- area electronic textiles.  
+
+The extended conjugated and easily doped \(\pi\) - system along the backbone enables conducting polymers to possess intriguing transport, optical, and electrochemical properties, which have rarely been found in conventional polymers and metal conductors \(^{1 - 3}\) . Processing conducting polymers into macroscopically fibrous materials makes it possible to translate their nano- object features to human- friendly products in a continuous manner. The combined merits, including but not limited to mechanical flexibility, intrinsic conductivity, and electrochemical activity, of conducting polymer fibres (CPFs) have introduced a new era of “electronic textiles” \(^{4}\) . For instance, highly conductive and electrochemically active poly(3- methylthiophene) fibres have been achieved by in situ electrochemical oxidation of monomers \(^{5}\) . Fast ion transport between CPFs and ionic liquids has given birth to long- term operation actuators, electrochromic windows, and numeric displays \(^{6}\) . In recent studies, the wet- spun poly (3,4- ethylene dioxythiophene) (PEDOT) fibres have been widely used in various frontier fields, such as flexible energy storage electrodes, implantable bioelectronics, and organic transistors \(^{7,8}\) .  
+
+Unfortunately, due primarily to the large diameters, the performance and expectations of most achieved continuous CPFs have been limited by their insufficient electroactive surfaces and weak breaking strengths. Electropinning and wet spinning are two mainstream strategies to produce continuous CPFs. In the case of electrospinning, the fairly rigid backbone due to the high aromaticity results into an insufficient elasticity of conducting polymer solutions, which fails to be solely electrospun into fine fibres \(^{9}\) . Although a two- fluid electrospinning technique has been proposed by coating a soluble and electrospinable fluid on the conducting polymer cores, the complex procedures
+
+<--- Page Split --->
+
+involving the addition and removal of second components defy the mass production of electrospun CPFs<sup>10,11</sup>. In the case of conventional wet spinning, conducting polymer dopes tend to occur a transient solidification in poor solvents, induced by the strong interactions of conducting polymer chains. The rapidly hardened gels suppress the post- stretching and slenderizing procedures, and cause the wet- spun CPFs to show a large diameter, generally beyond \(10 \mu \mathrm{m}^{12 - 14}\) . The large diameters largely discount the mechanical properties and electrochemical activities of CPFs<sup>4,15</sup>. Thus, there is an urgent need to realize the mass production of ultrafine CPFs, which remains challenging. In this work, we report a good solvent exchange strategy in a modified wet spinning technique to prepare the ultrafine polyaniline (PAni) fibres (UFPFs) at the large scale. Beyond conventional wet spinning protocol, we replaced poor solvents by good solvents as the coagulation bath to decrease the viscosity of gel protofibres, which were subject to an ultrahigh drawing ratio and reduced to an ultrafine morphology. The obtained UFPFs own a small diameter below \(5 \mu \mathrm{m}\) , an unprecedented mechanical strength of \(1080 \pm 71 \mathrm{MPa}\) , a high area capacitance beyond \(1008 \mathrm{mF} \mathrm{cm}^{- 2}\) , and an enormous charge storage capacity of \(5.25 \times 10^{4} \mathrm{mC} \mathrm{cm}^{- 2}\) . Based on the structural and electrochemical merits of UFPFs, we demonstrated an ultrathin all- solid organic electrochemical transistor (OECT) with less one volt driving voltage, which substantially amplified drain- source electrical signals with a low power- consumption and responded to vertical pressure and horizontal friction forces at different levels. Our work opens an avenue to prepare continuous ultrafine CPFs and high- performance soft electronics.
+
+<--- Page Split --->
+![](images/Figure_1.jpg)
+
+<center>Fig. 1: Scalable production of UFPFs. a Schematic of the good solvent exchange strategy to prepare UFPFs in a modified wet spinning protocol. In the case of poor solvent exchange (light orange region, upper panel), PANi molecules are rapidly solidified into thick gels and protofibres with rough crystallized particles. In the case of good solvent exchange (light blue region, lower panel), the formed gels with low viscosity occur an impressive gel extension and are slenderized into ultrafine fibres. b Schematic of the modified wet spinning process. c Scanning electron microscope (SEM) image of the marked region in b, showing the sharp necking behavior of gel PANi fibres. The close observation to region 1 (d), region 2 (e), and region 3 (f) in the marked zone of c, illustrating the sharply necking process of PANi gels. g Photograph of a 5.4-kilometres-long UFPF collected in two hours. Scale bars: c 20 μm, d 2 μm, e 10 μm, g 150 mm. </center>
+
+<--- Page Split --->
+
+## Results  
+
+Preparation and characterization of UFPFs. In the modified one- step wet spinning process, we used good solvents as the coagulation bath to realize the mass production of UFPFs (Fig. 1a- b and Supplementary Fig. 1). After doping PANi power (emeraldine base) with camphor sulfonic acid (CSA) at a molar ratio of 2:1, we dispersed fully doped PANi into m- cresol as the raw spinning dopes (see the Methods section) \(^{16}\) . Significantly, the direct use of doped PANi solutions as the dopes saves the trouble of conventional post- doping procedures, and further permits the uniform charge distribution throughout the fibre length \(^{17}\) . A good solvent, dimethyl formamide (DMF), of PANi operated as the coagulation bath. A slow solvent exchange between m- cresol and DMF facilitated the formation of PANi gel protofibres with a quite low viscosity below 3000 cP. Subsequently, we observed a sharp decrease of diameter from \(\sim 0.1 \mathrm{mm}\) to \(\sim 4.7 \mu \mathrm{m}\) when stretching the gel fibres in bath (Fig. 1c- f), which, to our knowledge, is a record small value in the achieved wet- spun CPFs \(^{4}\) . The ultrafine fibre shows a smooth surface (Fig. 1f and Supplementary Fig. 2) and highly crystallized microstructures (Supplementary Fig.3). Moreover, such an impressive drawing ratio enables a very high production efficiency of UFPFs beyond 40 meters per minute. For example, we prepared a 5.4- kilometres- long UFPF in two hours (Fig. 1e).
+
+<--- Page Split --->
+![](images/Figure_2.jpg)
+
+<center>Fig. 2: Mechanism and mechanical properties of UFPPs. a SEM images of the PAni fibres produced in different solvating species. Specifically, the upper four panels showing the fibres prepared from poor solvents, and the lower two panels showing the fibres fabricated from good solvents. b Raman spectra of PAni fibres after placing in air for four weeks. c The diffusivity from PAni dispersions (in m-cresol) to various solvating species. d The viscosity of PAni gels formed in various solvating species. e Mechanics simulation results of extension behaviors of PAni gel fibres at different interfacial pressure. f Typical tensile stress-strain curves of UFPPs. g Ashby plot comparing the mechanical strength of UFPPs to previously reported CPFs. Scale bars in a: Water, Ethanol, EA, Acetone \(20\mu \mathrm{m}\) (left) \(10\mu \mathrm{m}\) (right); NMP \(20\mu \mathrm{m}\) (left) \(5\mu \mathrm{m}\) (right), DMF \(20\mu \mathrm{m}\) (left) \(2\mu \mathrm{m}\) (right). </center>  
+
+The sharp necking behaviors of gel protofibres are highly related to the use of good solvents as the coagulation bath. We recorded the evolution of surface morphologies of PAni fibres collected from different solvating species. As shown in Fig. 2a, the obtained fibres in poor solvating species, i.e., water, ethanol, ethyl acetate (EA), and acetone, generally present coarse surfaces and large diameters around \(20\mu \mathrm{m}\) . By comparison,
+
+<--- Page Split --->
+
+we clearly observed a necking phenomenon in both cases of good solvents, i.e., N- methyl- 2- pyrrolidone (NMP) and DMF. Such necking effects promoted the finally produced fibres to behave ultrafine morphologies, which assists PAni fibres to behave better structure and performance stabilities due to the higher degree of orientation and crystallization (see the X- ray diffraction analysis in Supplementary Fig. 3). We used Raman spectra to evaluate their structural evolution after placing fibres in air for four weeks. As shown in Fig. 2b, we did not find obvious de- doping signals in Raman spectra of the PAni fibres from good solvents, whereas various de- doping peaks (1223 \(\mathrm{cm^{- 1}}\) and \(1462\mathrm{cm^{- 1}}\) ) appeared in the cases of poor solvents.  
+
+We speculate that this sharp necking phenomenon may be caused by two factors: diffusion difference and interfacial pressure. In the conventional wet spinning protocol, the diffusion from good solvents to poor solvents occurs quickly to solidify dope fluids into gel fibres<sup>18,19</sup>. The rapid diffusion could be aggravated in the system of conducting polymers due to the strong interactions of rigid chains. Thus, PAni molecules tend to bond into irregularly crystallized particles prior to undergoing extensive drawing, as present in the upper panels of Fig. 1a. In previous reports using poor solvents as coagulation bath, although CPFs with a smooth surface could be collected by enhanced shear flow and strong stretching<sup>12,14</sup>, diameters are unable to be decreased to the ideal level due to the insufficient stretching slenderization of solidified gels. In contrast, the diffusion from dope fluids to good solvents is quite slow. Such slow diffusion allows the formation of fibrous gels with a low viscosity and the following high drawing ratios. Note that most conventional polymers are incapable of gelling in good solvents due to the poor chain interactions<sup>20,21</sup>.  
+
+We calculated the diffusivities between various solvents and measured the viscosity of corresponding formed gels to support our explanations. The diffusivity from A molecules to B molecules, \(D_{AB}^{0}\) , is determined by the equation  
+
+\[\frac{D_{AB}^{0}\mu_{B}}{T} = 8.52\times 10^{-8}V_{bB}^{-1 / 3}\left[1.40\left(\frac{V_{bB}}{V_{bA}}\right)^{1 / 3} + \frac{V_{bB}}{V_{bA}}\right]\]
+
+<--- Page Split --->
+
+, where \(\mu_{B}\) is the solvent viscosity, T is the temperature, and \(V_{b}\) is the molar volume of solvent at its normal boiling temperature \(^{22}\) . As displayed in Fig. 2c, the diffusivities from m- cresol to DMF \((7.5\times 10^{- 6} \mathrm{cm}^{2}\mathrm{s}^{- 1})\) and NMP \((7.71\times 10^{- 6} \mathrm{cm}^{2}\mathrm{s}^{- 1}\) ) are generally lower than that of poor solvents. Diffusion in bath further dominates the viscosity of protofibres. To monitor the viscosity of gel fibres in practical conditions, we conducted the viscometer tests at a low revolution (e.g., 10 Rev.). As summarized in Fig. 2d, the formed PANi gels in good solvents show a viscosity below \(3000\mathrm{cP}\) , much lower than that of poor solvents ( \(>4000\mathrm{cP}\) ). The established solvating specific- diffusivity- viscosity formula accords well with our proposed explanations.  
+
+Interfacial pressure during solvent exchange is another major factor relating to the necking behavior of PANi protofibres. In a two fluid system, the interfacial pressure between two kind of solvents is inclined to decrease with the improved solvent diffusion \(^{23}\) . Based on the slow diffusion from m- cresol to good solvents (Fig. 2c), the interfacial pressure between gel fibres and coagulation bath is considerable, which further induces the necking of protofibres. To understand this, we conducted a mechanic simulation to the stretching behavior of gel fibres at different interfacial pressure (see the progressive results in Supplementary Fig.4 and Method section). According to the simulation results in Fig. 2e, the higher interfacial pressure drives gel fibres to occur the sharper necking and thinning effects at a given tensile stress. This probably explains the formation of UFPFs in DMF bath.  
+
+UFPFs show impressive mechanical performance. Different from that of conventional polymer fibres, the typical linear strain- stress curves of UFPFs demonstrate a brittle fracture behavior with a small tensile strain of \(3.67\pm 0.64\%\) (Fig. 2f). It is reasonable if considering the rigid backbone of PANi chains, which likely gather and condense into fragile fibrous assemblies after undergoing strong shear flow in spinning microtubes. According to classical Griffith theory on brittle fracture, fibres' strength generally improves with the decrease of diameter due to the depressed structural defects \(^{24}\) . We compared the mechanical performance of UFPFs with previously reported CPFs.
+
+<--- Page Split --->
+![](images/Figure_3.jpg)
+
+<center>Fig. 3: Energy and charge storage capacities of UFPFs. a Schematic of a micro capacitor constructed using two UFPF electrodes on a substrate. b Cyclic voltammetry curves with the increasing scan rates from 10 to 20, 50, 80 and \(100\mathrm{mV}\mathrm{s}^{-1}\) . c Galvanostatic charge/discharge curves at various current densities increasing from 0.32 to 0.63, 1.59 and \(3.18\mathrm{mA}\mathrm{cm}^{-2}\) . d The area capacitance of UFPFs comparing to previous reported electrodes. e Cycle galvanostatic charge/discharge curves during 120 cycles between 0 and \(0.6\mathrm{V}\) at \(1.59\mathrm{mA}\mathrm{cm}^{-2}\) . f. The relationship between current and voltage at a slow rate of \(10\mathrm{mV}\mathrm{s}^{-1}\) . g The charge storage capacity of UFPFs comparing to other charge storage materials. </center>  
+
+Derived from the strain- stress curves, we concluded that UFPFs have a modulus of \(29.89 \pm 5.6\%\) GPa, and a strength of \(1080 \pm 71\) MPa, at least one order of magnitude higher than that of CPFs with larger diameters (Fig. 2g), mainly including PEDOT fibres (<450 MPa) \(^{7,25 - 29}\) and PAni fibres (<400 MPa) \(^{30 - 33}\) .  
+
+Energy and charge storage capacities. Ultrafine morphology optimizes the
+
+<--- Page Split --->
+
+electroactive surfaces, which enables UFPFs to exhibit superb energy and charge storage capacities. To evaluate the electrochemical activity of UFPFs, we constructed a micro capacitor using polyvinyl alcohol (PVA)- \(\mathrm{H}_3\mathrm{PO}_4\) gel electrolyte and two UFPF electrodes (Fig. 3a). The electrochemical properties were checked by cyclic voltammetry (CV) and galvanostatic charge- discharge (GCD) measurements. At different scan rates, the nearly rectangular shape of CV curves and instantaneous current response to voltage reversal at each end potential suggest the good electrochemical activity of UFPFs<sup>34</sup> (Fig. 3b). The nearly triangular shape of GCD curves at different current densities illustrates the formation of efficient electric double layers and charge propagation across the UFPF electrodes<sup>35</sup> (Fig. 3c). According to the GCD results, we determined the electrochemical properties of UFPFs. Among of them, the area capacitance, \(\mathrm{C_A}\) , is between 1008 and 1666 mF cm<sup>- 2</sup> at the current densities between 0.32 and 3.18 mA cm<sup>- 2</sup>, outperforming previously reported thick CPFs<sup>29</sup> and other electrodes, such as carbon nanomaterials<sup>3434,36</sup>, metal oxides<sup>37</sup> and conducting polymers<sup>38- 41</sup>, and approaching to that of PANi nanowires<sup>42</sup> (Fig. 3d). The volumetric capacitance, power density and energy density reach 83.8 F cm<sup>- 3</sup>, 0.96 W cm<sup>- 3</sup> and 4.19 mWh cm<sup>- 3</sup>, respectively (Supplementary Fig. 5). In lifetime tests of UFPF- based capacitor, both the potential and capacitance continued without significant decrease for 120 charge/discharge cycles at a low current density of 1.59 mA cm<sup>- 2</sup>, indicating the reliable electrochemical performance stability of UFPFs (Fig. 3e).  
+
+We were able to confirm the amount of transported charge per unit area to UFPF during the charge/discharge cycle. The charge during a triangular wave potential between - 0.9 V and 1.0 V (water window, see the Supplementary Fig. 6) was calculated by integrating the measured current with respect to the time of period at a low scan rate of 10 mV s<sup>- 1</sup> (Fig. 3f)<sup>6</sup>. We determined that the charge storage capacity of UFPF was \(5.25 \times 10^{4}\) mC cm<sup>- 2</sup>, a value at least two orders of magnitude higher than that of noble metals<sup>43</sup>, carbon bulk<sup>44 - 46</sup> and previously reported conducting polymers<sup>47</sup> (Fig. 3g). This value decreases slightly to \(2.015 \times 10^{4}\) mC cm<sup>- 2</sup> at a tenfold scan rate of 100 mV s<sup>- 1</sup> (Supplementary Fig. 7).
+
+<--- Page Split --->
+![](images/Figure_4.jpg)
+
+<center>Fig. 4: Demonstration and characterization of all-solid organic electrochemical transistor based on UFPFs. a Schematic of the all-solid OECT composed of three polymer layers, one silver wire as the gate electrode, and one UFPF as the drain-source channel. b Cross-section SEM image and schematic of OECT. The yellow break lines direct the charge flow along the fibre chains (green solid lines). c Transmittance of the OECT in the region of visible light. A typical output curve (d), transfer curve (e), and power consumption in operation (f) of OECT. Scale bars: b 20 μm. </center>  
+
+Structure and performance of all- solid OECT. Benefitting from the favorable energy and charge storage performance of UFPFs, we demonstrated a high- performance all- solid OECT. OECT amplifies drain- source current intensities at low operating voltages by ion penetration into the organic mixed ionic- electronic conductors, i.e., conducting
+
+<--- Page Split --->
+
+polymers<sup>48,49</sup>. This process is controlled by the gate bias, and, to date, has generally conducted in aqueous electrolytes. To preclude the interference of external environment, we promoted the working conditions of OECT from aqueous environments to all- solid state by using gel electrolytes as the ion matrix. As shown in Fig. 4a- b, our OECT is mainly constructed by three polymer layers. The upper layer is the cured polyurethane (PU) working as the dielectric coating and also protecting the device from the invasion of external action<sup>50</sup>. A fibrous silver gate electrode with a diameter of \(7 \mu \mathrm{m}\) is fixed in PU. Since UFPFs have demonstrated reliable electrochemical activities in PVA- \(\mathrm{H}_3\mathrm{PO}_4\) gel, we used PVA- \(\mathrm{H}_3\mathrm{PO}_4\) gel as the middle layer to inject ions to or uptake ions from the drain- source channel materials. A UFPF right below the silver gate is fused in the ion gel, and operates as the channel material. The bottom layer is also pure PU acting as the supporter of the whole device. Due to the remarkable flexibility and transparency of PVA and PU, the all- solid OECT is very soft, and shows a transmittance beyond \(80\%\) in the region of visible light (Fig. 4c), and a small thickness below \(300 \mu \mathrm{m}\) .  
+
+Despite the long channel length ( \(\sim 0.48 \mathrm{cm}\) ), much larger than of conventional micrometer- scale device, the all- solid OECT showed favorable amplification performance with a high on- off current ratio ( \(>10^3\) , Fig. 4e) at low voltages ( \(< 1 \mathrm{V}\) , Fig. 4d). The relatively fair transconductance ( \(\mathrm{g}_{\mathrm{m}} < 60 \mu \mathrm{S}\) ) is probably ascribed to the small cross- sectional area, which dramatically magnifies the resistance of fibrillar channel. Note that the all- solid OECT is an energy saving device with extremely low power consumptions. For example, at a given drain- source voltage of \(0.6 \mathrm{V}\) , the consumed power is below \(18 \mu \mathrm{W}\) (Fig. 4f).
+
+<--- Page Split --->
+![](images/Figure_5.jpg)
+
+<center>Fig. 5: Electrical response of the all-solid OECT to mechanical deformations. a Schematic of the mechanism explaining the response to the action of external pressure. b Relative drain-source change \((\Delta \mathrm{I}_{\mathrm{DS}} / \mathrm{I}_{\mathrm{DS0}})\) and sensitivity as a function of pressure. c Response time of the all-solid OECT when pressing (rising edge) and releasing (falling part) under the instantaneous pressure of 17.8 KPa. d Cyclic response at three different pressure levels (0.92, 6.8, and 22.2 KPa). e, Schematic of the working principle of the response to friction. f Cyclic response at three different frictions (1.84, 4.69, and 5.55 KPa). g An enlarged curve of the marked part in (f). h Cyclic response at different friction speeds from 4, 6, 8, 10, 15, to \(20\mathrm{mm}\mathrm{s}^{-1}\) . </center>  
+
+We proved that the all- solid OECT functioned to amplify small electrical signals in gel environments and respond to mechanical deformation as a tactile sensor. As illustrated in Fig. 5a, the applied vertical pressure on the surface of the all- solid OECT adjusted the ion penetration due to the improved gate- source electric field and the redistribution of intrinsic capacitance<sup>51</sup>. At a \(\mathrm{V}_{\mathrm{G}}\) of - 0.1 V and a \(\mathrm{V}_{\mathrm{D}}\) of 0.35 V, we observed a stable increase of drain- source current, \(\mathrm{I}_{\mathrm{DS}}\) , with the increasing pressure, up to a 92% amplification from 0 to 40 KPa (Fig. 5b). The sensitivity is at the level of 0.01- 0.1 KPa<sup>1</sup> in this process (dark cyan dots in Fig. 5b). As shown in Fig. 5c, the average rising time and falling time under instantaneous pressure of 17.8 KPa is \(\sim 536\) ms and \(\sim 698\) ms,
+
+<--- Page Split --->
+
+respectively. Such integrated parameters facilitated the all- solid OECT to respond to different pressure levels from 0.92 to \(22.2\mathrm{KPa}\) (Fig. 5d). In addition to the response to pressure at the vertical direction, the all- solid OECT also reacted to friction at the horizontal direction (Fig. 5e and Supplementary Fig. 8). The forward and backward friction of a load on the surface changed the real- time distance between silver gate and UFPP channel repeatedly, thus producing a bimodal response curve (Fig. 5g). Note that, to enable the enlargement of \(\mathrm{I_{DS}}\) with the increasing gate- channel distance under the repeated friction, we applied a positive \(\mathrm{V_G}\) of \(0.1\mathrm{V}\) at a \(\mathrm{V_D}\) of \(0.55\mathrm{V}\) . The all- solid OECT responded stably to friction at different magnitudes (Fig. 5f, from 1.84 to 5.55 \(\mathrm{KPa}\) ) and different speeds (Fig. 5h, from 4 to \(20\mathrm{mm}\mathrm{s}^{- 1}\) ) during our cyclic tests. For example, \(\mathrm{I_{DS}}\) increased \(\sim 86\%\) at \(5.55\mathrm{KPa}\) .  
+
+## Discussion  
+
+The past decades have witnessed great achievements in preparing high- performance CPFs, which made a vast difference to the rapid development of advanced electronics. However, due to the limitations of both technology and strategy, it is still difficult to produce ultrafine CPFs at the large scale. We proposed a good solvents strategy in a modified wet spinning technology. With a principle of diffusion- controlled slow gelation of protofibres, the new system successfully downsized the diameter of PANi fibres to below \(5\mu \mathrm{m}\) , a value smaller than that of most previous work. Furthermore, the ultrafine morphology with highly improved electroactive surfaces promotes UFPPs to behave superb electrochemical activities and mechanical performance.  
+
+It is of great importance to realize the mass production of ultrafine CPFs. We constructed an all- solid OECT to employ the impressive energy and charge storage capacities of UFPPs. A handful of fibres are robust enough to satisfy the operation as the tactile sensor. In view of the ability to produce on the industrial scale, UFPPs are promised to be extended to large- area electronics, such as textile- scale numeric displays, soft electrochromic windows, and wearable energy harvesting systems.
+
+<--- Page Split --->
+
+## Methods  
+
+Characteristics. All the SEM images were collected on a tungsten thermionic emission SEM system (the Tescan VEGA3). XRD spectra were obtained from XRD system (Rigaku SmartLab) equipped with \(9\mathrm{kW}\) rotating anode X- ray source ( \(\lambda \sim 1.54\mathrm{\AA}\) ) coupling with high- quality semiconductor detector that supports 0D, 1D or 2D x- ray diffraction measurement. Raman spectra were recorded from Renishaw Micro- Raman Spectroscopy system fully integrated with confocal microscope spectrometer and a \(785\mathrm{nm}\) laser source. Mechanical tests were conducted on an advanced rheometric expansion system at the Hong Kong University of Science and Technology. All the electrochemical tests were processed on an electrochemical workstation (VersaSTAT3). The measurements of OECT were conducted on probe station (Micromanipulator) with Keithley 4200A- SCS parameter analyzer.  
+
+The fabrication of UFPFs. PAni power (emeraldine base, purchased from Sigma- Aldrich) was mixed with CSA at a molar ratio of 2:1. After being milled for 15 minutes, the uniform doped PAni was dispersed in m- cresol (after degassing) at a concentration of \(0.05\mathrm{gmL^{- 1}}\) . The dispersions were used as spinning dopes after blending in air for 8 hours, and extruded through a PEEK microtube with an inner diameter of \(100\mu \mathrm{m}\) at a rate of \(1\mathrm{mL}\mathrm{min}^{- 1}\) . Coagulation bath was chosen according to the experimental requirements. PAni fibres were directly drawn out from bath and collected on a graphite roller continuously.  
+
+Numerical method. The experimental result is verified by numerical method using commercial software ANSYS. The simulation is performed using workbench 18.0. In the simplified computational model, a geometric model of gel tube is developed, in which the ratio of diameter to length is chosen as 1:18, and the mechanical properties, density, Young's modulus and Poisson's ratio are selected as \(300\mathrm{kg / m3}\) , \(1000\mathrm{Pa}\) and 0.01, respectively. For the boundary conditions, one end of the gel model set as fixed support, and another end applies extend displacement to mimic the stretching effect in the actual situation. Meanwhile, the corresponding pressure is applied on the outer surface of the gel model to account for the function of the impressive interfacial pressure on the surface of gel fibres. To ensure the convergence of the result, a grid independence test is conducted by refining mesh size sequentially, and the finite element mesh with 162641 nodes and 37128 hexahedral elements are adopted finally.  
+
+The fabrication of micro capacitor. Micro capacitor composed of two UFPF electrodes and the
+
+<--- Page Split --->
+
+gel electrolyte was constructed on a glass substrate. To prepare the gel electrolyte, PVA power was dispersed into deionized water at a mass ratio of 9:1. PVA was dissolved after being heated for 5 hours at \(85^{\circ}\mathrm{C}\) . Then phosphoric acid was added at a mass ratio of 1:10 with deionized water. The mixture cooled at room temperature and were ready for use. Two UFPFs were placed in parallel on the glass slide. The transparent PVA- \(\mathrm{H}_3\mathrm{PO}_4\) gel was dropped between UFPFs. Two cooper wires connected to the UPPFs with silver paste worked as the conductor lines. After condensing for 10 minutes at \(40^{\circ}\mathrm{C}\) , the whole device was subject to electrochemical tests.  
+
+The fabrication of all- solid OECT. The OECT was built from three layers: two PU layers and one ion gel layer. PU dispersion in DMF was casted on a PVDF substrate. After being treated in oven at \(60^{\circ}\mathrm{C}\) , a thin and transparent layer of pure PU was obtained. One drop of PVA- \(\mathrm{H}_3\mathrm{PO}_4\) gel electrolyte was added on the surface of solidified PU. An UFPF was immersed into gel. After been dried at \(45^{\circ}\mathrm{C}\) for 15 minutes, a UFPF channel locked in PVA- \(\mathrm{H}_3\mathrm{PO}_4\) gel was obtained. Afterwards, another drop of PU was added and a silver wire operation as the gate electrode was putted in PU at the liquid state. After being dried at \(60^{\circ}\mathrm{C}\) , an all- solid OECT was prepared. Note that all the three electrodes were connected to cooper electrodes for following measurements.  
+
+## Data availability  
+
+The data that support the findings of this study are available from the corresponding author upon reasonable request. Correspondence and requests for materials should be addressed to Y.C. and X.T.  
+
+## Acknowledgements  
+
+This work is supported by the Research Grants Council of Hong Kong (No. 15201419), Hong Kong Polytechnic University Postdoctoral Fellowship and Endowed Professorship Fund (No. 847A).  
+
+## Author contributions  
+
+X. T. supervised this study. B. F. designed and conducted the main experiments. J. Y., Y. C. and B. F. constructed and characterized the transistor. D. C. helped to build the wet spinning equipment and discussed the results. J. P. did the mechanic simulations. K. M. M. helped to draw a part of schematics. Q. G. and P. G. helped to conduct the mechanical tests. B.F., X. T. and Y. C. wrote the manuscript.  
+
+## Competing interests  
+
+The authors declare no competing interests.  
+
+## References
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+<--- Page Split --->
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+
+<--- Page Split --->
+
+## Supplementary Files  
+
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+Supplementaryinformation1130. pdf
+
+<--- Page Split --->

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+<|ref|>title<|/ref|><|det|>[[44, 107, 955, 175]]<|/det|>
+# Scalable production of ultrafine polyaniline fibres for tactile organic electrochemical transistors  
+
+<|ref|>text<|/ref|><|det|>[[44, 196, 352, 238]]<|/det|>
+Bo Fang Hong Kong Polytechnic University  
+
+<|ref|>text<|/ref|><|det|>[[44, 243, 350, 284]]<|/det|>
+Jianmin Yan Hong Kong Polytechnic University  
+
+<|ref|>text<|/ref|><|det|>[[44, 290, 225, 330]]<|/det|>
+Dan Chang Zhejiang University  
+
+<|ref|>text<|/ref|><|det|>[[44, 336, 350, 377]]<|/det|>
+Jinli Piao Hong Kong Polytechnic University  
+
+<|ref|>text<|/ref|><|det|>[[44, 382, 350, 423]]<|/det|>
+Kit Ming Ma Hong Kong Polytechnic University  
+
+<|ref|>text<|/ref|><|det|>[[44, 429, 484, 470]]<|/det|>
+Qiao Du Hong Kong University of Science and Technology  
+
+<|ref|>text<|/ref|><|det|>[[44, 475, 484, 516]]<|/det|>
+Ping Gao Hong Kong University of Science and Technology  
+
+<|ref|>text<|/ref|><|det|>[[44, 521, 707, 562]]<|/det|>
+Yang Chai Hong Kong Polytechnic University https://orcid.org/0000- 0002- 8943- 0861  
+
+<|ref|>text<|/ref|><|det|>[[44, 566, 707, 608]]<|/det|>
+Xiaoming Tao ( \(\boxed{\times}\) xiao- ming.tao@polyu.edu.hk) Hong Kong Polytechnic University https://orcid.org/0000- 0002- 2406- 0695  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 650, 102, 667]]<|/det|>
+## Article  
+
+<|ref|>title<|/ref|><|det|>[[44, 688, 135, 706]]<|/det|>
+# Keywords:  
+
+<|ref|>text<|/ref|><|det|>[[44, 725, 333, 744]]<|/det|>
+Posted Date: December 8th, 2021  
+
+<|ref|>text<|/ref|><|det|>[[44, 763, 473, 782]]<|/det|>
+DOI: https://doi.org/10.21203/rs.3.rs- 1126903/v1  
+
+<|ref|>text<|/ref|><|det|>[[44, 800, 909, 844]]<|/det|>
+License: \(\circledcirc\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+<|ref|>text<|/ref|><|det|>[[42, 879, 909, 921]]<|/det|>
+Version of Record: A version of this preprint was published at Nature Communications on April 19th, 2022. See the published version at https://doi.org/10.1038/s41467- 022- 29773- 9.
+
+<--- Page Split --->
+<|ref|>title<|/ref|><|det|>[[147, 92, 850, 151]]<|/det|>
+# Scalable production of ultrafine polyaniline fibres for tactile organic electrochemical transistors  
+
+<|ref|>text<|/ref|><|det|>[[147, 161, 852, 210]]<|/det|>
+Bo Fang<sup>1,2</sup>, Jianmin Yan<sup>1,3</sup>, Dan Chang<sup>4</sup>, Jinli Piao<sup>1,2</sup>, Kit Ming Ma<sup>1,2</sup>, Qiao Gu<sup>5</sup>, Ping Gao<sup>5</sup>, Yang Chai<sup>1,3\*</sup> Xiaoming Tao<sup>1,2\*</sup>  
+
+<|ref|>text<|/ref|><|det|>[[147, 218, 850, 266]]<|/det|>
+<sup>1</sup>Research Institute for Intelligent Wearable Systems, Hong Kong Polytechnic University, Hong Kong, 999077 China  
+
+<|ref|>text<|/ref|><|det|>[[147, 275, 853, 321]]<|/det|>
+<sup>2</sup>Institute of Textiles and Clothing, Hong Kong Polytechnic University, Hong Kong, 999077 China  
+
+<|ref|>text<|/ref|><|det|>[[147, 330, 850, 377]]<|/det|>
+<sup>3</sup>Department of Applied Physics, Hong Kong Polytechnic University, Hong Kong, 999077 China  
+
+<|ref|>text<|/ref|><|det|>[[147, 386, 853, 433]]<|/det|>
+<sup>4</sup>Department of Polymer Science and Engineering, Zhejiang University, Hangzhou, 310027 China  
+
+<|ref|>text<|/ref|><|det|>[[147, 441, 850, 488]]<|/det|>
+<sup>5</sup>Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong, 999077 China  
+
+<|ref|>text<|/ref|><|det|>[[147, 496, 653, 515]]<|/det|>
+Email: ychai@polyu.edu.hk; xiao- ming.tao@polyu.edu.hk  
+
+<|ref|>text<|/ref|><|det|>[[147, 551, 853, 905]]<|/det|>
+The development of continuous conducting polymer fibres is essential for applications ranging from advanced fibrous devices to frontier fabric electronics. The use of continuous conducting polymer fibres requires a small diameter to maximize their electroactive surfaces, microstructural orientations, and mechanical strengths. However, regularly used wet spinning techniques have rarely achieved this goal due primarily to the insufficient slenderization of rapidly solidified conducting polymer molecules in poor solvents. Here we report a good solvent exchange strategy to wet spin the ultrafine polyaniline fibres at the large scale. The slow diffusion between good solvents distinctly decreases the viscosity of gel protofibers, which undergo an impressive drawing ratio. The continuously collected polyaniline fibres have a previously unattained diameter below \(5 \mu \mathrm{m}\) , high energy and charge storage capacities, and favorable mechanical performance. We demonstrated an ultrathin all- solid organic electrochemical transistor based
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 88, 852, 247]]<|/det|>
+on ultrafine polyaniline fibres, which substantially amplified microampere drain- source electrical signals with less one volt driving voltage and effectively operated as a tactile sensor detecting pressure and friction forces at different levels. The aggressive electronical and electrochemical merits of ultrafine polyaniline fibres and their great potentials to prepare on industrial scale offer new opportunities for high- performance soft electronics and large- area electronic textiles.  
+
+<|ref|>text<|/ref|><|det|>[[147, 282, 852, 692]]<|/det|>
+The extended conjugated and easily doped \(\pi\) - system along the backbone enables conducting polymers to possess intriguing transport, optical, and electrochemical properties, which have rarely been found in conventional polymers and metal conductors \(^{1 - 3}\) . Processing conducting polymers into macroscopically fibrous materials makes it possible to translate their nano- object features to human- friendly products in a continuous manner. The combined merits, including but not limited to mechanical flexibility, intrinsic conductivity, and electrochemical activity, of conducting polymer fibres (CPFs) have introduced a new era of “electronic textiles” \(^{4}\) . For instance, highly conductive and electrochemically active poly(3- methylthiophene) fibres have been achieved by in situ electrochemical oxidation of monomers \(^{5}\) . Fast ion transport between CPFs and ionic liquids has given birth to long- term operation actuators, electrochromic windows, and numeric displays \(^{6}\) . In recent studies, the wet- spun poly (3,4- ethylene dioxythiophene) (PEDOT) fibres have been widely used in various frontier fields, such as flexible energy storage electrodes, implantable bioelectronics, and organic transistors \(^{7,8}\) .  
+
+<|ref|>text<|/ref|><|det|>[[147, 700, 852, 914]]<|/det|>
+Unfortunately, due primarily to the large diameters, the performance and expectations of most achieved continuous CPFs have been limited by their insufficient electroactive surfaces and weak breaking strengths. Electropinning and wet spinning are two mainstream strategies to produce continuous CPFs. In the case of electrospinning, the fairly rigid backbone due to the high aromaticity results into an insufficient elasticity of conducting polymer solutions, which fails to be solely electrospun into fine fibres \(^{9}\) . Although a two- fluid electrospinning technique has been proposed by coating a soluble and electrospinable fluid on the conducting polymer cores, the complex procedures
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 81, 853, 696]]<|/det|>
+involving the addition and removal of second components defy the mass production of electrospun CPFs<sup>10,11</sup>. In the case of conventional wet spinning, conducting polymer dopes tend to occur a transient solidification in poor solvents, induced by the strong interactions of conducting polymer chains. The rapidly hardened gels suppress the post- stretching and slenderizing procedures, and cause the wet- spun CPFs to show a large diameter, generally beyond \(10 \mu \mathrm{m}^{12 - 14}\) . The large diameters largely discount the mechanical properties and electrochemical activities of CPFs<sup>4,15</sup>. Thus, there is an urgent need to realize the mass production of ultrafine CPFs, which remains challenging. In this work, we report a good solvent exchange strategy in a modified wet spinning technique to prepare the ultrafine polyaniline (PAni) fibres (UFPFs) at the large scale. Beyond conventional wet spinning protocol, we replaced poor solvents by good solvents as the coagulation bath to decrease the viscosity of gel protofibres, which were subject to an ultrahigh drawing ratio and reduced to an ultrafine morphology. The obtained UFPFs own a small diameter below \(5 \mu \mathrm{m}\) , an unprecedented mechanical strength of \(1080 \pm 71 \mathrm{MPa}\) , a high area capacitance beyond \(1008 \mathrm{mF} \mathrm{cm}^{- 2}\) , and an enormous charge storage capacity of \(5.25 \times 10^{4} \mathrm{mC} \mathrm{cm}^{- 2}\) . Based on the structural and electrochemical merits of UFPFs, we demonstrated an ultrathin all- solid organic electrochemical transistor (OECT) with less one volt driving voltage, which substantially amplified drain- source electrical signals with a low power- consumption and responded to vertical pressure and horizontal friction forces at different levels. Our work opens an avenue to prepare continuous ultrafine CPFs and high- performance soft electronics.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[152, 85, 848, 617]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[147, 624, 852, 891]]<|/det|>
+<center>Fig. 1: Scalable production of UFPFs. a Schematic of the good solvent exchange strategy to prepare UFPFs in a modified wet spinning protocol. In the case of poor solvent exchange (light orange region, upper panel), PANi molecules are rapidly solidified into thick gels and protofibres with rough crystallized particles. In the case of good solvent exchange (light blue region, lower panel), the formed gels with low viscosity occur an impressive gel extension and are slenderized into ultrafine fibres. b Schematic of the modified wet spinning process. c Scanning electron microscope (SEM) image of the marked region in b, showing the sharp necking behavior of gel PANi fibres. The close observation to region 1 (d), region 2 (e), and region 3 (f) in the marked zone of c, illustrating the sharply necking process of PANi gels. g Photograph of a 5.4-kilometres-long UFPF collected in two hours. Scale bars: c 20 μm, d 2 μm, e 10 μm, g 150 mm. </center>
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[148, 91, 214, 107]]<|/det|>
+## Results  
+
+<|ref|>text<|/ref|><|det|>[[147, 115, 854, 584]]<|/det|>
+Preparation and characterization of UFPFs. In the modified one- step wet spinning process, we used good solvents as the coagulation bath to realize the mass production of UFPFs (Fig. 1a- b and Supplementary Fig. 1). After doping PANi power (emeraldine base) with camphor sulfonic acid (CSA) at a molar ratio of 2:1, we dispersed fully doped PANi into m- cresol as the raw spinning dopes (see the Methods section) \(^{16}\) . Significantly, the direct use of doped PANi solutions as the dopes saves the trouble of conventional post- doping procedures, and further permits the uniform charge distribution throughout the fibre length \(^{17}\) . A good solvent, dimethyl formamide (DMF), of PANi operated as the coagulation bath. A slow solvent exchange between m- cresol and DMF facilitated the formation of PANi gel protofibres with a quite low viscosity below 3000 cP. Subsequently, we observed a sharp decrease of diameter from \(\sim 0.1 \mathrm{mm}\) to \(\sim 4.7 \mu \mathrm{m}\) when stretching the gel fibres in bath (Fig. 1c- f), which, to our knowledge, is a record small value in the achieved wet- spun CPFs \(^{4}\) . The ultrafine fibre shows a smooth surface (Fig. 1f and Supplementary Fig. 2) and highly crystallized microstructures (Supplementary Fig.3). Moreover, such an impressive drawing ratio enables a very high production efficiency of UFPFs beyond 40 meters per minute. For example, we prepared a 5.4- kilometres- long UFPF in two hours (Fig. 1e).
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[157, 90, 849, 455]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[147, 460, 852, 728]]<|/det|>
+<center>Fig. 2: Mechanism and mechanical properties of UFPPs. a SEM images of the PAni fibres produced in different solvating species. Specifically, the upper four panels showing the fibres prepared from poor solvents, and the lower two panels showing the fibres fabricated from good solvents. b Raman spectra of PAni fibres after placing in air for four weeks. c The diffusivity from PAni dispersions (in m-cresol) to various solvating species. d The viscosity of PAni gels formed in various solvating species. e Mechanics simulation results of extension behaviors of PAni gel fibres at different interfacial pressure. f Typical tensile stress-strain curves of UFPPs. g Ashby plot comparing the mechanical strength of UFPPs to previously reported CPFs. Scale bars in a: Water, Ethanol, EA, Acetone \(20\mu \mathrm{m}\) (left) \(10\mu \mathrm{m}\) (right); NMP \(20\mu \mathrm{m}\) (left) \(5\mu \mathrm{m}\) (right), DMF \(20\mu \mathrm{m}\) (left) \(2\mu \mathrm{m}\) (right). </center>  
+
+<|ref|>text<|/ref|><|det|>[[147, 760, 852, 891]]<|/det|>
+The sharp necking behaviors of gel protofibres are highly related to the use of good solvents as the coagulation bath. We recorded the evolution of surface morphologies of PAni fibres collected from different solvating species. As shown in Fig. 2a, the obtained fibres in poor solvating species, i.e., water, ethanol, ethyl acetate (EA), and acetone, generally present coarse surfaces and large diameters around \(20\mu \mathrm{m}\) . By comparison,
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 88, 852, 330]]<|/det|>
+we clearly observed a necking phenomenon in both cases of good solvents, i.e., N- methyl- 2- pyrrolidone (NMP) and DMF. Such necking effects promoted the finally produced fibres to behave ultrafine morphologies, which assists PAni fibres to behave better structure and performance stabilities due to the higher degree of orientation and crystallization (see the X- ray diffraction analysis in Supplementary Fig. 3). We used Raman spectra to evaluate their structural evolution after placing fibres in air for four weeks. As shown in Fig. 2b, we did not find obvious de- doping signals in Raman spectra of the PAni fibres from good solvents, whereas various de- doping peaks (1223 \(\mathrm{cm^{- 1}}\) and \(1462\mathrm{cm^{- 1}}\) ) appeared in the cases of poor solvents.  
+
+<|ref|>text<|/ref|><|det|>[[147, 366, 852, 748]]<|/det|>
+We speculate that this sharp necking phenomenon may be caused by two factors: diffusion difference and interfacial pressure. In the conventional wet spinning protocol, the diffusion from good solvents to poor solvents occurs quickly to solidify dope fluids into gel fibres<sup>18,19</sup>. The rapid diffusion could be aggravated in the system of conducting polymers due to the strong interactions of rigid chains. Thus, PAni molecules tend to bond into irregularly crystallized particles prior to undergoing extensive drawing, as present in the upper panels of Fig. 1a. In previous reports using poor solvents as coagulation bath, although CPFs with a smooth surface could be collected by enhanced shear flow and strong stretching<sup>12,14</sup>, diameters are unable to be decreased to the ideal level due to the insufficient stretching slenderization of solidified gels. In contrast, the diffusion from dope fluids to good solvents is quite slow. Such slow diffusion allows the formation of fibrous gels with a low viscosity and the following high drawing ratios. Note that most conventional polymers are incapable of gelling in good solvents due to the poor chain interactions<sup>20,21</sup>.  
+
+<|ref|>text<|/ref|><|det|>[[148, 783, 852, 857]]<|/det|>
+We calculated the diffusivities between various solvents and measured the viscosity of corresponding formed gels to support our explanations. The diffusivity from A molecules to B molecules, \(D_{AB}^{0}\) , is determined by the equation  
+
+<|ref|>equation<|/ref|><|det|>[[283, 867, 711, 911]]<|/det|>
+\[\frac{D_{AB}^{0}\mu_{B}}{T} = 8.52\times 10^{-8}V_{bB}^{-1 / 3}\left[1.40\left(\frac{V_{bB}}{V_{bA}}\right)^{1 / 3} + \frac{V_{bB}}{V_{bA}}\right]\]
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 88, 852, 331]]<|/det|>
+, where \(\mu_{B}\) is the solvent viscosity, T is the temperature, and \(V_{b}\) is the molar volume of solvent at its normal boiling temperature \(^{22}\) . As displayed in Fig. 2c, the diffusivities from m- cresol to DMF \((7.5\times 10^{- 6} \mathrm{cm}^{2}\mathrm{s}^{- 1})\) and NMP \((7.71\times 10^{- 6} \mathrm{cm}^{2}\mathrm{s}^{- 1}\) ) are generally lower than that of poor solvents. Diffusion in bath further dominates the viscosity of protofibres. To monitor the viscosity of gel fibres in practical conditions, we conducted the viscometer tests at a low revolution (e.g., 10 Rev.). As summarized in Fig. 2d, the formed PANi gels in good solvents show a viscosity below \(3000\mathrm{cP}\) , much lower than that of poor solvents ( \(>4000\mathrm{cP}\) ). The established solvating specific- diffusivity- viscosity formula accords well with our proposed explanations.  
+
+<|ref|>text<|/ref|><|det|>[[147, 366, 852, 664]]<|/det|>
+Interfacial pressure during solvent exchange is another major factor relating to the necking behavior of PANi protofibres. In a two fluid system, the interfacial pressure between two kind of solvents is inclined to decrease with the improved solvent diffusion \(^{23}\) . Based on the slow diffusion from m- cresol to good solvents (Fig. 2c), the interfacial pressure between gel fibres and coagulation bath is considerable, which further induces the necking of protofibres. To understand this, we conducted a mechanic simulation to the stretching behavior of gel fibres at different interfacial pressure (see the progressive results in Supplementary Fig.4 and Method section). According to the simulation results in Fig. 2e, the higher interfacial pressure drives gel fibres to occur the sharper necking and thinning effects at a given tensile stress. This probably explains the formation of UFPFs in DMF bath.  
+
+<|ref|>text<|/ref|><|det|>[[147, 700, 852, 912]]<|/det|>
+UFPFs show impressive mechanical performance. Different from that of conventional polymer fibres, the typical linear strain- stress curves of UFPFs demonstrate a brittle fracture behavior with a small tensile strain of \(3.67\pm 0.64\%\) (Fig. 2f). It is reasonable if considering the rigid backbone of PANi chains, which likely gather and condense into fragile fibrous assemblies after undergoing strong shear flow in spinning microtubes. According to classical Griffith theory on brittle fracture, fibres' strength generally improves with the decrease of diameter due to the depressed structural defects \(^{24}\) . We compared the mechanical performance of UFPFs with previously reported CPFs.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[150, 90, 850, 451]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[147, 480, 852, 693]]<|/det|>
+<center>Fig. 3: Energy and charge storage capacities of UFPFs. a Schematic of a micro capacitor constructed using two UFPF electrodes on a substrate. b Cyclic voltammetry curves with the increasing scan rates from 10 to 20, 50, 80 and \(100\mathrm{mV}\mathrm{s}^{-1}\) . c Galvanostatic charge/discharge curves at various current densities increasing from 0.32 to 0.63, 1.59 and \(3.18\mathrm{mA}\mathrm{cm}^{-2}\) . d The area capacitance of UFPFs comparing to previous reported electrodes. e Cycle galvanostatic charge/discharge curves during 120 cycles between 0 and \(0.6\mathrm{V}\) at \(1.59\mathrm{mA}\mathrm{cm}^{-2}\) . f. The relationship between current and voltage at a slow rate of \(10\mathrm{mV}\mathrm{s}^{-1}\) . g The charge storage capacity of UFPFs comparing to other charge storage materials. </center>  
+
+<|ref|>text<|/ref|><|det|>[[147, 729, 852, 833]]<|/det|>
+Derived from the strain- stress curves, we concluded that UFPFs have a modulus of \(29.89 \pm 5.6\%\) GPa, and a strength of \(1080 \pm 71\) MPa, at least one order of magnitude higher than that of CPFs with larger diameters (Fig. 2g), mainly including PEDOT fibres (<450 MPa) \(^{7,25 - 29}\) and PAni fibres (<400 MPa) \(^{30 - 33}\) .  
+
+<|ref|>text<|/ref|><|det|>[[147, 869, 850, 888]]<|/det|>
+Energy and charge storage capacities. Ultrafine morphology optimizes the
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 80, 853, 640]]<|/det|>
+electroactive surfaces, which enables UFPFs to exhibit superb energy and charge storage capacities. To evaluate the electrochemical activity of UFPFs, we constructed a micro capacitor using polyvinyl alcohol (PVA)- \(\mathrm{H}_3\mathrm{PO}_4\) gel electrolyte and two UFPF electrodes (Fig. 3a). The electrochemical properties were checked by cyclic voltammetry (CV) and galvanostatic charge- discharge (GCD) measurements. At different scan rates, the nearly rectangular shape of CV curves and instantaneous current response to voltage reversal at each end potential suggest the good electrochemical activity of UFPFs<sup>34</sup> (Fig. 3b). The nearly triangular shape of GCD curves at different current densities illustrates the formation of efficient electric double layers and charge propagation across the UFPF electrodes<sup>35</sup> (Fig. 3c). According to the GCD results, we determined the electrochemical properties of UFPFs. Among of them, the area capacitance, \(\mathrm{C_A}\) , is between 1008 and 1666 mF cm<sup>- 2</sup> at the current densities between 0.32 and 3.18 mA cm<sup>- 2</sup>, outperforming previously reported thick CPFs<sup>29</sup> and other electrodes, such as carbon nanomaterials<sup>3434,36</sup>, metal oxides<sup>37</sup> and conducting polymers<sup>38- 41</sup>, and approaching to that of PANi nanowires<sup>42</sup> (Fig. 3d). The volumetric capacitance, power density and energy density reach 83.8 F cm<sup>- 3</sup>, 0.96 W cm<sup>- 3</sup> and 4.19 mWh cm<sup>- 3</sup>, respectively (Supplementary Fig. 5). In lifetime tests of UFPF- based capacitor, both the potential and capacitance continued without significant decrease for 120 charge/discharge cycles at a low current density of 1.59 mA cm<sup>- 2</sup>, indicating the reliable electrochemical performance stability of UFPFs (Fig. 3e).  
+
+<|ref|>text<|/ref|><|det|>[[147, 672, 853, 915]]<|/det|>
+We were able to confirm the amount of transported charge per unit area to UFPF during the charge/discharge cycle. The charge during a triangular wave potential between - 0.9 V and 1.0 V (water window, see the Supplementary Fig. 6) was calculated by integrating the measured current with respect to the time of period at a low scan rate of 10 mV s<sup>- 1</sup> (Fig. 3f)<sup>6</sup>. We determined that the charge storage capacity of UFPF was \(5.25 \times 10^{4}\) mC cm<sup>- 2</sup>, a value at least two orders of magnitude higher than that of noble metals<sup>43</sup>, carbon bulk<sup>44 - 46</sup> and previously reported conducting polymers<sup>47</sup> (Fig. 3g). This value decreases slightly to \(2.015 \times 10^{4}\) mC cm<sup>- 2</sup> at a tenfold scan rate of 100 mV s<sup>- 1</sup> (Supplementary Fig. 7).
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[156, 85, 848, 547]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[147, 604, 852, 760]]<|/det|>
+<center>Fig. 4: Demonstration and characterization of all-solid organic electrochemical transistor based on UFPFs. a Schematic of the all-solid OECT composed of three polymer layers, one silver wire as the gate electrode, and one UFPF as the drain-source channel. b Cross-section SEM image and schematic of OECT. The yellow break lines direct the charge flow along the fibre chains (green solid lines). c Transmittance of the OECT in the region of visible light. A typical output curve (d), transfer curve (e), and power consumption in operation (f) of OECT. Scale bars: b 20 μm. </center>  
+
+<|ref|>text<|/ref|><|det|>[[147, 797, 852, 900]]<|/det|>
+Structure and performance of all- solid OECT. Benefitting from the favorable energy and charge storage performance of UFPFs, we demonstrated a high- performance all- solid OECT. OECT amplifies drain- source current intensities at low operating voltages by ion penetration into the organic mixed ionic- electronic conductors, i.e., conducting
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[146, 87, 855, 470]]<|/det|>
+polymers<sup>48,49</sup>. This process is controlled by the gate bias, and, to date, has generally conducted in aqueous electrolytes. To preclude the interference of external environment, we promoted the working conditions of OECT from aqueous environments to all- solid state by using gel electrolytes as the ion matrix. As shown in Fig. 4a- b, our OECT is mainly constructed by three polymer layers. The upper layer is the cured polyurethane (PU) working as the dielectric coating and also protecting the device from the invasion of external action<sup>50</sup>. A fibrous silver gate electrode with a diameter of \(7 \mu \mathrm{m}\) is fixed in PU. Since UFPFs have demonstrated reliable electrochemical activities in PVA- \(\mathrm{H}_3\mathrm{PO}_4\) gel, we used PVA- \(\mathrm{H}_3\mathrm{PO}_4\) gel as the middle layer to inject ions to or uptake ions from the drain- source channel materials. A UFPF right below the silver gate is fused in the ion gel, and operates as the channel material. The bottom layer is also pure PU acting as the supporter of the whole device. Due to the remarkable flexibility and transparency of PVA and PU, the all- solid OECT is very soft, and shows a transmittance beyond \(80\%\) in the region of visible light (Fig. 4c), and a small thickness below \(300 \mu \mathrm{m}\) .  
+
+<|ref|>text<|/ref|><|det|>[[147, 504, 853, 721]]<|/det|>
+Despite the long channel length ( \(\sim 0.48 \mathrm{cm}\) ), much larger than of conventional micrometer- scale device, the all- solid OECT showed favorable amplification performance with a high on- off current ratio ( \(>10^3\) , Fig. 4e) at low voltages ( \(< 1 \mathrm{V}\) , Fig. 4d). The relatively fair transconductance ( \(\mathrm{g}_{\mathrm{m}} < 60 \mu \mathrm{S}\) ) is probably ascribed to the small cross- sectional area, which dramatically magnifies the resistance of fibrillar channel. Note that the all- solid OECT is an energy saving device with extremely low power consumptions. For example, at a given drain- source voltage of \(0.6 \mathrm{V}\) , the consumed power is below \(18 \mu \mathrm{W}\) (Fig. 4f).
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[151, 85, 841, 402]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[147, 409, 852, 620]]<|/det|>
+<center>Fig. 5: Electrical response of the all-solid OECT to mechanical deformations. a Schematic of the mechanism explaining the response to the action of external pressure. b Relative drain-source change \((\Delta \mathrm{I}_{\mathrm{DS}} / \mathrm{I}_{\mathrm{DS0}})\) and sensitivity as a function of pressure. c Response time of the all-solid OECT when pressing (rising edge) and releasing (falling part) under the instantaneous pressure of 17.8 KPa. d Cyclic response at three different pressure levels (0.92, 6.8, and 22.2 KPa). e, Schematic of the working principle of the response to friction. f Cyclic response at three different frictions (1.84, 4.69, and 5.55 KPa). g An enlarged curve of the marked part in (f). h Cyclic response at different friction speeds from 4, 6, 8, 10, 15, to \(20\mathrm{mm}\mathrm{s}^{-1}\) . </center>  
+
+<|ref|>text<|/ref|><|det|>[[147, 657, 852, 900]]<|/det|>
+We proved that the all- solid OECT functioned to amplify small electrical signals in gel environments and respond to mechanical deformation as a tactile sensor. As illustrated in Fig. 5a, the applied vertical pressure on the surface of the all- solid OECT adjusted the ion penetration due to the improved gate- source electric field and the redistribution of intrinsic capacitance<sup>51</sup>. At a \(\mathrm{V}_{\mathrm{G}}\) of - 0.1 V and a \(\mathrm{V}_{\mathrm{D}}\) of 0.35 V, we observed a stable increase of drain- source current, \(\mathrm{I}_{\mathrm{DS}}\) , with the increasing pressure, up to a 92% amplification from 0 to 40 KPa (Fig. 5b). The sensitivity is at the level of 0.01- 0.1 KPa<sup>1</sup> in this process (dark cyan dots in Fig. 5b). As shown in Fig. 5c, the average rising time and falling time under instantaneous pressure of 17.8 KPa is \(\sim 536\) ms and \(\sim 698\) ms,
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 88, 853, 388]]<|/det|>
+respectively. Such integrated parameters facilitated the all- solid OECT to respond to different pressure levels from 0.92 to \(22.2\mathrm{KPa}\) (Fig. 5d). In addition to the response to pressure at the vertical direction, the all- solid OECT also reacted to friction at the horizontal direction (Fig. 5e and Supplementary Fig. 8). The forward and backward friction of a load on the surface changed the real- time distance between silver gate and UFPP channel repeatedly, thus producing a bimodal response curve (Fig. 5g). Note that, to enable the enlargement of \(\mathrm{I_{DS}}\) with the increasing gate- channel distance under the repeated friction, we applied a positive \(\mathrm{V_G}\) of \(0.1\mathrm{V}\) at a \(\mathrm{V_D}\) of \(0.55\mathrm{V}\) . The all- solid OECT responded stably to friction at different magnitudes (Fig. 5f, from 1.84 to 5.55 \(\mathrm{KPa}\) ) and different speeds (Fig. 5h, from 4 to \(20\mathrm{mm}\mathrm{s}^{- 1}\) ) during our cyclic tests. For example, \(\mathrm{I_{DS}}\) increased \(\sim 86\%\) at \(5.55\mathrm{KPa}\) .  
+
+<|ref|>sub_title<|/ref|><|det|>[[148, 424, 242, 441]]<|/det|>
+## Discussion  
+
+<|ref|>text<|/ref|><|det|>[[147, 450, 853, 693]]<|/det|>
+The past decades have witnessed great achievements in preparing high- performance CPFs, which made a vast difference to the rapid development of advanced electronics. However, due to the limitations of both technology and strategy, it is still difficult to produce ultrafine CPFs at the large scale. We proposed a good solvents strategy in a modified wet spinning technology. With a principle of diffusion- controlled slow gelation of protofibres, the new system successfully downsized the diameter of PANi fibres to below \(5\mu \mathrm{m}\) , a value smaller than that of most previous work. Furthermore, the ultrafine morphology with highly improved electroactive surfaces promotes UFPPs to behave superb electrochemical activities and mechanical performance.  
+
+<|ref|>text<|/ref|><|det|>[[147, 700, 855, 858]]<|/det|>
+It is of great importance to realize the mass production of ultrafine CPFs. We constructed an all- solid OECT to employ the impressive energy and charge storage capacities of UFPPs. A handful of fibres are robust enough to satisfy the operation as the tactile sensor. In view of the ability to produce on the industrial scale, UFPPs are promised to be extended to large- area electronics, such as textile- scale numeric displays, soft electrochromic windows, and wearable energy harvesting systems.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[148, 91, 226, 107]]<|/det|>
+## Methods  
+
+<|ref|>text<|/ref|><|det|>[[147, 117, 853, 386]]<|/det|>
+Characteristics. All the SEM images were collected on a tungsten thermionic emission SEM system (the Tescan VEGA3). XRD spectra were obtained from XRD system (Rigaku SmartLab) equipped with \(9\mathrm{kW}\) rotating anode X- ray source ( \(\lambda \sim 1.54\mathrm{\AA}\) ) coupling with high- quality semiconductor detector that supports 0D, 1D or 2D x- ray diffraction measurement. Raman spectra were recorded from Renishaw Micro- Raman Spectroscopy system fully integrated with confocal microscope spectrometer and a \(785\mathrm{nm}\) laser source. Mechanical tests were conducted on an advanced rheometric expansion system at the Hong Kong University of Science and Technology. All the electrochemical tests were processed on an electrochemical workstation (VersaSTAT3). The measurements of OECT were conducted on probe station (Micromanipulator) with Keithley 4200A- SCS parameter analyzer.  
+
+<|ref|>text<|/ref|><|det|>[[147, 396, 853, 580]]<|/det|>
+The fabrication of UFPFs. PAni power (emeraldine base, purchased from Sigma- Aldrich) was mixed with CSA at a molar ratio of 2:1. After being milled for 15 minutes, the uniform doped PAni was dispersed in m- cresol (after degassing) at a concentration of \(0.05\mathrm{gmL^{- 1}}\) . The dispersions were used as spinning dopes after blending in air for 8 hours, and extruded through a PEEK microtube with an inner diameter of \(100\mu \mathrm{m}\) at a rate of \(1\mathrm{mL}\mathrm{min}^{- 1}\) . Coagulation bath was chosen according to the experimental requirements. PAni fibres were directly drawn out from bath and collected on a graphite roller continuously.  
+
+<|ref|>text<|/ref|><|det|>[[147, 589, 853, 886]]<|/det|>
+Numerical method. The experimental result is verified by numerical method using commercial software ANSYS. The simulation is performed using workbench 18.0. In the simplified computational model, a geometric model of gel tube is developed, in which the ratio of diameter to length is chosen as 1:18, and the mechanical properties, density, Young's modulus and Poisson's ratio are selected as \(300\mathrm{kg / m3}\) , \(1000\mathrm{Pa}\) and 0.01, respectively. For the boundary conditions, one end of the gel model set as fixed support, and another end applies extend displacement to mimic the stretching effect in the actual situation. Meanwhile, the corresponding pressure is applied on the outer surface of the gel model to account for the function of the impressive interfacial pressure on the surface of gel fibres. To ensure the convergence of the result, a grid independence test is conducted by refining mesh size sequentially, and the finite element mesh with 162641 nodes and 37128 hexahedral elements are adopted finally.  
+
+<|ref|>text<|/ref|><|det|>[[147, 896, 850, 913]]<|/det|>
+The fabrication of micro capacitor. Micro capacitor composed of two UFPF electrodes and the
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[147, 89, 853, 274]]<|/det|>
+gel electrolyte was constructed on a glass substrate. To prepare the gel electrolyte, PVA power was dispersed into deionized water at a mass ratio of 9:1. PVA was dissolved after being heated for 5 hours at \(85^{\circ}\mathrm{C}\) . Then phosphoric acid was added at a mass ratio of 1:10 with deionized water. The mixture cooled at room temperature and were ready for use. Two UFPFs were placed in parallel on the glass slide. The transparent PVA- \(\mathrm{H}_3\mathrm{PO}_4\) gel was dropped between UFPFs. Two cooper wires connected to the UPPFs with silver paste worked as the conductor lines. After condensing for 10 minutes at \(40^{\circ}\mathrm{C}\) , the whole device was subject to electrochemical tests.  
+
+<|ref|>text<|/ref|><|det|>[[147, 283, 868, 496]]<|/det|>
+The fabrication of all- solid OECT. The OECT was built from three layers: two PU layers and one ion gel layer. PU dispersion in DMF was casted on a PVDF substrate. After being treated in oven at \(60^{\circ}\mathrm{C}\) , a thin and transparent layer of pure PU was obtained. One drop of PVA- \(\mathrm{H}_3\mathrm{PO}_4\) gel electrolyte was added on the surface of solidified PU. An UFPF was immersed into gel. After been dried at \(45^{\circ}\mathrm{C}\) for 15 minutes, a UFPF channel locked in PVA- \(\mathrm{H}_3\mathrm{PO}_4\) gel was obtained. Afterwards, another drop of PU was added and a silver wire operation as the gate electrode was putted in PU at the liquid state. After being dried at \(60^{\circ}\mathrm{C}\) , an all- solid OECT was prepared. Note that all the three electrodes were connected to cooper electrodes for following measurements.  
+
+<|ref|>sub_title<|/ref|><|det|>[[148, 508, 275, 523]]<|/det|>
+## Data availability  
+
+<|ref|>text<|/ref|><|det|>[[148, 535, 850, 580]]<|/det|>
+The data that support the findings of this study are available from the corresponding author upon reasonable request. Correspondence and requests for materials should be addressed to Y.C. and X.T.  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 592, 296, 607]]<|/det|>
+## Acknowledgements  
+
+<|ref|>text<|/ref|><|det|>[[149, 618, 850, 662]]<|/det|>
+This work is supported by the Research Grants Council of Hong Kong (No. 15201419), Hong Kong Polytechnic University Postdoctoral Fellowship and Endowed Professorship Fund (No. 847A).  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 674, 312, 689]]<|/det|>
+## Author contributions  
+
+<|ref|>text<|/ref|><|det|>[[147, 700, 852, 828]]<|/det|>
+X. T. supervised this study. B. F. designed and conducted the main experiments. J. Y., Y. C. and B. F. constructed and characterized the transistor. D. C. helped to build the wet spinning equipment and discussed the results. J. P. did the mechanic simulations. K. M. M. helped to draw a part of schematics. Q. G. and P. G. helped to conduct the mechanical tests. B.F., X. T. and Y. C. wrote the manuscript.  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 841, 302, 856]]<|/det|>
+## Competing interests  
+
+<|ref|>text<|/ref|><|det|>[[150, 868, 458, 884]]<|/det|>
+The authors declare no competing interests.  
+
+<|ref|>sub_title<|/ref|><|det|>[[149, 895, 245, 911]]<|/det|>
+## References
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[144, 102, 852, 895]]<|/det|>
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+for conductive polyaniline fibres. Synth. Met. 101, 724- 725 (1999). (PAni 100 MPa)33. Hsu, C. H., Cohen, J. D. & Tietz, R. F. Polyaniline spinning solutions and fibers. Synth. Met. 59, 37- 41 (1993). (PAni 400 MPa)34. Kou, L. et al. Coaxial wet- spun yarn supercapacitors for high- energy density and safe wearable electronics. Nat. Commun. 5, 1- 10. (2014). (RGO/CNT fibers)35. Yoo, J. J. et al. Ultrathin planar graphene supercapacitors. Nano Lett. 11, 1423- 1427 (2011).36. Yu, D. et al. Scalable synthesis of hierarchically structured carbon nanotube- graphene fibres for capacitive energy storage. Nat. Nanotechnol. 9, 555- 562 (2014). (RGO/SWCNT fiber)37. Lu, X. et al. Oxygen- deficient hematite nanorods as high- performance and novel negative electrodes for flexible asymmetric supercapacitors. Adv. Mater. 26, 3148- 3155 (2014).38. Zheng, Y. et al. Thermally- treated and acid- etched carbon fiber cloth based on pre- oxidized polyacrylonitrile as self- standing and high area- capacitance electrodes for flexible supercapacitors. Chem. Eng. J. 364, 70- 78 (2019). (PAN clothes)39. Zeng, S. et al. Electrochemical fabrication of carbon nanotube/polyaniline hydrogel film for all- solid- state flexible supercapacitor with high areal capacitance. J. Mater. Chem. A 3, 23864- 23870 (2015). (PAni/CNT)40. Yuan, L. et al. Polypyrrole- coated paper for flexible solid- state energy storage. Energ. Environ. Sci. 6, 470- 476 (2013). (PPy)41. Chi, K. et al. Freestanding graphene paper supported three- dimensional porous graphene- polyaniline nanocomposite synthesized by inkjet printing and in flexible all- solid- state supercapacitor. ACS Appl. Mater. Inter. 6, 16312- 16319 (2014). (PAni/graphene)42. Horng, Y. Y. et al. Flexible supercapacitor based on polyaniline nanowires/carbon cloth with both high gravimetric and area- normalized capacitance. J. Power Sources 195, 4418- 4422 (2010).43. Lu, Y. et al. Electrodeposited polypyrrole/carbon nanotubes composite films electrodes for neural interfaces. Biomaterials 31, 5169- 5181 (2010). (Pt, PPy/Cl)44. Gerwig, R. et al. PEDOT- CNT composite microelectrodes for recording and electrostimulation applications: fabrication, morphology, and electrical properties. Front. Neuroeng. 5, 8 (2012). (PEDOT/CNT, PEDOT/CI04)45. Vitale, F., Summerson, S. R., Aazhang, B., Kemere, C. & Pasquali, M. Neural stimulation and recording with bidirectional, soft carbon nanotube fiber microelectrodes. ACS Nano 9, 4465- 4474 (2015). (CNT fiber)46. Wang, K. et al. High- performance graphene- fiber- based neural recording microelectrodes. Adv. Mater. 31, 1805867 (2019). (Graphene/Pt)47. Venkatraman, S. et al. In vitro and in vivo evaluation of PEDOT microelectrodes for neural stimulation and recording. IEEE T. Neur. Sys. Reh. 19, 307- 316 (2011). (PEDOT/PSS, PPy/CNT, PPy/PSS)48. Inal, S., Malliaras, G. G. & Rivnay, J. Benchmarking organic mixed conductors for transistors.
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+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[44, 42, 311, 70]]<|/det|>
+## Supplementary Files  
+
+<|ref|>text<|/ref|><|det|>[[44, 93, 765, 113]]<|/det|>
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+<|ref|>text<|/ref|><|det|>[[60, 130, 397, 150]]<|/det|>
+Supplementaryinformation1130. pdf
+
+<--- Page Split --->

preprint/preprint__01e7a1078c78ab368216c5666b306391adfa28f34c3a711786fcea2c12e51345/images_list.json

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+    "type": "image",
+    "img_path": "images/Figure_1.jpg",
+    "caption": "Figure 1. On-chip micro-vascularization enabled by soft microfluidics. (a) 3D printed tissue culture chip designed for eight multiplexed soft 3D soft microfluidic capillary grids. Inset: microfluidic capillary grid fabricated on a plastic baseplate and diagram of working principle. (b) of microfluidic capillary grid (left) and the close up image of the same grid showing individual hydrogel capillaries (right). (c) Microvessel 3D printing by 3D printing of microfluidic grid using high-resolution 2-photon stereo-lithography with non-swelling photo-polymerizable hydrogel precursors enables reproduction of features as small as \\(10\\mu \\mathrm{m}\\) . The top view photograph demonstrates an array of cylinders of various outer diameter in micrometers (top row) and wall thickness in micrometers (columns). (d) CAD image of microfluidic grid (left) with capillaries shown in red and the structural components shown in grey. The fence at the circumference of the structure makes",
+    "footnote": [],
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+    ],
+    "page_idx": 33
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_2.jpg",
+    "caption": "Figure 2. Transcriptomic changes upon perfusion. (a) Combined dataset UMAP (control, non-perfused and perfused samples). (b) Combined dataset UMAP with neuroepithelial cells (NE), pluripotent-neuroepithelial transitioning cells (P-NE), proliferating cells with medium mitochondrial (mito.) content, pluripotent cells (P) with low glycolysis (glyc.) and medium mitochondrial content, glycolytic pluripotent cells with low mitochondrial content, highly glycolytic pluripotent cells, a highly glycolytic hypoxic (hypo.) identity and a stressed cluster. (c) Pseudotime trajectory on combined dataset UMAP. (d) Cluster specific expressions of selected marker genes. Expression values are normalized and centered. Sample fractions for each identified cluster. (e) GO enrichment analysis for key processes upregulated in perfused and non-perfused samples.",
+    "footnote": [],
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+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_3.jpg",
+    "caption": "Figure 3. Changes in proliferation mediated by hypoxia. a) Representative experimental results demonstrating differences in proliferation and viability between standard organoid culture(left column) and the tissue constructs without (middle column) and with perfusion (right column). Top row: bright field images of organoids, non-perfused and perfused constructs correspondingly. Middle row: immunofluorescent images of apoptotic marker cleaved Caspase 3 (green) expressed in the three conditions. Bottom row: immunofluorescent images of hypoxia marker HIF1a (green). Hoechst staining of nuclei shown in blue. The images represent transverse cross-sections of the tissue constructs. (b) Top: average proportion of the number of live cells in perfused and non-perfused constructs quantified by flow cytometry (90.95.1% in perfused vs 89.2±4.4% in non-perfused tissue, n.s., n=4). Middle: average expression of cleaved Caspase 3 in control organoids (8±1% area, n=7), non-perfused(35.7±5%, pControl=0.02, n=4) and perfused tissue constructs (5.1±1% of total area, pControl=0.02, n=9). Bottom: average expression of hypoxia marker HIF-1a in control organoids (100±3%, n=4), non-perfused (55±3%, pControl=0.0003, n=3) and perfused constructs (26±2%, pControl=0.0004, n=2). Control organoids, non-perfused and perfused constructs are denoted as Ctrl, NP and P correspondingly, data are represented as mean ± SEM, asterisks (*) denote statistical significance of the difference between control organoids and the tissue constructs (unpaired two-tailed Student's t-test, 95% confidence interval). (c) Representative fluo-cytometry data, demonstrating the difference in total number of cells between perfused and non-perfused constructs. (d) Distribution of gene expression levels across the single cell population for hypoxia (left), G2M cell cycle (middle) and S cell cycle (right) associated gene sets. Scale bar: 1mm",
+    "footnote": [],
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+  {
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+    "img_path": "images/Figure_4.jpg",
+    "caption": "Figure 4. Rapid neural differentiation in perfused systems. (a) Representative experimental results demonstrating the result of 2 days of neuronal induction in control organoids(left column), non-perfused (middle column) and perfused (right column) tissue constructs. Top, middle and bottom row: immunofluorescent images of stem cell marker NANOG, early neural marker PAX6 and cell adhesion proteins N-Cadherin (green) and E-Cadherin (red) correspondingly. The images represent transverse cross-sections of the tissue constructs. In most cases, the cross-sections of capillaries are visible as circular structures (marked by triangles) (b) Average expression of PAX6(Ctrl: Not detected; NP: \\(0.2\\% \\pm 0.2\\%\\) , pControl \\(= 0.35(n.s.)\\) , \\(n = 9\\) ; P: \\(21.5 \\pm 2\\%\\) , pControl \\(= 0.00002\\) , \\(n = 12\\) ), NANOG(Ctrl: \\(73.6 \\pm 8\\%\\) , \\(n = 9\\) ; NP: \\(16.1 \\pm 2\\%\\) , pControl \\(= 0.00007\\) , \\(n = 11\\) ; P: not detected), NCad(CDH2) (Ctrl: \\(100 \\pm 1\\%\\) , \\(n = 8\\) ; NP: \\(7.6 \\pm 1\\%\\) , pControl \\(= 4e - 13\\) , \\(n = 3\\) ; P: \\(55.7 \\pm 9\\%\\) (pControl \\(= 0.009\\) , \\(n = 5\\) ) and ECad(CDH1) (Ctrl: \\(99 \\pm 2\\%\\) , \\(n = 12\\) ; NP: \\(26.6 \\pm 7\\%\\) , pControl \\(= 0.0001\\) , \\(n = 6\\) ; P: \\(4.5 \\pm 2\\%\\) , pControl \\(= 2e - 10\\) , \\(n = 5\\) ) markers in control organoids (Ctrl), non-perfused(NP) and perfused(P) constructs. Data are represented as mean \\(\\pm\\) SEM, asterisks (*) denote statistical significance of the difference between control organoids and the tissue constructs (unpaired two-tailed Student's \\(t\\) -test, \\(95\\%\\) confidence interval) (c) UMAP plot of the combined dataset showing the localization of cells from control organoids, non-perfused and perfused constructs in the UMAP space. (d) UMAP plot of the combined dataset highlighting locations of PAX6, NANOG, NCad (CDH2) and ECad(CDH1) expressing cells in the UMAP space. Scale bar: \\(250 \\mu m\\)",
+    "footnote": [],
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+    "page_idx": 37
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_5.jpg",
+    "caption": "Figure 5. Functional improvement in perfused liver microtissues. (a) Hematoxylin and eosin of a transverse section of liver tissue construct with hepatocyte-like morphology and no visible indication of apoptosis. (b) Immunofluorescence images showing presence of AFP and CYP3A4, (c) Alpha-1 antitrypsin (AAT) and Cytokeratin 19 (KRT19), and (d) basic hepatic markers albumin (ALB) and hepatic nuclear factor HNF4α expression (f) Heatmap representation of fold change gene expression levels normalized to control 2D cell culture and compared to standard hepatic organoids and perfused liver constructs; rows are centered and scaled. Gene expression data for hepatic 2D culture and hepatic organoids are from Kumar et al<sup>74</sup>. Scale bar: \\(50\\mu \\mathrm{m}\\)",
+    "footnote": [],
+    "bbox": [
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+    "page_idx": 38
+  },
+  {
+    "type": "image",
+    "img_path": "images/Supplementary_Figure_3.jpg",
+    "caption": "Supplementary Figure 3. Two-photon stereo-lithography enables precision fabrication in a wide range of scales. Microfluidic grids of different dimensions (in mm): 1.2mm x 1.2mm x 1.2mm (left), 2.6mm x 2.6 mm x 1.5mm (middle), 6.5mm x 6.5mm x 5mm (right). Perfusion vessels in each grid have identical 50μm diameter and inter-vessel distance of 250μm. Scale bar 5 mm.",
+    "footnote": [],
+    "bbox": [],
+    "page_idx": 40
+  },
+  {
+    "type": "image",
+    "img_path": "images/Supplementary_Figure_4.jpg",
+    "caption": "Supplementary Figure 4. Daily brightfield imaging of neural tissue in perfused and non-perfused chips. (a) Bright field images of the perfused (top) and non-perfused (bottom) tissue constructs taken every 2 days during the culturing protocol. (b) Live sections of perfused (left) and non-perfused (right) tissue constructs taken at the end of the culturing protocol. Scale bar 500μm.",
+    "footnote": [],
+    "bbox": [
+      [
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+      ]
+    ],
+    "page_idx": 41
+  },
+  {
+    "type": "image",
+    "img_path": "images/Supplementary_Figure_6.jpg",
+    "caption": "Supplementary Figure 6. Gene-set analysis for various processes. Combined dataset UMAP with scores for pluripotent, glycolysis and neural progenitor gene-set as well as the \\(\\%\\) mitochondrial genes. Hierarchical clustering of gene-set scores and \\(\\%\\) mitochondrial genes. Score is column scaled.",
+    "footnote": [],
+    "bbox": [],
+    "page_idx": 41
+  },
+  {
+    "type": "image",
+    "img_path": "images/Supplementary_Figure_7.jpg",
+    "caption": "Supplementary Figure 7. Gene expression for unannotated hNTO clusters with the top 25 marker genes for each cluster.",
+    "footnote": [],
+    "bbox": [
+      [
+        260,
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+    ],
+    "page_idx": 42
+  },
+  {
+    "type": "image",
+    "img_path": "images/Supplementary_Figure_8.jpg",
+    "caption": "Supplementary Figure 8. Cluster-specific analysis of scRNAseq dataset. Dot-plot heatmap of hypoxia and cell cycle markers for each identified cluster. The average gene expression is represented by the color intensity of each dot, whereas the dot size represents the percentage of the gene-expressing cells for each sample within each cluster.",
+    "footnote": [],
+    "bbox": [
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+    "page_idx": 43
+  },
+  {
+    "type": "image",
+    "img_path": "images/Supplementary_Figure_9.jpg",
+    "caption": "Supplementary Figure 9. Difference in localized expression of hypoxia marker HIF1α and apoptosis marker cleaved Caspase-3. (a) HIF1α expression is localized to regions containing intact cell bodies (middle), evidenced by intact nuclei in the outlined region (left) as well as well-defined cytoplasmic regions stained with E-Cad antibody (right). (b) Cleaved Caspase-3 expression (middle) is localized to regions with apoptotic cell bodies evidenced by Hoechst stain of fragmented nuclei (outside of the outlined live region on the left image). Scale bar 100μm.",
+    "footnote": [],
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+    "page_idx": 44
+  },
+  {
+    "type": "image",
+    "img_path": "images/Supplementary_Figure_10.jpg",
+    "caption": "Supplementary Figure 10. Brightfield images of perfused liver cultures. Bright field images of the perfused liver-like tissue constructs taken during tissue culturing. Scale bar 500μm.",
+    "footnote": [],
+    "bbox": [
+      [
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+    "page_idx": 45
+  }
+]

preprint/preprint__01e7a1078c78ab368216c5666b306391adfa28f34c3a711786fcea2c12e51345/preprint__01e7a1078c78ab368216c5666b306391adfa28f34c3a711786fcea2c12e51345.mmd

@@ -0,0 +1,515 @@
+
+# Engineering large-scale perfused tissues via synthetic 3D soft microfluidics  
+
+Sergei Grebenyuk KU Leuven  
+
+Abdel Rahman Abdel Fattah KU Leuven https://orcid.org/0000- 0002- 7817- 5586  
+
+Gregorius Rustandi KU Leuven  
+
+Manoj Kumar KU Leuven https://orcid.org/0000- 0002- 0572- 5786  
+
+Burak Toprakhisar  
+
+Stem Cell Institute, Department of Stem Cell and Developmental Biology, KU Leuven, Leuven, Belgium  
+
+Idris Salmon KU Leuven  
+
+Catherine Verfaillie  
+
+KU Leuven https://orcid.org/0000- 0001- 7564- 4079  
+
+Adrian Ranga ( adrian.ranga@kuleuven.be )  
+
+adrian.ranga@kuleuven.be https://orcid.org/0000- 0002- 6400- 9472  
+
+Article  
+
+Keywords:  
+
+Posted Date: September 8th, 2021  
+
+DOI: https://doi.org/10.21203/rs.3.rs- 867063/v1  
+
+License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License
+
+<--- Page Split --->
+
+# Engineering large-scale perfused tissues via synthetic 3D soft microfluidics  
+
+Sergei Grebenyuk \(^{1}\) , Abdel Rahman Abdel Fattah \(^{1}\) , Gregorius Rustandi \(^{1}\) , Manoj Kumar \(^{2}\) , Burak Toprakhisar \(^{2}\) , Idris Salmon \(^{1}\) , Catherine Verfaillie \(^{2}\) , Adrian Ranga \(^{1*}\)  
+
+\(^{1}\) Laboratory of Bioengineering and Morphogenesis, Biomechanics Section, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium  
+
+\(^{2}\) Stem Cell Institute Leuven and Department of Development and Regeneration, Faculty of Medicine, KU Leuven, Leuven, Belgium  
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+\* email: adrian.ranga@kuleuven.be  
+
+## Abstract  
+
+The vascularization of engineered tissues and organoids has remained a major unresolved challenge in regenerative medicine. While multiple approaches have been developed to vascularize in vitro tissues, it has thus far not been possible to generate sufficiently dense networks of small- scale vessels to perfuse large de novo tissues. Here, we achieve the perfusion of multi- mm \(^{3}\) tissue constructs by generating networks of synthetic capillary- scale 3D vessels. Our 3D soft microfluidic strategy is uniquely enabled by a 3D- printable 2- photon- polymerizable hydrogel formulation, which allows for precise microvessel printing at scales below the diffusion limit of living tissues. We demonstrate that these large- scale engineered tissues are viable, proliferative and exhibit complex morphogenesis during long- term in- vitro culture, while avoiding hypoxia and necrosis. We show by scRNAseq and immunohistochemistry that neural differentiation is significantly accelerated in perfused neural constructs. Additionally, we illustrate the versatility of this platform by demonstrating long- term perfusion of developing liver tissue. This fully synthetic vascularization platform opens the door to the generation of human tissue models at unprecedented scale and complexity.
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+## Introduction  
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+Human engineered tissue and organoids are potentially transformational model systems, which could create dramatic efficiencies in the drug discovery process and function as key building blocks for regenerative medicine applications. In particular, larger- scale tissues have the possibility to recapitulate complex functional and organizational characteristics of their in vivo counterparts, and could therefore become a long- sought alternative to animal models \(^{1 - 3}\) . However, the poorly defined structural organization, small size and slow maturation of these tissues have remained major limitations in engineering fully functional and reproducible organoids and tissues.  
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+In vivo, the development of tissues is supported by a complex network of blood vessels which provide oxygen, nutrients and waste exchange and mediate paracrine interactions via growth and differentiation factors \(^{4}\) . The size of the microvasculature is a critical parameter for local tissue perfusion: to maintain sufficient diffusion of oxygen, nutrients, and waste products most cells in vivo lie within \(200 \mu \mathrm{m}\) of a capillary. In the absence of vascular support, normal physiological conditions can be maintained only within this narrow range. Similar to the diffusion limits in normal tissue, the generation of solid tissue in vitro requires both vascularization and flow to maintain cell viability throughout the entire construct \(^{1}\) . The lack of vascularization in engineered tissues therefore prevents oxygen and nutrient exchange, which is thought to be the main reason for the commonly observed development of a necrotic core within organoids once they reach a critical size, as well as for apoptosis within engineered tissues. These issues have been widely recognized, and various approaches have been reported in order to overcome them \(^{5}\) .  
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+The extrinsic induction of angiogenesis has been frequently used in the context of organoid vascularization. Vessel sprouts have been shown to infiltrate organoids maintained with endothelial cells in separate compartments of microfluidic culture devices \(^{6,7}\) , or co- cultured with pre- established microvascular beds \(^{8,9}\) . These results have suggested that the presence of a perfusable vasculature can enhance organoid growth \(^{8}\) , confirming the importance of systemic cross- talk between vasculature and organoids in the developmental process. The resulting organoids have nonetheless been limited in size, and to date, functional organoid vascularization has only been achieved by grafting organoids into host animals \(^{10,11}\) .  
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+A number of studies have focused on creating artificial vessels though the use of templating approaches based on patterned layer- by- layer deposition of gelled material, in the form of thin filaments \(^{12}\) ,
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+droplets \(^{13 - 18}\) or layer- by- layer polymerization by stereolithography \(^{19 - 21}\) . Large tissue constructs have been generated by bioprinting of bioinks comprised of gels carrying different cell types \(^{12,22,23}\) , with vascular templates generated by depositing endothelial cells interleaved with tissue- specific cells using filament extrusion \(^{24 - 28}\) or stereolithography \(^{19,21}\) . While multiphoton lithography has been used to fabricate capillary- sized tubular fragments \(^{29}\) and vascular mimics \(^{30}\) , these vessels have thus far not been successfully perfused. The dissolution of sacrificial networks to form lumenized vessels has been proposed as an alternative strategy, with resorbable gel filaments being created via stereolithography \(^{31}\) , pre- polymer extrusion \(^{32 - 38}\) or molding \(^{39}\) . Artificial vessels have also been formed by the direct removal of diverse hydrogel material such as silk fibroin and PEG hydrogels using laser photo- ablation \(^{40 - 44}\) including in the presence of cells \(^{45 - 47}\) .  
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+Despite the versatility of these vessel templating approaches, the minimal diameter of perfusable engineered vessels reported thus far has been limited to \(150\mu \mathrm{m}^{33,39}\) and, in parallel with strategies based on extrinsic angiogenesis in organ- on- chip implementations \(^{48}\) , the size of generated tissues has been limited to \(400 - 500\mu \mathrm{m}\) in at least one dimension \(^{49 - 53}\) . Because of their small size, engineered tissues which have been implemented thus far do not preserve a physiologically relevant signaling context within the tissue, nor do they develop to a level of complexity comparable to in vivo organs.  
+
+## Photo-polymerizable non-swelling hydrogels enable 3D soft microfluidics  
+
+In order to vascularize tissue at large scale, we hypothesized that a microfluidic approach which could bridge the capillary to tissue scale would be necessary to enable the perfusion of thick three- dimensional tissue constructs. We therefore designed a dense, regularly spaced capillary network whose tubular walls were made of a hydrogel allowing diffusion (Fig. 1a). Tissues growing within this soft grid- like hydrated network would interface with an external perfusion pumping system, circulating cell culture medium throughout the volume of the tissue (Fig. 1a and Supplementary Fig. 1). We developed an approach whereby the perfusable grid would be printed directly on a hard plastic base (Fig. 1a), thereby forming a tight seal. This base, which contains perfusion holes, would then be incorporated into a perfusion chip linked to a peristaltic pump circulating cell culture medium (Supplementary Fig. 1)
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+An important requirement of this platform was the need to have capillary- like tubing at scales of a few \(\mu \mathrm{m}\) in diameter and thickness, while perfusing across a large, multi- \(\mathrm{mm}^3\) three- dimensional space. The geometrical complexity of the design, properties of the biopolymer and fabrication scale ranging from \(10\mu \mathrm{m}\) to \(2000\mu \mathrm{m}\) featured by our design made two- photon laser scanning photo- polymerization the ideal technology for this purpose. Initial 3D prints with the 2- photon Nanoscribe printer using commonly used photopolymerizable materials, including gelatin and PEG diacrylate, resulted in significant swelling of the material upon polymerization and hydration, which disrupted the seal between the soft microfluidic grid and the rigid plastic plate, and generated mismatched tubular segments (Supplementary Fig. 2).  
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+To overcome this post- printing distortion, we developed a custom formulated hydrophilic photopolymer based on polyethylene glycol diacrylate (PEGDA). While PEGDA exhibits cell- repelling surface properties, the polymer surface could be rendered cell- binding by the addition of the photocrosslinker pentaerythritol triacrylate (PETA)54. We reasoned that the addition of a significant amount of PETA as a crosslinker would increase the toughness of the polymer, and balanced with the addition of an inert "filler" component (Triton- X 100) would on the other hand retain sufficient porosity of the polymer to enable rapid diffusion.  
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+The combination of 2- photon printing with this non- swelling hydrogel material allowed printing of a variety of microfluidic grids, ranging in size from \(1.2 \times 1.2 \times 1.2\mathrm{mm}\) up to \(6.5 \times 6.5 \times 5\mathrm{mm}\) (Fig. 1b and Supplementary Fig. 3) with vessel diameters from \(10\mu \mathrm{m}\) to \(>70\mu \mathrm{m}\) and vessel wall thickness from \(2\mu \mathrm{m}\) to \(10\mu \mathrm{m}\) (Fig. 1c). Importantly, the printing with our novel formulation resin resulted in a 1:1 fidelity between the generated CAD geometry and the printed parts, thereby ensuring no distortion and a tight seal (Supplementary Fig. 2). The standard size used for most of the subsequent experiments was \(2.6\mathrm{mm} \times 2.6\mathrm{mm} \times 1.5\mathrm{mm}\) , with an inter- vessel distance of \(250\mu \mathrm{m}\) (Fig. 1b). These grids could be incorporated into a multi- plexed perfusion chip allowing up to 8 grids to be perfused simultaneously (Fig. 1a). Single cells or organoids smaller than the \(250\mu \mathrm{m}\) inter- capillary distance, previously mixed within a liquid hydrogel (eg. Matrigel) precursor solution, could be seeded into the platform, yielding a "gel- in- gel" 3- dimensional construct (Fig. 1d). Vessel permeability to water- soluble molecules within the chips overlayed with Matrigel was verified using fluorescein, with diffusion throughout the three- dimensional space seen in less than 10 minutes (Fig. 1e).
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+To perform a biological proof of concept experiment, we generated hundreds of organoids of less than \(200 \mu m\) diameter by aggregating human pluripotent stem cells (hPSCs) in microwells in pluripotency medium over 24 hours. We collected these aggregates in cold liquid Matrigel, which were then pipetted into the grids. Initial seeding demonstrated that these aggregates filled the grids and, over a period of 8 days of growth and neural differentiation, merged and filled the whole volume (Fig. 1f and Supplementary Fig. 4).  
+
+## scRNAseq reveals changes in differentiation, hypoxia, cell cycle regulation and differentiation upon perfusion  
+
+To assess how perfusion affected cellular processes and differentiation in large- scale in- vitro tissue, we dissociated cells from tissue constructs in a perfused and a non- perfused grid, as well as from organoids in conventional suspension culture after the 8- day culture period, and performed single cell RNA sequencing. Graph- based clustering and Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction technique on the 8,625 total cells retained after QC revealed significant transcriptomic differences between the tissue constructs (perfused and non- perfused) and the control organoids as evidenced by largely separated clustering of these cell populations (Fig. 2a). Correlation analysis using the 100 most differentially expressed marker genes revealed the most difference between perfused tissue and conventional organoid culture, with non- perfused tissue sharing gene expression profiles with the other two conditions (Supplementary Fig. 5). Differential gene expression analysis was then used to annotate eight clusters, which were largely differentiated by fate, as well as by metabolic, hypoxia and cell cycle regulation (Fig. 2b, Fig. 2d, Supplementary Fig. 6, Supplementary Fig. 7). Cells from control organoids were found in clusters with low, medium and high glycolytic processes marked by a pluripotent identity, including highly expressed markers such as NANOG and OCT4 (POUF5F1). Both perfused and non- perfused tissues expressed varying degrees of hypoxia, proliferation, mitochondrial gene expression and neuroepithelial markers. The non- perfused tissue made up the largest proportion of the hypoxic and stressed clusters (HIF1α, FOS) while the perfused tissue represented the majority of the neuroepithelial cluster (PAX6).  
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+In particular, control organoids had lower expression of mitochondrial genes (Supplementary Fig. 7, Supplementary Fig. 8) compared to the tissue constructs. This is in line with previous reports of lower
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+mitochondrial activity in human embryonic stem cells that increase upon differentiation to fit the energy needs of resultant cell identities<sup>55</sup>. Indeed, differentiation towards the neuroepithelial fates from pluripotent cells was characterized by an increase in mitochondrial activity, and a simultaneous decrease in glycolysis (Supplementary Fig. 8). These results suggest a metabolic switch from anaerobic glycolysis to oxidative phosphorylation in the tissue constructs, as has been reported to occur during cell differentiation after exit from pluripotency<sup>56,57</sup>. Such transitions, which were additionally evidenced in pseudotime analysis (Fig. 2c) distinguished the control organoids from the tissue constructs in the microfluidic grids, and further characterized differences between non- perfused and perfused tissues.  
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+The perfused tissue was also characterized by the expression of the neuroepithelial markers PAX6, PAX7, PAX3, and CDH2, while sharing IRX1 and IRX2 markers with non- perfused tissue (Fig. 2d). Moreover, while non- perfused and perfused tissues represented similar proportions of the pluripotent proliferating cluster, the perfused tissue expressed the highest proliferation markers such as CDC6 (Supplementary Fig. 7). Additionally, the high expression of proliferation markers such MKi67 and TOP2A in the neuroepithelial cluster suggested the retained proliferative capacity of perfused tissues after neuroepithelial differentiation, while maintaining overall minimal hypoxic/stress response markers VEGF, FOS, HIF1a (Supplementary Fig. 8). By contrast, non- perfused tissue was distinguished by significant presence of pluripotency (NANOG, POU5F1/OCT- 4) (Fig. 2d) and hypoxia/stress markers (Fig. 2d, Supplementary Fig. 8). The control organoids, on the other hand, were mostly represented by pluripotent and hypoxic cells with complete absence of neuroepithelial identity (Fig. 2d and Supplementary Fig. 8), likely due to the short time in neural differentiation conditions.  
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+To investigate transcriptional changes and associated cellular processes upon perfusion in a systematic manner, we performed differential gene expression analysis between perfused and non- perfused tissue constructs followed by Gene Ontology (GO) enrichment analysis (Fig. 2e). This analysis confirmed up- regulation of processes related to cell division and proliferation in the perfused sample, as well as regulation of neural precursors and neural precursor cell proliferation. By contrast, cell stress, hypoxia and cellular death processes were upregulated in non- perfused samples. Taken together our analysis of the transcriptomic data suggested that perfusion of large tissue constructs dramatically decreased apoptosis and hypoxia, and accelerated the process of neural differentiation.
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+## Perfusion rescues hypoxia and necrotic core in large tissue constructs  
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+To further investigate how perfusion modulates the spatial distribution of hypoxia and apoptosis, we imaged the constructs in bright field microscopy and sectioned whole samples transversely, perpendicular to the direction of grid perfusion, followed by immunohistochemistry (Fig. 3a and associated quantification in Fig. 3b). Control organoids demonstrated a characteristic dark dense tissue core with occasional lighter voidlike structures, surrounded by more translucent peripheral tissue. In non- perfused samples, tissue growth was restricted to the internal volume of the microfluidic grid, with generally dense central tissue interspersed with patchy lighter areas. Strikingly, perfused tissue covered the entire volume of the grid in a uniformly dense manner, with bulging epithelial outgrowths characteristic of cerebral organoids at the periphery. These observations suggested that cell proliferation was much higher in the perfused grids, compared to the non- perfused samples. To confirm this observation quantitatively, we dissociated the tissue constructs into single cells, stained with calcein- AM and ethidium homodimer to label live and dead cells respectively, followed by quantitative flow cytometry (Fig. 3b and Fig. 3c). Our analysis revealed a 5- fold difference in total cell number in the perfused tissue constructs over the non- perfused ones, while the proportion of live cells was similar (90.9±5.1% in perfused vs 89.2±4.4% in non- perfused tissue) (Fig. 3a and Fig. 3b).  
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+To determine whether these changes in viability and proliferation were due to apoptosis, we stained sections of the grids for cleaved Caspase 3, an active form of the Caspase- 3 enzyme responsible for the degradation of multiple cellular proteins and ultimately for cell fragmentation into apoptotic bodies. Upon sectioning, the control organoids were empty in the center, suggesting, as has previously been reported<sup>58</sup>, the loss of apoptotic cells during the sectioning process. Closer to this inner core, signs of apoptosis were evident (8±1% of total area), (Fig. 3a,b), consistent with the large size of these organoids. Tissue in the non- perfused grids exhibited a clear inner core of apoptotic cellular fragments (35.7±5% of total section area) (Fig. 3a) along with empty regions completely lacking cells, with some analogous features to control organoids. Strikingly, nearly the entire perfused tissue did not show signs of cleaved Caspase 3 (5.1±1% of total area), indicating that perfusion successfully prevented cell apoptosis throughout the course of differentiation.
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+To verify whether hypoxia could be involved in initiating the observed apoptosis in the non- perfused samples, we next stained for HIF1α, a heterodimer protein complex playing a key role in oxygen homeostasis<sup>59,60</sup>. The rapid buildup of HIF- 1α in low- oxygen conditions is known to trigger a hypoxic response ultimately leading to apoptosis<sup>61,62</sup>. In line with scRNA analysis data (Fig. 3d), high HIF1α expression levels were detected in the control organoids (100±3% mean fluorescent intensity), as well as in the non- perfused samples (55±3% of organoid control). Conversely, low levels of HIF1α expression were observed in the perfused samples (26±2% of organoid control) (Fig. 3a), associated with a higher proportion of cycling G2, M and S phase cells (Fig. 3d).  
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+The patterns of HIF- 1α and cleaved Caspase3 expression in non- perfused tissue sections therefore suggest that in these samples, cells in the center of the construct were in a transient hypoxic state ultimately leading to apoptotic cell death. This transition from hypoxic to apoptotic cell state continued until the volume of the tissue was small enough to allow sufficient oxygen supply by a passive diffusion, with the accumulation of apoptotic bodies and cellular debris preserving the cleaved Caspase 3 expression in the bulk of the non- perfused tissue (Supplementary Fig. 9). Conversely, this phenomenon was completely absent in the perfused samples, clearly confirming that thick tissues at multi- mm<sup>3</sup> scale could be grown with high viability, and with little to no apoptosis or hypoxia within this platform.  
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+## Accelerated neural differentiation in perfused tissue constructs  
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+Our scRNAseq data suggested that perfusion not only improved proliferation, prevented apoptosis and hypoxia, but could also direct fate specification. To confirm these findings, we analyzed specific markers of pluripotency and neural differentiation by immunohistochemistry (Fig. 3a and associated image quantification in Fig. 3b). NANOG, a canonical marker of pluripotency was abundantly expressed in organoids (73.6±8% of cells) and significantly expressed in non- perfused constructs (16.1±2%) at the protein level, but was completely missing in perfused samples. Conversely, PAX6, the earliest marker of neural differentiation was clearly evidenced in the perfused samples (21.5±2%), but not in organoid controls and non- perfused samples. These results were in line with the scRNAseq data, which indicated that NANOG- expressing cells were present in the control and non- perfused samples, while PAX6 was largely expressed in the perfused sample (Fig. 4c). The transition of stem cells from pluripotency to neural identity
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+is regulated by a loss of cellular expression of E- CADHERIN (CDH1) and a gain of N- CADHERIN (CDH2) expression which drives neural differentiation by inhibiting FGF- mediated pathways<sup>63</sup>. In order to assess whether perfusion enhanced this transition, we stained for E- CADHERIN to evidence the epithelial state associated with pluripotency, and for N- CADHERIN to confirm the transition to early neuro- epithelial identity. Perfused samples were indeed largely N- CADHERIN positive (55.7±9% vs 7.6±1% in non- perfused samples), while more cells in the non- perfused samples maintained E- CADHERIN+ identity (26.6±7% vs 4.5±2% in perfused samples). These results were confirmed by the scRNA data (Fig. 4c), indicating the predominant expression of N- CADHERIN in cells from perfused tissue, and E- CADHERIN in non- perfused and control tissue. Taken together, these results demonstrate that perfusion rapidly accelerates the transition from pluripotency to early neuroepithelial identity, while cells which lack perfusion remain in a state of pluripotency.  
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+## Long-term perfusion of liver tissue constructs  
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+To demonstrate the versatility of this platform, we went on to assess the differentiation of PSC- derived liver progenitors in perfused grids. hPSC were differentiated for 8 days in conventional 2D culture, followed by spheroid generation, seeding into the chip and perfusion for an additional 32 days. As was the case with neural differentiation, cells merged over time into a continuous tissue (Supplementary Fig. 10), with histological analysis identifying tightly packed cells with an eosinophilic and clear vacuolated cytoplasm reminiscent of hepatocytes, with no evidence of apoptotic bodies or necrosis throughout the tissue (Fig. 5a). The expression of many hepatocyte- specific genes was upregulated in perfused tissue constructs compared to both control hepatic organoids and 2D hepatocyte differentiation, including hepatocyte nuclear factor 6 (HNF6), Na+/taurocholate co- transporting polypeptide (NTCP), albumin (ALB), alpha1- antitrypsin (AAT) and two major cytochrome P450 enzymes CYP2C9 and CYP3A4 (Fig. 5f). The presence of CYP3A4 as well as of the hepatocyte progenitor marker AFP were confirmed at the protein level in the perfused sample by immunohistochemistry (Fig. 5b). Interestingly, the two major gluconeogenesis enzymes phosphoenolpyruvate carboxykinase (PEPCK) and glucose 6- phosphatase alpha (G6PC) were also expressed at higher levels in the perfused constructs, compared to control organoids (1.6- and 1.3- fold higher expression, respectively) (Fig. 5f).
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+We next assessed whether non- parenchymal cells in the developing liver, such as cytokeratin 19 (KRT19)- expressing cholangiocytes, which contribute to bile secretion and hepatocyte survival<sup>64</sup>, were also present in our perfused culture system. These cells are generated in- vivo from hepatoblasts surrounding the portal veins, while hepatoblasts located away from portal vein areas differentiate into hepatocytes<sup>65</sup>. We observed a similar localization pattern of KRT19+ cells, with such cells tightly surrounding every vessel in the microfluidic grid (Fig. 5c), while the hepatocytic markers HNF4α<sup>66</sup> was expressed in cells scattered in the inter- vessel space (Fig. 5d). Additionally, the functional production of albumin (ALB) was confirmed by staining (Fig. 5d).  
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+Taken together, our results confirmed the feasibility of using this synthetic micro- vascularization approach as a generic strategy to building large, viable, perfused in- vitro tissues.  
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+## Discussion  
+
+Here, we demonstrated for the first time an integrated3D culture platform which provides a physiologically relevant micro- perfusion for engineered tissues, resulting in enhanced tissue growth and differentiation compared to previously reported in- vitro tissue vascularization strategies.  
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+We showed that microvascular networks could be created using 2- photon hydrogel polymerization, demonstrating that this technology can be used to achieve micro- vasculature with previously unreported accuracy, resolution and scale. A significant limitation of current photo- polymerizable hydrogel materials is the significant swelling of the material, which prevents the robust, leak- free interface between printed structures and microfluidic perfusion systems. Our development of a non- swelling photo- polymerizable material formulation was a critical component to overcome this limitation, and enabled the printing of 3D soft microfluidic systems which could be reliably perfused over multiple weeks. The exchange of nutrients and oxygen as well as the removal of waste products was achieved via simple diffusion as the printed vascular network is permeable to water- soluble molecules and gases. The direct fabrication of capillaries of a defined topology delivers an unprecedented control over tissue perfusion parameters, and the design of the vascular network is highly flexible and can be adapted to more complex geometrical and structural requirements. To provide a complete extracellular milieu with structural 3D support to the growing engineered tissue, the space around the perfused vessels was filled with hydrogel. In the experiments
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+presented here, the hydrogel component consisted of the commonly used proteinaceous matrix Matrigel, however the platform can also readily accommodate without any additional modifications the use of other naturally derived matrices such as collagen, as well as synthetic artificial extracellular matrices such as poly(ethylene) glycol PEG \(^{67}\) or alginate \(^{68}\) . This technology provides a reliable fluidic coupling between the microfluidic grid and the host perfusion device, such that a continuous peristaltic pump driven perfusion is possible. By integrating these printed microfluidic grids into a perfusion system, we were able to demonstrate that that large- scale (>15mm \(^{3}\) ) fully perfused neural and liver tissues could be generated with this platform.  
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+Our experiments with neural tissue demonstrate that the differentiation trajectory of cells in this perfused system is significantly enhanced. While control organoids remained largely in a pluripotent state, scRNAseq analysis revealed that perfused tissue rapidly differentiated towards the neural fate, together with a switch from glycolysis to oxidative phosphorylation. Imaging and flow cytometry confirmed that this tissue was highly viable, and immunohistochemistry showed markers of neural differentiation, which were absent in the non- perfused sample, as well as hallmarks of epithelial to mesenchymal transition. This was underscored by our observations that the lack of active micro- perfusion of the engineered tissue construct triggered a stress response in the cells within the inner core of the tissue. The accelerated differentiation of tissues upon perfusion is thought to be due not only to increased availability of nutrients and oxygen but also to the rapid diffusion of differentiation factors within the tissue via the tightly spaced capillary network.  
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+The possibility of long- term perfusion within this platform was demonstrated by the maintenance of viable engineered liver tissue demonstrating enhanced phenotypic and functional features compared to standard 2D and 3D organoid culture. As cells in our current capillary design were not sufficiently distant from the source of oxygen to generate an oxygen gradient, we did not observe a clear spatial segregation of hepatocytic markers, known as zonation, in the perfused liver tissue, however this feature could be engineered by wider inter- vessel distance.  
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+Overall, the 3D soft microfluidic technology presented here overcomes one of the major challenge in engineering tissues and organoids: the lack of tissue perfusion from the initiation of tissue growth, and enables the generation of large engineered tissues which are vascularized from within and their maintenance over long periods of time. In applications such as disease modeling and drug development,
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+such a highly defined synthetic perfusion system would be beneficial in avoiding the complexity and variability introduced by exogenous angiogenesis- driven vascularization. While the current implementation of the platform does not recapitulate features of in vivo vascular networks such as adaptive vascular remodeling or selective blood brain barrier interactions, it addresses the major problem of oxygen, nutrient and growth factor and small molecule supply as well as of waste removal, allowing to generate viable tissues beyond currently available dimensions. The incorporating of endothelial vasculature with this synthetic capillaries could be implemented, where the hydrogel micro- vascularization could provide a temporary tissue support during the time required for angiogenesis- driven capillarization to establish a perfusable network. We expect that this approach is widely applicable in overcoming the current size limitations of bio- printed tissues and provides a technological foundation for the development of perfusable in vitro models of increased complexity and scale.  
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+## Acknowledgements  
+
+This work was supported by FWO grant G087018N, Interreg Biomat- on- Chip grant and Vlaams- Brabant and Flemish Government co- financing, KU Leuven grants C14/17/111 and C32/17/027.
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+## Materials and Methods  
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+Human PSC culture. Human PSCs were cultured in Matrigel (356277, Becton Dickinson) coated 6 well plates up to \(60 - 70\%\) confluency. Passages were performed by a 3 min treatment of Dispase II (D4693, Sigma) at \(37^{\circ}C\) , followed by 2- 3 PBS washings at RT. 1 mL of E8- Flex medium (A2858501, Thermo Scientific) was added and the colonies were scraped and gently pipetted 4- 5 times through 1ml plastic tip to break the colonies. The colony suspension was then diluted at 1:5 ratio and plated to a Matrigel coated wells in \(2\mathsf{mL}\) of E8Flex medium supplemented with \(10\mu \mathsf{M}\) Rock inhibitor(Y- 27632, Hellobio) for \(24h\) . The medium was then replaced by \(2\mathsf{mL}\) of fresh E8- Flex medium and incubation was continued for \(48h\) , at which point the colonies usually reached \(60 - 70\%\) confluency and were ready for next passage.  
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+Human PSC- derived cerebral organoids and perfused cerebral tissue. We adapted the protocol by Lancaster et al. \(^{69}\) to our experimental conditions. Upon reaching a confluency of \(60 - 70\%\) hPSCs were dissociated by treatment the colonies with \(250\mu \mathsf{I}\) Accutase (A1110501, Gibco) for 7 min at \(37^{\circ}C\) and resuspended in E8Flex medium containing \(10\mu \mathsf{M}\) Rock inhibitor.  
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+Organoids were generated in U- bottom 96- well plates (#351177, Falcon). The plates were rinsed with Anti- Adherence Solution (#07010, Stemcell Technologies) and cells were plated at 9000cells/well density. Plates were spun at 300rcf at RT and left in CO2 incubator for 24 hours for hPSC spheroid aggregation, after which culture medium was replaced by fresh E8- Flex medium, without Rock inhibitor and changed afterwards every 2 days. At day 2, the spheroids were embedded in growth factor reduced (GFR) Matrigel (354230, Becton Dickinson) and kept in 6- well plates, pre- rinsed with Anti Adherence Solution in CO2 incubator. At day 6, neuronal induction was started by replacing E8- Flex medium with DMEM/F12 medium (31330038, Gibco) containing \(1\%\) MEM- NEAA (11140035, Gibco), \(1\%\) Glutamax (35050038, Gibco), \(1\%\) Pen- Strep (15140122, Gibco), \(0.5\%\) N2 supplement (17502048, Gibco) and \(1\mathsf{ug / ml}\) Heparin (H3149, Sigma) (neural induction medium). Day 8 spheroids were used for characterization.  
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+For the formation of perfused tissue, at day 0, we first generated micro- hPSC spheroids using 24- well Aggrewell plates (34411, Stemcell Technologies) following a protocol supplied by the manufacturer. Specifically, we seeded Aggrewells with hPSCs to obtain 350- 400 cells per micro- well. After 24 hours,
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+spheroids formed and the medium was replaced by fresh E8- Flex without Rock inhibitor. At day 2, the spheroids typically reached a diameter of 180- 200um, and were harvested and resuspended in ice cold GFR Matrigel at a density of 3500 spheroids per 200- 250ul GFR Matrigel. The grids were placed under stereomicroscope in a 35mm petri dish on ice and seeded with the spheroid suspension at final density of \(\sim 1800\) spheroids per grid. \(200\mu l\) plastic tips pre- chilled on ice were used to dispense the suspension under stereomicroscope. Seeding was performed in several stages in order to allow dispensed spheroids to settle into the grids. After seeding, the grids were kept in a CO2 incubator for 40- 50 min to allow Matrigel polymerization, after which the grids where placed in a perfusion chip and perfusion was started. E8- Flex/PenStrep medium was changed every 2 days (3- 4ml per grid). At day 6, E8- Flex medium was replaced by neural induction medium for 2 days. At day 8, the grids were extracted and the tissue used for characterizations.  
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+Immunofluorescence analysis of cerebral tissue samples. Samples were fixed with \(4\%\) paraformaldehyde (158127, Sigma- Aldrich) for 24- 36 hours at \(4^{\circ}C\) and washed by 3 incubations in PBS for 15- 20 min at room temperature. Fixed tissue was sectioned either by embedding in low melting point agarose and sectioning on vibratome (Leica VT1000S) into 100- 150um sections or cryopreserved in OCT(6502, Thermo APD Consumables) overnight at \(4^{\circ}C\) , re- embedded into fresh OCT, frozen in isopropanol/dry ice slurry, cut into 50um sections on cryotome (Leica CM1850) and affixed on SuperFrost Plus (10019419, Thermo Scientific) microscope glass slides.  
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+Sections were incubated in a permeabilization and blocking solution of \(0.3\%\) Triton X (A4975, PanREAC Applichem) and \(3\%\) BSA (A7906, Sigma) in PBS for 24 hours at \(4^{\circ}C\) . Primary antibodies were diluted in the permeabilization and blocking solution and applied to sections for 24h at \(4^{\circ}C\) , after which three PBS washes were performed over another 24h period. Secondary antibodies and Hoechst were also diluted in the permeabilization and blocking solution and applied to sections overnight at \(4^{\circ}C\) , followed by washing in PBS 3- 4 times over another 24 hours. Antibodies used in this study are listed in Table 1. Stained agarose- embedded sections were stored in 2mM sodium azide solution in PBS. Cryosections were mounted in Fluoromount- G medium and stored at \(4^{\circ}C\) .
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+Table 1. List of antibodies used in this study   
+
+<table><tr><td>Primary</td><td>Host</td><td>Dilution</td><td>Manufacturer</td></tr><tr><td>Pax6, monoclonal</td><td>Mouse</td><td>1:200</td><td>DSHB</td></tr><tr><td>Nanog</td><td>Goat</td><td>1:200</td><td>R&amp;amp;D Systems, AF1997</td></tr><tr><td>ECad</td><td>Mouse</td><td>1:500</td><td>Abcam, ab76055</td></tr><tr><td>NCad</td><td>Rat</td><td>1:200</td><td>DSHB</td></tr><tr><td>Cleaved Caspase3</td><td>Rabbit</td><td>1:400</td><td>Cell Signaling Technology, 9661</td></tr><tr><td>HIF1a</td><td>Rabbit</td><td>1:500</td><td>Abcam, ab51608</td></tr><tr><td>HNF4α</td><td>Mouse</td><td>1:200</td><td>Abcam, ab41898</td></tr><tr><td>Alpha-1-Antitrypsin</td><td>Rabbit</td><td>1:200</td><td>DAKO, A0012 (00092029)</td></tr><tr><td>PEPCK</td><td>Mouse</td><td>1/1000</td><td>Santa Cruz, sc-271204</td></tr><tr><td>MRP2</td><td>Mouse</td><td>1/500</td><td>Abcam, ab3373</td></tr><tr><td>KRT19</td><td>goat</td><td>1/500</td><td>Santa Cruz, sc-33120</td></tr><tr><td>ALB</td><td>Rabbit</td><td>1/500</td><td>Abcam, ab207327</td></tr><tr><td>Secondary</td><td></td><td></td><td></td></tr><tr><td>Hoechst</td><td></td><td></td><td>Sigma-Aldrich, 14533</td></tr><tr><td>Anti-mouse Alexa 647</td><td>Donkey</td><td>1:500</td><td>Invitrogen, A31571</td></tr><tr><td>Anti-goat Alexa 647</td><td>Donkey</td><td>1:500</td><td>Invitrogen, A21447</td></tr><tr><td>Anti-rat Alexa 555</td><td>Goat</td><td>1:500</td><td>Invitrogen, A21434</td></tr><tr><td>Anti-goat Alexa 555</td><td>Donkey</td><td>1:500</td><td>Invitrogen, A21432</td></tr><tr><td>Anti-rabbit Alexa 555</td><td>Donkey</td><td>1:500</td><td>Invitrogen, A31572</td></tr><tr><td>Anti-mouse Alexa 488</td><td>Donky</td><td>1:500</td><td>Invitrogen, A11029</td></tr></table>  
+
+Human PSC- derived perfused hepatic tissue. All liver differentiation experiments were performed with the H3CX hiPSC line previously generated 70. H3CX is a hiPSC line (Sigma 0028, Sigma- Aldrich) genetically engineered to overexpress 3 transcription factors HNF1A, FOXA3 and PROX1 upon Doxycycline induction, which allows for rapid generation of hepatocyte- like progeny. H3CX cells were
+
+<--- Page Split --->
+
+expanded feeder- free on Matrigel (BD Biosciences)- coated plates in E8 or E8 Flex (Thermo Fisher Scientific). HC3X cells were differentiated towards HLCs as previously described71. Briefly, HC3X cells were dissociated to single cells using StemPro™Accutase™ Cell dissociation Reagent (Thermo Fisher Scientific) and plated on Matrigel- coated plates at \(\pm 8.75 \times 10^{4}\) cells/cm² in mTeSR medium (Stem Cell Technologies) supplemented with RevitaCell (Thermo Fisher Scientific). When cells reached 70- 80% confluence, differentiation was performed during 40 days in liver differentiation medium (LDM) containing to comprise 500ml total volume: 285 ml of DMEM low glucose (Invitrogen 31885023), 200 ml of MCDB- 201 solution in water (Sigma M- 6770) adjusted to pH 7.2, \(0.25 \times\) of Linoleic acid—Bovine serum albumin (LA- BSA, Sigma L- 9530), \(0.25 \times\) of Insulin- transferrin- selenium (ITS, Sigma I- 3146), 50 U of Penicillin/Streptomycin (Invitrogen 15140122), 100 nM of I- ascorbic acid (Sigma A8960), 1 μM dexamethasone (Sigma D2915) and 50 μM of \(\beta\) - mercaptoethanol (Invitrogen 31350010). Differentiation medium was supplemented with \(0.6\%\) dimethylsulfoxide (DMSO) during the first 12 days of the culture. \(2.0\%\) DMSO and 3x concentrate of non- essential amino- acids (NEAA) was added to LDM medium between days 12- 13, and from day 14 until the end of differentiation 20g/L glycine was added to LDM medium supplemented with NEAA. Differentiation was performed in presence of the following factors: day 0- 1: 100ng/ml Activin- A and 50ng/ml Wnt3a, day 2- 3: 100ng/ml Activin- A, day 4- 7: 50ng/ml BMP4, day 8- 11: 20ng/ml FGF1, and 20ng/ml HGF during the rest of differentiation. Doxycycline (5ug/ml) was applied from day 4 until the end of differentiation. All cytokines were purchased from Peprotech.  
+
+RNA extraction and quantitative reverse- transcription PCR. RNA extraction was performed using TR1zol reagent (Invitrogen) following manufacturer's instructions. At least 1μg of RNA was transcribed to cDNA using the Superscript III First- Strand synthesis (Invitrogen). Gene expression analysis was performed using the Platinum SYBR green qPCR supermix- UDG kit (Invitrogen) in a ViiA 7 Real- Time PCR instrument (Thermo Fisher Scientific). The sequences of all used RT- qPCR primers are listed in Table 2. The ribosomal protein L19 transcript (RPL19) was used as a housekeeping gene for normalization.  
+
+<table><tr><td>RPL19</td><td>ATTGGTCTCATTGGGGTCTAAC<br>AGTATGCTCAGGCTTCAGAAGA</td></tr><tr><td>AAT</td><td>AGGGCCGGAAGCTAGTGAGT<br>TCCTCGGTGTTCCTTGACTTC</td></tr></table>
+
+<--- Page Split --->
+
+
+Table 2. List of primers used in this study   
+
+<table><tr><td>NTCP</td><td>ATGCTGAGGCAAGGATGTTC<br>AGCAGCAGCACGACAGAGTA</td></tr><tr><td>G6PC</td><td>GTGTCCGTGATCGCAAGCC<br>GACGAGGTTGAGCCAGTCTC</td></tr><tr><td>CYP3A4</td><td>TTCCTCCCTGAAAGATTCAGC<br>GTTGAAGAAGTCTCCTAAGCT</td></tr><tr><td>PEPCK</td><td>AAGAAGTGCTTTGCTCTCAG<br>CCTTAAATGACCTTGTCGT</td></tr><tr><td>CYP2D6</td><td>CATACCTGCCCTACTACCAAA<br>TGTCTGCCTGGTCCTC</td></tr><tr><td>PGC1α</td><td>CCTTCGAGCACAAGAAAACA<br>TGCTTCGTCGTCAAAAACAG</td></tr><tr><td>HNF6</td><td>AAATCACCATTTCCCAGCAG<br>ACTCCTCCTTCTTGCGTCA</td></tr><tr><td>ALB</td><td>ATGCTGAGGCAAGGATGTTC<br>AGCAGCAGCACGACAGAGTA</td></tr><tr><td>AAT</td><td>AGGGGCTGAAGCTAGTGGAT<br>TCCTCGGTGTCCTTGACTTC</td></tr></table>  
+
+Histology. Samples were fixed with \(4\%\) (w/v) paraformaldehyde (PFA, Sigma- Aldrich) overnight at \(4^{\circ}\mathrm{C}\) washed 3 times with PBS and submerged in PBS- sodium azide ( \(0.01\%\) v/v) solution at \(4^{\circ}\mathrm{C}\) until embedded in paraffin. Hydrogel sections (5 \(\mu \mathrm{m}\) ) were prepared using a microtome (Microm HM 360, Marshall Scientific.) For Hematoxylin and Eosin (H&E) staining, sections were treated with xylene solution to remove the paraffin, and gradually rehydrated in ethanol (100 to \(70\%\) , v/v). H&E staining was performed by submerging rehydrated hydrogel sections in Harris Hematoxylin solution, acid alcohol, bluing reagent and Eosin- Y solution by order. Stained samples were dehydrated with ascending alcohol series, washed in xylene solution, and mounted with DPX mountant (Sigma- Aldrich).  
+
+Immunofluorescence analysis of liver tissue samples. Following deparaffinization in xylene and rehydration in descending alcohol series, heat- mediated antigen retrieval was performed by incubating hydrogel sections in Dako antigen retrieval solution (Dako) for 20 min at \(98^{\circ}\mathrm{C}\) . This step was followed by cell permeabilization with \(0.01\%\) (v/v) Triton- X (Sigma- Aldrich) solution in PBS, for 20 minutes. Samples
+
+<--- Page Split --->
+
+were then incubated with \(5\%\) (v/v) Goat or Donkey Serum (Dako) for 30 min. Primary antibodies diluted in Dako antibody diluent solution, were incubated overnight at \(4^{\circ}C\) , followed by washing steps and incubation with Alexa- coupled secondary antibody (1:500) and Hoechst 33412 (1:500) solution for 1 hour at room temperature. Finally, samples were washed in PBS, and mounted with Vectashield antifade mounting medium (Vector Laboratories). Stained sections were imaged using laser scanning confocal microscope (LSM 880, Zeiss, Germany), and image processing were performed on ZEN Blue software (Zeiss, Germany).  
+
+Image acquisition and analysis of cerebral tissue samples. Fluorescence images were obtained using a confocal microscope (Leica SP8 DIVE, Leica Microsystems) equipped with 10x NA0.4 dry objective. Acquisition parameters were kept constant for all samples obtained in the same experiment. Image processing was performed using Fiji/ImageJ (NIH) and custom- written ImageJ Java plugin. At the data preprocessing stage, image stacks were collapsed by maximum intensity projection. Resulting images were translated to 8- bit lookup table representation, manually cleaned from grid structure elements and thresholded by replacing all sub- threshold pixels with black (zero value) pixels. Lookup tables of the images were then remapped from [Threshold..255] back to full [0..255] range. Threshold values were chosen manually for Hoechst and marker channels for each image in order to remove background from the images. For all markers except HIF1α, total protein expression levels in a tissue were quantified as a relative number of cells expressing the protein, i.e.  
+
+\[Protein expression = \frac{Marker^{+}cellnumber}{Totalcellnumber} \times 100\%\]  
+
+On the assumption that cell size is constant across the samples, the relative number of cells was estimated as a ratio of areas occupied by cells on marker and Hoechst channels. Corresponding areas were calculated by counting all non- zero pixels on marker and Hoechst channels. For statistical comparison, results for each marker were pooled from all experiments.  
+
+As HIF1α marker is constitutively expressed in cells under normoxic conditions, its expression level in a tissue was estimated as the average fluorescence intensity on the marker channel. Threshold values were kept constant for organoids, non- perfused and perfused samples obtained in the same experiment;
+
+<--- Page Split --->
+
+expression values obtained in the experiment were normalized to organoid value and normalized data from different experiments were averaged.  
+
+All results are expressed as mean \(\pm\) SEM. Statistical comparisons between two data groups were analyzed using unpaired two- tailed Student's \(t\) - test with a \(95\%\) confidence interval (Origin Pro v9.5, OriginLab Inc.). Significance was marked on plots by \*, \*\* and \*\*\* for \(p< 0.05\) , \(p< 0.01\) , and \(p< 0.001\) correspondingly. If not stated otherwise, the statistical significance was assessed for comparisons of perfused and non- perfused samples to organoid control samples and denoted by "pControl".  
+
+Organoid dissociation and single- cell RNA sequencing. For dissociation, the microfluidic grids of non- perfused and perfused samples were removed from the perfusion chips and, along with the control organoids, placed temporarily in DMEM/F12 media in independent \(15 \text{mL}\) Falcon tubes until all 3 samples were ready for the next step (time \(< 5 \text{min}\) ). The DMEM was then replaced with \(1 \text{mL}\) of pre- warmed TrypLE Express (12605010, Gibco) at \(37^{\circ} \text{C}\) . The samples were transferred to a warm water bath at \(37^{\circ} \text{C}\) for 7.5 minutes with gentle agitation every 1 minute after the first 3 minutes. A visual inspection was performed using an inverted microscope to ensure complete dissociation of tissues and the presence of single cells. The \(1 \text{mL}\) solution was introduced to \(9 \text{mL}\) of DMEM supplemented with \(20\%\) FBS for TrypLE Express neutralization, and centrifuged at \(500 \text{rcf}\) for 5 minutes. The pellet was resuspended in \(200 \text{uL}\) of N2B27 media and put on ice. This dissociation protocol yielded an average viability of above \(80\%\) across all samples.  
+
+Library preparations for the scRNAseq was performed using 10X Genomics Chromium Single Cell 3' Kit, v3 (10X Genomics, Pleasanton, CA, USA). The cell count and the viability of the samples were accessed using LUNA dual fluorescence cell counter (Logos Biosystems) and a targeted cell recovery of 6000 cells was aimed for all the samples. Post cell count and QC, the samples were immediately loaded onto the Chromium Controller. Single cell RNAseq libraries were prepared using manufacturers recommendations (Single cell 3' reagent kits v3 user guide; CG00052 Rev B), and at the different check points, the library quality was accessed using Qubit (ThermoFisher) and Bioanalyzer (Agilent). Single cell libraries were sequenced using paired- end sequencing workflow and with recommended 10X; v3 read
+
+<--- Page Split --->
+
+parameters (28- 8- 0- 91 cycles). The data generated from sequencing was de- multiplexed and mapped against human genome reference using CellRanger v3.0.2.  
+
+Single Cell RNA sequencing Data Processing. We have sequenced 4,818, 5,453, and 2,804 cells for non- perfused, perfused and control organoids samples respectively for a total of 13,075 cells, which were reduced after QC steps to 8,625 cells with an average of 1,852 detected genes per cell. Data processing and subsequent steps were performed using the Seurat72 tool for single cell genomics version 3 in R version 4.0.3. A filtering step was performed to ensure the quality of the data, where the counts of mitochondrial reads and total genes reads were assessed. Cells with more than \(15\%\) of identifiable genes rising from the mitochondrial genome were filtered out. Similarly, cells having fewer identifiable genes than 200 (low quality) and above 7,500 (probable doublets) were filtered out. Data normalization was performed, followed by the identification of 2,000 highly variable genes using the FindVariableFeatures(). S- phase and G2M- phase cell cycle regression was performed to allow cell clustering purely on cell identity and fate, which otherwise was biased by cell cycle phases. Auto scaling of the data was performed and described using principal component analysis (PCA) using the RunPCA() function.  
+
+Correlation analysis. Correlation heatmap between samples was performed in R using the top 100 marker genes for each sample using the FindAllMarkers() function followed by the selection of the top 100 marker genes using the top_n() function with \(n = 100\) and wt = avg.logFC. The correlation analysis was generated using the cor() function using the Pearson method and the heatmap using the heatmap.2() function in R.  
+
+Data Clustering. Graph- based clustering using the FindNeighbors() function (using top 15 principal components (PCs)) and FindCluster() function (resolution = 0.5) was performed to group cells based on their transcriptional profiles. No batch correction was performed on the data set. Data visualization was performed using the Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction technique using the RunUMAP package while employing the top 15 PCs identified in the previous PCA step. Cluster annotation was based on hallmark genes to identify Neuroepithelial cluster (PAX6, PAX7, PAX3), Pluripotent- Neuroepithelial transitioning (P- NE) cells (WNT4, IRX1, IRX2), Proliferating cells
+
+<--- Page Split --->
+
+(CDC6, PTN, MK167, TOP2A), medium mitochondrial gene content and low glycolysis pluripotent cells (MK167, TOP2A, with the expression of NANOG, POU5F1, DPPA4), Pluripotent cells (NANOG, POU5F1, DPPA4), highly glycolytic pluripotent cells that link to the Cycling pluripotent and Pluripotent cells but include the expressions (LDHA, ENO1, HK2), a cluster undergoing EMT/migration with hypoxic markers (EPCAM, VIM, TWIST2, VEGFA) and finally a stressed cluster (FOS, HIF1A, JUN). One cluster was manually removed due to very low unique molecular identifier (UMI) count, after which the data was reprocessed to account for the removal of the cluster.  
+
+Pseudotime trajectories. Trajectory analysis was performed using Monocle \(3^{73}\) on R by employing the monocole3 and SeuratWrappers libraries. The Seurat obtained object containing all combined samples was passed to the Monocle 3 pipeline using the as.cell_data_set() function followed by processing using the cluster_cells() function. The pseudotime trajectory was inferred by first using the subset function on the only partition detected, followed by the learn_graph() function. Cells were colored in their inferred pseudotime using the which.max() function with the FetchData() (AVP) function, followed by the order_cells() function with the root_cell = max.avp. The UMAP superimposed with inferred pseudotime trajectory was generated using the plot_cells() function.  
+
+Gene- set analysis and hierarchical clustering. Gene- sets were created using the AddModuleScore() function in R. three gene- set were created to explore the dataset, pluripotency gene- set (POU5F1, NANOG, CDH1), glycolysis gene- set (ALDOA, BPGM, ENO1, ENO2, GPI, HK1, HK2, PFKL, PFKM, PGAM1, PGK1, PKM, TP11), and a neural progenitor gene- set (PAX3, PAX6, PAX7, OTX2, CDH2). The % mitochondrial genes were obtain as evaluated by the Seurat pipeline. Each cell was evaluated and scored and its expression plotted on UMAPS. Cluster level hierarchical clustering of the gene- set scores and % mitochondrial genes was employed using R package heatmap.2 after scaling the expression across rows (clusters).  
+
+Design and fabrication of soft micro- fluidic grids. The microfluidic grids were designed as a multitude of parallel capillaries stemming from a common reservoir. The dimensions of the grid (2.6x2.6x1.5mm) were
+
+<--- Page Split --->
+
+chosen as a compromise between size and the fabrication time. The grids were micro- fabricated on custom baseplates using high- resolution 3D printer (Photonic Professional GT2, Nanoscribe GmbH) equipped with 2- photon femtosecond laser. CAD model of the capillary grid was pre- processed by DeScribe software (Nanoscribe GmbH) to produce a printing job with defined printing parameters. A custom- formulation photopolymerizable resin was used to print microfluidic grids which contained \(2\%\) 2- Benzyl- 2- (dimethylamino)- 4'- morpholinobutyrophenone (lrgacure 369), \(7\%\) propylene glycol methyl ether acetate (PGMEA), \(36\%\) poly(ethylene glycol) diacrylate, MW700 (PEGDA 700), \(25\%\) pentaerythritol triacrylate (PETA), \(20\%\) Triton X- 100 and \(10\%\) water. lrgacure 369 was purchased from Tokyo Chemical Industry Ltd, the other components were from Sigma- Aldrich.  
+
+The microfluidic grids were printed on custom plates 3D printed on Formlabs Form 2 printer from a biocompatible resin (Dental SG, Formlabs Inc), post- cured by UV light (Formcure station, Formlabs Inc) for 2h at \(80^{\circ}C\) and washed in 2- propanol for 3- 4 days with daily change of the solvent. The plates had 1mm thickness and 10mm diameter, with 0.5mm perfusion holes. The grid printing process was set up such that the inlets in the microfluidic grid were aligned with the perfusion holes in a baseplate. After fabrication, baseplate- grid assemblies were washed during 2 days in PGMEA, 12 days in 2- propanol and then kept in PBS until use. The solvents were refreshed every 1- 2 days and PBS was refreshed daily for the first week and every 2- 3 days afterwards.  
+
+Fabrication of perfusion chips and setting up perfusion. The perfusion chips were implemented as an assembly as shown in Supplementary Fig. 1. Two microfluidic grids were sandwiched between two polydimethylsiloxane (PDMS) custom- profiled blocks. Two metal plates clamped with screws provided a tight junction between PDMS blocks and cover slips, thereby forming fluidic channels within the chip. The chips were designed to only allow contact of the medium with PDMS blocks and cover slips, thereby isolating the flow from contact with other materials of the chip. Guiding frames were used to simplify the alignment of the PDMS blocks during the chip assembly process.  
+
+The PDMS blocks were made by casting liquid PDMS compound into custom- designed plastic molds and curing overnight at \(80^{\circ}C\) . The plastic molds were fabricated by stereo- lithography (SLA) 3D printer Form 2 (Formlabs Inc.) using a bio- compatible Formlabs Dental SG resin. The printed molds were
+
+<--- Page Split --->
+
+post- processed by thorough washing in 2- propanol and post- cured for 90 min at \(80^{\circ}C\) (Formcure, Formlabs Inc.). Metal plates were made from 1mm stainless steel sheet by laser cutting and stainless steel screws were used for clamping the assembly. The guiding frames were 3D printed and cover slips were glued to the metal plates with a bio- compatible UV/heat curable epoxy (NOA 86H, Norland) to provide a final two- part setup shown in Supplementary Fig. 1d.  
+
+Two chambers of the chip, containing perfused and non- perfused grids, a peristaltic pump and a medium reservoir (50ml Falcon tube) were connected by flexible PVC tubing (ID/OD 0.8/1.6mm) in series (Supplementary Fig. 1e). The medium was taken from the reservoir by the pump and fed through chambers containing the non- perfused and then the perfused grid, after which the medium was sent back to the reservoir for \(\mathrm{CO_2 / O_2}\) equilibration. A 0.22um filter was connected between the two chambers of the chip to protect capillaries of the perfused grid from potential clogging with tissue debris. The medium reservoir and the chip were kept in a CO2 incubator, while the peristaltic pump remained outside to protect electronics from humidity and to ensure proper cooling. The medium reservoir and the chip were installed on a custom made 3D printed holder (Supplementary Fig. 1f) to simplify transitions between a CO2 incubator and a laminar hood. At day 0 of an experiment, all parts of the perfusion were sterilized by ethanol and UV light in a laminar hood, connected together and filled with equilibrated medium. The microfluidic grids, seeded with hPSC spheroids, were installed in the chip (Supplementary Fig. 1d) and the chip was clamped by screws (Supplementary Fig. 1f- i). Air bubbles were removed from the system and the perfusion started at approx. \(400\mu \mathrm{L} / \mathrm{min}\) flow rate (Supplementary Fig. 1, f- iii).  
+
+Multi- grid perfusion chips were designed to provide an equal multiplexed perfusion for all microfluidic grids. The fluidic inlet of the chip, via three steps of bifurcations, was split into eight fluidic channels feeding eight wells. All fluidic channels had equal length and cross- section and, therefore, equal hydraulic resistance. The multi- grid chip were fabricated by stereo- lithography (SLA) 3D printer Form 2 (Formlabs Inc.) using Formlabs Dental SG resin. The printed chips were post- processed by thorough washing in 2- propanol and post- cured for 90 min at \(60^{\circ}C\) . 7- 8mm pieces of syringe needles (0.9mm) were cut by hand drill/cutter (Precision drill, Proxxon Inc.) and glued into chip inlets by a biocompatible UV/heat curable epoxy glue to serve as connectors for flexible tubing. This assembly was again post- cured in FormCure station for 120 min at \(80^{\circ}C\) to achieve complete hardening of the glue. To integrate microfluidic
+
+<--- Page Split --->
+
+grids, a custom profile gaskets, cut from 0.5mm PDMS sheet by a laser cutter, were placed under the grid substrates to provide a liquid- tight contact and the substrates were fixed in place by 3D printed fasteners.  
+
+Data availability. All raw sequencing data, and the combined processed and metadata files generated in this study are available at GEO. The accession number for the reported data is GSE181290. This study did not generate any unique code. For review purposes please use the following token (ybchyyqofpejzgv) to access the sequencing data at: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE181290. The ImageJ Java code used for analysis of the image data are available upon request.
+
+<--- Page Split --->
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+## References  
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+71. Roelandt, P., Vanhove, J. & Verfaillie, C. Directed Differentiation of Pluripotent Stem Cells to Functional Hepatocytes. in Pluripotent Stem Cells (eds. Lakshmipathy, U. & Vemuri, M. C.) vol. 997 141-147 (Humana Press, 2013).  
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+<--- Page Split --->
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+73. Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).  
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+74. Kumar, M. et al. A fully defined matrix to support a pluripotent stem cell derived multi-cell-liver steatohepatitis and fibrosis model. Biomaterials 276, 121006 (2021).
+
+<--- Page Split --->
+
+Figures
+
+<--- Page Split --->
+![](images/Figure_1.jpg)
+
+<center>Figure 1. On-chip micro-vascularization enabled by soft microfluidics. (a) 3D printed tissue culture chip designed for eight multiplexed soft 3D soft microfluidic capillary grids. Inset: microfluidic capillary grid fabricated on a plastic baseplate and diagram of working principle. (b) of microfluidic capillary grid (left) and the close up image of the same grid showing individual hydrogel capillaries (right). (c) Microvessel 3D printing by 3D printing of microfluidic grid using high-resolution 2-photon stereo-lithography with non-swelling photo-polymerizable hydrogel precursors enables reproduction of features as small as \(10\mu \mathrm{m}\) . The top view photograph demonstrates an array of cylinders of various outer diameter in micrometers (top row) and wall thickness in micrometers (columns). (d) CAD image of microfluidic grid (left) with capillaries shown in red and the structural components shown in grey. The fence at the circumference of the structure makes </center>
+
+<--- Page Split --->
+
+up a "basket" that can be filled (right) with cell aggregates (spheroids) which merge and produce a solid tissue incorporating hydrogel capillaries. (e) Hydrogel capillaries are readily permeable. \(25\mu \mathrm{M}\) of Fluorescein, perfused through the microfluidic grid, embedded in Matrigel, passes across capillary walls and saturates the gel within several minutes. (f) Process of tissue generation. Photograph of an empty capillary grid with the tip of \(200\mu \mathrm{l}\) pipette tip visible above the grid (left), close up image of the greed seeded with hIPSC spheroids (middle) manually dispensed from the pipette shown on the left image, image of the spheroids fused into a solid tissue after 24- 36 hours of culturing on chip. The top row schematically represents the same process, where spheroids and the resulting tissue are shown in blue.
+
+<--- Page Split --->
+![](images/Figure_2.jpg)
+
+<center>Figure 2. Transcriptomic changes upon perfusion. (a) Combined dataset UMAP (control, non-perfused and perfused samples). (b) Combined dataset UMAP with neuroepithelial cells (NE), pluripotent-neuroepithelial transitioning cells (P-NE), proliferating cells with medium mitochondrial (mito.) content, pluripotent cells (P) with low glycolysis (glyc.) and medium mitochondrial content, glycolytic pluripotent cells with low mitochondrial content, highly glycolytic pluripotent cells, a highly glycolytic hypoxic (hypo.) identity and a stressed cluster. (c) Pseudotime trajectory on combined dataset UMAP. (d) Cluster specific expressions of selected marker genes. Expression values are normalized and centered. Sample fractions for each identified cluster. (e) GO enrichment analysis for key processes upregulated in perfused and non-perfused samples. </center>
+
+<--- Page Split --->
+![](images/Figure_3.jpg)
+
+<center>Figure 3. Changes in proliferation mediated by hypoxia. a) Representative experimental results demonstrating differences in proliferation and viability between standard organoid culture(left column) and the tissue constructs without (middle column) and with perfusion (right column). Top row: bright field images of organoids, non-perfused and perfused constructs correspondingly. Middle row: immunofluorescent images of apoptotic marker cleaved Caspase 3 (green) expressed in the three conditions. Bottom row: immunofluorescent images of hypoxia marker HIF1a (green). Hoechst staining of nuclei shown in blue. The images represent transverse cross-sections of the tissue constructs. (b) Top: average proportion of the number of live cells in perfused and non-perfused constructs quantified by flow cytometry (90.95.1% in perfused vs 89.2±4.4% in non-perfused tissue, n.s., n=4). Middle: average expression of cleaved Caspase 3 in control organoids (8±1% area, n=7), non-perfused(35.7±5%, pControl=0.02, n=4) and perfused tissue constructs (5.1±1% of total area, pControl=0.02, n=9). Bottom: average expression of hypoxia marker HIF-1a in control organoids (100±3%, n=4), non-perfused (55±3%, pControl=0.0003, n=3) and perfused constructs (26±2%, pControl=0.0004, n=2). Control organoids, non-perfused and perfused constructs are denoted as Ctrl, NP and P correspondingly, data are represented as mean ± SEM, asterisks (*) denote statistical significance of the difference between control organoids and the tissue constructs (unpaired two-tailed Student's t-test, 95% confidence interval). (c) Representative fluo-cytometry data, demonstrating the difference in total number of cells between perfused and non-perfused constructs. (d) Distribution of gene expression levels across the single cell population for hypoxia (left), G2M cell cycle (middle) and S cell cycle (right) associated gene sets. Scale bar: 1mm </center>
+
+<--- Page Split --->
+![](images/Figure_4.jpg)
+
+<center>Figure 4. Rapid neural differentiation in perfused systems. (a) Representative experimental results demonstrating the result of 2 days of neuronal induction in control organoids(left column), non-perfused (middle column) and perfused (right column) tissue constructs. Top, middle and bottom row: immunofluorescent images of stem cell marker NANOG, early neural marker PAX6 and cell adhesion proteins N-Cadherin (green) and E-Cadherin (red) correspondingly. The images represent transverse cross-sections of the tissue constructs. In most cases, the cross-sections of capillaries are visible as circular structures (marked by triangles) (b) Average expression of PAX6(Ctrl: Not detected; NP: \(0.2\% \pm 0.2\%\) , pControl \(= 0.35(n.s.)\) , \(n = 9\) ; P: \(21.5 \pm 2\%\) , pControl \(= 0.00002\) , \(n = 12\) ), NANOG(Ctrl: \(73.6 \pm 8\%\) , \(n = 9\) ; NP: \(16.1 \pm 2\%\) , pControl \(= 0.00007\) , \(n = 11\) ; P: not detected), NCad(CDH2) (Ctrl: \(100 \pm 1\%\) , \(n = 8\) ; NP: \(7.6 \pm 1\%\) , pControl \(= 4e - 13\) , \(n = 3\) ; P: \(55.7 \pm 9\%\) (pControl \(= 0.009\) , \(n = 5\) ) and ECad(CDH1) (Ctrl: \(99 \pm 2\%\) , \(n = 12\) ; NP: \(26.6 \pm 7\%\) , pControl \(= 0.0001\) , \(n = 6\) ; P: \(4.5 \pm 2\%\) , pControl \(= 2e - 10\) , \(n = 5\) ) markers in control organoids (Ctrl), non-perfused(NP) and perfused(P) constructs. Data are represented as mean \(\pm\) SEM, asterisks (*) denote statistical significance of the difference between control organoids and the tissue constructs (unpaired two-tailed Student's \(t\) -test, \(95\%\) confidence interval) (c) UMAP plot of the combined dataset showing the localization of cells from control organoids, non-perfused and perfused constructs in the UMAP space. (d) UMAP plot of the combined dataset highlighting locations of PAX6, NANOG, NCad (CDH2) and ECad(CDH1) expressing cells in the UMAP space. Scale bar: \(250 \mu m\) </center>
+
+<--- Page Split --->
+![](images/Figure_5.jpg)
+
+<center>Figure 5. Functional improvement in perfused liver microtissues. (a) Hematoxylin and eosin of a transverse section of liver tissue construct with hepatocyte-like morphology and no visible indication of apoptosis. (b) Immunofluorescence images showing presence of AFP and CYP3A4, (c) Alpha-1 antitrypsin (AAT) and Cytokeratin 19 (KRT19), and (d) basic hepatic markers albumin (ALB) and hepatic nuclear factor HNF4α expression (f) Heatmap representation of fold change gene expression levels normalized to control 2D cell culture and compared to standard hepatic organoids and perfused liver constructs; rows are centered and scaled. Gene expression data for hepatic 2D culture and hepatic organoids are from Kumar et al<sup>74</sup>. Scale bar: \(50\mu \mathrm{m}\) </center>
+
+<--- Page Split --->
+
+Supplementary Figures
+
+<--- Page Split --->
+![](images/Supplementary_Figure_3.jpg)
+  
+
+Supplementary Figure 1. Assembly and operation of the chip device. (a) Cutaway view of the fully assembled culture chip, the chamber with perfused capillary grid is shown. Cell culture medium enters the culture chamber through the inlet (1mm syringe needle, cut into 15mm pieces), passes through the capillary grid and leaves the chamber through outlet (not shown). (b) Exploded view of the chip. (c) Side view cross section of the chip. Tissue constructs can be observed through the glass windows with inverted or upright microscopes. The view also demonstrates that the cell culture medium only comes into contact with PDMS(pink) and glass(blue) parts of the chip. (d) During the assembly, a user needs to handle only two pre- assembled "halves" of the chip. (e) Schematic representation of the perfusion system driven by a peristaltic pump, with culture medium circulating between the perfusion chip and the medium reservoir. Here, the grid in the right chamber is surrounded by a constant medium flow but the flow through its capillaries is absent, while in the downstream grid (left chamber) the flow passes through the capillaries and diffuses across the walls into the tissue. (f) Photographs of fully assembled chip and perfusion system. The non-perfused and perfused chambers are connected by external tubing (left), with \(0.22\mu m\) filter connected inline between the chambers. The chip and the medium reservoir resides in a custom 3D-printed holder (middle) and connected to a peristaltic pump installed outside of \(\mathrm{CO_2}\) incubator (right). The image shows three experiments running in parallel.
+
+<--- Page Split --->
+![](images/Supplementary_Figure_4.jpg)
+  
+
+![](images/Supplementary_Figure_6.jpg)
+  
+
+Supplementary Figure 2. Optimization of printable material properties. (a) Test structure printed from PEGDA or PEGDA \(90\% /\) PETA \(10\%\) mixture. Both ends of the part are connected to a baseplate. When submerged to cell culture medium, swelling of the material leads to a breakage of the part. (b) Our custom formulation resin effectively prevents swelling in aqueous media and preserves the geometry of the microfabricated structures. Scale bar \(100\mu \mathrm{m}\) .
+
+<--- Page Split --->
+![](images/Supplementary_Figure_7.jpg)
+
+<center>Supplementary Figure 3. Two-photon stereo-lithography enables precision fabrication in a wide range of scales. Microfluidic grids of different dimensions (in mm): 1.2mm x 1.2mm x 1.2mm (left), 2.6mm x 2.6 mm x 1.5mm (middle), 6.5mm x 6.5mm x 5mm (right). Perfusion vessels in each grid have identical 50μm diameter and inter-vessel distance of 250μm. Scale bar 5 mm. </center>
+
+<--- Page Split --->
+![](images/Supplementary_Figure_8.jpg)
+
+<center>Supplementary Figure 4. Daily brightfield imaging of neural tissue in perfused and non-perfused chips. (a) Bright field images of the perfused (top) and non-perfused (bottom) tissue constructs taken every 2 days during the culturing protocol. (b) Live sections of perfused (left) and non-perfused (right) tissue constructs taken at the end of the culturing protocol. Scale bar 500μm. </center>
+
+<--- Page Split --->
+![](images/Supplementary_Figure_9.jpg)
+  
+
+Supplementary Figure 5. Correlation analysis between perfused-, non-perfused neural tissue constructs and conventional neural organoid culture. Correlation analysis using the top 100 differentially expressed marker genes for each of the three experimental conditions.
+
+<--- Page Split --->
+![](images/Supplementary_Figure_10.jpg)
+
+<center>Supplementary Figure 6. Gene-set analysis for various processes. Combined dataset UMAP with scores for pluripotent, glycolysis and neural progenitor gene-set as well as the \(\%\) mitochondrial genes. Hierarchical clustering of gene-set scores and \(\%\) mitochondrial genes. Score is column scaled. </center>
+
+<--- Page Split --->
+![PLACEHOLDER_46_0]
+
+<center>Supplementary Figure 7. Gene expression for unannotated hNTO clusters with the top 25 marker genes for each cluster. </center>
+
+<--- Page Split --->
+![PLACEHOLDER_47_0]
+
+<center>Supplementary Figure 8. Cluster-specific analysis of scRNAseq dataset. Dot-plot heatmap of hypoxia and cell cycle markers for each identified cluster. The average gene expression is represented by the color intensity of each dot, whereas the dot size represents the percentage of the gene-expressing cells for each sample within each cluster. </center>
+
+<--- Page Split --->
+![PLACEHOLDER_48_0]
+
+<center>Supplementary Figure 9. Difference in localized expression of hypoxia marker HIF1α and apoptosis marker cleaved Caspase-3. (a) HIF1α expression is localized to regions containing intact cell bodies (middle), evidenced by intact nuclei in the outlined region (left) as well as well-defined cytoplasmic regions stained with E-Cad antibody (right). (b) Cleaved Caspase-3 expression (middle) is localized to regions with apoptotic cell bodies evidenced by Hoechst stain of fragmented nuclei (outside of the outlined live region on the left image). Scale bar 100μm. </center>
+
+<--- Page Split --->
+![PLACEHOLDER_49_0]
+
+<center>Supplementary Figure 10. Brightfield images of perfused liver cultures. Bright field images of the perfused liver-like tissue constructs taken during tissue culturing. Scale bar 500μm. </center>
+
+<--- Page Split --->

preprint/preprint__01e7a1078c78ab368216c5666b306391adfa28f34c3a711786fcea2c12e51345/preprint__01e7a1078c78ab368216c5666b306391adfa28f34c3a711786fcea2c12e51345_det.mmd

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+<|ref|>title<|/ref|><|det|>[[44, 107, 820, 175]]<|/det|>
+# Engineering large-scale perfused tissues via synthetic 3D soft microfluidics  
+
+<|ref|>text<|/ref|><|det|>[[44, 196, 197, 234]]<|/det|>
+Sergei Grebenyuk KU Leuven  
+
+<|ref|>text<|/ref|><|det|>[[44, 241, 508, 283]]<|/det|>
+Abdel Rahman Abdel Fattah KU Leuven https://orcid.org/0000- 0002- 7817- 5586  
+
+<|ref|>text<|/ref|><|det|>[[44, 289, 212, 328]]<|/det|>
+Gregorius Rustandi KU Leuven  
+
+<|ref|>text<|/ref|><|det|>[[44, 335, 508, 377]]<|/det|>
+Manoj Kumar KU Leuven https://orcid.org/0000- 0002- 0572- 5786  
+
+<|ref|>text<|/ref|><|det|>[[44, 382, 208, 400]]<|/det|>
+Burak Toprakhisar  
+
+<|ref|>text<|/ref|><|det|>[[44, 403, 931, 423]]<|/det|>
+Stem Cell Institute, Department of Stem Cell and Developmental Biology, KU Leuven, Leuven, Belgium  
+
+<|ref|>text<|/ref|><|det|>[[44, 428, 155, 465]]<|/det|>
+Idris Salmon KU Leuven  
+
+<|ref|>text<|/ref|><|det|>[[44, 472, 212, 490]]<|/det|>
+Catherine Verfaillie  
+
+<|ref|>text<|/ref|><|det|>[[44, 494, 508, 513]]<|/det|>
+KU Leuven https://orcid.org/0000- 0001- 7564- 4079  
+
+<|ref|>text<|/ref|><|det|>[[44, 519, 452, 539]]<|/det|>
+Adrian Ranga ( adrian.ranga@kuleuven.be )  
+
+<|ref|>text<|/ref|><|det|>[[50, 541, 647, 560]]<|/det|>
+adrian.ranga@kuleuven.be https://orcid.org/0000- 0002- 6400- 9472  
+
+<|ref|>text<|/ref|><|det|>[[44, 603, 102, 620]]<|/det|>
+Article  
+
+<|ref|>text<|/ref|><|det|>[[44, 641, 135, 660]]<|/det|>
+Keywords:  
+
+<|ref|>text<|/ref|><|det|>[[44, 678, 340, 698]]<|/det|>
+Posted Date: September 8th, 2021  
+
+<|ref|>text<|/ref|><|det|>[[44, 716, 463, 736]]<|/det|>
+DOI: https://doi.org/10.21203/rs.3.rs- 867063/v1  
+
+<|ref|>text<|/ref|><|det|>[[42, 753, 910, 797]]<|/det|>
+License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License
+
+<--- Page Split --->
+<|ref|>title<|/ref|><|det|>[[115, 88, 705, 106]]<|/det|>
+# Engineering large-scale perfused tissues via synthetic 3D soft microfluidics  
+
+<|ref|>text<|/ref|><|det|>[[115, 145, 884, 194]]<|/det|>
+Sergei Grebenyuk \(^{1}\) , Abdel Rahman Abdel Fattah \(^{1}\) , Gregorius Rustandi \(^{1}\) , Manoj Kumar \(^{2}\) , Burak Toprakhisar \(^{2}\) , Idris Salmon \(^{1}\) , Catherine Verfaillie \(^{2}\) , Adrian Ranga \(^{1*}\)  
+
+<|ref|>text<|/ref|><|det|>[[115, 233, 884, 281]]<|/det|>
+\(^{1}\) Laboratory of Bioengineering and Morphogenesis, Biomechanics Section, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium  
+
+<|ref|>text<|/ref|><|det|>[[115, 291, 884, 339]]<|/det|>
+\(^{2}\) Stem Cell Institute Leuven and Department of Development and Regeneration, Faculty of Medicine, KU Leuven, Leuven, Belgium  
+
+<|ref|>text<|/ref|><|det|>[[117, 350, 375, 367]]<|/det|>
+\* email: adrian.ranga@kuleuven.be  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 437, 184, 453]]<|/det|>
+## Abstract  
+
+<|ref|>text<|/ref|><|det|>[[113, 492, 885, 833]]<|/det|>
+The vascularization of engineered tissues and organoids has remained a major unresolved challenge in regenerative medicine. While multiple approaches have been developed to vascularize in vitro tissues, it has thus far not been possible to generate sufficiently dense networks of small- scale vessels to perfuse large de novo tissues. Here, we achieve the perfusion of multi- mm \(^{3}\) tissue constructs by generating networks of synthetic capillary- scale 3D vessels. Our 3D soft microfluidic strategy is uniquely enabled by a 3D- printable 2- photon- polymerizable hydrogel formulation, which allows for precise microvessel printing at scales below the diffusion limit of living tissues. We demonstrate that these large- scale engineered tissues are viable, proliferative and exhibit complex morphogenesis during long- term in- vitro culture, while avoiding hypoxia and necrosis. We show by scRNAseq and immunohistochemistry that neural differentiation is significantly accelerated in perfused neural constructs. Additionally, we illustrate the versatility of this platform by demonstrating long- term perfusion of developing liver tissue. This fully synthetic vascularization platform opens the door to the generation of human tissue models at unprecedented scale and complexity.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[115, 89, 213, 105]]<|/det|>
+## Introduction  
+
+<|ref|>text<|/ref|><|det|>[[113, 116, 885, 310]]<|/det|>
+Human engineered tissue and organoids are potentially transformational model systems, which could create dramatic efficiencies in the drug discovery process and function as key building blocks for regenerative medicine applications. In particular, larger- scale tissues have the possibility to recapitulate complex functional and organizational characteristics of their in vivo counterparts, and could therefore become a long- sought alternative to animal models \(^{1 - 3}\) . However, the poorly defined structural organization, small size and slow maturation of these tissues have remained major limitations in engineering fully functional and reproducible organoids and tissues.  
+
+<|ref|>text<|/ref|><|det|>[[113, 320, 886, 629]]<|/det|>
+In vivo, the development of tissues is supported by a complex network of blood vessels which provide oxygen, nutrients and waste exchange and mediate paracrine interactions via growth and differentiation factors \(^{4}\) . The size of the microvasculature is a critical parameter for local tissue perfusion: to maintain sufficient diffusion of oxygen, nutrients, and waste products most cells in vivo lie within \(200 \mu \mathrm{m}\) of a capillary. In the absence of vascular support, normal physiological conditions can be maintained only within this narrow range. Similar to the diffusion limits in normal tissue, the generation of solid tissue in vitro requires both vascularization and flow to maintain cell viability throughout the entire construct \(^{1}\) . The lack of vascularization in engineered tissues therefore prevents oxygen and nutrient exchange, which is thought to be the main reason for the commonly observed development of a necrotic core within organoids once they reach a critical size, as well as for apoptosis within engineered tissues. These issues have been widely recognized, and various approaches have been reported in order to overcome them \(^{5}\) .  
+
+<|ref|>text<|/ref|><|det|>[[113, 639, 885, 833]]<|/det|>
+The extrinsic induction of angiogenesis has been frequently used in the context of organoid vascularization. Vessel sprouts have been shown to infiltrate organoids maintained with endothelial cells in separate compartments of microfluidic culture devices \(^{6,7}\) , or co- cultured with pre- established microvascular beds \(^{8,9}\) . These results have suggested that the presence of a perfusable vasculature can enhance organoid growth \(^{8}\) , confirming the importance of systemic cross- talk between vasculature and organoids in the developmental process. The resulting organoids have nonetheless been limited in size, and to date, functional organoid vascularization has only been achieved by grafting organoids into host animals \(^{10,11}\) .  
+
+<|ref|>text<|/ref|><|det|>[[115, 843, 884, 890]]<|/det|>
+A number of studies have focused on creating artificial vessels though the use of templating approaches based on patterned layer- by- layer deposition of gelled material, in the form of thin filaments \(^{12}\) ,
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 87, 885, 367]]<|/det|>
+droplets \(^{13 - 18}\) or layer- by- layer polymerization by stereolithography \(^{19 - 21}\) . Large tissue constructs have been generated by bioprinting of bioinks comprised of gels carrying different cell types \(^{12,22,23}\) , with vascular templates generated by depositing endothelial cells interleaved with tissue- specific cells using filament extrusion \(^{24 - 28}\) or stereolithography \(^{19,21}\) . While multiphoton lithography has been used to fabricate capillary- sized tubular fragments \(^{29}\) and vascular mimics \(^{30}\) , these vessels have thus far not been successfully perfused. The dissolution of sacrificial networks to form lumenized vessels has been proposed as an alternative strategy, with resorbable gel filaments being created via stereolithography \(^{31}\) , pre- polymer extrusion \(^{32 - 38}\) or molding \(^{39}\) . Artificial vessels have also been formed by the direct removal of diverse hydrogel material such as silk fibroin and PEG hydrogels using laser photo- ablation \(^{40 - 44}\) including in the presence of cells \(^{45 - 47}\) .  
+
+<|ref|>text<|/ref|><|det|>[[113, 378, 885, 542]]<|/det|>
+Despite the versatility of these vessel templating approaches, the minimal diameter of perfusable engineered vessels reported thus far has been limited to \(150\mu \mathrm{m}^{33,39}\) and, in parallel with strategies based on extrinsic angiogenesis in organ- on- chip implementations \(^{48}\) , the size of generated tissues has been limited to \(400 - 500\mu \mathrm{m}\) in at least one dimension \(^{49 - 53}\) . Because of their small size, engineered tissues which have been implemented thus far do not preserve a physiologically relevant signaling context within the tissue, nor do they develop to a level of complexity comparable to in vivo organs.  
+
+<|ref|>sub_title<|/ref|><|det|>[[114, 581, 688, 599]]<|/det|>
+## Photo-polymerizable non-swelling hydrogels enable 3D soft microfluidics  
+
+<|ref|>text<|/ref|><|det|>[[113, 609, 885, 861]]<|/det|>
+In order to vascularize tissue at large scale, we hypothesized that a microfluidic approach which could bridge the capillary to tissue scale would be necessary to enable the perfusion of thick three- dimensional tissue constructs. We therefore designed a dense, regularly spaced capillary network whose tubular walls were made of a hydrogel allowing diffusion (Fig. 1a). Tissues growing within this soft grid- like hydrated network would interface with an external perfusion pumping system, circulating cell culture medium throughout the volume of the tissue (Fig. 1a and Supplementary Fig. 1). We developed an approach whereby the perfusable grid would be printed directly on a hard plastic base (Fig. 1a), thereby forming a tight seal. This base, which contains perfusion holes, would then be incorporated into a perfusion chip linked to a peristaltic pump circulating cell culture medium (Supplementary Fig. 1)
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 87, 885, 310]]<|/det|>
+An important requirement of this platform was the need to have capillary- like tubing at scales of a few \(\mu \mathrm{m}\) in diameter and thickness, while perfusing across a large, multi- \(\mathrm{mm}^3\) three- dimensional space. The geometrical complexity of the design, properties of the biopolymer and fabrication scale ranging from \(10\mu \mathrm{m}\) to \(2000\mu \mathrm{m}\) featured by our design made two- photon laser scanning photo- polymerization the ideal technology for this purpose. Initial 3D prints with the 2- photon Nanoscribe printer using commonly used photopolymerizable materials, including gelatin and PEG diacrylate, resulted in significant swelling of the material upon polymerization and hydration, which disrupted the seal between the soft microfluidic grid and the rigid plastic plate, and generated mismatched tubular segments (Supplementary Fig. 2).  
+
+<|ref|>text<|/ref|><|det|>[[113, 320, 885, 513]]<|/det|>
+To overcome this post- printing distortion, we developed a custom formulated hydrophilic photopolymer based on polyethylene glycol diacrylate (PEGDA). While PEGDA exhibits cell- repelling surface properties, the polymer surface could be rendered cell- binding by the addition of the photocrosslinker pentaerythritol triacrylate (PETA)54. We reasoned that the addition of a significant amount of PETA as a crosslinker would increase the toughness of the polymer, and balanced with the addition of an inert "filler" component (Triton- X 100) would on the other hand retain sufficient porosity of the polymer to enable rapid diffusion.  
+
+<|ref|>text<|/ref|><|det|>[[113, 523, 886, 892]]<|/det|>
+The combination of 2- photon printing with this non- swelling hydrogel material allowed printing of a variety of microfluidic grids, ranging in size from \(1.2 \times 1.2 \times 1.2\mathrm{mm}\) up to \(6.5 \times 6.5 \times 5\mathrm{mm}\) (Fig. 1b and Supplementary Fig. 3) with vessel diameters from \(10\mu \mathrm{m}\) to \(>70\mu \mathrm{m}\) and vessel wall thickness from \(2\mu \mathrm{m}\) to \(10\mu \mathrm{m}\) (Fig. 1c). Importantly, the printing with our novel formulation resin resulted in a 1:1 fidelity between the generated CAD geometry and the printed parts, thereby ensuring no distortion and a tight seal (Supplementary Fig. 2). The standard size used for most of the subsequent experiments was \(2.6\mathrm{mm} \times 2.6\mathrm{mm} \times 1.5\mathrm{mm}\) , with an inter- vessel distance of \(250\mu \mathrm{m}\) (Fig. 1b). These grids could be incorporated into a multi- plexed perfusion chip allowing up to 8 grids to be perfused simultaneously (Fig. 1a). Single cells or organoids smaller than the \(250\mu \mathrm{m}\) inter- capillary distance, previously mixed within a liquid hydrogel (eg. Matrigel) precursor solution, could be seeded into the platform, yielding a "gel- in- gel" 3- dimensional construct (Fig. 1d). Vessel permeability to water- soluble molecules within the chips overlayed with Matrigel was verified using fluorescein, with diffusion throughout the three- dimensional space seen in less than 10 minutes (Fig. 1e).
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 88, 884, 223]]<|/det|>
+To perform a biological proof of concept experiment, we generated hundreds of organoids of less than \(200 \mu m\) diameter by aggregating human pluripotent stem cells (hPSCs) in microwells in pluripotency medium over 24 hours. We collected these aggregates in cold liquid Matrigel, which were then pipetted into the grids. Initial seeding demonstrated that these aggregates filled the grids and, over a period of 8 days of growth and neural differentiation, merged and filled the whole volume (Fig. 1f and Supplementary Fig. 4).  
+
+<|ref|>sub_title<|/ref|><|det|>[[114, 262, 883, 309]]<|/det|>
+## scRNAseq reveals changes in differentiation, hypoxia, cell cycle regulation and differentiation upon perfusion  
+
+<|ref|>text<|/ref|><|det|>[[112, 317, 885, 833]]<|/det|>
+To assess how perfusion affected cellular processes and differentiation in large- scale in- vitro tissue, we dissociated cells from tissue constructs in a perfused and a non- perfused grid, as well as from organoids in conventional suspension culture after the 8- day culture period, and performed single cell RNA sequencing. Graph- based clustering and Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction technique on the 8,625 total cells retained after QC revealed significant transcriptomic differences between the tissue constructs (perfused and non- perfused) and the control organoids as evidenced by largely separated clustering of these cell populations (Fig. 2a). Correlation analysis using the 100 most differentially expressed marker genes revealed the most difference between perfused tissue and conventional organoid culture, with non- perfused tissue sharing gene expression profiles with the other two conditions (Supplementary Fig. 5). Differential gene expression analysis was then used to annotate eight clusters, which were largely differentiated by fate, as well as by metabolic, hypoxia and cell cycle regulation (Fig. 2b, Fig. 2d, Supplementary Fig. 6, Supplementary Fig. 7). Cells from control organoids were found in clusters with low, medium and high glycolytic processes marked by a pluripotent identity, including highly expressed markers such as NANOG and OCT4 (POUF5F1). Both perfused and non- perfused tissues expressed varying degrees of hypoxia, proliferation, mitochondrial gene expression and neuroepithelial markers. The non- perfused tissue made up the largest proportion of the hypoxic and stressed clusters (HIF1α, FOS) while the perfused tissue represented the majority of the neuroepithelial cluster (PAX6).  
+
+<|ref|>text<|/ref|><|det|>[[113, 843, 883, 890]]<|/det|>
+In particular, control organoids had lower expression of mitochondrial genes (Supplementary Fig. 7, Supplementary Fig. 8) compared to the tissue constructs. This is in line with previous reports of lower
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 87, 885, 309]]<|/det|>
+mitochondrial activity in human embryonic stem cells that increase upon differentiation to fit the energy needs of resultant cell identities<sup>55</sup>. Indeed, differentiation towards the neuroepithelial fates from pluripotent cells was characterized by an increase in mitochondrial activity, and a simultaneous decrease in glycolysis (Supplementary Fig. 8). These results suggest a metabolic switch from anaerobic glycolysis to oxidative phosphorylation in the tissue constructs, as has been reported to occur during cell differentiation after exit from pluripotency<sup>56,57</sup>. Such transitions, which were additionally evidenced in pseudotime analysis (Fig. 2c) distinguished the control organoids from the tissue constructs in the microfluidic grids, and further characterized differences between non- perfused and perfused tissues.  
+
+<|ref|>text<|/ref|><|det|>[[112, 319, 886, 659]]<|/det|>
+The perfused tissue was also characterized by the expression of the neuroepithelial markers PAX6, PAX7, PAX3, and CDH2, while sharing IRX1 and IRX2 markers with non- perfused tissue (Fig. 2d). Moreover, while non- perfused and perfused tissues represented similar proportions of the pluripotent proliferating cluster, the perfused tissue expressed the highest proliferation markers such as CDC6 (Supplementary Fig. 7). Additionally, the high expression of proliferation markers such MKi67 and TOP2A in the neuroepithelial cluster suggested the retained proliferative capacity of perfused tissues after neuroepithelial differentiation, while maintaining overall minimal hypoxic/stress response markers VEGF, FOS, HIF1a (Supplementary Fig. 8). By contrast, non- perfused tissue was distinguished by significant presence of pluripotency (NANOG, POU5F1/OCT- 4) (Fig. 2d) and hypoxia/stress markers (Fig. 2d, Supplementary Fig. 8). The control organoids, on the other hand, were mostly represented by pluripotent and hypoxic cells with complete absence of neuroepithelial identity (Fig. 2d and Supplementary Fig. 8), likely due to the short time in neural differentiation conditions.  
+
+<|ref|>text<|/ref|><|det|>[[113, 668, 885, 891]]<|/det|>
+To investigate transcriptional changes and associated cellular processes upon perfusion in a systematic manner, we performed differential gene expression analysis between perfused and non- perfused tissue constructs followed by Gene Ontology (GO) enrichment analysis (Fig. 2e). This analysis confirmed up- regulation of processes related to cell division and proliferation in the perfused sample, as well as regulation of neural precursors and neural precursor cell proliferation. By contrast, cell stress, hypoxia and cellular death processes were upregulated in non- perfused samples. Taken together our analysis of the transcriptomic data suggested that perfusion of large tissue constructs dramatically decreased apoptosis and hypoxia, and accelerated the process of neural differentiation.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[115, 117, 668, 136]]<|/det|>
+## Perfusion rescues hypoxia and necrotic core in large tissue constructs  
+
+<|ref|>text<|/ref|><|det|>[[112, 145, 886, 545]]<|/det|>
+To further investigate how perfusion modulates the spatial distribution of hypoxia and apoptosis, we imaged the constructs in bright field microscopy and sectioned whole samples transversely, perpendicular to the direction of grid perfusion, followed by immunohistochemistry (Fig. 3a and associated quantification in Fig. 3b). Control organoids demonstrated a characteristic dark dense tissue core with occasional lighter voidlike structures, surrounded by more translucent peripheral tissue. In non- perfused samples, tissue growth was restricted to the internal volume of the microfluidic grid, with generally dense central tissue interspersed with patchy lighter areas. Strikingly, perfused tissue covered the entire volume of the grid in a uniformly dense manner, with bulging epithelial outgrowths characteristic of cerebral organoids at the periphery. These observations suggested that cell proliferation was much higher in the perfused grids, compared to the non- perfused samples. To confirm this observation quantitatively, we dissociated the tissue constructs into single cells, stained with calcein- AM and ethidium homodimer to label live and dead cells respectively, followed by quantitative flow cytometry (Fig. 3b and Fig. 3c). Our analysis revealed a 5- fold difference in total cell number in the perfused tissue constructs over the non- perfused ones, while the proportion of live cells was similar (90.9±5.1% in perfused vs 89.2±4.4% in non- perfused tissue) (Fig. 3a and Fig. 3b).  
+
+<|ref|>text<|/ref|><|det|>[[112, 553, 886, 860]]<|/det|>
+To determine whether these changes in viability and proliferation were due to apoptosis, we stained sections of the grids for cleaved Caspase 3, an active form of the Caspase- 3 enzyme responsible for the degradation of multiple cellular proteins and ultimately for cell fragmentation into apoptotic bodies. Upon sectioning, the control organoids were empty in the center, suggesting, as has previously been reported<sup>58</sup>, the loss of apoptotic cells during the sectioning process. Closer to this inner core, signs of apoptosis were evident (8±1% of total area), (Fig. 3a,b), consistent with the large size of these organoids. Tissue in the non- perfused grids exhibited a clear inner core of apoptotic cellular fragments (35.7±5% of total section area) (Fig. 3a) along with empty regions completely lacking cells, with some analogous features to control organoids. Strikingly, nearly the entire perfused tissue did not show signs of cleaved Caspase 3 (5.1±1% of total area), indicating that perfusion successfully prevented cell apoptosis throughout the course of differentiation.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 87, 885, 310]]<|/det|>
+To verify whether hypoxia could be involved in initiating the observed apoptosis in the non- perfused samples, we next stained for HIF1α, a heterodimer protein complex playing a key role in oxygen homeostasis<sup>59,60</sup>. The rapid buildup of HIF- 1α in low- oxygen conditions is known to trigger a hypoxic response ultimately leading to apoptosis<sup>61,62</sup>. In line with scRNA analysis data (Fig. 3d), high HIF1α expression levels were detected in the control organoids (100±3% mean fluorescent intensity), as well as in the non- perfused samples (55±3% of organoid control). Conversely, low levels of HIF1α expression were observed in the perfused samples (26±2% of organoid control) (Fig. 3a), associated with a higher proportion of cycling G2, M and S phase cells (Fig. 3d).  
+
+<|ref|>text<|/ref|><|det|>[[113, 320, 885, 543]]<|/det|>
+The patterns of HIF- 1α and cleaved Caspase3 expression in non- perfused tissue sections therefore suggest that in these samples, cells in the center of the construct were in a transient hypoxic state ultimately leading to apoptotic cell death. This transition from hypoxic to apoptotic cell state continued until the volume of the tissue was small enough to allow sufficient oxygen supply by a passive diffusion, with the accumulation of apoptotic bodies and cellular debris preserving the cleaved Caspase 3 expression in the bulk of the non- perfused tissue (Supplementary Fig. 9). Conversely, this phenomenon was completely absent in the perfused samples, clearly confirming that thick tissues at multi- mm<sup>3</sup> scale could be grown with high viability, and with little to no apoptosis or hypoxia within this platform.  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 581, 610, 599]]<|/det|>
+## Accelerated neural differentiation in perfused tissue constructs  
+
+<|ref|>text<|/ref|><|det|>[[113, 608, 886, 890]]<|/det|>
+Our scRNAseq data suggested that perfusion not only improved proliferation, prevented apoptosis and hypoxia, but could also direct fate specification. To confirm these findings, we analyzed specific markers of pluripotency and neural differentiation by immunohistochemistry (Fig. 3a and associated image quantification in Fig. 3b). NANOG, a canonical marker of pluripotency was abundantly expressed in organoids (73.6±8% of cells) and significantly expressed in non- perfused constructs (16.1±2%) at the protein level, but was completely missing in perfused samples. Conversely, PAX6, the earliest marker of neural differentiation was clearly evidenced in the perfused samples (21.5±2%), but not in organoid controls and non- perfused samples. These results were in line with the scRNAseq data, which indicated that NANOG- expressing cells were present in the control and non- perfused samples, while PAX6 was largely expressed in the perfused sample (Fig. 4c). The transition of stem cells from pluripotency to neural identity
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[112, 87, 886, 398]]<|/det|>
+is regulated by a loss of cellular expression of E- CADHERIN (CDH1) and a gain of N- CADHERIN (CDH2) expression which drives neural differentiation by inhibiting FGF- mediated pathways<sup>63</sup>. In order to assess whether perfusion enhanced this transition, we stained for E- CADHERIN to evidence the epithelial state associated with pluripotency, and for N- CADHERIN to confirm the transition to early neuro- epithelial identity. Perfused samples were indeed largely N- CADHERIN positive (55.7±9% vs 7.6±1% in non- perfused samples), while more cells in the non- perfused samples maintained E- CADHERIN+ identity (26.6±7% vs 4.5±2% in perfused samples). These results were confirmed by the scRNA data (Fig. 4c), indicating the predominant expression of N- CADHERIN in cells from perfused tissue, and E- CADHERIN in non- perfused and control tissue. Taken together, these results demonstrate that perfusion rapidly accelerates the transition from pluripotency to early neuroepithelial identity, while cells which lack perfusion remain in a state of pluripotency.  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 437, 475, 454]]<|/det|>
+## Long-term perfusion of liver tissue constructs  
+
+<|ref|>text<|/ref|><|det|>[[112, 465, 886, 891]]<|/det|>
+To demonstrate the versatility of this platform, we went on to assess the differentiation of PSC- derived liver progenitors in perfused grids. hPSC were differentiated for 8 days in conventional 2D culture, followed by spheroid generation, seeding into the chip and perfusion for an additional 32 days. As was the case with neural differentiation, cells merged over time into a continuous tissue (Supplementary Fig. 10), with histological analysis identifying tightly packed cells with an eosinophilic and clear vacuolated cytoplasm reminiscent of hepatocytes, with no evidence of apoptotic bodies or necrosis throughout the tissue (Fig. 5a). The expression of many hepatocyte- specific genes was upregulated in perfused tissue constructs compared to both control hepatic organoids and 2D hepatocyte differentiation, including hepatocyte nuclear factor 6 (HNF6), Na+/taurocholate co- transporting polypeptide (NTCP), albumin (ALB), alpha1- antitrypsin (AAT) and two major cytochrome P450 enzymes CYP2C9 and CYP3A4 (Fig. 5f). The presence of CYP3A4 as well as of the hepatocyte progenitor marker AFP were confirmed at the protein level in the perfused sample by immunohistochemistry (Fig. 5b). Interestingly, the two major gluconeogenesis enzymes phosphoenolpyruvate carboxykinase (PEPCK) and glucose 6- phosphatase alpha (G6PC) were also expressed at higher levels in the perfused constructs, compared to control organoids (1.6- and 1.3- fold higher expression, respectively) (Fig. 5f).
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 88, 885, 310]]<|/det|>
+We next assessed whether non- parenchymal cells in the developing liver, such as cytokeratin 19 (KRT19)- expressing cholangiocytes, which contribute to bile secretion and hepatocyte survival<sup>64</sup>, were also present in our perfused culture system. These cells are generated in- vivo from hepatoblasts surrounding the portal veins, while hepatoblasts located away from portal vein areas differentiate into hepatocytes<sup>65</sup>. We observed a similar localization pattern of KRT19+ cells, with such cells tightly surrounding every vessel in the microfluidic grid (Fig. 5c), while the hepatocytic markers HNF4α<sup>66</sup> was expressed in cells scattered in the inter- vessel space (Fig. 5d). Additionally, the functional production of albumin (ALB) was confirmed by staining (Fig. 5d).  
+
+<|ref|>text<|/ref|><|det|>[[115, 321, 883, 368]]<|/det|>
+Taken together, our results confirmed the feasibility of using this synthetic micro- vascularization approach as a generic strategy to building large, viable, perfused in- vitro tissues.  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 407, 205, 423]]<|/det|>
+## Discussion  
+
+<|ref|>text<|/ref|><|det|>[[114, 436, 884, 514]]<|/det|>
+Here, we demonstrated for the first time an integrated3D culture platform which provides a physiologically relevant micro- perfusion for engineered tissues, resulting in enhanced tissue growth and differentiation compared to previously reported in- vitro tissue vascularization strategies.  
+
+<|ref|>text<|/ref|><|det|>[[113, 525, 885, 891]]<|/det|>
+We showed that microvascular networks could be created using 2- photon hydrogel polymerization, demonstrating that this technology can be used to achieve micro- vasculature with previously unreported accuracy, resolution and scale. A significant limitation of current photo- polymerizable hydrogel materials is the significant swelling of the material, which prevents the robust, leak- free interface between printed structures and microfluidic perfusion systems. Our development of a non- swelling photo- polymerizable material formulation was a critical component to overcome this limitation, and enabled the printing of 3D soft microfluidic systems which could be reliably perfused over multiple weeks. The exchange of nutrients and oxygen as well as the removal of waste products was achieved via simple diffusion as the printed vascular network is permeable to water- soluble molecules and gases. The direct fabrication of capillaries of a defined topology delivers an unprecedented control over tissue perfusion parameters, and the design of the vascular network is highly flexible and can be adapted to more complex geometrical and structural requirements. To provide a complete extracellular milieu with structural 3D support to the growing engineered tissue, the space around the perfused vessels was filled with hydrogel. In the experiments
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 87, 885, 310]]<|/det|>
+presented here, the hydrogel component consisted of the commonly used proteinaceous matrix Matrigel, however the platform can also readily accommodate without any additional modifications the use of other naturally derived matrices such as collagen, as well as synthetic artificial extracellular matrices such as poly(ethylene) glycol PEG \(^{67}\) or alginate \(^{68}\) . This technology provides a reliable fluidic coupling between the microfluidic grid and the host perfusion device, such that a continuous peristaltic pump driven perfusion is possible. By integrating these printed microfluidic grids into a perfusion system, we were able to demonstrate that that large- scale (>15mm \(^{3}\) ) fully perfused neural and liver tissues could be generated with this platform.  
+
+<|ref|>text<|/ref|><|det|>[[113, 321, 886, 602]]<|/det|>
+Our experiments with neural tissue demonstrate that the differentiation trajectory of cells in this perfused system is significantly enhanced. While control organoids remained largely in a pluripotent state, scRNAseq analysis revealed that perfused tissue rapidly differentiated towards the neural fate, together with a switch from glycolysis to oxidative phosphorylation. Imaging and flow cytometry confirmed that this tissue was highly viable, and immunohistochemistry showed markers of neural differentiation, which were absent in the non- perfused sample, as well as hallmarks of epithelial to mesenchymal transition. This was underscored by our observations that the lack of active micro- perfusion of the engineered tissue construct triggered a stress response in the cells within the inner core of the tissue. The accelerated differentiation of tissues upon perfusion is thought to be due not only to increased availability of nutrients and oxygen but also to the rapid diffusion of differentiation factors within the tissue via the tightly spaced capillary network.  
+
+<|ref|>text<|/ref|><|det|>[[113, 611, 885, 775]]<|/det|>
+The possibility of long- term perfusion within this platform was demonstrated by the maintenance of viable engineered liver tissue demonstrating enhanced phenotypic and functional features compared to standard 2D and 3D organoid culture. As cells in our current capillary design were not sufficiently distant from the source of oxygen to generate an oxygen gradient, we did not observe a clear spatial segregation of hepatocytic markers, known as zonation, in the perfused liver tissue, however this feature could be engineered by wider inter- vessel distance.  
+
+<|ref|>text<|/ref|><|det|>[[113, 785, 884, 891]]<|/det|>
+Overall, the 3D soft microfluidic technology presented here overcomes one of the major challenge in engineering tissues and organoids: the lack of tissue perfusion from the initiation of tissue growth, and enables the generation of large engineered tissues which are vascularized from within and their maintenance over long periods of time. In applications such as disease modeling and drug development,
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[112, 87, 886, 398]]<|/det|>
+such a highly defined synthetic perfusion system would be beneficial in avoiding the complexity and variability introduced by exogenous angiogenesis- driven vascularization. While the current implementation of the platform does not recapitulate features of in vivo vascular networks such as adaptive vascular remodeling or selective blood brain barrier interactions, it addresses the major problem of oxygen, nutrient and growth factor and small molecule supply as well as of waste removal, allowing to generate viable tissues beyond currently available dimensions. The incorporating of endothelial vasculature with this synthetic capillaries could be implemented, where the hydrogel micro- vascularization could provide a temporary tissue support during the time required for angiogenesis- driven capillarization to establish a perfusable network. We expect that this approach is widely applicable in overcoming the current size limitations of bio- printed tissues and provides a technological foundation for the development of perfusable in vitro models of increased complexity and scale.  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 466, 271, 483]]<|/det|>
+## Acknowledgements  
+
+<|ref|>text<|/ref|><|det|>[[115, 494, 884, 541]]<|/det|>
+This work was supported by FWO grant G087018N, Interreg Biomat- on- Chip grant and Vlaams- Brabant and Flemish Government co- financing, KU Leuven grants C14/17/111 and C32/17/027.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[115, 89, 294, 105]]<|/det|>
+## Materials and Methods  
+
+<|ref|>text<|/ref|><|det|>[[113, 144, 885, 368]]<|/det|>
+Human PSC culture. Human PSCs were cultured in Matrigel (356277, Becton Dickinson) coated 6 well plates up to \(60 - 70\%\) confluency. Passages were performed by a 3 min treatment of Dispase II (D4693, Sigma) at \(37^{\circ}C\) , followed by 2- 3 PBS washings at RT. 1 mL of E8- Flex medium (A2858501, Thermo Scientific) was added and the colonies were scraped and gently pipetted 4- 5 times through 1ml plastic tip to break the colonies. The colony suspension was then diluted at 1:5 ratio and plated to a Matrigel coated wells in \(2\mathsf{mL}\) of E8Flex medium supplemented with \(10\mu \mathsf{M}\) Rock inhibitor(Y- 27632, Hellobio) for \(24h\) . The medium was then replaced by \(2\mathsf{mL}\) of fresh E8- Flex medium and incubation was continued for \(48h\) , at which point the colonies usually reached \(60 - 70\%\) confluency and were ready for next passage.  
+
+<|ref|>text<|/ref|><|det|>[[113, 408, 884, 515]]<|/det|>
+Human PSC- derived cerebral organoids and perfused cerebral tissue. We adapted the protocol by Lancaster et al. \(^{69}\) to our experimental conditions. Upon reaching a confluency of \(60 - 70\%\) hPSCs were dissociated by treatment the colonies with \(250\mu \mathsf{I}\) Accutase (A1110501, Gibco) for 7 min at \(37^{\circ}C\) and resuspended in E8Flex medium containing \(10\mu \mathsf{M}\) Rock inhibitor.  
+
+<|ref|>text<|/ref|><|det|>[[113, 525, 885, 806]]<|/det|>
+Organoids were generated in U- bottom 96- well plates (#351177, Falcon). The plates were rinsed with Anti- Adherence Solution (#07010, Stemcell Technologies) and cells were plated at 9000cells/well density. Plates were spun at 300rcf at RT and left in CO2 incubator for 24 hours for hPSC spheroid aggregation, after which culture medium was replaced by fresh E8- Flex medium, without Rock inhibitor and changed afterwards every 2 days. At day 2, the spheroids were embedded in growth factor reduced (GFR) Matrigel (354230, Becton Dickinson) and kept in 6- well plates, pre- rinsed with Anti Adherence Solution in CO2 incubator. At day 6, neuronal induction was started by replacing E8- Flex medium with DMEM/F12 medium (31330038, Gibco) containing \(1\%\) MEM- NEAA (11140035, Gibco), \(1\%\) Glutamax (35050038, Gibco), \(1\%\) Pen- Strep (15140122, Gibco), \(0.5\%\) N2 supplement (17502048, Gibco) and \(1\mathsf{ug / ml}\) Heparin (H3149, Sigma) (neural induction medium). Day 8 spheroids were used for characterization.  
+
+<|ref|>text<|/ref|><|det|>[[113, 816, 884, 892]]<|/det|>
+For the formation of perfused tissue, at day 0, we first generated micro- hPSC spheroids using 24- well Aggrewell plates (34411, Stemcell Technologies) following a protocol supplied by the manufacturer. Specifically, we seeded Aggrewells with hPSCs to obtain 350- 400 cells per micro- well. After 24 hours,
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 87, 886, 397]]<|/det|>
+spheroids formed and the medium was replaced by fresh E8- Flex without Rock inhibitor. At day 2, the spheroids typically reached a diameter of 180- 200um, and were harvested and resuspended in ice cold GFR Matrigel at a density of 3500 spheroids per 200- 250ul GFR Matrigel. The grids were placed under stereomicroscope in a 35mm petri dish on ice and seeded with the spheroid suspension at final density of \(\sim 1800\) spheroids per grid. \(200\mu l\) plastic tips pre- chilled on ice were used to dispense the suspension under stereomicroscope. Seeding was performed in several stages in order to allow dispensed spheroids to settle into the grids. After seeding, the grids were kept in a CO2 incubator for 40- 50 min to allow Matrigel polymerization, after which the grids where placed in a perfusion chip and perfusion was started. E8- Flex/PenStrep medium was changed every 2 days (3- 4ml per grid). At day 6, E8- Flex medium was replaced by neural induction medium for 2 days. At day 8, the grids were extracted and the tissue used for characterizations.  
+
+<|ref|>text<|/ref|><|det|>[[113, 435, 886, 628]]<|/det|>
+Immunofluorescence analysis of cerebral tissue samples. Samples were fixed with \(4\%\) paraformaldehyde (158127, Sigma- Aldrich) for 24- 36 hours at \(4^{\circ}C\) and washed by 3 incubations in PBS for 15- 20 min at room temperature. Fixed tissue was sectioned either by embedding in low melting point agarose and sectioning on vibratome (Leica VT1000S) into 100- 150um sections or cryopreserved in OCT(6502, Thermo APD Consumables) overnight at \(4^{\circ}C\) , re- embedded into fresh OCT, frozen in isopropanol/dry ice slurry, cut into 50um sections on cryotome (Leica CM1850) and affixed on SuperFrost Plus (10019419, Thermo Scientific) microscope glass slides.  
+
+<|ref|>text<|/ref|><|det|>[[113, 639, 886, 860]]<|/det|>
+Sections were incubated in a permeabilization and blocking solution of \(0.3\%\) Triton X (A4975, PanREAC Applichem) and \(3\%\) BSA (A7906, Sigma) in PBS for 24 hours at \(4^{\circ}C\) . Primary antibodies were diluted in the permeabilization and blocking solution and applied to sections for 24h at \(4^{\circ}C\) , after which three PBS washes were performed over another 24h period. Secondary antibodies and Hoechst were also diluted in the permeabilization and blocking solution and applied to sections overnight at \(4^{\circ}C\) , followed by washing in PBS 3- 4 times over another 24 hours. Antibodies used in this study are listed in Table 1. Stained agarose- embedded sections were stored in 2mM sodium azide solution in PBS. Cryosections were mounted in Fluoromount- G medium and stored at \(4^{\circ}C\) .
+
+<--- Page Split --->
+<|ref|>table<|/ref|><|det|>[[114, 112, 710, 744]]<|/det|>
+<|ref|>table_caption<|/ref|><|det|>[[115, 742, 462, 758]]<|/det|>
+Table 1. List of antibodies used in this study   
+
+<table><tr><td>Primary</td><td>Host</td><td>Dilution</td><td>Manufacturer</td></tr><tr><td>Pax6, monoclonal</td><td>Mouse</td><td>1:200</td><td>DSHB</td></tr><tr><td>Nanog</td><td>Goat</td><td>1:200</td><td>R&amp;amp;D Systems, AF1997</td></tr><tr><td>ECad</td><td>Mouse</td><td>1:500</td><td>Abcam, ab76055</td></tr><tr><td>NCad</td><td>Rat</td><td>1:200</td><td>DSHB</td></tr><tr><td>Cleaved Caspase3</td><td>Rabbit</td><td>1:400</td><td>Cell Signaling Technology, 9661</td></tr><tr><td>HIF1a</td><td>Rabbit</td><td>1:500</td><td>Abcam, ab51608</td></tr><tr><td>HNF4α</td><td>Mouse</td><td>1:200</td><td>Abcam, ab41898</td></tr><tr><td>Alpha-1-Antitrypsin</td><td>Rabbit</td><td>1:200</td><td>DAKO, A0012 (00092029)</td></tr><tr><td>PEPCK</td><td>Mouse</td><td>1/1000</td><td>Santa Cruz, sc-271204</td></tr><tr><td>MRP2</td><td>Mouse</td><td>1/500</td><td>Abcam, ab3373</td></tr><tr><td>KRT19</td><td>goat</td><td>1/500</td><td>Santa Cruz, sc-33120</td></tr><tr><td>ALB</td><td>Rabbit</td><td>1/500</td><td>Abcam, ab207327</td></tr><tr><td>Secondary</td><td></td><td></td><td></td></tr><tr><td>Hoechst</td><td></td><td></td><td>Sigma-Aldrich, 14533</td></tr><tr><td>Anti-mouse Alexa 647</td><td>Donkey</td><td>1:500</td><td>Invitrogen, A31571</td></tr><tr><td>Anti-goat Alexa 647</td><td>Donkey</td><td>1:500</td><td>Invitrogen, A21447</td></tr><tr><td>Anti-rat Alexa 555</td><td>Goat</td><td>1:500</td><td>Invitrogen, A21434</td></tr><tr><td>Anti-goat Alexa 555</td><td>Donkey</td><td>1:500</td><td>Invitrogen, A21432</td></tr><tr><td>Anti-rabbit Alexa 555</td><td>Donkey</td><td>1:500</td><td>Invitrogen, A31572</td></tr><tr><td>Anti-mouse Alexa 488</td><td>Donky</td><td>1:500</td><td>Invitrogen, A11029</td></tr></table>  
+
+<|ref|>text<|/ref|><|det|>[[113, 781, 884, 888]]<|/det|>
+Human PSC- derived perfused hepatic tissue. All liver differentiation experiments were performed with the H3CX hiPSC line previously generated 70. H3CX is a hiPSC line (Sigma 0028, Sigma- Aldrich) genetically engineered to overexpress 3 transcription factors HNF1A, FOXA3 and PROX1 upon Doxycycline induction, which allows for rapid generation of hepatocyte- like progeny. H3CX cells were
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[112, 85, 886, 600]]<|/det|>
+expanded feeder- free on Matrigel (BD Biosciences)- coated plates in E8 or E8 Flex (Thermo Fisher Scientific). HC3X cells were differentiated towards HLCs as previously described71. Briefly, HC3X cells were dissociated to single cells using StemPro™Accutase™ Cell dissociation Reagent (Thermo Fisher Scientific) and plated on Matrigel- coated plates at \(\pm 8.75 \times 10^{4}\) cells/cm² in mTeSR medium (Stem Cell Technologies) supplemented with RevitaCell (Thermo Fisher Scientific). When cells reached 70- 80% confluence, differentiation was performed during 40 days in liver differentiation medium (LDM) containing to comprise 500ml total volume: 285 ml of DMEM low glucose (Invitrogen 31885023), 200 ml of MCDB- 201 solution in water (Sigma M- 6770) adjusted to pH 7.2, \(0.25 \times\) of Linoleic acid—Bovine serum albumin (LA- BSA, Sigma L- 9530), \(0.25 \times\) of Insulin- transferrin- selenium (ITS, Sigma I- 3146), 50 U of Penicillin/Streptomycin (Invitrogen 15140122), 100 nM of I- ascorbic acid (Sigma A8960), 1 μM dexamethasone (Sigma D2915) and 50 μM of \(\beta\) - mercaptoethanol (Invitrogen 31350010). Differentiation medium was supplemented with \(0.6\%\) dimethylsulfoxide (DMSO) during the first 12 days of the culture. \(2.0\%\) DMSO and 3x concentrate of non- essential amino- acids (NEAA) was added to LDM medium between days 12- 13, and from day 14 until the end of differentiation 20g/L glycine was added to LDM medium supplemented with NEAA. Differentiation was performed in presence of the following factors: day 0- 1: 100ng/ml Activin- A and 50ng/ml Wnt3a, day 2- 3: 100ng/ml Activin- A, day 4- 7: 50ng/ml BMP4, day 8- 11: 20ng/ml FGF1, and 20ng/ml HGF during the rest of differentiation. Doxycycline (5ug/ml) was applied from day 4 until the end of differentiation. All cytokines were purchased from Peprotech.  
+
+<|ref|>text<|/ref|><|det|>[[113, 637, 886, 802]]<|/det|>
+RNA extraction and quantitative reverse- transcription PCR. RNA extraction was performed using TR1zol reagent (Invitrogen) following manufacturer's instructions. At least 1μg of RNA was transcribed to cDNA using the Superscript III First- Strand synthesis (Invitrogen). Gene expression analysis was performed using the Platinum SYBR green qPCR supermix- UDG kit (Invitrogen) in a ViiA 7 Real- Time PCR instrument (Thermo Fisher Scientific). The sequences of all used RT- qPCR primers are listed in Table 2. The ribosomal protein L19 transcript (RPL19) was used as a housekeeping gene for normalization.  
+
+<|ref|>table<|/ref|><|det|>[[114, 812, 457, 897]]<|/det|>
+
+<table><tr><td>RPL19</td><td>ATTGGTCTCATTGGGGTCTAAC<br>AGTATGCTCAGGCTTCAGAAGA</td></tr><tr><td>AAT</td><td>AGGGCCGGAAGCTAGTGAGT<br>TCCTCGGTGTTCCTTGACTTC</td></tr></table>
+
+<--- Page Split --->
+<|ref|>table<|/ref|><|det|>[[114, 88, 459, 461]]<|/det|>
+<|ref|>table_caption<|/ref|><|det|>[[115, 461, 441, 478]]<|/det|>
+Table 2. List of primers used in this study   
+
+<table><tr><td>NTCP</td><td>ATGCTGAGGCAAGGATGTTC<br>AGCAGCAGCACGACAGAGTA</td></tr><tr><td>G6PC</td><td>GTGTCCGTGATCGCAAGCC<br>GACGAGGTTGAGCCAGTCTC</td></tr><tr><td>CYP3A4</td><td>TTCCTCCCTGAAAGATTCAGC<br>GTTGAAGAAGTCTCCTAAGCT</td></tr><tr><td>PEPCK</td><td>AAGAAGTGCTTTGCTCTCAG<br>CCTTAAATGACCTTGTCGT</td></tr><tr><td>CYP2D6</td><td>CATACCTGCCCTACTACCAAA<br>TGTCTGCCTGGTCCTC</td></tr><tr><td>PGC1α</td><td>CCTTCGAGCACAAGAAAACA<br>TGCTTCGTCGTCAAAAACAG</td></tr><tr><td>HNF6</td><td>AAATCACCATTTCCCAGCAG<br>ACTCCTCCTTCTTGCGTCA</td></tr><tr><td>ALB</td><td>ATGCTGAGGCAAGGATGTTC<br>AGCAGCAGCACGACAGAGTA</td></tr><tr><td>AAT</td><td>AGGGGCTGAAGCTAGTGGAT<br>TCCTCGGTGTCCTTGACTTC</td></tr></table>  
+
+<|ref|>text<|/ref|><|det|>[[113, 529, 886, 754]]<|/det|>
+Histology. Samples were fixed with \(4\%\) (w/v) paraformaldehyde (PFA, Sigma- Aldrich) overnight at \(4^{\circ}\mathrm{C}\) washed 3 times with PBS and submerged in PBS- sodium azide ( \(0.01\%\) v/v) solution at \(4^{\circ}\mathrm{C}\) until embedded in paraffin. Hydrogel sections (5 \(\mu \mathrm{m}\) ) were prepared using a microtome (Microm HM 360, Marshall Scientific.) For Hematoxylin and Eosin (H&E) staining, sections were treated with xylene solution to remove the paraffin, and gradually rehydrated in ethanol (100 to \(70\%\) , v/v). H&E staining was performed by submerging rehydrated hydrogel sections in Harris Hematoxylin solution, acid alcohol, bluing reagent and Eosin- Y solution by order. Stained samples were dehydrated with ascending alcohol series, washed in xylene solution, and mounted with DPX mountant (Sigma- Aldrich).  
+
+<|ref|>text<|/ref|><|det|>[[113, 792, 884, 900]]<|/det|>
+Immunofluorescence analysis of liver tissue samples. Following deparaffinization in xylene and rehydration in descending alcohol series, heat- mediated antigen retrieval was performed by incubating hydrogel sections in Dako antigen retrieval solution (Dako) for 20 min at \(98^{\circ}\mathrm{C}\) . This step was followed by cell permeabilization with \(0.01\%\) (v/v) Triton- X (Sigma- Aldrich) solution in PBS, for 20 minutes. Samples
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 87, 884, 282]]<|/det|>
+were then incubated with \(5\%\) (v/v) Goat or Donkey Serum (Dako) for 30 min. Primary antibodies diluted in Dako antibody diluent solution, were incubated overnight at \(4^{\circ}C\) , followed by washing steps and incubation with Alexa- coupled secondary antibody (1:500) and Hoechst 33412 (1:500) solution for 1 hour at room temperature. Finally, samples were washed in PBS, and mounted with Vectashield antifade mounting medium (Vector Laboratories). Stained sections were imaged using laser scanning confocal microscope (LSM 880, Zeiss, Germany), and image processing were performed on ZEN Blue software (Zeiss, Germany).  
+
+<|ref|>text<|/ref|><|det|>[[112, 320, 885, 630]]<|/det|>
+Image acquisition and analysis of cerebral tissue samples. Fluorescence images were obtained using a confocal microscope (Leica SP8 DIVE, Leica Microsystems) equipped with 10x NA0.4 dry objective. Acquisition parameters were kept constant for all samples obtained in the same experiment. Image processing was performed using Fiji/ImageJ (NIH) and custom- written ImageJ Java plugin. At the data preprocessing stage, image stacks were collapsed by maximum intensity projection. Resulting images were translated to 8- bit lookup table representation, manually cleaned from grid structure elements and thresholded by replacing all sub- threshold pixels with black (zero value) pixels. Lookup tables of the images were then remapped from [Threshold..255] back to full [0..255] range. Threshold values were chosen manually for Hoechst and marker channels for each image in order to remove background from the images. For all markers except HIF1α, total protein expression levels in a tissue were quantified as a relative number of cells expressing the protein, i.e.  
+
+<|ref|>equation<|/ref|><|det|>[[310, 655, 685, 688]]<|/det|>
+\[Protein expression = \frac{Marker^{+}cellnumber}{Totalcellnumber} \times 100\%\]  
+
+<|ref|>text<|/ref|><|det|>[[113, 713, 884, 820]]<|/det|>
+On the assumption that cell size is constant across the samples, the relative number of cells was estimated as a ratio of areas occupied by cells on marker and Hoechst channels. Corresponding areas were calculated by counting all non- zero pixels on marker and Hoechst channels. For statistical comparison, results for each marker were pooled from all experiments.  
+
+<|ref|>text<|/ref|><|det|>[[113, 830, 884, 907]]<|/det|>
+As HIF1α marker is constitutively expressed in cells under normoxic conditions, its expression level in a tissue was estimated as the average fluorescence intensity on the marker channel. Threshold values were kept constant for organoids, non- perfused and perfused samples obtained in the same experiment;
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[114, 88, 883, 135]]<|/det|>
+expression values obtained in the experiment were normalized to organoid value and normalized data from different experiments were averaged.  
+
+<|ref|>text<|/ref|><|det|>[[113, 146, 884, 282]]<|/det|>
+All results are expressed as mean \(\pm\) SEM. Statistical comparisons between two data groups were analyzed using unpaired two- tailed Student's \(t\) - test with a \(95\%\) confidence interval (Origin Pro v9.5, OriginLab Inc.). Significance was marked on plots by \*, \*\* and \*\*\* for \(p< 0.05\) , \(p< 0.01\) , and \(p< 0.001\) correspondingly. If not stated otherwise, the statistical significance was assessed for comparisons of perfused and non- perfused samples to organoid control samples and denoted by "pControl".  
+
+<|ref|>text<|/ref|><|det|>[[112, 319, 885, 629]]<|/det|>
+Organoid dissociation and single- cell RNA sequencing. For dissociation, the microfluidic grids of non- perfused and perfused samples were removed from the perfusion chips and, along with the control organoids, placed temporarily in DMEM/F12 media in independent \(15 \text{mL}\) Falcon tubes until all 3 samples were ready for the next step (time \(< 5 \text{min}\) ). The DMEM was then replaced with \(1 \text{mL}\) of pre- warmed TrypLE Express (12605010, Gibco) at \(37^{\circ} \text{C}\) . The samples were transferred to a warm water bath at \(37^{\circ} \text{C}\) for 7.5 minutes with gentle agitation every 1 minute after the first 3 minutes. A visual inspection was performed using an inverted microscope to ensure complete dissociation of tissues and the presence of single cells. The \(1 \text{mL}\) solution was introduced to \(9 \text{mL}\) of DMEM supplemented with \(20\%\) FBS for TrypLE Express neutralization, and centrifuged at \(500 \text{rcf}\) for 5 minutes. The pellet was resuspended in \(200 \text{uL}\) of N2B27 media and put on ice. This dissociation protocol yielded an average viability of above \(80\%\) across all samples.  
+
+<|ref|>text<|/ref|><|det|>[[112, 639, 885, 861]]<|/det|>
+Library preparations for the scRNAseq was performed using 10X Genomics Chromium Single Cell 3' Kit, v3 (10X Genomics, Pleasanton, CA, USA). The cell count and the viability of the samples were accessed using LUNA dual fluorescence cell counter (Logos Biosystems) and a targeted cell recovery of 6000 cells was aimed for all the samples. Post cell count and QC, the samples were immediately loaded onto the Chromium Controller. Single cell RNAseq libraries were prepared using manufacturers recommendations (Single cell 3' reagent kits v3 user guide; CG00052 Rev B), and at the different check points, the library quality was accessed using Qubit (ThermoFisher) and Bioanalyzer (Agilent). Single cell libraries were sequenced using paired- end sequencing workflow and with recommended 10X; v3 read
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[114, 88, 883, 136]]<|/det|>
+parameters (28- 8- 0- 91 cycles). The data generated from sequencing was de- multiplexed and mapped against human genome reference using CellRanger v3.0.2.  
+
+<|ref|>text<|/ref|><|det|>[[112, 174, 885, 516]]<|/det|>
+Single Cell RNA sequencing Data Processing. We have sequenced 4,818, 5,453, and 2,804 cells for non- perfused, perfused and control organoids samples respectively for a total of 13,075 cells, which were reduced after QC steps to 8,625 cells with an average of 1,852 detected genes per cell. Data processing and subsequent steps were performed using the Seurat72 tool for single cell genomics version 3 in R version 4.0.3. A filtering step was performed to ensure the quality of the data, where the counts of mitochondrial reads and total genes reads were assessed. Cells with more than \(15\%\) of identifiable genes rising from the mitochondrial genome were filtered out. Similarly, cells having fewer identifiable genes than 200 (low quality) and above 7,500 (probable doublets) were filtered out. Data normalization was performed, followed by the identification of 2,000 highly variable genes using the FindVariableFeatures(). S- phase and G2M- phase cell cycle regression was performed to allow cell clustering purely on cell identity and fate, which otherwise was biased by cell cycle phases. Auto scaling of the data was performed and described using principal component analysis (PCA) using the RunPCA() function.  
+
+<|ref|>text<|/ref|><|det|>[[113, 551, 884, 660]]<|/det|>
+Correlation analysis. Correlation heatmap between samples was performed in R using the top 100 marker genes for each sample using the FindAllMarkers() function followed by the selection of the top 100 marker genes using the top_n() function with \(n = 100\) and wt = avg.logFC. The correlation analysis was generated using the cor() function using the Pearson method and the heatmap using the heatmap.2() function in R.  
+
+<|ref|>text<|/ref|><|det|>[[113, 697, 885, 891]]<|/det|>
+Data Clustering. Graph- based clustering using the FindNeighbors() function (using top 15 principal components (PCs)) and FindCluster() function (resolution = 0.5) was performed to group cells based on their transcriptional profiles. No batch correction was performed on the data set. Data visualization was performed using the Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction technique using the RunUMAP package while employing the top 15 PCs identified in the previous PCA step. Cluster annotation was based on hallmark genes to identify Neuroepithelial cluster (PAX6, PAX7, PAX3), Pluripotent- Neuroepithelial transitioning (P- NE) cells (WNT4, IRX1, IRX2), Proliferating cells
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 87, 885, 280]]<|/det|>
+(CDC6, PTN, MK167, TOP2A), medium mitochondrial gene content and low glycolysis pluripotent cells (MK167, TOP2A, with the expression of NANOG, POU5F1, DPPA4), Pluripotent cells (NANOG, POU5F1, DPPA4), highly glycolytic pluripotent cells that link to the Cycling pluripotent and Pluripotent cells but include the expressions (LDHA, ENO1, HK2), a cluster undergoing EMT/migration with hypoxic markers (EPCAM, VIM, TWIST2, VEGFA) and finally a stressed cluster (FOS, HIF1A, JUN). One cluster was manually removed due to very low unique molecular identifier (UMI) count, after which the data was reprocessed to account for the removal of the cluster.  
+
+<|ref|>text<|/ref|><|det|>[[113, 320, 885, 543]]<|/det|>
+Pseudotime trajectories. Trajectory analysis was performed using Monocle \(3^{73}\) on R by employing the monocole3 and SeuratWrappers libraries. The Seurat obtained object containing all combined samples was passed to the Monocle 3 pipeline using the as.cell_data_set() function followed by processing using the cluster_cells() function. The pseudotime trajectory was inferred by first using the subset function on the only partition detected, followed by the learn_graph() function. Cells were colored in their inferred pseudotime using the which.max() function with the FetchData() (AVP) function, followed by the order_cells() function with the root_cell = max.avp. The UMAP superimposed with inferred pseudotime trajectory was generated using the plot_cells() function.  
+
+<|ref|>text<|/ref|><|det|>[[113, 580, 885, 802]]<|/det|>
+Gene- set analysis and hierarchical clustering. Gene- sets were created using the AddModuleScore() function in R. three gene- set were created to explore the dataset, pluripotency gene- set (POU5F1, NANOG, CDH1), glycolysis gene- set (ALDOA, BPGM, ENO1, ENO2, GPI, HK1, HK2, PFKL, PFKM, PGAM1, PGK1, PKM, TP11), and a neural progenitor gene- set (PAX3, PAX6, PAX7, OTX2, CDH2). The % mitochondrial genes were obtain as evaluated by the Seurat pipeline. Each cell was evaluated and scored and its expression plotted on UMAPS. Cluster level hierarchical clustering of the gene- set scores and % mitochondrial genes was employed using R package heatmap.2 after scaling the expression across rows (clusters).  
+
+<|ref|>text<|/ref|><|det|>[[113, 842, 883, 890]]<|/det|>
+Design and fabrication of soft micro- fluidic grids. The microfluidic grids were designed as a multitude of parallel capillaries stemming from a common reservoir. The dimensions of the grid (2.6x2.6x1.5mm) were
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 87, 885, 339]]<|/det|>
+chosen as a compromise between size and the fabrication time. The grids were micro- fabricated on custom baseplates using high- resolution 3D printer (Photonic Professional GT2, Nanoscribe GmbH) equipped with 2- photon femtosecond laser. CAD model of the capillary grid was pre- processed by DeScribe software (Nanoscribe GmbH) to produce a printing job with defined printing parameters. A custom- formulation photopolymerizable resin was used to print microfluidic grids which contained \(2\%\) 2- Benzyl- 2- (dimethylamino)- 4'- morpholinobutyrophenone (lrgacure 369), \(7\%\) propylene glycol methyl ether acetate (PGMEA), \(36\%\) poly(ethylene glycol) diacrylate, MW700 (PEGDA 700), \(25\%\) pentaerythritol triacrylate (PETA), \(20\%\) Triton X- 100 and \(10\%\) water. lrgacure 369 was purchased from Tokyo Chemical Industry Ltd, the other components were from Sigma- Aldrich.  
+
+<|ref|>text<|/ref|><|det|>[[113, 349, 886, 570]]<|/det|>
+The microfluidic grids were printed on custom plates 3D printed on Formlabs Form 2 printer from a biocompatible resin (Dental SG, Formlabs Inc), post- cured by UV light (Formcure station, Formlabs Inc) for 2h at \(80^{\circ}C\) and washed in 2- propanol for 3- 4 days with daily change of the solvent. The plates had 1mm thickness and 10mm diameter, with 0.5mm perfusion holes. The grid printing process was set up such that the inlets in the microfluidic grid were aligned with the perfusion holes in a baseplate. After fabrication, baseplate- grid assemblies were washed during 2 days in PGMEA, 12 days in 2- propanol and then kept in PBS until use. The solvents were refreshed every 1- 2 days and PBS was refreshed daily for the first week and every 2- 3 days afterwards.  
+
+<|ref|>text<|/ref|><|det|>[[113, 608, 885, 803]]<|/det|>
+Fabrication of perfusion chips and setting up perfusion. The perfusion chips were implemented as an assembly as shown in Supplementary Fig. 1. Two microfluidic grids were sandwiched between two polydimethylsiloxane (PDMS) custom- profiled blocks. Two metal plates clamped with screws provided a tight junction between PDMS blocks and cover slips, thereby forming fluidic channels within the chip. The chips were designed to only allow contact of the medium with PDMS blocks and cover slips, thereby isolating the flow from contact with other materials of the chip. Guiding frames were used to simplify the alignment of the PDMS blocks during the chip assembly process.  
+
+<|ref|>text<|/ref|><|det|>[[113, 813, 884, 890]]<|/det|>
+The PDMS blocks were made by casting liquid PDMS compound into custom- designed plastic molds and curing overnight at \(80^{\circ}C\) . The plastic molds were fabricated by stereo- lithography (SLA) 3D printer Form 2 (Formlabs Inc.) using a bio- compatible Formlabs Dental SG resin. The printed molds were
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 88, 884, 223]]<|/det|>
+post- processed by thorough washing in 2- propanol and post- cured for 90 min at \(80^{\circ}C\) (Formcure, Formlabs Inc.). Metal plates were made from 1mm stainless steel sheet by laser cutting and stainless steel screws were used for clamping the assembly. The guiding frames were 3D printed and cover slips were glued to the metal plates with a bio- compatible UV/heat curable epoxy (NOA 86H, Norland) to provide a final two- part setup shown in Supplementary Fig. 1d.  
+
+<|ref|>text<|/ref|><|det|>[[112, 233, 885, 630]]<|/det|>
+Two chambers of the chip, containing perfused and non- perfused grids, a peristaltic pump and a medium reservoir (50ml Falcon tube) were connected by flexible PVC tubing (ID/OD 0.8/1.6mm) in series (Supplementary Fig. 1e). The medium was taken from the reservoir by the pump and fed through chambers containing the non- perfused and then the perfused grid, after which the medium was sent back to the reservoir for \(\mathrm{CO_2 / O_2}\) equilibration. A 0.22um filter was connected between the two chambers of the chip to protect capillaries of the perfused grid from potential clogging with tissue debris. The medium reservoir and the chip were kept in a CO2 incubator, while the peristaltic pump remained outside to protect electronics from humidity and to ensure proper cooling. The medium reservoir and the chip were installed on a custom made 3D printed holder (Supplementary Fig. 1f) to simplify transitions between a CO2 incubator and a laminar hood. At day 0 of an experiment, all parts of the perfusion were sterilized by ethanol and UV light in a laminar hood, connected together and filled with equilibrated medium. The microfluidic grids, seeded with hPSC spheroids, were installed in the chip (Supplementary Fig. 1d) and the chip was clamped by screws (Supplementary Fig. 1f- i). Air bubbles were removed from the system and the perfusion started at approx. \(400\mu \mathrm{L} / \mathrm{min}\) flow rate (Supplementary Fig. 1, f- iii).  
+
+<|ref|>text<|/ref|><|det|>[[113, 639, 885, 890]]<|/det|>
+Multi- grid perfusion chips were designed to provide an equal multiplexed perfusion for all microfluidic grids. The fluidic inlet of the chip, via three steps of bifurcations, was split into eight fluidic channels feeding eight wells. All fluidic channels had equal length and cross- section and, therefore, equal hydraulic resistance. The multi- grid chip were fabricated by stereo- lithography (SLA) 3D printer Form 2 (Formlabs Inc.) using Formlabs Dental SG resin. The printed chips were post- processed by thorough washing in 2- propanol and post- cured for 90 min at \(60^{\circ}C\) . 7- 8mm pieces of syringe needles (0.9mm) were cut by hand drill/cutter (Precision drill, Proxxon Inc.) and glued into chip inlets by a biocompatible UV/heat curable epoxy glue to serve as connectors for flexible tubing. This assembly was again post- cured in FormCure station for 120 min at \(80^{\circ}C\) to achieve complete hardening of the glue. To integrate microfluidic
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 88, 883, 136]]<|/det|>
+grids, a custom profile gaskets, cut from 0.5mm PDMS sheet by a laser cutter, were placed under the grid substrates to provide a liquid- tight contact and the substrates were fixed in place by 3D printed fasteners.  
+
+<|ref|>text<|/ref|><|det|>[[113, 175, 884, 309]]<|/det|>
+Data availability. All raw sequencing data, and the combined processed and metadata files generated in this study are available at GEO. The accession number for the reported data is GSE181290. This study did not generate any unique code. For review purposes please use the following token (ybchyyqofpejzgv) to access the sequencing data at: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE181290. The ImageJ Java code used for analysis of the image data are available upon request.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[115, 89, 206, 105]]<|/det|>
+## References  
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+Figures
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[171, 110, 825, 744]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[114, 770, 883, 904]]<|/det|>
+<center>Figure 1. On-chip micro-vascularization enabled by soft microfluidics. (a) 3D printed tissue culture chip designed for eight multiplexed soft 3D soft microfluidic capillary grids. Inset: microfluidic capillary grid fabricated on a plastic baseplate and diagram of working principle. (b) of microfluidic capillary grid (left) and the close up image of the same grid showing individual hydrogel capillaries (right). (c) Microvessel 3D printing by 3D printing of microfluidic grid using high-resolution 2-photon stereo-lithography with non-swelling photo-polymerizable hydrogel precursors enables reproduction of features as small as \(10\mu \mathrm{m}\) . The top view photograph demonstrates an array of cylinders of various outer diameter in micrometers (top row) and wall thickness in micrometers (columns). (d) CAD image of microfluidic grid (left) with capillaries shown in red and the structural components shown in grey. The fence at the circumference of the structure makes </center>
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[115, 89, 882, 207]]<|/det|>
+up a "basket" that can be filled (right) with cell aggregates (spheroids) which merge and produce a solid tissue incorporating hydrogel capillaries. (e) Hydrogel capillaries are readily permeable. \(25\mu \mathrm{M}\) of Fluorescein, perfused through the microfluidic grid, embedded in Matrigel, passes across capillary walls and saturates the gel within several minutes. (f) Process of tissue generation. Photograph of an empty capillary grid with the tip of \(200\mu \mathrm{l}\) pipette tip visible above the grid (left), close up image of the greed seeded with hIPSC spheroids (middle) manually dispensed from the pipette shown on the left image, image of the spheroids fused into a solid tissue after 24- 36 hours of culturing on chip. The top row schematically represents the same process, where spheroids and the resulting tissue are shown in blue.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[125, 240, 899, 565]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[113, 628, 882, 761]]<|/det|>
+<center>Figure 2. Transcriptomic changes upon perfusion. (a) Combined dataset UMAP (control, non-perfused and perfused samples). (b) Combined dataset UMAP with neuroepithelial cells (NE), pluripotent-neuroepithelial transitioning cells (P-NE), proliferating cells with medium mitochondrial (mito.) content, pluripotent cells (P) with low glycolysis (glyc.) and medium mitochondrial content, glycolytic pluripotent cells with low mitochondrial content, highly glycolytic pluripotent cells, a highly glycolytic hypoxic (hypo.) identity and a stressed cluster. (c) Pseudotime trajectory on combined dataset UMAP. (d) Cluster specific expressions of selected marker genes. Expression values are normalized and centered. Sample fractions for each identified cluster. (e) GO enrichment analysis for key processes upregulated in perfused and non-perfused samples. </center>
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[115, 105, 901, 420]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[113, 450, 882, 730]]<|/det|>
+<center>Figure 3. Changes in proliferation mediated by hypoxia. a) Representative experimental results demonstrating differences in proliferation and viability between standard organoid culture(left column) and the tissue constructs without (middle column) and with perfusion (right column). Top row: bright field images of organoids, non-perfused and perfused constructs correspondingly. Middle row: immunofluorescent images of apoptotic marker cleaved Caspase 3 (green) expressed in the three conditions. Bottom row: immunofluorescent images of hypoxia marker HIF1a (green). Hoechst staining of nuclei shown in blue. The images represent transverse cross-sections of the tissue constructs. (b) Top: average proportion of the number of live cells in perfused and non-perfused constructs quantified by flow cytometry (90.95.1% in perfused vs 89.2±4.4% in non-perfused tissue, n.s., n=4). Middle: average expression of cleaved Caspase 3 in control organoids (8±1% area, n=7), non-perfused(35.7±5%, pControl=0.02, n=4) and perfused tissue constructs (5.1±1% of total area, pControl=0.02, n=9). Bottom: average expression of hypoxia marker HIF-1a in control organoids (100±3%, n=4), non-perfused (55±3%, pControl=0.0003, n=3) and perfused constructs (26±2%, pControl=0.0004, n=2). Control organoids, non-perfused and perfused constructs are denoted as Ctrl, NP and P correspondingly, data are represented as mean ± SEM, asterisks (*) denote statistical significance of the difference between control organoids and the tissue constructs (unpaired two-tailed Student's t-test, 95% confidence interval). (c) Representative fluo-cytometry data, demonstrating the difference in total number of cells between perfused and non-perfused constructs. (d) Distribution of gene expression levels across the single cell population for hypoxia (left), G2M cell cycle (middle) and S cell cycle (right) associated gene sets. Scale bar: 1mm </center>
+
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+<|ref|>image<|/ref|><|det|>[[125, 155, 876, 570]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[113, 606, 883, 856]]<|/det|>
+<center>Figure 4. Rapid neural differentiation in perfused systems. (a) Representative experimental results demonstrating the result of 2 days of neuronal induction in control organoids(left column), non-perfused (middle column) and perfused (right column) tissue constructs. Top, middle and bottom row: immunofluorescent images of stem cell marker NANOG, early neural marker PAX6 and cell adhesion proteins N-Cadherin (green) and E-Cadherin (red) correspondingly. The images represent transverse cross-sections of the tissue constructs. In most cases, the cross-sections of capillaries are visible as circular structures (marked by triangles) (b) Average expression of PAX6(Ctrl: Not detected; NP: \(0.2\% \pm 0.2\%\) , pControl \(= 0.35(n.s.)\) , \(n = 9\) ; P: \(21.5 \pm 2\%\) , pControl \(= 0.00002\) , \(n = 12\) ), NANOG(Ctrl: \(73.6 \pm 8\%\) , \(n = 9\) ; NP: \(16.1 \pm 2\%\) , pControl \(= 0.00007\) , \(n = 11\) ; P: not detected), NCad(CDH2) (Ctrl: \(100 \pm 1\%\) , \(n = 8\) ; NP: \(7.6 \pm 1\%\) , pControl \(= 4e - 13\) , \(n = 3\) ; P: \(55.7 \pm 9\%\) (pControl \(= 0.009\) , \(n = 5\) ) and ECad(CDH1) (Ctrl: \(99 \pm 2\%\) , \(n = 12\) ; NP: \(26.6 \pm 7\%\) , pControl \(= 0.0001\) , \(n = 6\) ; P: \(4.5 \pm 2\%\) , pControl \(= 2e - 10\) , \(n = 5\) ) markers in control organoids (Ctrl), non-perfused(NP) and perfused(P) constructs. Data are represented as mean \(\pm\) SEM, asterisks (*) denote statistical significance of the difference between control organoids and the tissue constructs (unpaired two-tailed Student's \(t\) -test, \(95\%\) confidence interval) (c) UMAP plot of the combined dataset showing the localization of cells from control organoids, non-perfused and perfused constructs in the UMAP space. (d) UMAP plot of the combined dataset highlighting locations of PAX6, NANOG, NCad (CDH2) and ECad(CDH1) expressing cells in the UMAP space. Scale bar: \(250 \mu m\) </center>
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[128, 140, 888, 457]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[114, 477, 883, 595]]<|/det|>
+<center>Figure 5. Functional improvement in perfused liver microtissues. (a) Hematoxylin and eosin of a transverse section of liver tissue construct with hepatocyte-like morphology and no visible indication of apoptosis. (b) Immunofluorescence images showing presence of AFP and CYP3A4, (c) Alpha-1 antitrypsin (AAT) and Cytokeratin 19 (KRT19), and (d) basic hepatic markers albumin (ALB) and hepatic nuclear factor HNF4α expression (f) Heatmap representation of fold change gene expression levels normalized to control 2D cell culture and compared to standard hepatic organoids and perfused liver constructs; rows are centered and scaled. Gene expression data for hepatic 2D culture and hepatic organoids are from Kumar et al<sup>74</sup>. Scale bar: \(50\mu \mathrm{m}\) </center>
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[114, 442, 300, 459]]<|/det|>
+Supplementary Figures
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[238, 108, 763, 544]]<|/det|>  
+
+<|ref|>text<|/ref|><|det|>[[114, 574, 882, 812]]<|/det|>
+Supplementary Figure 1. Assembly and operation of the chip device. (a) Cutaway view of the fully assembled culture chip, the chamber with perfused capillary grid is shown. Cell culture medium enters the culture chamber through the inlet (1mm syringe needle, cut into 15mm pieces), passes through the capillary grid and leaves the chamber through outlet (not shown). (b) Exploded view of the chip. (c) Side view cross section of the chip. Tissue constructs can be observed through the glass windows with inverted or upright microscopes. The view also demonstrates that the cell culture medium only comes into contact with PDMS(pink) and glass(blue) parts of the chip. (d) During the assembly, a user needs to handle only two pre- assembled "halves" of the chip. (e) Schematic representation of the perfusion system driven by a peristaltic pump, with culture medium circulating between the perfusion chip and the medium reservoir. Here, the grid in the right chamber is surrounded by a constant medium flow but the flow through its capillaries is absent, while in the downstream grid (left chamber) the flow passes through the capillaries and diffuses across the walls into the tissue. (f) Photographs of fully assembled chip and perfusion system. The non-perfused and perfused chambers are connected by external tubing (left), with \(0.22\mu m\) filter connected inline between the chambers. The chip and the medium reservoir resides in a custom 3D-printed holder (middle) and connected to a peristaltic pump installed outside of \(\mathrm{CO_2}\) incubator (right). The image shows three experiments running in parallel.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[125, 123, 824, 339]]<|/det|>  
+
+<|ref|>image<|/ref|><|det|>[[125, 375, 825, 620]]<|/det|>  
+
+<|ref|>text<|/ref|><|det|>[[114, 647, 882, 722]]<|/det|>
+Supplementary Figure 2. Optimization of printable material properties. (a) Test structure printed from PEGDA or PEGDA \(90\% /\) PETA \(10\%\) mixture. Both ends of the part are connected to a baseplate. When submerged to cell culture medium, swelling of the material leads to a breakage of the part. (b) Our custom formulation resin effectively prevents swelling in aqueous media and preserves the geometry of the microfabricated structures. Scale bar \(100\mu \mathrm{m}\) .
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[125, 180, 904, 425]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[113, 453, 883, 513]]<|/det|>
+<center>Supplementary Figure 3. Two-photon stereo-lithography enables precision fabrication in a wide range of scales. Microfluidic grids of different dimensions (in mm): 1.2mm x 1.2mm x 1.2mm (left), 2.6mm x 2.6 mm x 1.5mm (middle), 6.5mm x 6.5mm x 5mm (right). Perfusion vessels in each grid have identical 50μm diameter and inter-vessel distance of 250μm. Scale bar 5 mm. </center>
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[130, 240, 840, 744]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[113, 761, 883, 821]]<|/det|>
+<center>Supplementary Figure 4. Daily brightfield imaging of neural tissue in perfused and non-perfused chips. (a) Bright field images of the perfused (top) and non-perfused (bottom) tissue constructs taken every 2 days during the culturing protocol. (b) Live sections of perfused (left) and non-perfused (right) tissue constructs taken at the end of the culturing protocol. Scale bar 500μm. </center>
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[339, 195, 652, 407]]<|/det|>  
+
+<|ref|>text<|/ref|><|det|>[[114, 439, 883, 485]]<|/det|>
+Supplementary Figure 5. Correlation analysis between perfused-, non-perfused neural tissue constructs and conventional neural organoid culture. Correlation analysis using the top 100 differentially expressed marker genes for each of the three experimental conditions.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[112, 87, 880, 480]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[113, 506, 883, 553]]<|/det|>
+<center>Supplementary Figure 6. Gene-set analysis for various processes. Combined dataset UMAP with scores for pluripotent, glycolysis and neural progenitor gene-set as well as the \(\%\) mitochondrial genes. Hierarchical clustering of gene-set scores and \(\%\) mitochondrial genes. Score is column scaled. </center>
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[260, 90, 732, 830]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[112, 840, 880, 870]]<|/det|>
+<center>Supplementary Figure 7. Gene expression for unannotated hNTO clusters with the top 25 marker genes for each cluster. </center>
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[115, 168, 856, 455]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[113, 475, 882, 536]]<|/det|>
+<center>Supplementary Figure 8. Cluster-specific analysis of scRNAseq dataset. Dot-plot heatmap of hypoxia and cell cycle markers for each identified cluster. The average gene expression is represented by the color intensity of each dot, whereas the dot size represents the percentage of the gene-expressing cells for each sample within each cluster. </center>
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[232, 216, 760, 488]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[114, 531, 882, 619]]<|/det|>
+<center>Supplementary Figure 9. Difference in localized expression of hypoxia marker HIF1α and apoptosis marker cleaved Caspase-3. (a) HIF1α expression is localized to regions containing intact cell bodies (middle), evidenced by intact nuclei in the outlined region (left) as well as well-defined cytoplasmic regions stained with E-Cad antibody (right). (b) Cleaved Caspase-3 expression (middle) is localized to regions with apoptotic cell bodies evidenced by Hoechst stain of fragmented nuclei (outside of the outlined live region on the left image). Scale bar 100μm. </center>
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[113, 94, 928, 310]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[113, 320, 884, 354]]<|/det|>
+<center>Supplementary Figure 10. Brightfield images of perfused liver cultures. Bright field images of the perfused liver-like tissue constructs taken during tissue culturing. Scale bar 500μm. </center>
+
+<--- Page Split --->

preprint/preprint__01f0e3bff18307bf43bc81d95d1730c9c3bec79b418b6773f6d9e518508e2289/images_list.json

@@ -0,0 +1,33 @@
+[
+  {
+    "type": "image",
+    "img_path": "images/Figure_1.jpg",
+    "caption": "Fig. 1 | Study site and double approach for iron export estimation. a). Sentinel-2 10 m resolution image of Deception Island (South Shetland Islands) – Vapour Col (magenta square), where the data for this study were collected form, is the second largest chinstrap penguin rookery on Deception Island, gathering more than 19,000 breeding pairs<sup>37</sup>. b). Chinstrap penguins feeding on krill, which is their main food source<sup>28</sup>. c). Chinstrap population census estimated by the deep learning model. Despite certain limitations of the inference over areas with Cp-like artifacts, such as the rocky coastal areas, the model succeeded in accurately detecting the individuals in relevant, high density Cp areas (Extended Data in supplementary Table 1). d) Vapour Col photographically available extension. The northern tip of Vapour Col is outlined in yellow and correspond to the terrain of the panel e. Blue and green patches correspond to the output of the non-supervised classification for guano areas<sup>36</sup>. e). Northern tip of Vapour Col – penguin density is shown as a heatmap, the brightest areas correspond to the breeding zones where the penguins are tightly clustered, matching with the guano accumulation zones.",
+    "footnote": [],
+    "bbox": [
+      [
+        100,
+        100,
+        904,
+        468
+      ]
+    ],
+    "page_idx": 4
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_3.jpg",
+    "caption": "Fig. 3 | Iron recycling and net primary production stimulation in the Southern Ocean pelagic areas. A perspective of Chinstrap penguins relative Fe input and net primary production stimulation compared to post-whaling Mysticeti<sup>38</sup> and Antarctic krill<sup>39,40</sup> Fe fertilizing rates, estimated in this study from a literature model<sup>10</sup> (see Methods). Carbon assimilation associated with primary production (2 g C m<sup>-2</sup> day<sup>-1</sup>, here expressed as annual assimilated C)<sup>41</sup> refers to the Southern Ocean coastal areas, such as polynyas, marginal ice zones and the continental shelf. Despite the lower penguin Fe contribution compared to that from baleen whales, this number accounts for only one species in contrast with the Mysticeti genus, with Chinstrap numbers being 50% lower than four decades ago<sup>15</sup>, when whales were already subjected to whaling pressure. Solid arrows indicate energy fluxes within the ecosystem. Dashed arrows indicate Fe input and primary production stimulation.",
+    "footnote": [],
+    "bbox": [],
+    "page_idx": 6
+  },
+  {
+    "type": "image",
+    "img_path": "images/Extended_Data_Figure_2.jpg",
+    "caption": "Extended Data Fig. 2 | Photographic available data of Vapour Col colony and Regions of Interest for deep learning model",
+    "footnote": [],
+    "bbox": [],
+    "page_idx": 8
+  }
+]

preprint/preprint__01f0e3bff18307bf43bc81d95d1730c9c3bec79b418b6773f6d9e518508e2289/preprint__01f0e3bff18307bf43bc81d95d1730c9c3bec79b418b6773f6d9e518508e2289.mmd

@@ -0,0 +1,439 @@
+
+# The contribution of penguin guano to the Southern Ocean iron pool  
+
+Oleg Korolev ( \(\boxed{\infty}\) o.belyaev@csic.es) Institute of Marine Sciences of Andalusia, Spanish National Research Council https://orcid.org/0000- 0002- 8851- 2996  
+
+Erica Sparaventi Institute of Marine Sciences of Andalusia, Spanish National Research Council Gabriel Navarro Consejo Superior de Investigaciones Cientificas (CSIC) https://orcid.org/0000- 0002- 8919- 0060  
+
+Article  
+
+Keywords:  
+
+Posted Date: July 22nd, 2022  
+
+DOI: https://doi.org/10.21203/rs.3.rs- 1804836/v1  
+
+License: © \(\circledast\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License
+
+<--- Page Split --->
+
+## The contribution of penguin guano to the Southern Ocean iron pool  
+
+Oleg B. Korolev\*,1, Erica Sparaventi1, Gabriel Navarro1, Araceli Rodríguez-Romero2, Antonio Tovar- Sánchez1  
+
+1 Department of Ecology and Coastal Management, Institute of Marine Sciences of Andalusia  
+
+(ICMAN), Spanish National Research Council (CSIC), 11510 Puerto Real, Cádiz, Spain.  
+
+2 Department of Analytical Chemistry. Faculty of Marine and Environmental Sciences, Marine  
+
+Research Institute (INMAR), University of Cádiz, Campus Río San Pedro, 11510 Puerto Real,  
+
+Cádiz, Spain.  
+
+Iron plays a crucial role in the high- nutrient, low- chlorophyll Southern Ocean regions, promoting phytoplankton growth and enhancing atmospheric carbon sequestration1- 5. In this area, the recycling of biogenic iron is partially driven by the iron- rich Antarctic krill (Euphausia superba), and baleen whale species as one of their main predators6- 10. However, although oceanic iron fertilization by Antarctic seabirds has been addressed by previous studies11- 13, penguins have received limited attention, despite their representing the largest seabird biomass in the southern polar region14. Here, we use breeding site guano volumes estimated from drone images and cross- validated with deep learning- powered penguin census, in combination with guano chemical composition to assess the relative iron export to the Antarctic waters from one the most abundant penguin species, the Chinstrap penguin (Pygoscelis antarcticus). Our results show that these seabirds are a relevant but previously ignored contributor to the iron remobilization pool in the Southern Ocean. With an average guano concentration of 3 mg iron g- 1, we estimate that the Chinstrap penguin population is recycling 514 tonnes iron yr- 1. The current iron contribution represents half of the input these penguins were able to recycle four decades ago, as they have declined in more than 50% since then15. Our results evidence the need for in- depth
+
+<--- Page Split --->
+
+understanding how these seabirds' population dynamics impact the surrounding Antarctic marine ecosystems.  
+
+Areas of the Southern Ocean are considered high- nutrient, low- chlorophyll regions<sup>1</sup>, where photosynthetic biota is limited by iron availability<sup>16- 19</sup>. Despite that limitation, this oceanic region is one of the major sinks of anthropogenic carbon dioxide<sup>2,4,5</sup>, removing about \(5.5 \times 10^{11}\) kg C from the pelagic zone each year<sup>20- 24</sup>. Contrary to the well understood physical processes that mediate carbon acquisition, the biological pump remains poorly described due to the high spatial and temporal availability of mechanisms such as uptake, remineralization, or export of key elements. However, these Fe inputs are known to have physical source- like contribution magnitude for Fe enrichment of the southern pelagic regions<sup>6,7,25</sup>.  
+
+Surface exchange of recycled biogenic Fe has previously shown to be driven by Antarctic krill (Euphausia superba), a key Fe rich species in the Southern Ocean ecosystem<sup>6,7</sup>, and several whale species mainly via excretion products<sup>6,8,10,26</sup>. However, information about the role of seabirds, especially penguins, as a source of Fe, remains limited. Unlike whales, that roam free in northern latitude waters throughout their life stages, Antarctic penguins, especially the Pygoscelis genus (i.e., Chinstrap, Adélie and Papua), are restricted to the Southern Ocean<sup>27</sup>; they feed almost exclusively (> 90% of their diet) on krill<sup>28,29</sup>; and this feeding takes place within the upper 100 m of the water column, where primary production and photosynthetic fixation of carbon by phytoplankton occurs<sup>30</sup>. Furthermore, their significant abundance leads to the accumulation of high densities of Fe- containing guano in the coastal ecosystem, reaching concentrations of up to \(3 \times 10^{3}\) times higher over the background in the waters surrounding the rookeries<sup>31</sup>. Therefore, penguins play a fundamental role in the Southern Ocean through Fe recycling<sup>11- 13,32</sup>. Their role in the local ecosystem is even more relevant when overall declining trends in specific Antarctic penguin species populations have been shown during the last four decades<sup>15,33- 35</sup>.
+
+<--- Page Split --->
+
+To analyse the relative Fe input of these seabirds into the marine environment, we have applied a holistic approach which addresses both penguin population dynamics and the amount of Fe exported through their excretion products in relation to penguin biomass. Using Deception Island's Vapour Col rookery (Fig. 1a, d, e) as a case study, we focused on one of the most abundant Antarctic penguin species, the Chinstrap penguin (Cp) (Pygoscelis antarcticus) (Fig. 1b). We assessed the Cp relative population status by collecting drone images of the breeding site, data which served as an input for a deep learning model trained to detect penguin individuals (Fig. 1c). With unsupervised classification from drone imagery<sup>36</sup> (Fig. 1d), we estimated the amount of guano present in the colony, which we cross- validated with the Cp census- based guano accumulation (Fig. 1e). We quantified the Fe contents of guano and calculated the relative guano discharge, based on the Fe concentrations measured in the waters surrounding the rookery and the areas away from it. Subsequently, we extrapolated the input of Fe for the global population of the Cp and estimated the magnitude of its contribution to yearly carbon accumulation in the Southern Ocean. These calculations serve as an empirical basis to highlight the role of these penguins as one of the major vertebrate mediators in the pelagic Fe recycling system, prompting a possible ecosystem imbalance caused by their significant population decrease.
+
+<--- Page Split --->
+![](images/Figure_1.jpg)
+
+<center>Fig. 1 | Study site and double approach for iron export estimation. a). Sentinel-2 10 m resolution image of Deception Island (South Shetland Islands) – Vapour Col (magenta square), where the data for this study were collected form, is the second largest chinstrap penguin rookery on Deception Island, gathering more than 19,000 breeding pairs<sup>37</sup>. b). Chinstrap penguins feeding on krill, which is their main food source<sup>28</sup>. c). Chinstrap population census estimated by the deep learning model. Despite certain limitations of the inference over areas with Cp-like artifacts, such as the rocky coastal areas, the model succeeded in accurately detecting the individuals in relevant, high density Cp areas (Extended Data in supplementary Table 1). d) Vapour Col photographically available extension. The northern tip of Vapour Col is outlined in yellow and correspond to the terrain of the panel e. Blue and green patches correspond to the output of the non-supervised classification for guano areas<sup>36</sup>. e). Northern tip of Vapour Col – penguin density is shown as a heatmap, the brightest areas correspond to the breeding zones where the penguins are tightly clustered, matching with the guano accumulation zones. </center>  
+
+## Results and discussion  
+
+## Water iron enrichment through penguin guano  
+
+To evaluate the magnitude of the Cp contribution to the Fe pool in the surrounding waters, we analysed its relative accumulation in the colony and further discharge to the seawater. We examined three guano
+
+<--- Page Split --->
+
+sample types collected on: ice, soil and from a specific collector designed for sampling uncontaminated soil- free guano (see Methods). Among all guano types, Fe exhibited the highest concentration of 3.0 \(\pm 1.4\mathrm{mg}\mathrm{g}^{- 1}\) guano, ranging from \(2.3 - 4.0\mathrm{mg}\mathrm{g}^{- 1}\) in the guano collector, with a peak reaching \(5.8\mathrm{mg}\) \(\mathrm{g}^{- 1}\) in the soil, likely reflecting a certain degree of influence from soil sources. These Fe concentrations of guano samples are consistent with the Cp main food source, the Antarctic krill, where the high content of Fe might reflect the geothermal activity of the region<sup>7</sup>. To assess the amount of guano excreted in the colony and subsequently obtain its Fe content, we volumetrically characterized, using unsupervised classification<sup>36</sup>, the nesting areas where the majority of the penguins are clustered, i.e. guano- rich zones (GRZs, Fig. 1d, e), resulting in 165.5 tonnes of dry weight guano containing \(500\pm 241\mathrm{kg}\) Fe (see Methods). To cross- check this estimation, we examined an individual- level approximation<sup>32</sup> using the Cp census as estimated by the deep learning model to produce an output based on the mean excretion rate of each individual per day and its Fe content. We found that according to the \(16,725\pm 907\) penguins censored by the model, a total of \(169.4\mathrm{t}\) of dry weight guano can be expected in the breeding site considering 120 days as breeding season duration, containing \(512\pm 246\mathrm{kg}\) Fe (see Methods). The survey (February 8, 2021) coincided with the end of the breeding season, when penguins tend to leave the colony and therefore the deep learning census corresponded to the lower limit of the whole nesting period. Subsequently, considering the Fe concentration (0.324 mg L<sup>- 1</sup>) in the coastal water at the specific time of study, we obtained an approximate Fe content of 26 kg in \(8\times 10^{4}\mathrm{m}^{3}\) of water under the influence of the Cp plume (Fig. 2a, b). Thus, considering the accumulated amount of Fe in the GRZs of the Vapour Col colony using the mean between GRZ volumes and deep learning census, at a certain time, \(6.8\pm 4.2\%\) of the Fe content is drained into the surrounding waters.  
+
+In order to quantify the fraction of guano Fe that reaches the Antarctic waters, it is necessary to consider multiple inherent climatic, terrain and biological factors. We consider that guano accumulation and washing responds to a dynamic interaction between factors, including penguin movement, in- situ snow
+
+<--- Page Split --->
+
+accumulation or melting runoff from surrounding elevated terrain. Therefore, guano can be stored in and released from the colony at any given time, depending on specific environmental conditions, consequently varying the amount of Fe released. Furthermore, terrain geomorphology highly conditions the flow of guano towards the ocean, with higher rates in steep terrains such as Vapour Col. Although we estimated close to \(7\%\) Fe discharge from the colony (see Methods), this fraction can be significantly higher if areas of guano accumulation are located near terrain with sufficient inclination and exposed to climate events. For instance, if we consider the northern tip of the Vapour Col colony, the only contributor to the Fe pool in the surrounding waters, as an example of a steep terrain, we estimate that the amount of Fe released from this zone oscillates between 20 and \(30\%\) of the total accumulated Fe. Furthermore, considering the seasonal stage at the time of the survey, our guano discharge estimates probably represent a minimal fraction of what is actually being released during the breeding peak.  
+
+![](images/Figure_3.jpg)
+
+
+<--- Page Split --->
+
+Fig. 2 | Deception Island coastal high turbidity waters and considered volume for iron discharge estimation from Vapour Col. a). Sentinel- 2 3- D composition of Deception Island, where areas of high turbidity can be seen flowing westwards, next to the Vapour Col colony. b). Volume section of coastal water under the influence of the penguin colony. The white dot is the surface water sampling location (62°59'29" S, 60°43'32" W) for Fe analysis. To estimate the relative discharge of Fe to the Antarctic waters we consider a calculated seawater volume of \(8 \times 10^{4} \mathrm{~m}^{3}\) with a \(2 \mathrm{~m}\) average depth near-shore region of \(4 \times 10^{4} \mathrm{~m}^{2}\) (white cuboid). TP: Turbidity Plumes originating from Vapour Col breeding site. VC: Vapour Col colony.  
+
+## Chinstrap penguin global iron input  
+
+Iron raises special attention when its concentration in the Cp guano is compared to other krill- feeding Antarctic species. For example, in baleen whales, who currently recycle up to \(1.2 \times 10^{3}\) tonnes Fe \(\mathrm{yr}^{- 1}\) (38), Fe content in depositions range between \(145.9 \pm 133.7 \mathrm{mg} \mathrm{kg}^{- 1}\) (42), being at its most more than ten times lower than the Fe content in Cp guano found in the present study. However, penguin populations potentially gain importance because of the number and distribution of their individuals, making them important ocean fertilizers. It is complex to evaluate the Fe contribution of the global population of Cp due to its seasonal life cycle; however, once the estimate of the relative Fe input from the breeding season period is assessed, we suggest that this input corresponds to a minimal fraction of what in reality is exported to the ocean when the breeding season is over, and the population migrates to the pack ice. If we assume the same guano production rates and that the deposited guano on the ice shelves would be almost entirely washed into the ocean during the summer season, we estimate that the current population of Cp is able to produce up to \(514 \pm 248\) tonnes Fe \(\mathrm{yr}^{- 1}\) (see Methods) (Fig. 3), considering a minimum of \(10\%\) average efficiency of Fe export during the breeding season. Therefore, this Fe input to the Southern Ocean represents close to half of the input produced by baleen whales, which highlights the evident environmental relevance of Cp populations.  
+
+We further explored the yearly net primary productivity (NPP) that is stimulated by Cp input of Fe in the Antarctic waters. Using a Fe recycling model proposed for baleen whales (10), we first calculated the bioavailable Fe, to then estimate total NPP in \(\mathrm{g} \mathrm{C} \mathrm{m}^{- 2} \mathrm{yr}^{- 1}\) for the extension of the Southern Ocean \((2 \times 10^{7} \mathrm{~km}^{2})\) . We found that considering the 514 tonnes Fe \(\mathrm{yr}^{- 1}\) exported by the global Cp population,
+
+<--- Page Split --->
+![](images/Extended_Data_Figure_2.jpg)
+
+<center>Fig. 3 | Iron recycling and net primary production stimulation in the Southern Ocean pelagic areas. A perspective of Chinstrap penguins relative Fe input and net primary production stimulation compared to post-whaling Mysticeti<sup>38</sup> and Antarctic krill<sup>39,40</sup> Fe fertilizing rates, estimated in this study from a literature model<sup>10</sup> (see Methods). Carbon assimilation associated with primary production (2 g C m<sup>-2</sup> day<sup>-1</sup>, here expressed as annual assimilated C)<sup>41</sup> refers to the Southern Ocean coastal areas, such as polynyas, marginal ice zones and the continental shelf. Despite the lower penguin Fe contribution compared to that from baleen whales, this number accounts for only one species in contrast with the Mysticeti genus, with Chinstrap numbers being 50% lower than four decades ago<sup>15</sup>, when whales were already subjected to whaling pressure. Solid arrows indicate energy fluxes within the ecosystem. Dashed arrows indicate Fe input and primary production stimulation. </center>  
+
+Cp can be contributing with \(0.11 \pm 0.05\) to \(1.04 \pm 0.5 \mathrm{g C m^{- 2} y r^{- 1}}\) , with an estimated base rate of 0.46 \(\pm 0.22 \mathrm{g C m^{- 2} y r^{- 1}}\) (see Methods). These values are of the same magnitude range as the NPP driven by the entire Mysticeti genus' Fe input<sup>38</sup>, which currently represents an estimated average of \(1.08 \mathrm{g C m^{- 2} y r^{- 1}}\) NPP stimulation, highlighting the significance of Cp when comparing single to multi- species Fe export to the Antarctic waters.  
+
+Although fluctuating patterns are appreciated among the Cp populations, mainly due to the complexity of spatial distribution, an overall decrease in their numbers is evident, especially in the South Shetland Islands, where various studies showed significant drops in the number of individuals<sup>33,34,43,44</sup>. Similar to the rest of the regions inhabited by Cp, Vapour Col presented a similar negative fluctuation, where a decrease in population of 36% between 1991 and 2008 has been reported<sup>35</sup>. This decrease of Cp
+
+<--- Page Split --->
+
+numbers however is common to almost all the breeding sites on the Antarctic Peninsula, possibly leading to a similar situation as that experienced by baleen whales, where its Fe recycling is now up to ten times less than in the pre- whaling period<sup>42</sup>. Overall, a global decline of >50% in Cp numbers has been reported since the 1980s<sup>15</sup>, therefore suggesting that only four decades ago, Fe recycling by these seabirds had approximately the same magnitude as the one produced by baleen whales subjected to current whaling pressure.  
+
+## Conclusion  
+
+Here we show that the Cp can be considered one of the biological pillars upon which Fe is being recycled in the Southern Ocean. Furthermore, if we include the Adélie penguin (Pygoscelis adeliae) as the other widely distributed Pygoscelid species, with increasing population numbers reaching \(10^{7}\) individuals<sup>45</sup> with a mainly krill- based diet<sup>29</sup>, Pygoscelid Fe input to the Southern Ocean could potentially reach the amount that baleen whales are currently recycling. The current maximum guano Fe concentration values known for this species<sup>32</sup> are an order of magnitude lower than those found in this study for the Cp. However, as measured here, these Fe concentrations could be even greater, leading to a significance increase of recycled Fe by these seabirds.  
+
+The effect that Cp have as ocean fertilizers has been decreasing as a consequence of rapid populational decline. This general negative population trend in the Antarctic Peninsula and, by default, in the rest of their breeding sites, can be related to the environmental changes faced by the Southern Ocean region mainly due to climate change effects<sup>46</sup>. However, several hypotheses are currently considered as a possible explanation of the decline of Cp populations, arguing possible krill biomass related causes, where a decoupling between krill concentrations and nonbreeding winter season is responsible for the dropping Cp numbers<sup>15</sup>. Thus, a deeper understanding of Cp life and prey- consumption cycles would help improve their conservation status and their impact on Fe recycling in the Antarctic marine ecosystem.
+
+<--- Page Split --->
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+205 6. Tovar-Sanchez, A., Duarte, C. M., Hernández-León, S. & Sañudo-Wilhelmy, S. A. Krill as a central node for iron cycling in the Southern Ocean. Geophys. Res. Lett. 32, L11601 (2007).  
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+212 9. Ratnarajah, L., Bowie, A. R., Lannuzel, D., Meiners, K. M. & Nicol, S. The Biogeochemical Role of Baleen Whales and Krill in Southern Ocean Nutrient Cycling. PLoS ONE 9, e114067 (2014).  
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+214 10. Ratnarajah, L. et al. A preliminary model of iron fertilisation by baleen whales and Antarctic krill in the Southern Ocean: Sensitivity of primary productivity estimates to parameter uncertainty. Ecological Modelling 320, 203–212 (2016).  
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+217 11. Wing, S. et al. Seabirds and marine mammals redistribute bioavailable iron in the Southern Ocean. Mar. Ecol. Prog. Ser. 510, 1–13 (2014).  
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+219 12. Shatova, O., Wing, S. R., Gault-Ringold, M., Wing, L. & Hoffmann, L. J. Seabird guano enhances phytoplankton production in the Southern Ocean. Journal of Experimental Marine Biology and Ecology 483, 74–87 (2016).  
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+18. de Baar, H. J. W. Synthesis of iron fertilization experiments: From the Iron Age in the Age of Enlightenment. J. Geophys. Res. 110, C09S16 (2005).  
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+23. Hauck, J. et al. On the Southern Ocean CO₂ uptake and the role of the biological carbon pump in the 21st century. Global Biogeochem. Cycles 29, 1451-1470 (2015).  
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+24. Long, M. C. et al. Strong Southern Ocean carbon uptake evident in airborne observations. Science 374, 1275-1280 (2021).  
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+25. Ratnarajah, L., Nicol, S. & Bowie, A. R. Pelagic Iron Recycling in the Southern Ocean: Exploring the Contribution of Marine Animals. Front. Mar. Sci. 5, 109 (2018).  
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+26. Schmidt, K. et al. Seabed foraging by Antarctic krill: Implications for stock assessment, bentho-pelagic coupling, and the vertical transfer of iron. Limnol. Oceanogr. 56, 1411-1428 (2011).  
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+27. Ballard, G. et al. Responding to climate change: Adélie Penguins confront astronomical and ocean boundaries. Ecology 91, 2056-2069 (2010).
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+28. Lynnes, A. S., Reid, K. & Croxall, J. P. Diet and reproductive success of Adélie and chinstrap penguins: linking response of predators to prey population dynamics. Polar Biol 27, (2004).  
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+29. Juáres, M. A. et al. Diet of Adélie penguins (Pygoscelis adeliae) at Stranger Point (25 de Mayo/King George Island, Antarctica) over a 13-year period (2003–2015). Polar Biol 41, 303–311 (2018).  
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+30. Mahadevan, A. The Impact of Submesoscale Physics on Primary Productivity of Plankton. Annu. Rev. Mar. Sci. 8, 161–184 (2016).  
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+31. Sparaventi, E., Rodríguez-Romero, A., Navarro, G. & Tovar-Sánchez, A. A Novel Automatic Water Autosampler Operated From UAVs for Determining Dissolved Trace Elements. Front. Mar. Sci. 9, 879953 (2022).  
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+32. Sparaventi, E., Rodríguez-Romero, A., Barbosa, A., Ramajo, L. & Tovar-Sánchez, A. Trace elements in Antarctic penguins and the potential role of guano as source of recycled metals in the Southern Ocean. Chemosphere 285, 131423 (2021).  
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+33. Ciaputa, P. & Sierakowski, K. Long-term population changes of Adelie, chinstrap, and gentoo penguins in the regions of SSSI No.8 and SSSI No.34, King George Island, Antarctica. Polish Polar Research 20, 355–365 (1999).  
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+34. Sander, M., Balbão, T. C., Polito, M. J., Costa, E. S. & Carneiro, A. P. B. Recent decrease in chinstrap penguin (Pygoscelis antarctica) populations at two of Admiralty Bay’s islets on King George Island, South Shetland Islands, Antarctica. Polar Biol 30, 659–661 (2007).  
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+35. Barbosa, A., Benzal, J., De León, A. & Moreno, J. Population decline of chinstrap penguins (Pygoscelis antarctica) on Deception Island, South Shetlands, Antarctica. Polar Biol 35, 1453–1457 (2012).  
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+36. Tovar-Sánchez, A., Román, A., Roque-Atienza, D. & Navarro, G. Applications of unmanned aerial vehicles in Antarctic environmental research. Sci Rep 11, 21717 (2021).  
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+37. Naveen, R., Lynch, H. J., Forrest, S., Mueller, T. & Polito, M. First direct, site-wide penguin survey at Deception Island, Antarctica, suggests significant declines in breeding chinstrap penguins. Polar Biol (2012) doi:10.1007/s00300-012-1230-3.  
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+38. Savoca, M. S. et al. Baleen whale prey consumption based on high-resolution foraging measurements. Nature 599, 85–90 (2021).  
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+39. Maldonado, M. T., Surma, S. & Pakhomov, E. A. Southern Ocean biological iron cycling in the prewhaling and present ecosystems. Phil. Trans. R. Soc. A. 374, 20150292 (2016).  
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+40. Böckmann, S. et al. Salp fecal pellets release more bioavailable iron to Southern Ocean phytoplankton than krill fecal pellets. Current Biology 31, 2737-2746.e3 (2021).
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+<--- Page Split --->
+
+41. Arrigo, K. R., van Dijken, G. L. & Bushinsky, S. Primary production in the Southern Ocean, 1997–2006. J. Geophys. Res. 113, C08004 (2008).  
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+42. Nicol, S. et al. Southern Ocean iron fertilization by baleen whales and Antarctic krill: Whales, Antarctic krill and iron fertilization. Fish and Fisheries 11, 203–209 (2010).  
+
+43. Lynch, H. J., Naveen, R. & Fagan, W. Censuses of penguin, Blue-eyed Shag Phalacrocorax atriceps and Southern Giant Petrel Macronectes giganteus populations on the Antarctic Peninsula, 2001-2007. Marine Ornithology 36, 83–97 (2008).  
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+44. Lynch, H. J. et al. In stark contrast to widespread declines along the Scotia Arc, a survey of the South Sandwich Islands finds a robust seabird community. Polar Biol 39, 1615–1625 (2016).  
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+45. BirdLife International (2022) Species factsheet: Pygoscelis adeliae. BirdLife International http://www.birdlife.org/ (2022).  
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+46. Forcada, J. & Trathan, P. N. Penguin responses to climate change in the Southern Ocean. Global Change Biology 15, 1618–1630 (2009).  
+
+## Methods  
+
+## Guano sample collection and chemical analysis  
+
+During January and February, 2021, fresh guano samples were collected at the Vapour Col (VC) breeding site. The samples have been differentiated according to the collection substrate, since three types of samples have been analysed: soil, ice and guano collected in a “trap”. The traps consist of a polyethylene plastic plate with a PVC frame (40×30 cm long), placed in the colony 24 hours prior to sampling, to guarantee the collection of fresh samples, avoiding possible contamination from the soil. Fresh guano samples were collected in VC \((n = 23)\) , and placed manually using a plastic spoon in polyethylene bags or in acid-cleaned vials and stored frozen at - 20 °C until analysis. Fe was later extracted with a microwave acid digestion system (MARS-V, CEM) in accordance with the SW- 846 EPA Method 3051A⁴⁷. Approximately 0.2 g of guano sample were digested with 10 mL of nitric acid (65%, Suprapur quality) in Teflon vessels, in triplicate. After digestion, Fe contents were analysed (see Fe concentrations in Extended Data Fig. 1) using an inductively coupled plasma-mass spectrometry
+
+<--- Page Split --->
+
+(ICP- MS, iCAP Thermo). Blanks and certified material for digestion and analysis were treated like the samples. The accuracy of the analytical methods was checked using a certified reference material (Lobster hepatopancreas TORT- 2), with Fe concentration of \(109.0 \pm 5.3 \mu \mathrm{g} \mathrm{g}^{- 1}\) , and a recovery of 103.8 \(\pm 5.0\%\) . The Detection Limit calculated as three times the standard deviation of the blank values was \(0.43 \mu \mathrm{g} \mathrm{g}^{- 1}\) .  
+
+## Identification of guano-rich zones  
+
+To identify the regions of the colony that have the greatest accumulation of guano, i.e. GRZ, the result of a non- supervised classification<sup>36</sup> over the VC colony terrain was used (Fig. 1d). Then, the total area of the GRZ predicted by the classification was calculated, representing a time- specific guano layer within VC. The area of these regions served as a baseline for subsequent guano volume and Cp population estimation in VC, as it is considered that the greatest percentage of the Fe export is derived from these zones.  
+
+## Chinstrap penguin deep learning-powered census  
+
+On February 8, 2021, Deception Island Cp rookeries were photographed during the PiMetAn Project XXXIV Spanish Antarctic campaign<sup>48</sup> using unmanned aerial vehicles (UAV), commonly known as drones. The photographic dataset used here does not cover the entire extension of VC but only the northern tip of it (NTvc) (Extended Data Fig. 2a), and the rest of VC was photographed at a height of 150 m (Extended Data Fig. 2a), thus not suitable for penguin detection due to their small body size. Subsequently, 377 RGB images of NTvc were selected with a resolution of 3,000x4,000 pixels, captured from a flight height of 30 m and 4.9 m s<sup>- 1</sup>. The photographic data has been obtained using the DJI Mavic 2 Pro UAV equipped with an RGB sensor (Hasselblad Camera) and flights were configured using DJI's Ground Station Pro photogrammetric flight planning software. The 377 photographs were used to generate an orthomosaic using Agisoft Metashape photogrammetric software.  
+
+The model Faster R- CNN<sup>49</sup> (FRCNN) was used to perform object detection tasks. In the present research FRCNN with ResNet- 101 backbone. Training and evaluation tasks were performed using the
+
+<--- Page Split --->
+
+TensorFlow 2.0 machine learning platform by Google. The model performance was later evaluated by analysing the resulting detection metrics of three regions of interest (RoI) selected within the orthomosaic: RoI 1 and RoI 3, corresponding to a non- coastal area and RoI 2, corresponding to a representative region of a coastal area (Extended Data Fig. 2b, Table 1).  
+
+Deep learning- powered census set the number of Cp in \(\mathrm{NT_{VC}}\) 's GRZ at \(2,265 \pm 159\) . The number of individuals located in outer zones (OZ, i.e. those that are not GRZ) of \(\mathrm{NT_{VC}}\) was estimated at \(1,853 \pm 130\) . The previous data were used to calculate the density of penguins for both zones, obtaining \(0.52 \pm 0.03\) ind. \(\mathrm{m}^{- 2}\) for the GRZ and \(0.028 \pm 0.01\) ind. \(\mathrm{m}^{- 2}\) for the OZ. Subsequently, the density extrapolation for the highly overflown ( \(150 \mathrm{~m}\) ) rest of VC was carried out, using the densities obtained for \(\mathrm{NT_{VC}}\) and applying them to these new GRZ and OZ. A final number of \(7,611 \pm 439\) individuals were found grouped in GRZs and \(4,996 \pm 179\) in the OZs. Finally, the total number of Cp in the entire VC colony was obtained by adding the output for both zones (Extended Data Table 2).  
+
+## Vapour Col Fe accumulation and export dynamics  
+
+Two approaches were explored to quantitatively assess the total Fe abundance at a given time in the studied region of the colony (see calculations in Extended Data Table 3). Firstly, the GRZ volumes were calculated to obtain an estimate of the amount of Cp deposition accumulated in the area. Determining an exact thickness of the guano layer for volume calculation was challenging, mainly due to the irregularity of the terrain, varying substrates, accumulation and discharge dynamics, and the scarcity of supporting references for this specific region. As a result, only a thin layer of \(2 \mathrm{~cm}\) as a representative thickness of fresh guano accumulation was taken into account. Then, the volume was used to estimate the total Fe content for each of GRZ, following the next equation:  
+
+\[g_{Fe} = w[Fe]a_{GRZ}t d_{guano}\]  
+
+where \(g_{Fe}\) is the total Fe in \(\mathrm{g}\) , \(w\) is the dry weight guano fraction, which for seabirds has been estimated at \(0.4 \mathrm{~g}^{50}\) ; \([Fe]\) is the Fe concentration in \(\mu \mathrm{g}\) per \(\mathrm{g}\) of dry guano, \(a_{GRZ}\) is the area in \(\mathrm{m}^2\) as determined
+
+<--- Page Split --->
+
+using the non- supervised classification<sup>36</sup>, \(t\) is the guano layer thickness and \(d_{\text{guano}}\) is the density of the guano obtained in Vapour Col (1,088.6 kg m<sup>- 3</sup>).  
+
+To cross- check the output of the Fe content estimation using the GRZ volumes, an individual- level approach presented by Sparaventi et al.<sup>32</sup>, was used according to the following equation:  
+
+\[g_{Fe} = C_p[Fe]e d\]  
+
+where \(C_p\) is the deep learning- based chinstrap census, \(e\) is the penguin excretion as g of dry guano per day, estimated at \(84.4 \mathrm{g}^{51}\) ; \(d\) is the number of days a certain number of penguins remain in the area. As the breeding season begins when \(C_p\) arrive at their colonies in early October through to November until the abandon them at the end of February, 120 days were used as the guano production period in the above equation. Comparatively, the total number of \(C_p\) present in the colony and not only in its GRZ was considered, as it was assumed that each individual must spend time within the nesting areas, thus contributing to guano accumulation.  
+
+## Primary production from iron fertilization  
+
+The NPP stimulated by CP Fe input in the Southern Ocean was calculated using a model for Fe cycling proposed by Ratnarajah et al.<sup>10</sup>. In this way, varying Fe retention in the photic zone and phytoplankton Fe assimilation between assumed fractions 0.25 (min), 0.5 (base) and 0.75 (max.) and considering a 3 \(\mu \mathrm{mol Fe}:\mathrm{mol C}\) phytoplankton ratio<sup>52,53</sup>, an estimate of NPP was obtained for the extension of the Southern Ocean (South of \(60^{\circ}\mathrm{S}\) ). In the same way, to obtain a comparative perspective, NPP was calculated for the current Fe input estimation for baleen whales<sup>38</sup> and krill<sup>40</sup> (see calculations in Extended Data Table 4).  
+
+## Reporting summary  
+
+Extra information about this study design is available on the Nature Research Reporting Summary file attached to this article.
+
+<--- Page Split --->
+
+## Data availability  
+
+The datasets generated during the current study and the code used to reproduce the results are available on https://github.com/obkorolev/penguin_iron_paper.  
+
+47. Lyman, W. J., Glazer, A. E., Ong, J. H. & Coons, S. F. Overview of sediment quality in the United States. Final report. (1987).  
+
+48. Navarro, G., Román, A., Roque, D. & Tovar-Sánchez, A. UAV imagery in Deception Island (Antarctica): PiMetAn Antarctic campaign 2020-2021. (2021) doi:10.20350/DIGITALCSIC/13850.  
+
+49. Ren, S., He, K., Girshick, R. & Sun, J. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. (2015) doi:10.48550/ARXIV.1506.01497.  
+
+50. De La Peña-Lastra, S. Seabird droppings: Effects on a global and local level. Science of The Total Environment 754, 142148 (2021).  
+
+51. Sun, L. & Xie, Z. Relics: Penguin Population Programs. Science Progress 84, 31-44 (2001).  
+
+52. Strzepek, R. F., Maldonado, M. T., Hunter, K. A., Frew, R. D. & Boyd, P. W. Adaptive strategies by Southern Ocean phytoplankton to lessen iron limitation: Uptake of organically complexed iron and reduced cellular iron requirements. Limnol. Oceanogr. 56, 1983-2002 (2011).  
+
+53. Twining, B. S., Baines, S. B. & Fisher, N. S. Element stoichiometries of individual plankton cells collected during the Southern Ocean Iron Experiment (SOFeX). Limnol. Oceanogr. 49, 2115-2128 (2004).  
+
+## Acknowledgements  
+
+This research has been funded by the Spanish Government projects PIMETAN (ref. RTI2018- 098048- B- I00), EQC2018- 004275- P and EQC2019- 005721- P. E. Sparaventi is supported by the Spanish FPI grant (Ref: PRE2019- 089679) and A. Rodríguez- Romero is supported by the Spanish grant, Juan de la Cierva Incorporación (Ref: IJC2018- 037545- I). We thank to D. Roque for supporting flight operations. This research is part of the POLARCSIC research initiative. Permissions to work and collect guano samples in the study area were granted by the Spanish Polar Committee. We thank the military staff of the Spanish Antarctic Base Gabriel de Castilla, the crew of the Sarmiento de Gamboa oceanographic vessel and the Marine Technology Unit (UTM- CSIC) for their logistic support, for making the XXXIV Spanish Antarctic campaign possible. We thank M. B. Dunbar, from the Institute of Marine Science of Andalusia, Spanish National Research Council
+
+<--- Page Split --->
+
+(ICMAN- CSIC), for the English language revision and M. Roca for advice on the quality of the illustrations.  
+
+## Author contributions  
+
+A.T.- S., A.R. and G.B. designed the research; A.T.- S., and G.B. participated in the Antarctic campaign flying unoccupied aerial vehicles and collecting data and samples; O.B.K. and E.S. performed the chemical analysis, aerial data processing and deep learning model implementation; O.B.K, E.S, G.N, A.R. and A.T.- S. wrote the manuscript.  
+
+## Inclusion & ethics  
+
+This study was designed and developed by a multicultural research team, with gender, nationality and religion diversity, where all members and external participants were treated with equity and respect.  
+
+## Competing interests  
+
+The authors declare no competing interests.  
+
+Correspondence and requests for materials should be addressed to Oleg B. Korolev.
+
+<--- Page Split --->
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+# Extended Data  
+
+![PLACEHOLDER_19_0]
+  
+
+Extended Data Fig. 1 | Iron concentration in fresh guano samples collected at Vapour Col. Twelve samples from 10 to \(500\mathrm{g}\) directly from the soil, six samples from 35 to \(450\mathrm{g}\) from the ice and five samples from 13 to \(50\mathrm{g}\) from the guano collectors or "traps". a). Soil, ice and trap Fe concentrations. Displaying median, Q1 and Q3, 25th and 75th percentile. b). Mean and standard deviation of \(n = 23\) guano samples, \(3.0 \pm 1.4\mathrm{mg}\mathrm{g}^{- 1}\) guano.
+
+<--- Page Split --->
+![PLACEHOLDER_20_0]
+
+<center>Extended Data Fig. 2 | Photographic available data of Vapour Col colony and Regions of Interest for deep learning model </center>  
+
+evaluation. a). Vapour Col breeding site. Outlined in yellow, is the northern tip of Vapour Col, NTvc, which was captured 30 m above the ground, yielding an optimum quality for penguin detection. The rest of the colony outside NTvc was captured at a height of 150 m. b). Close-up look of NTvc, where the RoIs were selected for deep learning model evaluation. RoI 1 and 3 correspond to a plain, artifact- free area, where the detection had optimum results. RoI 2 correspond to a rocky coast, with many similar features that compromised the performance of the model. However, this coastal area did not account for a large number of Chinstrap individuals in a visual examination of the terrain in the orthomosaic.
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+**Extended Data Table 1 | Detection metric yielded by the deep learning model for Chinstrap census.** For RoI 1 and 2, the ability of the model to find the maximum number of penguin individuals (recall) and the confidence that they were actually penguins (precision) is very high (max=1), mainly due to the relatively plain terrain and scarcity of penguin-like objects. RoI 2 showed good precision but a low recall, due to a relatively high confidence threshold (0.6) and the presence of rocks and white water foam.
+
+<table><tr><td rowspan="2">Precision (P)</td><td>Equation</td><td>RoI 1</td><td>RoI 2</td><td>RoI 3</td></tr><tr><td>true positives / (true positives + false positives)</td><td>0.98</td><td>0.9</td><td>0.97</td></tr><tr><td rowspan="2">Recall (R)</td><td>true positives / (true positives + false<br>negatives)</td><td>0.89</td><td>0.31</td><td>0.92</td></tr><tr><td>\(2\times P\times R/(P+R)\)</td><td>0.93</td><td>0.47</td><td>0.94</td></tr></table>
+
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+520 **Extended Data Table 2 | Detected Chinstrap penguins, areas and penguin densities.** To calculate their abundance in the highly
+
+521 overflow area (150 m) density extrapolation was performed for GRZ and for the outer guano-free zones (OZ).
+
+<table><tr><td></td><td>GRZ(m²)</td><td>OZ(m²)</td><td>Individuals in<br>GRZ</td><td>Individuals in<br>OZ</td><td>Density GRZ<br>(ind.m-2)</td><td>Density OZ<br>(ind.m-2)</td><td>Total individuals</td></tr><tr><td>NTVC</td><td>4,370</td><td>66,216</td><td>2,265±159</td><td>1,853±130</td><td>0.52±0.03</td><td>0.028±0.01</td><td>4,118±289</td></tr><tr><td>Rest of Vapour Col<br>overflow at 150 m</td><td>14,636</td><td>178,400</td><td>7,611±439</td><td>4,996±179</td><td>0.52±0.03</td><td>0.028±0.01</td><td>12,607±618</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>16,725±907</td></tr></table>
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+**Extended Data Table 3 | Calculations of the relative Fe content in Vapour Col.** Two methods were proposed to cross-validate the amount of Fe present in the colony at a certain time. The first accounts for Fe obtained using guano-rich zone volumes from non-supervised classification36, and the second uses the estimated deposition per penguin individual, whose total number is obtained from the deep-learning census of the present study. Referenced parameters50,51.
+
+<table><tr><td>Parameter</td><td>Reference</td><td>Value</td><td>Equation</td><td>Result</td><td>Uncertainty</td></tr><tr><td colspan="6">Fe calculation based on unsupervised guano rich zone classification</td></tr><tr><td>W<br>\([Fe]\)</td><td>50</td><td>0.4</td><td></td><td></td><td></td></tr><tr><td>\(a_{GRZ}\)</td><td>Present study</td><td>3.02 mg g-1</td><td>\(kgFe=0.4\times 3.02\times\)</td><td rowspan="3">500 kg Fe</td><td rowspan="3">268-732 kg Fe</td></tr><tr><td>\(d_{guano}\)</td><td>Present study<br>\(Present\ study\)</td><td>19,006 \(m^{3}\) \(1.0886\times 10^{6}g\ m^{3}\)</td><td>19,006x1.0886x0.02</td></tr><tr><td>t</td><td>Assumed, present study</td><td>0.02 m</td><td></td></tr><tr><td colspan="6">Fe calculation based on deep learning Chinstrap penguin census</td></tr><tr><td>Cp<br>\([Fe]\)</td><td>Present study</td><td>16,725</td><td>\(kgFe=(16,725\times 3,02\)</td><td rowspan="3">512 kg Fe</td><td rowspan="3">266-758 kg Fe</td></tr><tr><td>e</td><td>51</td><td>84.4 g</td><td>x84.4x120)</td></tr><tr><td>d</td><td>Assumed, present study</td><td>120 days</td><td></td></tr></table>
+
+<--- Page Split --->
+
+**Extended Data Table 4 | Calculations for net primary production stimulated by Chinstrap penguin Fe input.** In the same way as calculated here for the Chinstrap penguin, NPP was also obtained for krill, based on ref.39,40 and for baleen whales, based on ref.38. 
+
+Referenced parameters10,38,52,53. * Refers to the selected parameter of retained Fe in the photic zone and the bioavailable Fe for the 
+
+phytoplankton, as the model proposed by Ratnarajah et al.10 assumed values of 0.25 and 0.75 as max. and min. 
+
+<table><tr><td>Step / Parameter</td><td>Reference</td><td>Value</td><td>Equation</td><td>Result</td><td>Uncertainty</td></tr><tr><td>Chinstrap penguin annual Fe input in the Southern Ocean</td><td>Present study</td><td>514 tonnes Fe yr<sup>-1</sup></td><td></td><td></td><td>266 - 762 tonnes Fe yr<sup>-1</sup></td></tr><tr><td>Fraction of Fe retained in the photic zone</td><td>10</td><td>0.25/0.5*/0.75</td><td>5.14x10<sup>8</sup> g Fe yr<sup>-1</sup> x 0.5</td><td>2.57x10<sup>8</sup> g Fe yr<sup>-1</sup> retained in the photic zone</td><td>1.33 - 3.81 x10<sup>8</sup> g Fe yr<sup>-1</sup></td></tr><tr><td>Fraction of Fe bioavailable for phytoplankton</td><td>10</td><td>0.25/0.5*/0.75</td><td>2.57x10<sup>8</sup> g Fe yr<sup>-1</sup> x 0.5</td><td>1.28 x10<sup>8</sup> g Fe yr<sup>-1</sup> bioavailable for phyto.</td><td>0.66 - 1.9 x10<sup>8</sup> g Fe yr<sup>-1</sup></td></tr><tr><td>Fe : C ratio of phytoplankton in the Southern Ocean</td><td>38,52,53</td><td>3 μmol Fe : mol C</td><td></td><td></td><td>1 - 6 μmol Fe : mol C</td></tr><tr><td>Fe molecular weight</td><td></td><td>55.845 g mol<sup>-1</sup></td><td></td><td></td><td></td></tr><tr><td>g of carbon incorporated into phytoplankton biomass (NPP)</td><td>Present study</td><td></td><td>(1.28 x10<sup>8</sup> g Fe yr<sup>-1</sup> x 0.018 mol Fe x 10<sup>6</sup> μmol Fe x mol C x 12.01 g C) / (g Fe x mol Fe x 3 μmol Fe x mol C)</td><td>9.22 x10<sup>12</sup> g C yr<sup>-1</sup> incorporated into phyto.</td><td>4.75 - 13.7 x10<sup>12</sup> g C yr<sup>-1</sup></td></tr><tr><td>Southern Ocean area (South of 60°S)</td><td></td><td>2 x10<sup>13</sup> m<sup>2</sup></td><td></td><td></td><td></td></tr><tr><td>Rate of NPP stimulation by Cp guano input</td><td>Present study</td><td></td><td>(9.22 x10<sup>12</sup> g C yr<sup>-1</sup> incorporated into phyto.) / (2 x10<sup>13</sup> m<sup>2</sup>)</td><td>0.46 g C m<sup>-2</sup> yr<sup>-1</sup> incorporated into phyto.</td><td>0.24 - 0.68 g C m<sup>-2</sup> yr<sup>-1</sup></td></tr></table>
+
+<--- Page Split --->
+
+## Supplementary Files  
+
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+NCOMMS2226937Trs.pdf
+
+<--- Page Split --->

preprint/preprint__01f0e3bff18307bf43bc81d95d1730c9c3bec79b418b6773f6d9e518508e2289/preprint__01f0e3bff18307bf43bc81d95d1730c9c3bec79b418b6773f6d9e518508e2289_det.mmd

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+<|ref|>title<|/ref|><|det|>[[44, 106, 927, 175]]<|/det|>
+# The contribution of penguin guano to the Southern Ocean iron pool  
+
+<|ref|>text<|/ref|><|det|>[[44, 195, 940, 260]]<|/det|>
+Oleg Korolev ( \(\boxed{\infty}\) o.belyaev@csic.es) Institute of Marine Sciences of Andalusia, Spanish National Research Council https://orcid.org/0000- 0002- 8851- 2996  
+
+<|ref|>text<|/ref|><|det|>[[44, 265, 930, 450]]<|/det|>
+Erica Sparaventi Institute of Marine Sciences of Andalusia, Spanish National Research Council Gabriel Navarro Consejo Superior de Investigaciones Cientificas (CSIC) https://orcid.org/0000- 0002- 8919- 0060  
+
+<|ref|>text<|/ref|><|det|>[[44, 487, 101, 504]]<|/det|>
+Article  
+
+<|ref|>text<|/ref|><|det|>[[44, 524, 137, 542]]<|/det|>
+Keywords:  
+
+<|ref|>text<|/ref|><|det|>[[44, 561, 300, 580]]<|/det|>
+Posted Date: July 22nd, 2022  
+
+<|ref|>text<|/ref|><|det|>[[44, 600, 475, 619]]<|/det|>
+DOI: https://doi.org/10.21203/rs.3.rs- 1804836/v1  
+
+<|ref|>text<|/ref|><|det|>[[42, 636, 910, 680]]<|/det|>
+License: © \(\circledast\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[85, 85, 770, 106]]<|/det|>
+## The contribution of penguin guano to the Southern Ocean iron pool  
+
+<|ref|>text<|/ref|><|det|>[[85, 132, 910, 184]]<|/det|>
+Oleg B. Korolev\*,1, Erica Sparaventi1, Gabriel Navarro1, Araceli Rodríguez-Romero2, Antonio Tovar- Sánchez1  
+
+<|ref|>text<|/ref|><|det|>[[85, 207, 853, 228]]<|/det|>
+1 Department of Ecology and Coastal Management, Institute of Marine Sciences of Andalusia  
+
+<|ref|>text<|/ref|><|det|>[[85, 240, 808, 260]]<|/det|>
+(ICMAN), Spanish National Research Council (CSIC), 11510 Puerto Real, Cádiz, Spain.  
+
+<|ref|>text<|/ref|><|det|>[[85, 283, 860, 303]]<|/det|>
+2 Department of Analytical Chemistry. Faculty of Marine and Environmental Sciences, Marine  
+
+<|ref|>text<|/ref|><|det|>[[85, 315, 856, 335]]<|/det|>
+Research Institute (INMAR), University of Cádiz, Campus Río San Pedro, 11510 Puerto Real,  
+
+<|ref|>text<|/ref|><|det|>[[85, 348, 198, 367]]<|/det|>
+Cádiz, Spain.  
+
+<|ref|>text<|/ref|><|det|>[[82, 390, 913, 870]]<|/det|>
+Iron plays a crucial role in the high- nutrient, low- chlorophyll Southern Ocean regions, promoting phytoplankton growth and enhancing atmospheric carbon sequestration1- 5. In this area, the recycling of biogenic iron is partially driven by the iron- rich Antarctic krill (Euphausia superba), and baleen whale species as one of their main predators6- 10. However, although oceanic iron fertilization by Antarctic seabirds has been addressed by previous studies11- 13, penguins have received limited attention, despite their representing the largest seabird biomass in the southern polar region14. Here, we use breeding site guano volumes estimated from drone images and cross- validated with deep learning- powered penguin census, in combination with guano chemical composition to assess the relative iron export to the Antarctic waters from one the most abundant penguin species, the Chinstrap penguin (Pygoscelis antarcticus). Our results show that these seabirds are a relevant but previously ignored contributor to the iron remobilization pool in the Southern Ocean. With an average guano concentration of 3 mg iron g- 1, we estimate that the Chinstrap penguin population is recycling 514 tonnes iron yr- 1. The current iron contribution represents half of the input these penguins were able to recycle four decades ago, as they have declined in more than 50% since then15. Our results evidence the need for in- depth
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[85, 84, 911, 135]]<|/det|>
+understanding how these seabirds' population dynamics impact the surrounding Antarctic marine ecosystems.  
+
+<|ref|>text<|/ref|><|det|>[[83, 156, 913, 406]]<|/det|>
+Areas of the Southern Ocean are considered high- nutrient, low- chlorophyll regions<sup>1</sup>, where photosynthetic biota is limited by iron availability<sup>16- 19</sup>. Despite that limitation, this oceanic region is one of the major sinks of anthropogenic carbon dioxide<sup>2,4,5</sup>, removing about \(5.5 \times 10^{11}\) kg C from the pelagic zone each year<sup>20- 24</sup>. Contrary to the well understood physical processes that mediate carbon acquisition, the biological pump remains poorly described due to the high spatial and temporal availability of mechanisms such as uptake, remineralization, or export of key elements. However, these Fe inputs are known to have physical source- like contribution magnitude for Fe enrichment of the southern pelagic regions<sup>6,7,25</sup>.  
+
+<|ref|>text<|/ref|><|det|>[[83, 416, 913, 867]]<|/det|>
+Surface exchange of recycled biogenic Fe has previously shown to be driven by Antarctic krill (Euphausia superba), a key Fe rich species in the Southern Ocean ecosystem<sup>6,7</sup>, and several whale species mainly via excretion products<sup>6,8,10,26</sup>. However, information about the role of seabirds, especially penguins, as a source of Fe, remains limited. Unlike whales, that roam free in northern latitude waters throughout their life stages, Antarctic penguins, especially the Pygoscelis genus (i.e., Chinstrap, Adélie and Papua), are restricted to the Southern Ocean<sup>27</sup>; they feed almost exclusively (> 90% of their diet) on krill<sup>28,29</sup>; and this feeding takes place within the upper 100 m of the water column, where primary production and photosynthetic fixation of carbon by phytoplankton occurs<sup>30</sup>. Furthermore, their significant abundance leads to the accumulation of high densities of Fe- containing guano in the coastal ecosystem, reaching concentrations of up to \(3 \times 10^{3}\) times higher over the background in the waters surrounding the rookeries<sup>31</sup>. Therefore, penguins play a fundamental role in the Southern Ocean through Fe recycling<sup>11- 13,32</sup>. Their role in the local ecosystem is even more relevant when overall declining trends in specific Antarctic penguin species populations have been shown during the last four decades<sup>15,33- 35</sup>.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[82, 81, 914, 564]]<|/det|>
+To analyse the relative Fe input of these seabirds into the marine environment, we have applied a holistic approach which addresses both penguin population dynamics and the amount of Fe exported through their excretion products in relation to penguin biomass. Using Deception Island's Vapour Col rookery (Fig. 1a, d, e) as a case study, we focused on one of the most abundant Antarctic penguin species, the Chinstrap penguin (Cp) (Pygoscelis antarcticus) (Fig. 1b). We assessed the Cp relative population status by collecting drone images of the breeding site, data which served as an input for a deep learning model trained to detect penguin individuals (Fig. 1c). With unsupervised classification from drone imagery<sup>36</sup> (Fig. 1d), we estimated the amount of guano present in the colony, which we cross- validated with the Cp census- based guano accumulation (Fig. 1e). We quantified the Fe contents of guano and calculated the relative guano discharge, based on the Fe concentrations measured in the waters surrounding the rookery and the areas away from it. Subsequently, we extrapolated the input of Fe for the global population of the Cp and estimated the magnitude of its contribution to yearly carbon accumulation in the Southern Ocean. These calculations serve as an empirical basis to highlight the role of these penguins as one of the major vertebrate mediators in the pelagic Fe recycling system, prompting a possible ecosystem imbalance caused by their significant population decrease.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[100, 100, 904, 468]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[85, 483, 911, 718]]<|/det|>
+<center>Fig. 1 | Study site and double approach for iron export estimation. a). Sentinel-2 10 m resolution image of Deception Island (South Shetland Islands) – Vapour Col (magenta square), where the data for this study were collected form, is the second largest chinstrap penguin rookery on Deception Island, gathering more than 19,000 breeding pairs<sup>37</sup>. b). Chinstrap penguins feeding on krill, which is their main food source<sup>28</sup>. c). Chinstrap population census estimated by the deep learning model. Despite certain limitations of the inference over areas with Cp-like artifacts, such as the rocky coastal areas, the model succeeded in accurately detecting the individuals in relevant, high density Cp areas (Extended Data in supplementary Table 1). d) Vapour Col photographically available extension. The northern tip of Vapour Col is outlined in yellow and correspond to the terrain of the panel e. Blue and green patches correspond to the output of the non-supervised classification for guano areas<sup>36</sup>. e). Northern tip of Vapour Col – penguin density is shown as a heatmap, the brightest areas correspond to the breeding zones where the penguins are tightly clustered, matching with the guano accumulation zones. </center>  
+
+<|ref|>sub_title<|/ref|><|det|>[[88, 768, 315, 787]]<|/det|>
+## Results and discussion  
+
+<|ref|>sub_title<|/ref|><|det|>[[87, 806, 496, 825]]<|/det|>
+## Water iron enrichment through penguin guano  
+
+<|ref|>text<|/ref|><|det|>[[85, 838, 911, 889]]<|/det|>
+To evaluate the magnitude of the Cp contribution to the Fe pool in the surrounding waters, we analysed its relative accumulation in the colony and further discharge to the seawater. We examined three guano
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[82, 78, 914, 800]]<|/det|>
+sample types collected on: ice, soil and from a specific collector designed for sampling uncontaminated soil- free guano (see Methods). Among all guano types, Fe exhibited the highest concentration of 3.0 \(\pm 1.4\mathrm{mg}\mathrm{g}^{- 1}\) guano, ranging from \(2.3 - 4.0\mathrm{mg}\mathrm{g}^{- 1}\) in the guano collector, with a peak reaching \(5.8\mathrm{mg}\) \(\mathrm{g}^{- 1}\) in the soil, likely reflecting a certain degree of influence from soil sources. These Fe concentrations of guano samples are consistent with the Cp main food source, the Antarctic krill, where the high content of Fe might reflect the geothermal activity of the region<sup>7</sup>. To assess the amount of guano excreted in the colony and subsequently obtain its Fe content, we volumetrically characterized, using unsupervised classification<sup>36</sup>, the nesting areas where the majority of the penguins are clustered, i.e. guano- rich zones (GRZs, Fig. 1d, e), resulting in 165.5 tonnes of dry weight guano containing \(500\pm 241\mathrm{kg}\) Fe (see Methods). To cross- check this estimation, we examined an individual- level approximation<sup>32</sup> using the Cp census as estimated by the deep learning model to produce an output based on the mean excretion rate of each individual per day and its Fe content. We found that according to the \(16,725\pm 907\) penguins censored by the model, a total of \(169.4\mathrm{t}\) of dry weight guano can be expected in the breeding site considering 120 days as breeding season duration, containing \(512\pm 246\mathrm{kg}\) Fe (see Methods). The survey (February 8, 2021) coincided with the end of the breeding season, when penguins tend to leave the colony and therefore the deep learning census corresponded to the lower limit of the whole nesting period. Subsequently, considering the Fe concentration (0.324 mg L<sup>- 1</sup>) in the coastal water at the specific time of study, we obtained an approximate Fe content of 26 kg in \(8\times 10^{4}\mathrm{m}^{3}\) of water under the influence of the Cp plume (Fig. 2a, b). Thus, considering the accumulated amount of Fe in the GRZs of the Vapour Col colony using the mean between GRZ volumes and deep learning census, at a certain time, \(6.8\pm 4.2\%\) of the Fe content is drained into the surrounding waters.  
+
+<|ref|>text<|/ref|><|det|>[[85, 805, 912, 888]]<|/det|>
+In order to quantify the fraction of guano Fe that reaches the Antarctic waters, it is necessary to consider multiple inherent climatic, terrain and biological factors. We consider that guano accumulation and washing responds to a dynamic interaction between factors, including penguin movement, in- situ snow
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[85, 82, 913, 465]]<|/det|>
+accumulation or melting runoff from surrounding elevated terrain. Therefore, guano can be stored in and released from the colony at any given time, depending on specific environmental conditions, consequently varying the amount of Fe released. Furthermore, terrain geomorphology highly conditions the flow of guano towards the ocean, with higher rates in steep terrains such as Vapour Col. Although we estimated close to \(7\%\) Fe discharge from the colony (see Methods), this fraction can be significantly higher if areas of guano accumulation are located near terrain with sufficient inclination and exposed to climate events. For instance, if we consider the northern tip of the Vapour Col colony, the only contributor to the Fe pool in the surrounding waters, as an example of a steep terrain, we estimate that the amount of Fe released from this zone oscillates between 20 and \(30\%\) of the total accumulated Fe. Furthermore, considering the seasonal stage at the time of the survey, our guano discharge estimates probably represent a minimal fraction of what is actually being released during the breeding peak.  
+
+<|ref|>image<|/ref|><|det|>[[87, 518, 515, 884]]<|/det|>
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[85, 83, 910, 222]]<|/det|>
+Fig. 2 | Deception Island coastal high turbidity waters and considered volume for iron discharge estimation from Vapour Col. a). Sentinel- 2 3- D composition of Deception Island, where areas of high turbidity can be seen flowing westwards, next to the Vapour Col colony. b). Volume section of coastal water under the influence of the penguin colony. The white dot is the surface water sampling location (62°59'29" S, 60°43'32" W) for Fe analysis. To estimate the relative discharge of Fe to the Antarctic waters we consider a calculated seawater volume of \(8 \times 10^{4} \mathrm{~m}^{3}\) with a \(2 \mathrm{~m}\) average depth near-shore region of \(4 \times 10^{4} \mathrm{~m}^{2}\) (white cuboid). TP: Turbidity Plumes originating from Vapour Col breeding site. VC: Vapour Col colony.  
+
+<|ref|>sub_title<|/ref|><|det|>[[88, 264, 403, 283]]<|/det|>
+## Chinstrap penguin global iron input  
+
+<|ref|>text<|/ref|><|det|>[[85, 293, 912, 775]]<|/det|>
+Iron raises special attention when its concentration in the Cp guano is compared to other krill- feeding Antarctic species. For example, in baleen whales, who currently recycle up to \(1.2 \times 10^{3}\) tonnes Fe \(\mathrm{yr}^{- 1}\) (38), Fe content in depositions range between \(145.9 \pm 133.7 \mathrm{mg} \mathrm{kg}^{- 1}\) (42), being at its most more than ten times lower than the Fe content in Cp guano found in the present study. However, penguin populations potentially gain importance because of the number and distribution of their individuals, making them important ocean fertilizers. It is complex to evaluate the Fe contribution of the global population of Cp due to its seasonal life cycle; however, once the estimate of the relative Fe input from the breeding season period is assessed, we suggest that this input corresponds to a minimal fraction of what in reality is exported to the ocean when the breeding season is over, and the population migrates to the pack ice. If we assume the same guano production rates and that the deposited guano on the ice shelves would be almost entirely washed into the ocean during the summer season, we estimate that the current population of Cp is able to produce up to \(514 \pm 248\) tonnes Fe \(\mathrm{yr}^{- 1}\) (see Methods) (Fig. 3), considering a minimum of \(10\%\) average efficiency of Fe export during the breeding season. Therefore, this Fe input to the Southern Ocean represents close to half of the input produced by baleen whales, which highlights the evident environmental relevance of Cp populations.  
+
+<|ref|>text<|/ref|><|det|>[[85, 788, 912, 905]]<|/det|>
+We further explored the yearly net primary productivity (NPP) that is stimulated by Cp input of Fe in the Antarctic waters. Using a Fe recycling model proposed for baleen whales (10), we first calculated the bioavailable Fe, to then estimate total NPP in \(\mathrm{g} \mathrm{C} \mathrm{m}^{- 2} \mathrm{yr}^{- 1}\) for the extension of the Southern Ocean \((2 \times 10^{7} \mathrm{~km}^{2})\) . We found that considering the 514 tonnes Fe \(\mathrm{yr}^{- 1}\) exported by the global Cp population,
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[88, 85, 909, 380]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[87, 383, 911, 570]]<|/det|>
+<center>Fig. 3 | Iron recycling and net primary production stimulation in the Southern Ocean pelagic areas. A perspective of Chinstrap penguins relative Fe input and net primary production stimulation compared to post-whaling Mysticeti<sup>38</sup> and Antarctic krill<sup>39,40</sup> Fe fertilizing rates, estimated in this study from a literature model<sup>10</sup> (see Methods). Carbon assimilation associated with primary production (2 g C m<sup>-2</sup> day<sup>-1</sup>, here expressed as annual assimilated C)<sup>41</sup> refers to the Southern Ocean coastal areas, such as polynyas, marginal ice zones and the continental shelf. Despite the lower penguin Fe contribution compared to that from baleen whales, this number accounts for only one species in contrast with the Mysticeti genus, with Chinstrap numbers being 50% lower than four decades ago<sup>15</sup>, when whales were already subjected to whaling pressure. Solid arrows indicate energy fluxes within the ecosystem. Dashed arrows indicate Fe input and primary production stimulation. </center>  
+
+<|ref|>text<|/ref|><|det|>[[87, 591, 911, 740]]<|/det|>
+Cp can be contributing with \(0.11 \pm 0.05\) to \(1.04 \pm 0.5 \mathrm{g C m^{- 2} y r^{- 1}}\) , with an estimated base rate of 0.46 \(\pm 0.22 \mathrm{g C m^{- 2} y r^{- 1}}\) (see Methods). These values are of the same magnitude range as the NPP driven by the entire Mysticeti genus' Fe input<sup>38</sup>, which currently represents an estimated average of \(1.08 \mathrm{g C m^{- 2} y r^{- 1}}\) NPP stimulation, highlighting the significance of Cp when comparing single to multi- species Fe export to the Antarctic waters.  
+
+<|ref|>text<|/ref|><|det|>[[87, 755, 911, 905]]<|/det|>
+Although fluctuating patterns are appreciated among the Cp populations, mainly due to the complexity of spatial distribution, an overall decrease in their numbers is evident, especially in the South Shetland Islands, where various studies showed significant drops in the number of individuals<sup>33,34,43,44</sup>. Similar to the rest of the regions inhabited by Cp, Vapour Col presented a similar negative fluctuation, where a decrease in population of 36% between 1991 and 2008 has been reported<sup>35</sup>. This decrease of Cp
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[85, 83, 912, 266]]<|/det|>
+numbers however is common to almost all the breeding sites on the Antarctic Peninsula, possibly leading to a similar situation as that experienced by baleen whales, where its Fe recycling is now up to ten times less than in the pre- whaling period<sup>42</sup>. Overall, a global decline of >50% in Cp numbers has been reported since the 1980s<sup>15</sup>, therefore suggesting that only four decades ago, Fe recycling by these seabirds had approximately the same magnitude as the one produced by baleen whales subjected to current whaling pressure.  
+
+<|ref|>sub_title<|/ref|><|det|>[[86, 293, 203, 312]]<|/det|>
+## Conclusion  
+
+<|ref|>text<|/ref|><|det|>[[85, 330, 912, 579]]<|/det|>
+Here we show that the Cp can be considered one of the biological pillars upon which Fe is being recycled in the Southern Ocean. Furthermore, if we include the Adélie penguin (Pygoscelis adeliae) as the other widely distributed Pygoscelid species, with increasing population numbers reaching \(10^{7}\) individuals<sup>45</sup> with a mainly krill- based diet<sup>29</sup>, Pygoscelid Fe input to the Southern Ocean could potentially reach the amount that baleen whales are currently recycling. The current maximum guano Fe concentration values known for this species<sup>32</sup> are an order of magnitude lower than those found in this study for the Cp. However, as measured here, these Fe concentrations could be even greater, leading to a significance increase of recycled Fe by these seabirds.  
+
+<|ref|>text<|/ref|><|det|>[[85, 592, 913, 872]]<|/det|>
+The effect that Cp have as ocean fertilizers has been decreasing as a consequence of rapid populational decline. This general negative population trend in the Antarctic Peninsula and, by default, in the rest of their breeding sites, can be related to the environmental changes faced by the Southern Ocean region mainly due to climate change effects<sup>46</sup>. However, several hypotheses are currently considered as a possible explanation of the decline of Cp populations, arguing possible krill biomass related causes, where a decoupling between krill concentrations and nonbreeding winter season is responsible for the dropping Cp numbers<sup>15</sup>. Thus, a deeper understanding of Cp life and prey- consumption cycles would help improve their conservation status and their impact on Fe recycling in the Antarctic marine ecosystem.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[30, 85, 911, 124]]<|/det|>
+196 1. Longhurst, A., Sathyendranath, S., Platt, T. & Caverhill, C. An estimate of global primary production in the ocean from satellite radiometer data. J Plankton Res 17, 1245–1271 (1995).  
+
+<|ref|>text<|/ref|><|det|>[[30, 142, 840, 161]]<|/det|>
+198 2. Sabine, C. L. et al. The Oceanic Sink for Anthropogenic CO₂. Science 305, 367–371 (2004).  
+
+<|ref|>text<|/ref|><|det|>[[30, 180, 911, 220]]<|/det|>
+199 3. Sagarin, R. et al. Iron fertilization in the ocean for climate mitigation: Legal, economic, and environmental challenges. Nichols School of the Environment, Duke University. (2007).  
+
+<|ref|>text<|/ref|><|det|>[[30, 238, 911, 278]]<|/det|>
+201 4. Arrigo, K. R., van Dijken, G. & Long, M. Coastal Southern Ocean: A strong anthropogenic CO₂ sink. Geophys. Res. Lett. 35, L21602 (2008).  
+
+<|ref|>text<|/ref|><|det|>[[30, 296, 911, 336]]<|/det|>
+203 5. Frölicher, T. L. et al. Dominance of the Southern Ocean in Anthropogenic Carbon and Heat Uptake in CMIP5 Models. Journal of Climate 28, 862–886 (2015).  
+
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+32. Sparaventi, E., Rodríguez-Romero, A., Barbosa, A., Ramajo, L. & Tovar-Sánchez, A. Trace elements in Antarctic penguins and the potential role of guano as source of recycled metals in the Southern Ocean. Chemosphere 285, 131423 (2021).  
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+33. Ciaputa, P. & Sierakowski, K. Long-term population changes of Adelie, chinstrap, and gentoo penguins in the regions of SSSI No.8 and SSSI No.34, King George Island, Antarctica. Polish Polar Research 20, 355–365 (1999).  
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+34. Sander, M., Balbão, T. C., Polito, M. J., Costa, E. S. & Carneiro, A. P. B. Recent decrease in chinstrap penguin (Pygoscelis antarctica) populations at two of Admiralty Bay’s islets on King George Island, South Shetland Islands, Antarctica. Polar Biol 30, 659–661 (2007).  
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+35. Barbosa, A., Benzal, J., De León, A. & Moreno, J. Population decline of chinstrap penguins (Pygoscelis antarctica) on Deception Island, South Shetlands, Antarctica. Polar Biol 35, 1453–1457 (2012).  
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+36. Tovar-Sánchez, A., Román, A., Roque-Atienza, D. & Navarro, G. Applications of unmanned aerial vehicles in Antarctic environmental research. Sci Rep 11, 21717 (2021).  
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+37. Naveen, R., Lynch, H. J., Forrest, S., Mueller, T. & Polito, M. First direct, site-wide penguin survey at Deception Island, Antarctica, suggests significant declines in breeding chinstrap penguins. Polar Biol (2012) doi:10.1007/s00300-012-1230-3.  
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+38. Savoca, M. S. et al. Baleen whale prey consumption based on high-resolution foraging measurements. Nature 599, 85–90 (2021).  
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+39. Maldonado, M. T., Surma, S. & Pakhomov, E. A. Southern Ocean biological iron cycling in the prewhaling and present ecosystems. Phil. Trans. R. Soc. A. 374, 20150292 (2016).  
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+40. Böckmann, S. et al. Salp fecal pellets release more bioavailable iron to Southern Ocean phytoplankton than krill fecal pellets. Current Biology 31, 2737-2746.e3 (2021).
+
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+41. Arrigo, K. R., van Dijken, G. L. & Bushinsky, S. Primary production in the Southern Ocean, 1997–2006. J. Geophys. Res. 113, C08004 (2008).  
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+42. Nicol, S. et al. Southern Ocean iron fertilization by baleen whales and Antarctic krill: Whales, Antarctic krill and iron fertilization. Fish and Fisheries 11, 203–209 (2010).  
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+43. Lynch, H. J., Naveen, R. & Fagan, W. Censuses of penguin, Blue-eyed Shag Phalacrocorax atriceps and Southern Giant Petrel Macronectes giganteus populations on the Antarctic Peninsula, 2001-2007. Marine Ornithology 36, 83–97 (2008).  
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+44. Lynch, H. J. et al. In stark contrast to widespread declines along the Scotia Arc, a survey of the South Sandwich Islands finds a robust seabird community. Polar Biol 39, 1615–1625 (2016).  
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+45. BirdLife International (2022) Species factsheet: Pygoscelis adeliae. BirdLife International http://www.birdlife.org/ (2022).  
+
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+46. Forcada, J. & Trathan, P. N. Penguin responses to climate change in the Southern Ocean. Global Change Biology 15, 1618–1630 (2009).  
+
+<|ref|>sub_title<|/ref|><|det|>[[86, 483, 179, 501]]<|/det|>
+## Methods  
+
+<|ref|>sub_title<|/ref|><|det|>[[86, 529, 495, 547]]<|/det|>
+## Guano sample collection and chemical analysis  
+
+<|ref|>text<|/ref|><|det|>[[85, 561, 912, 908]]<|/det|>
+During January and February, 2021, fresh guano samples were collected at the Vapour Col (VC) breeding site. The samples have been differentiated according to the collection substrate, since three types of samples have been analysed: soil, ice and guano collected in a “trap”. The traps consist of a polyethylene plastic plate with a PVC frame (40×30 cm long), placed in the colony 24 hours prior to sampling, to guarantee the collection of fresh samples, avoiding possible contamination from the soil. Fresh guano samples were collected in VC \((n = 23)\) , and placed manually using a plastic spoon in polyethylene bags or in acid-cleaned vials and stored frozen at - 20 °C until analysis. Fe was later extracted with a microwave acid digestion system (MARS-V, CEM) in accordance with the SW- 846 EPA Method 3051A⁴⁷. Approximately 0.2 g of guano sample were digested with 10 mL of nitric acid (65%, Suprapur quality) in Teflon vessels, in triplicate. After digestion, Fe contents were analysed (see Fe concentrations in Extended Data Fig. 1) using an inductively coupled plasma-mass spectrometry
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[85, 82, 912, 235]]<|/det|>
+(ICP- MS, iCAP Thermo). Blanks and certified material for digestion and analysis were treated like the samples. The accuracy of the analytical methods was checked using a certified reference material (Lobster hepatopancreas TORT- 2), with Fe concentration of \(109.0 \pm 5.3 \mu \mathrm{g} \mathrm{g}^{- 1}\) , and a recovery of 103.8 \(\pm 5.0\%\) . The Detection Limit calculated as three times the standard deviation of the blank values was \(0.43 \mu \mathrm{g} \mathrm{g}^{- 1}\) .  
+
+<|ref|>sub_title<|/ref|><|det|>[[88, 258, 380, 276]]<|/det|>
+## Identification of guano-rich zones  
+
+<|ref|>text<|/ref|><|det|>[[85, 289, 913, 473]]<|/det|>
+To identify the regions of the colony that have the greatest accumulation of guano, i.e. GRZ, the result of a non- supervised classification<sup>36</sup> over the VC colony terrain was used (Fig. 1d). Then, the total area of the GRZ predicted by the classification was calculated, representing a time- specific guano layer within VC. The area of these regions served as a baseline for subsequent guano volume and Cp population estimation in VC, as it is considered that the greatest percentage of the Fe export is derived from these zones.  
+
+<|ref|>sub_title<|/ref|><|det|>[[87, 496, 518, 515]]<|/det|>
+## Chinstrap penguin deep learning-powered census  
+
+<|ref|>text<|/ref|><|det|>[[85, 526, 913, 846]]<|/det|>
+On February 8, 2021, Deception Island Cp rookeries were photographed during the PiMetAn Project XXXIV Spanish Antarctic campaign<sup>48</sup> using unmanned aerial vehicles (UAV), commonly known as drones. The photographic dataset used here does not cover the entire extension of VC but only the northern tip of it (NTvc) (Extended Data Fig. 2a), and the rest of VC was photographed at a height of 150 m (Extended Data Fig. 2a), thus not suitable for penguin detection due to their small body size. Subsequently, 377 RGB images of NTvc were selected with a resolution of 3,000x4,000 pixels, captured from a flight height of 30 m and 4.9 m s<sup>- 1</sup>. The photographic data has been obtained using the DJI Mavic 2 Pro UAV equipped with an RGB sensor (Hasselblad Camera) and flights were configured using DJI's Ground Station Pro photogrammetric flight planning software. The 377 photographs were used to generate an orthomosaic using Agisoft Metashape photogrammetric software.  
+
+<|ref|>text<|/ref|><|det|>[[85, 857, 912, 906]]<|/det|>
+The model Faster R- CNN<sup>49</sup> (FRCNN) was used to perform object detection tasks. In the present research FRCNN with ResNet- 101 backbone. Training and evaluation tasks were performed using the
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[85, 83, 912, 201]]<|/det|>
+TensorFlow 2.0 machine learning platform by Google. The model performance was later evaluated by analysing the resulting detection metrics of three regions of interest (RoI) selected within the orthomosaic: RoI 1 and RoI 3, corresponding to a non- coastal area and RoI 2, corresponding to a representative region of a coastal area (Extended Data Fig. 2b, Table 1).  
+
+<|ref|>text<|/ref|><|det|>[[85, 214, 912, 465]]<|/det|>
+Deep learning- powered census set the number of Cp in \(\mathrm{NT_{VC}}\) 's GRZ at \(2,265 \pm 159\) . The number of individuals located in outer zones (OZ, i.e. those that are not GRZ) of \(\mathrm{NT_{VC}}\) was estimated at \(1,853 \pm 130\) . The previous data were used to calculate the density of penguins for both zones, obtaining \(0.52 \pm 0.03\) ind. \(\mathrm{m}^{- 2}\) for the GRZ and \(0.028 \pm 0.01\) ind. \(\mathrm{m}^{- 2}\) for the OZ. Subsequently, the density extrapolation for the highly overflown ( \(150 \mathrm{~m}\) ) rest of VC was carried out, using the densities obtained for \(\mathrm{NT_{VC}}\) and applying them to these new GRZ and OZ. A final number of \(7,611 \pm 439\) individuals were found grouped in GRZs and \(4,996 \pm 179\) in the OZs. Finally, the total number of Cp in the entire VC colony was obtained by adding the output for both zones (Extended Data Table 2).  
+
+<|ref|>sub_title<|/ref|><|det|>[[88, 487, 526, 506]]<|/det|>
+## Vapour Col Fe accumulation and export dynamics  
+
+<|ref|>text<|/ref|><|det|>[[85, 518, 912, 767]]<|/det|>
+Two approaches were explored to quantitatively assess the total Fe abundance at a given time in the studied region of the colony (see calculations in Extended Data Table 3). Firstly, the GRZ volumes were calculated to obtain an estimate of the amount of Cp deposition accumulated in the area. Determining an exact thickness of the guano layer for volume calculation was challenging, mainly due to the irregularity of the terrain, varying substrates, accumulation and discharge dynamics, and the scarcity of supporting references for this specific region. As a result, only a thin layer of \(2 \mathrm{~cm}\) as a representative thickness of fresh guano accumulation was taken into account. Then, the volume was used to estimate the total Fe content for each of GRZ, following the next equation:  
+
+<|ref|>equation<|/ref|><|det|>[[395, 791, 602, 810]]<|/det|>
+\[g_{Fe} = w[Fe]a_{GRZ}t d_{guano}\]  
+
+<|ref|>text<|/ref|><|det|>[[85, 832, 911, 885]]<|/det|>
+where \(g_{Fe}\) is the total Fe in \(\mathrm{g}\) , \(w\) is the dry weight guano fraction, which for seabirds has been estimated at \(0.4 \mathrm{~g}^{50}\) ; \([Fe]\) is the Fe concentration in \(\mu \mathrm{g}\) per \(\mathrm{g}\) of dry guano, \(a_{GRZ}\) is the area in \(\mathrm{m}^2\) as determined
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[85, 82, 911, 135]]<|/det|>
+using the non- supervised classification<sup>36</sup>, \(t\) is the guano layer thickness and \(d_{\text{guano}}\) is the density of the guano obtained in Vapour Col (1,088.6 kg m<sup>- 3</sup>).  
+
+<|ref|>text<|/ref|><|det|>[[85, 149, 911, 202]]<|/det|>
+To cross- check the output of the Fe content estimation using the GRZ volumes, an individual- level approach presented by Sparaventi et al.<sup>32</sup>, was used according to the following equation:  
+
+<|ref|>equation<|/ref|><|det|>[[425, 223, 573, 243]]<|/det|>
+\[g_{Fe} = C_p[Fe]e d\]  
+
+<|ref|>text<|/ref|><|det|>[[85, 264, 913, 483]]<|/det|>
+where \(C_p\) is the deep learning- based chinstrap census, \(e\) is the penguin excretion as g of dry guano per day, estimated at \(84.4 \mathrm{g}^{51}\) ; \(d\) is the number of days a certain number of penguins remain in the area. As the breeding season begins when \(C_p\) arrive at their colonies in early October through to November until the abandon them at the end of February, 120 days were used as the guano production period in the above equation. Comparatively, the total number of \(C_p\) present in the colony and not only in its GRZ was considered, as it was assumed that each individual must spend time within the nesting areas, thus contributing to guano accumulation.  
+
+<|ref|>sub_title<|/ref|><|det|>[[85, 505, 459, 524]]<|/det|>
+## Primary production from iron fertilization  
+
+<|ref|>text<|/ref|><|det|>[[85, 536, 913, 753]]<|/det|>
+The NPP stimulated by CP Fe input in the Southern Ocean was calculated using a model for Fe cycling proposed by Ratnarajah et al.<sup>10</sup>. In this way, varying Fe retention in the photic zone and phytoplankton Fe assimilation between assumed fractions 0.25 (min), 0.5 (base) and 0.75 (max.) and considering a 3 \(\mu \mathrm{mol Fe}:\mathrm{mol C}\) phytoplankton ratio<sup>52,53</sup>, an estimate of NPP was obtained for the extension of the Southern Ocean (South of \(60^{\circ}\mathrm{S}\) ). In the same way, to obtain a comparative perspective, NPP was calculated for the current Fe input estimation for baleen whales<sup>38</sup> and krill<sup>40</sup> (see calculations in Extended Data Table 4).  
+
+<|ref|>sub_title<|/ref|><|det|>[[86, 802, 293, 822]]<|/det|>
+## Reporting summary  
+
+<|ref|>text<|/ref|><|det|>[[85, 837, 911, 889]]<|/det|>
+Extra information about this study design is available on the Nature Research Reporting Summary file attached to this article.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[88, 85, 257, 105]]<|/det|>
+## Data availability  
+
+<|ref|>text<|/ref|><|det|>[[88, 122, 911, 175]]<|/det|>
+The datasets generated during the current study and the code used to reproduce the results are available on https://github.com/obkorolev/penguin_iron_paper.  
+
+<|ref|>text<|/ref|><|det|>[[88, 185, 890, 202]]<|/det|>
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+48. Navarro, G., Román, A., Roque, D. & Tovar-Sánchez, A. UAV imagery in Deception Island (Antarctica): PiMetAn Antarctic campaign 2020-2021. (2021) doi:10.20350/DIGITALCSIC/13850.  
+
+<|ref|>text<|/ref|><|det|>[[88, 258, 886, 299]]<|/det|>
+49. Ren, S., He, K., Girshick, R. & Sun, J. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. (2015) doi:10.48550/ARXIV.1506.01497.  
+
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+50. De La Peña-Lastra, S. Seabird droppings: Effects on a global and local level. Science of The Total Environment 754, 142148 (2021).  
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+51. Sun, L. & Xie, Z. Relics: Penguin Population Programs. Science Progress 84, 31-44 (2001).  
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+52. Strzepek, R. F., Maldonado, M. T., Hunter, K. A., Frew, R. D. & Boyd, P. W. Adaptive strategies by Southern Ocean phytoplankton to lessen iron limitation: Uptake of organically complexed iron and reduced cellular iron requirements. Limnol. Oceanogr. 56, 1983-2002 (2011).  
+
+<|ref|>text<|/ref|><|det|>[[88, 455, 896, 496]]<|/det|>
+53. Twining, B. S., Baines, S. B. & Fisher, N. S. Element stoichiometries of individual plankton cells collected during the Southern Ocean Iron Experiment (SOFeX). Limnol. Oceanogr. 49, 2115-2128 (2004).  
+
+<|ref|>sub_title<|/ref|><|det|>[[88, 536, 285, 556]]<|/det|>
+## Acknowledgements  
+
+<|ref|>text<|/ref|><|det|>[[87, 565, 884, 905]]<|/det|>
+This research has been funded by the Spanish Government projects PIMETAN (ref. RTI2018- 098048- B- I00), EQC2018- 004275- P and EQC2019- 005721- P. E. Sparaventi is supported by the Spanish FPI grant (Ref: PRE2019- 089679) and A. Rodríguez- Romero is supported by the Spanish grant, Juan de la Cierva Incorporación (Ref: IJC2018- 037545- I). We thank to D. Roque for supporting flight operations. This research is part of the POLARCSIC research initiative. Permissions to work and collect guano samples in the study area were granted by the Spanish Polar Committee. We thank the military staff of the Spanish Antarctic Base Gabriel de Castilla, the crew of the Sarmiento de Gamboa oceanographic vessel and the Marine Technology Unit (UTM- CSIC) for their logistic support, for making the XXXIV Spanish Antarctic campaign possible. We thank M. B. Dunbar, from the Institute of Marine Science of Andalusia, Spanish National Research Council
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[85, 83, 864, 135]]<|/det|>
+(ICMAN- CSIC), for the English language revision and M. Roca for advice on the quality of the illustrations.  
+
+<|ref|>sub_title<|/ref|><|det|>[[88, 160, 305, 180]]<|/det|>
+## Author contributions  
+
+<|ref|>text<|/ref|><|det|>[[85, 196, 912, 315]]<|/det|>
+A.T.- S., A.R. and G.B. designed the research; A.T.- S., and G.B. participated in the Antarctic campaign flying unoccupied aerial vehicles and collecting data and samples; O.B.K. and E.S. performed the chemical analysis, aerial data processing and deep learning model implementation; O.B.K, E.S, G.N, A.R. and A.T.- S. wrote the manuscript.  
+
+<|ref|>sub_title<|/ref|><|det|>[[88, 362, 273, 381]]<|/det|>
+## Inclusion & ethics  
+
+<|ref|>text<|/ref|><|det|>[[85, 398, 878, 485]]<|/det|>
+This study was designed and developed by a multicultural research team, with gender, nationality and religion diversity, where all members and external participants were treated with equity and respect.  
+
+<|ref|>sub_title<|/ref|><|det|>[[88, 498, 264, 516]]<|/det|>
+## Competing interests  
+
+<|ref|>text<|/ref|><|det|>[[88, 531, 442, 549]]<|/det|>
+The authors declare no competing interests.  
+
+<|ref|>text<|/ref|><|det|>[[88, 563, 794, 583]]<|/det|>
+Correspondence and requests for materials should be addressed to Oleg B. Korolev.
+
+<--- Page Split --->
+<|ref|>title<|/ref|><|det|>[[88, 85, 241, 103]]<|/det|>
+# Extended Data  
+
+<|ref|>image<|/ref|><|det|>[[225, 368, 770, 625]]<|/det|>  
+
+<|ref|>text<|/ref|><|det|>[[85, 647, 907, 737]]<|/det|>
+Extended Data Fig. 1 | Iron concentration in fresh guano samples collected at Vapour Col. Twelve samples from 10 to \(500\mathrm{g}\) directly from the soil, six samples from 35 to \(450\mathrm{g}\) from the ice and five samples from 13 to \(50\mathrm{g}\) from the guano collectors or "traps". a). Soil, ice and trap Fe concentrations. Displaying median, Q1 and Q3, 25th and 75th percentile. b). Mean and standard deviation of \(n = 23\) guano samples, \(3.0 \pm 1.4\mathrm{mg}\mathrm{g}^{- 1}\) guano.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[100, 333, 895, 660]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[85, 670, 875, 686]]<|/det|>
+<center>Extended Data Fig. 2 | Photographic available data of Vapour Col colony and Regions of Interest for deep learning model </center>  
+
+<|ref|>text<|/ref|><|det|>[[85, 694, 910, 833]]<|/det|>
+evaluation. a). Vapour Col breeding site. Outlined in yellow, is the northern tip of Vapour Col, NTvc, which was captured 30 m above the ground, yielding an optimum quality for penguin detection. The rest of the colony outside NTvc was captured at a height of 150 m. b). Close-up look of NTvc, where the RoIs were selected for deep learning model evaluation. RoI 1 and 3 correspond to a plain, artifact- free area, where the detection had optimum results. RoI 2 correspond to a rocky coast, with many similar features that compromised the performance of the model. However, this coastal area did not account for a large number of Chinstrap individuals in a visual examination of the terrain in the orthomosaic.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[30, 85, 60, 95]]<|/det|>
+482
+
+<|ref|>text<|/ref|><|det|>[[30, 125, 60, 134]]<|/det|>
+483
+
+<|ref|>text<|/ref|><|det|>[[30, 148, 60, 157]]<|/det|>
+484
+
+<|ref|>text<|/ref|><|det|>[[30, 187, 60, 196]]<|/det|>
+485
+
+<|ref|>text<|/ref|><|det|>[[30, 216, 60, 225]]<|/det|>
+486
+
+<|ref|>text<|/ref|><|det|>[[30, 244, 60, 253]]<|/det|>
+487
+
+<|ref|>text<|/ref|><|det|>[[30, 272, 60, 281]]<|/det|>
+488
+
+<|ref|>text<|/ref|><|det|>[[30, 300, 60, 309]]<|/det|>
+489
+
+<|ref|>text<|/ref|><|det|>[[30, 328, 60, 337]]<|/det|>
+490
+
+<|ref|>text<|/ref|><|det|>[[30, 355, 60, 365]]<|/det|>
+491
+
+<|ref|>text<|/ref|><|det|>[[30, 384, 896, 468]]<|/det|>
+**Extended Data Table 1 | Detection metric yielded by the deep learning model for Chinstrap census.** For RoI 1 and 2, the ability of the model to find the maximum number of penguin individuals (recall) and the confidence that they were actually penguins (precision) is very high (max=1), mainly due to the relatively plain terrain and scarcity of penguin-like objects. RoI 2 showed good precision but a low recall, due to a relatively high confidence threshold (0.6) and the presence of rocks and white water foam.
+
+<|ref|>table<|/ref|><|det|>[[92, 488, 910, 554]]<|/det|>
+
+<table><tr><td rowspan="2">Precision (P)</td><td>Equation</td><td>RoI 1</td><td>RoI 2</td><td>RoI 3</td></tr><tr><td>true positives / (true positives + false positives)</td><td>0.98</td><td>0.9</td><td>0.97</td></tr><tr><td rowspan="2">Recall (R)</td><td>true positives / (true positives + false<br>negatives)</td><td>0.89</td><td>0.31</td><td>0.92</td></tr><tr><td>\(2\times P\times R/(P+R)\)</td><td>0.93</td><td>0.47</td><td>0.94</td></tr></table>
+
+<|ref|>text<|/ref|><|det|>[[30, 560, 60, 569]]<|/det|>
+496
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+499
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+500
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+502
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+503
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+504
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+<|ref|>text<|/ref|><|det|>[[30, 816, 60, 825]]<|/det|>
+505
+
+<|ref|>text<|/ref|><|det|>[[30, 843, 60, 852]]<|/det|>
+506
+
+<|ref|>text<|/ref|><|det|>[[30, 870, 60, 879]]<|/det|>
+507
+
+<|ref|>text<|/ref|><|det|>[[30, 897, 60, 906]]<|/det|>
+508
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[33, 87, 60, 99]]<|/det|>
+509
+
+<|ref|>text<|/ref|><|det|>[[33, 115, 60, 125]]<|/det|>
+510
+
+<|ref|>text<|/ref|><|det|>[[33, 140, 60, 152]]<|/det|>
+511
+
+<|ref|>text<|/ref|><|det|>[[33, 167, 60, 178]]<|/det|>
+512
+
+<|ref|>text<|/ref|><|det|>[[33, 193, 60, 204]]<|/det|>
+513
+
+<|ref|>text<|/ref|><|det|>[[33, 220, 60, 231]]<|/det|>
+514
+
+<|ref|>text<|/ref|><|det|>[[33, 246, 60, 257]]<|/det|>
+515
+
+<|ref|>text<|/ref|><|det|>[[33, 272, 60, 283]]<|/det|>
+516
+
+<|ref|>text<|/ref|><|det|>[[33, 298, 60, 309]]<|/det|>
+517
+
+<|ref|>text<|/ref|><|det|>[[33, 324, 60, 335]]<|/det|>
+518
+
+<|ref|>text<|/ref|><|det|>[[33, 350, 60, 361]]<|/det|>
+519
+
+<|ref|>text<|/ref|><|det|>[[33, 376, 884, 390]]<|/det|>
+520 **Extended Data Table 2 | Detected Chinstrap penguins, areas and penguin densities.** To calculate their abundance in the highly
+
+<|ref|>text<|/ref|><|det|>[[33, 404, 763, 415]]<|/det|>
+521 overflow area (150 m) density extrapolation was performed for GRZ and for the outer guano-free zones (OZ).
+
+<|ref|>table<|/ref|><|det|>[[90, 437, 907, 500]]<|/det|>
+
+<table><tr><td></td><td>GRZ(m²)</td><td>OZ(m²)</td><td>Individuals in<br>GRZ</td><td>Individuals in<br>OZ</td><td>Density GRZ<br>(ind.m-2)</td><td>Density OZ<br>(ind.m-2)</td><td>Total individuals</td></tr><tr><td>NTVC</td><td>4,370</td><td>66,216</td><td>2,265±159</td><td>1,853±130</td><td>0.52±0.03</td><td>0.028±0.01</td><td>4,118±289</td></tr><tr><td>Rest of Vapour Col<br>overflow at 150 m</td><td>14,636</td><td>178,400</td><td>7,611±439</td><td>4,996±179</td><td>0.52±0.03</td><td>0.028±0.01</td><td>12,607±618</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>16,725±907</td></tr></table>
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[30, 88, 61, 99]]<|/det|>
+538
+
+<|ref|>text<|/ref|><|det|>[[30, 115, 61, 125]]<|/det|>
+539
+
+<|ref|>text<|/ref|><|det|>[[30, 141, 61, 152]]<|/det|>
+540
+
+<|ref|>text<|/ref|><|det|>[[30, 167, 61, 178]]<|/det|>
+541
+
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+542
+
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+543
+
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+544
+
+<|ref|>text<|/ref|><|det|>[[30, 273, 61, 284]]<|/det|>
+545
+
+<|ref|>text<|/ref|><|det|>[[30, 300, 61, 310]]<|/det|>
+546
+
+<|ref|>text<|/ref|><|det|>[[30, 326, 61, 337]]<|/det|>
+547
+
+<|ref|>text<|/ref|><|det|>[[30, 353, 61, 363]]<|/det|>
+548
+
+<|ref|>text<|/ref|><|det|>[[30, 379, 901, 464]]<|/det|>
+**Extended Data Table 3 | Calculations of the relative Fe content in Vapour Col.** Two methods were proposed to cross-validate the amount of Fe present in the colony at a certain time. The first accounts for Fe obtained using guano-rich zone volumes from non-supervised classification36, and the second uses the estimated deposition per penguin individual, whose total number is obtained from the deep-learning census of the present study. Referenced parameters50,51.
+
+<|ref|>table<|/ref|><|det|>[[88, 486, 907, 610]]<|/det|>
+
+<table><tr><td>Parameter</td><td>Reference</td><td>Value</td><td>Equation</td><td>Result</td><td>Uncertainty</td></tr><tr><td colspan="6">Fe calculation based on unsupervised guano rich zone classification</td></tr><tr><td>W<br>\([Fe]\)</td><td>50</td><td>0.4</td><td></td><td></td><td></td></tr><tr><td>\(a_{GRZ}\)</td><td>Present study</td><td>3.02 mg g-1</td><td>\(kgFe=0.4\times 3.02\times\)</td><td rowspan="3">500 kg Fe</td><td rowspan="3">268-732 kg Fe</td></tr><tr><td>\(d_{guano}\)</td><td>Present study<br>\(Present\ study\)</td><td>19,006 \(m^{3}\) \(1.0886\times 10^{6}g\ m^{3}\)</td><td>19,006x1.0886x0.02</td></tr><tr><td>t</td><td>Assumed, present study</td><td>0.02 m</td><td></td></tr><tr><td colspan="6">Fe calculation based on deep learning Chinstrap penguin census</td></tr><tr><td>Cp<br>\([Fe]\)</td><td>Present study</td><td>16,725</td><td>\(kgFe=(16,725\times 3,02\)</td><td rowspan="3">512 kg Fe</td><td rowspan="3">266-758 kg Fe</td></tr><tr><td>e</td><td>51</td><td>84.4 g</td><td>x84.4x120)</td></tr><tr><td>d</td><td>Assumed, present study</td><td>120 days</td><td></td></tr></table>
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[88, 245, 908, 280]]<|/det|>
+**Extended Data Table 4 | Calculations for net primary production stimulated by Chinstrap penguin Fe input.** In the same way as calculated here for the Chinstrap penguin, NPP was also obtained for krill, based on ref.39,40 and for baleen whales, based on ref.38. 
+
+<|ref|>text<|/ref|><|det|>[[88, 292, 880, 308]]<|/det|>
+Referenced parameters10,38,52,53. * Refers to the selected parameter of retained Fe in the photic zone and the bioavailable Fe for the 
+
+<|ref|>text<|/ref|><|det|>[[88, 319, 760, 333]]<|/det|>
+phytoplankton, as the model proposed by Ratnarajah et al.10 assumed values of 0.25 and 0.75 as max. and min. 
+
+<|ref|>table<|/ref|><|det|>[[78, 352, 920, 696]]<|/det|>
+<table><tr><td>Step / Parameter</td><td>Reference</td><td>Value</td><td>Equation</td><td>Result</td><td>Uncertainty</td></tr><tr><td>Chinstrap penguin annual Fe input in the Southern Ocean</td><td>Present study</td><td>514 tonnes Fe yr<sup>-1</sup></td><td></td><td></td><td>266 - 762 tonnes Fe yr<sup>-1</sup></td></tr><tr><td>Fraction of Fe retained in the photic zone</td><td>10</td><td>0.25/0.5*/0.75</td><td>5.14x10<sup>8</sup> g Fe yr<sup>-1</sup> x 0.5</td><td>2.57x10<sup>8</sup> g Fe yr<sup>-1</sup> retained in the photic zone</td><td>1.33 - 3.81 x10<sup>8</sup> g Fe yr<sup>-1</sup></td></tr><tr><td>Fraction of Fe bioavailable for phytoplankton</td><td>10</td><td>0.25/0.5*/0.75</td><td>2.57x10<sup>8</sup> g Fe yr<sup>-1</sup> x 0.5</td><td>1.28 x10<sup>8</sup> g Fe yr<sup>-1</sup> bioavailable for phyto.</td><td>0.66 - 1.9 x10<sup>8</sup> g Fe yr<sup>-1</sup></td></tr><tr><td>Fe : C ratio of phytoplankton in the Southern Ocean</td><td>38,52,53</td><td>3 μmol Fe : mol C</td><td></td><td></td><td>1 - 6 μmol Fe : mol C</td></tr><tr><td>Fe molecular weight</td><td></td><td>55.845 g mol<sup>-1</sup></td><td></td><td></td><td></td></tr><tr><td>g of carbon incorporated into phytoplankton biomass (NPP)</td><td>Present study</td><td></td><td>(1.28 x10<sup>8</sup> g Fe yr<sup>-1</sup> x 0.018 mol Fe x 10<sup>6</sup> μmol Fe x mol C x 12.01 g C) / (g Fe x mol Fe x 3 μmol Fe x mol C)</td><td>9.22 x10<sup>12</sup> g C yr<sup>-1</sup> incorporated into phyto.</td><td>4.75 - 13.7 x10<sup>12</sup> g C yr<sup>-1</sup></td></tr><tr><td>Southern Ocean area (South of 60°S)</td><td></td><td>2 x10<sup>13</sup> m<sup>2</sup></td><td></td><td></td><td></td></tr><tr><td>Rate of NPP stimulation by Cp guano input</td><td>Present study</td><td></td><td>(9.22 x10<sup>12</sup> g C yr<sup>-1</sup> incorporated into phyto.) / (2 x10<sup>13</sup> m<sup>2</sup>)</td><td>0.46 g C m<sup>-2</sup> yr<sup>-1</sup> incorporated into phyto.</td><td>0.24 - 0.68 g C m<sup>-2</sup> yr<sup>-1</sup></td></tr></table>
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[44, 42, 311, 70]]<|/det|>
+## Supplementary Files  
+
+<|ref|>text<|/ref|><|det|>[[44, 93, 765, 113]]<|/det|>
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+<|ref|>text<|/ref|><|det|>[[61, 130, 311, 150]]<|/det|>
+NCOMMS2226937Trs.pdf
+
+<--- Page Split --->

preprint/preprint__0207534b48d5dc7f202715e97467615fe845cf6d2005bff553f5b155b496fd65/images_list.json

@@ -0,0 +1,62 @@
+[
+  {
+    "type": "image",
+    "img_path": "images/Figure_1.jpg",
+    "caption": "Figure 1: (a) Log-scale global map of grid cell mean ‘average edge distance’ (AED, m), as used as model input in this study and interpretable as the average Euclidian distance from an average patch edge to its interior and calculated as described (Methods, Fig. S1) to remove the urban proportion of road area. (b) System diagram of fragmentation-fire relations implemented within the ORCHIDEE model structure, where fragmentation is affecting fire size, spread, number and propensity/rate of spread (ROS)/intensity, with counteracting effects with respect to BA. Green boxes denote where AED impinges on individual fire variables (blue boxes), given modulating factors (orange) that result in emergent grid-scale fire tendencies (red). Arrows denote positive (red) and negative (blue) relationships. FDI=Fire Danger Index; ECO2=Fire CO2 emissions.",
+    "footnote": [],
+    "bbox": [
+      [
+        120,
+        149,
+        545,
+        555
+      ]
+    ],
+    "page_idx": 3
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_2.jpg",
+    "caption": "Figure 2: (a,b): Gross fractional increase (a) and decrease (b) in simulated mean annual BA \\((f(\\Delta BA_{Frag.}))\\) versus a control simulation without fragmentation (log-scale). Grid cells where the absolute change in area burned \\(< 0.2\\%\\) of a grid cell \\((-5\\mathrm{km}^2\\mathrm{yr}^{-1})\\) were masked out. Aggregate annual increases and decreases in BA (Ha \\(\\mathrm{yr}^{-1}\\) ) due to fragmentation are included in million hectares (Mha). (c) Regression of logit link-transformed monthly mean BA against the square root of RD \\((\\mathrm{mkm}^2)\\) . Dashed black line: Observation-based regression model between BA and road density from ref. (Haas et al., 2022 71). Grey line: all simulated grid cells. Blue line and circles: only the simulated grids where fragmentation explicitly decreases mean fire size, plotted against the original road density data used in Haas et al. Red line and circles: same but plotted against the road density used in our simulations where the urban fraction of roads is removed. (d) Frequency counts of grid cells across bins of mean fragmented vegetation patch size (Ha) aggregated from the global AED map in Fig. 1a (red bars), with observed maximum (blue/purple), mean (green) and minimum (orange) observed fire patch size frequencies averaged from 2001-2020 for each grid cell, across the observed range of fire patch size using data from refs (85-88). The overlap between maximum obs. fire size (blue) and fragment size (red) is shaded purple to highlight their statistical overlap. This Figure facilitates interpretation of the fire size range and grid cell quantity affected (C,B) or unaffected (A,D) by fragmentation (see Fig. 2c).",
+    "footnote": [],
+    "bbox": [
+      [
+        120,
+        355,
+        868,
+        652
+      ]
+    ],
+    "page_idx": 4
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_3.jpg",
+    "caption": "Figure 3: (a) Simulated fractional changes in BA due to fragmentation in northern S. America \\((f(\\Delta BA_{Frag.}),\\) colour-bar), overlaid with BA and fragmentation-proxy trend data from Rosan et al. (2022)102, which were",
+    "footnote": [],
+    "bbox": [
+      [
+        125,
+        75,
+        744,
+        860
+      ]
+    ],
+    "page_idx": 7
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_4.jpg",
+    "caption": "Figure 4: (a) Time-averaged map showing where fragmentation relative to the control simulation without fragmentation leads to coupled (increasing or decreasing in the same direction) changes in BA \\((\\Delta \\mathrm{BA}_{\\mathrm{Frag}}\\) , Ha yr \\(^{-1}\\) ) and fire \\(\\mathrm{CO}_2\\) emissions intensity \\((\\Delta \\mathrm{ECO}_{2\\mathrm{Frag}}\\) , gC \\(\\mathrm{m}^{-2}\\) yr \\(^{-1}\\) ), shown in light colours, or decoupled changes (one increasing, the other decreasing), in dark colours. Blue depicts areas where \\(\\Delta \\mathrm{BA}_{\\mathrm{Frag}}\\) is negative, and red where it is positive. (b) Global spatial sensitivity of fire to hypothetical fragmentation levels: \\(\\mathrm{AED}_{\\mathrm{F2}}\\) simulation ensemble-averaged spatial distribution of fragmentation-doubling effects on fire burned area (unitless colour scale). All grid cells show a mean decrease in BA over the 9 simulations, but because of differential direction of change between simulations, we show in dark colours those grid cells where the average \\(\\mathrm{AED}_{\\mathrm{F2}}\\) impact is most likely to increase BA (highest fire susceptibility) and in light colours where it is most likely to decrease BA.",
+    "footnote": [],
+    "bbox": [
+      [
+        120,
+        129,
+        878,
+        290
+      ]
+    ],
+    "page_idx": 9
+  }
+]

preprint/preprint__022065845b0149dd1bde836e14da3448f70c4603767a53533c6448baf134c8c3/images_list.json

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+  {
+    "type": "image",
+    "img_path": "images/Figure_1.jpg",
+    "caption": "Figure 1",
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+    "bbox": [
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+    "img_path": "images/Figure_2.jpg",
+    "caption": "Figure 2",
+    "footnote": [],
+    "bbox": [
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+        880,
+        544
+      ]
+    ],
+    "page_idx": 5
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_3.jpg",
+    "caption": "Figure 3",
+    "footnote": [],
+    "bbox": [
+      [
+        170,
+        130,
+        781,
+        350
+      ]
+    ],
+    "page_idx": 7
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_4.jpg",
+    "caption": "Figure 4",
+    "footnote": [],
+    "bbox": [
+      [
+        173,
+        88,
+        832,
+        364
+      ]
+    ],
+    "page_idx": 8
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_5.jpg",
+    "caption": "Figure 5.",
+    "footnote": [],
+    "bbox": [
+      [
+        133,
+        319,
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+        500
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+    "page_idx": 9
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preprint/preprint__022065845b0149dd1bde836e14da3448f70c4603767a53533c6448baf134c8c3/preprint__022065845b0149dd1bde836e14da3448f70c4603767a53533c6448baf134c8c3.mmd

@@ -0,0 +1,305 @@
+
+# Crowding results from optimal integration of visual targets with contextual information  
+
+Guido Marco CicchiniConsiglio Nazionale delle Ricerche https://orcid.org/0000- 0002- 3303- 0420  
+
+Giovanni D'ErricoCNR Neuroscience Institute https://orcid.org/0000- 0002- 0491- 581XDavid Burr (Dave@in.cnr.it)University of Florence https://orcid.org/0000- 0003- 1541- 8832  
+
+## Article  
+
+# Keywords:  
+
+Posted Date: March 1st, 2022  
+
+DOI: https://doi.org/10.21203/rs.3.rs- 1296243/v1  
+
+License: ©  This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+Version of Record: A version of this preprint was published at Nature Communications on September 30th, 2022. See the published version at https://doi.org/10.1038/s41467- 022- 33508- 1.
+
+<--- Page Split --->
+
+# Crowding results from optimal integration of visual targets with contextual information  
+
+Guido Marco Cicchini<sup>1</sup>, Giovanni D'Errico<sup>1</sup> and David C. Burr<sup>1,2</sup>  
+
+1. Institute of Neuroscience, CNR, via Moruzzi, 1, 56124 – Pisa (ITALY)  
+2. Department of Neurosciences, Psychology, Drug Research and Child Health, University of Florence, viale Pieraccini, 6 – 50139 Firenze (ITALY)  
+
+## ABSTRACT  
+
+Crowding is the inability to recognize peripheral objects in clutter, usually considered a fundamental low- level bottleneck to object recognition. Here we advance and test an alternative hypothesis, that crowding, like “serial dependence”, results from optimizing strategies that exploit redundancies in natural scenes. This notion leads to several testable predictions: (1) crowding should be greatest for unreliable targets and reliable flankers; (2) crowding- induced biases should be maximal when target and flankers have similar orientations, falling off for differences around \(20^{\circ}\) ; (3) flanker interference should be associated with higher precision in orientation judgements, leading to lower overall error rate; (4) effects should be maximal when the orientation of the target is near that of the average of the flankers, rather than to that of individual flankers. All these effects were verified, and well simulated with ideal- observer models that maximize performance. The results suggest that while crowding can impact strongly on object recognition, it is best understood not as a processing bottleneck, but as a consequence of efficient exploitation of the spatial redundancies of the natural world.
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+
+## INTRODUCTION  
+
+Crowding is the inability to recognize and identify objects in clutter, despite their being clearly visible, and recognizable when presented in isolation<sup>1</sup> (see examples in Figure 1A). It is particularly elevated in the periphery, scaling linearly with eccentricity, such that the minimal spacing between targets and flanking elements for uncrowded vision is equal to about half the eccentricity (Bouma's law<sup>2</sup>). Crowding impacts on many important daily tasks, such as face recognition and reading (for reviews see<sup>3,4,5</sup>), to the extent it has been considered a major bottleneck to object recognition.  
+
+Most popular current models of crowding involve some form of compulsory pooling (or substitution) of targets with flankers. For example, Parkes and colleagues<sup>6</sup> showed that while the orientation of a single line cannot be determined when embedded in flankers, it does influence the perceived orientation of the ensemble: hence it is merged with the flankers, rather than suppressed. This is reinforced by several studies showing that the targets can take on characteristics of the flanker stimuli<sup>7- 9</sup>. Pelli and Tillmann<sup>3</sup> suggest that the compulsory integration occurs in higher cortical areas, such as V4, which have large receptive fields, appropriately sized to account for Bouma's law (see also<sup>10</sup>).  
+
+However, compulsory integration does not explain all the known facts about crowding. For example, flankers that are similar in size, colour or orientation cause more crowding than dissimilar ones<sup>11- 13</sup>. More difficult to explain are the recent demonstrations of Herzog and colleagues<sup>14</sup> of "uncrowding", where the addition of extra flanking stimuli around the flankers can reduce drastically their crowding effect, particularly if the extra flankers group with the original flankers to form coherent objects. These data do not fit easily with compulsory integration, even with appropriate linear filtering, which could in principle account for other effects, such as orientation or size selectivity.  
+
+Crowding has been studied for decades, and usually considered to be a defect in the system, "an essential bottleneck to object perception"<sup>15</sup>. Certainly, it impacts heavily on object recognition in tasks like or face recognition: but is it possible that it may reflect processes that are in principle advantageous to perception? Perception is strongly affected by contextual information, particularly temporal context, where recent and longer term perceptual history has been shown to exert a major influence on current perception<sup>16- 19</sup>. While the role of context and experience has been appreciated for some time<sup>20,21</sup>, it has
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+become particularly topical in recent years within the framework of Bayesian analysis. This approach has revealed an interesting phenomenon termed "serial dependence", where the appearance of many important attributes of a stimulus (including orientation, numerosity, facial identity, beauty etc) are biased towards previously viewed stimuli<sup>17,18,22,23</sup>. Counterintuitively, these consistent biases in perception have been shown to reflect an efficient perceptual strategy, exploiting temporal redundancies in natural viewing to reduce overall reproduction errors, despite the biases<sup>24,17,25</sup>.  
+
+Could crowding also be a consequence of efficient integration processes that exploit spatial (rather than temporal) redundancies to improve performance? We investigate this possibility by studying crowding with a similar paradigm used for serial dependence studies. If, like serial dependence, crowding is a by- product of efficient redundancy- reducing mechanisms, it should display several specific signature characteristics. One is that crowding- induced biases should be stronger for targets that are unreliably perceived, and for flankers that are reliably perceived. In addition, crowding should follow the signature pattern seen in serial dependence, highest when the orientations of target and flankers are similar, then steadily falling off. We verify these characteristics qualitatively and qualitatively, and show that crowding, while leading to biases, also improves overall performance. The results fit well with models simulating intelligent combination of signals from a small receptive field centred on the target with signals from a much larger integration region, following the same rules that govern serial dependence. On this view crowding should not be considered a defect, or bottleneck, in the system, but the unavoidable consequence of efficient exploitation of spatial redundancies of the natural world.
+
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+![](images/Figure_1.jpg)
+
+<center>Figure 1 </center>  
+
+a) Crowding is a visual phenomenon where items that can be easily identified in isolation are not identifiable if surrounded by similar items. The P and hand symbol on the right are difficult to recognize, while fixating the central red dots.  
+
+b) Stimuli employed in this experiment. Observers judged the orientation of a peripheral target (the central oval), which was flanked above and below by oval stimuli. Two conditions were tested: a rounded target with elongated flankers (Low reliability target, high reliability flankers, at left) or an elongated target with rounded ovals (at right). In the main condition the centre-to-centre distance of flankers and targets was 5.5 deg, and eccentricity 26 degrees, leading to a Bouma ratio of 0.21.  
+
+## RESULTS  
+
+To test if visual crowding follows the rules of optimal integration, which well describe serial dependence \(^{18,25}\) , we measured crowding with an orientation reproduction task. Participants reproduced the orientation of oval stimuli, which were either elongated (aspect ratio 1: 2.8) or rounded (aspect 1: 1.4). Targets were presented \(26^{\circ}\) to the right of fixation, and vertically flanked by similar oval stimuli, elongated if the target was rounded, and vice versa (see Fig. 1B). The orientation of the target was either \(35^{\circ}\) or \(55^{\circ}\) (at random). The orientations of the two flankers were yoked together, and varied randomly over a range of \(\pm 45^{\circ}\) from target orientation. The clear prediction from the efficient integration model \(^{24}\) (see Eqn 10) is that the effects of crowding will be far stronger for the rounded targets and elongated flankers than vice versa. The reasons are explained formally the modelling section, but the intuition is that the rounded stimuli have less reliable orientation signals and therefore benefit more from integration with contextual information, especially if it is reliable.  
+
+Figure 2A shows the bias in target reproduction as a function of difference in flanker orientation. Clearly, the rounded targets show the strongest contextual effects of crowding,
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+with peak biases varying by up to \(\pm 5.1^{\circ}\) , compared with \(\pm 1.9^{\circ}\) for the elongated targets. Furthermore, the pattern of bias follows closely that predicted and observed in serial dependence studies \(^{25}\) , varying non- linearly with difference between target and flanker orientation, increasing to a maximum around \(\pm 20^{\circ}\) , then decreasing. These data are well fit by derivative of gaussian functions (eqn. 15, light- coloured lines), commonly used in serial dependence studies \(^{18}\) , and expected from a causal inference model (see modelling section \(^{26}\) ). The dark lines show the predictions of another Bayesian model (eqn. 10), which has also proven successful with serial dependence data \(^{17,25}\) . While the models are detailed later, it is worth noting that they are almost entirely anchored by data, down to a simple scaling factor, suggesting that the data are consistent with ideal behaviour.  
+
+![](images/Figure_2.jpg)
+
+<center>Figure 2 </center>  
+
+a) Average response bias (response minus target orientation) as a function of the orientation of two identical flankers. Low reliability (rounded) targets in blue, high reliability (elongated) in red. Error bars show \(\pm 1\) SEM. Dark lines show predictions from an ideal-observer Bayesian model which scales the action of flankers according to their reliability and orientation difference (Eqn 10 of model section). Light blue and red curves show predictions for the causal inference model that doses flanker and target information according to their reliability and the probability of originating from a common cause (Eqn 15 of model section).  
+b) Response scatter as a function of the orientation of two identical flankers, together with model predictions. Colour coding as in A.  
+c) Response Scatter error plotted against bias errors for the two conditions. Dashed circles indicate regions with identical RMSE error (given by the Pythagorean sum of the two types of error). RMSE varies with orientation, and is least around \(0^{\circ}\) , when target and flankers coincide.  
+
+Another important prediction is that the contextual effects should improve performance. Figure 2B plots reproduction scatter (root- variance of reproduction trials) as a function of orientation difference. As expected, at all orientation differences, these are lower for the elongated than the rounded targets. However, for both targets, particularly the rounded
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+targets, the scatter decreased as the difference between target and flanker orientation decreased. Figure 2C plots scatter against bias, with points connected to follow the change in orientation. On this plot, total error (the Pythagorean sum of scatter and bias) is the radial distance from the origin. For the points with flanker orientation most distant from the target (near \(\pm 45^{\circ}\) ), the total error is around \(15^{\circ}\) . Between these extremes, total error falls off, despite the constant bias. When the flankers and targets have similar orientations, the error falls to around \(11^{\circ}\) , evidence that “crowding” improves overall performance.  
+
+If the effects shown in Figure 2 represent visual crowding, they should depend on critical spacing between target and flankers, and follow Bouma's law<sup>1</sup>. We therefore measured the effects as a function of target- flanker spacing, for 5 participants. Figure 3 shows the data for the rounded targets with elongated flankers (which show the strongest effects). For the two smallest spacings (5.5 and 7.5 deg), bias showed the characteristic S- shaped dependency on the orientation of the flankers. For the larger spacings (11.0 and 16.6 deg), however, the effect was much reduced and even inverted at 11 deg. As before, the curves are fit by a derivative of gaussian function (eqn 18), which is the product of a linear regression (illustrated by dashed line in Figure 3A) and a gaussian. The best fitting slope of this regression is an estimate of the weight given to the flankers when judging orientation. Figure 3B plots the fitted weight as a function of target- flanker spacing (lower abscissa), with the upper abscissa showing the Bouma constant, the distance between target and flanker centres divided by the eccentricity (26 deg). The weight drops from 0.5 to 0 for Bouma constants between 0.3 and 0.4, broadly in line with the literature, suggesting that the effects observed here relate to crowding.
+
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+![](images/Figure_3.jpg)
+
+<center>Figure 3 </center>  
+
+a) Response bias as function of flanker orientation for various target-flanker distances leading to four different Bouma ratios (distance between flanker and target centres divided by eccentricity. Data are fit with a derivative of gaussian function with free parameters (Eqn 18).  
+b) Weight of the flankers (maximal slope of the curves in panel A) as a function of the Bouma ratio (colour-code as before). Error bars show ±1 SEM.  
+
+The results so far show that integration is not obligatory, but depends on the reliability of both target and flankers, and on their orientation similarity. A remaining question is how the flankers integrate with the target: each separately, or after combination with each other. Figure 4 illustrates two possibilities (see also modelling section). One is feedforward model where the target integrates independently with low- level, high- resolution neural representations of each of the flankers. The other depicts integration with a broader representation including both flankers, potentially implemented through recurrent feedback.
+
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+![](images/Figure_4.jpg)
+
+<center>Figure 4 </center>  
+
+a) Rationale of Experiment 2. Flankers could either act independently on the target (as illustrated by purple arrows in top left panel), or first pooled into a larger RF, which in turn biases the target (illustrated by the large yellow circle and arrow in bottom left panel).  
+b) Predictions for the two hypotheses. If the flankers act independently, when one flanker is locked at \(+15^{\circ}\) and the other free to vary, the pattern should be similar to that of the main experiment (centre close to \(0^{\circ}\) ), but raised because of the action of the locked flanker (purple curve). If flankers are first integrated at a more global stage, maximal effect is expected when all the elements in the larger operator average \(0^{\circ}\) . Since one of the flankers is locked at \(+15^{\circ}\) , this occurs when the other flanker is \(-15^{\circ}\) , leading to a leftward shift of the curve of the main experiment (yellow curve)  
+
+To distinguish between these two plausible possibilities, we measured target bias with the orientation of the two flankers varying independently. Specifically, one flanker (randomly top or bottom) was always oriented \(+15^{\circ}\) from the target, while the other varied randomly over the range. The logic is that the gaussian function windowing the contextual effect should be centred where the orientations of target and context coincide. If the integration occurs directly between the target and individual flankers, then the maximum effects should occur when the variable flanker coincides with the target; on the other hand, if the integration is with a broader representation including both flankers, maximum integration should occur when the flanker mean is zero, which occurs when the variable flanker is \(-15^{\circ}\) . These predictions are illustrated in Figure 4B: note that the individual flanker effect also predicts the curve to be higher at all flanker orientations, as the fixed flanker will exert a constant effect at all orientations of the variable flanker.
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+The results for the rounded targets with elongated flankers are shown figure 5A. The biases clearly follow the signature pattern, well fit by a derivative of gaussian function. The centre of the function is \(- 12.1^{\circ}\) , closer to the \(15^{\circ}\) predicted by integration with the average orientation of the flankers, than \(0^{\circ}\) predicted by the individual flanker model. The mean height of the function is \(0.5^{\circ}\) , close to that observed in the previous experiment \((- 0.9^{\circ})\) , while the individual-flanker integration model predicts a constant average bias \(4.7^{\circ}\) . Figure 5B shows the scatter for this experiment, which was reduced over the region of bias, well described by an inverted Gaussian.  
+
+![](images/Figure_5.jpg)
+
+<center>Figure 5. </center>  
+
+a) Biasing errors as function of a single flanker orientation, while the other flanker was locked at \(+15^{\circ}\) . Colours and conventions as for Figure 2. Thick dark lines refer to the ideal observer model (Eqn 10), thick light blue lines to the causal inference model (Eqn 15). Thin dashed lines show best-fitting derivative of gaussian, with all parameters free to vary (Eqn 18).  
+b) Response Scatter as a function of the variable flanker orientation. Conventions as in panel a.  
+c) Histogram of the centres of the gaussian derivative for 1000 bootstrap fits.  
+
+To test significance, we bootstrapped the data 1000 times (sampling with replacement) and measured the centre of the gaussian derivative on each iteration. The results plotted in the histogram of Figure 5C show that on only 16 out of 1000 iterations (1.6%) was the centre closer to \(0^{\circ}\) (individual flanker prediction) than to \(- 15^{\circ}\) (joint-flanker prediction). This leads to a likelihood ratio (Bayes factor) of \(984 / 16 = 61.5\) , strong evidence in favour of the joint-flanker- integration model.
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+
+## MODELLING  
+
+We propose two plausible models to explain the pattern of data. Both are motivated by principles of "optimal cue integration" commonly used in multi- sensory perception \(^{27,28}\) , which predict optimal combination of information from multiple sources after appropriate weighting to minimize overall root- mean- square error. The first is based on an ideal- observer model successfully used to model serial dependence \(^{17}\) , the second on a "causal- inference" model of multi- sensory integration \(^{26}\) . Both models predict well the data.  
+
+## Ideal Observer  
+
+Total RMS error \((E)\) can be decomposed into bias \((B)\) and precision (scatter standard deviation: \(S\) ), whose squares sum to give total squared error:  
+
+\[E = \sqrt{B^{2} + S^{2}} \quad (eq.1)\]  
+
+The ideal responses \((R)\) in a pooling model can be expressed as a linear weighted combination of internal representation of target \((T)\) and flankers \((F_{1}\) and \(F_{2}\) ), each weighted by \(w_{1}\) and \(w_{2}\) .  
+
+\[R = w_{1}F_{1} + w_{2}F_{2} + (1 - w_{1} - w_{2})T \quad (eq.2)\]  
+
+As the two flankers of this study had the same aspect ratio they should be weighted equally, \((w_{1} = w_{2} = w)\) , so Eqn. 2 simplifies to:  
+
+\[R = wF_{1} + wF_{2} + (1 - 2w)T \quad (eq.3)\]  
+
+The mean of the responses \((\mu_{R})\) is a simple linear combination of the means of flankers and target \((\mu_{1}, \mu_{2}\) and \(\mu_{T}\) ).  
+
+\[\mu_{R} = w\mu_{1} + w\mu_{2} + (1 - 2w)\mu_{T} = w(\mu_{1} + \mu_{2}) + (1 - 2w)\mu_{T} \quad (eq.4)\]  
+
+Bias is the difference between the mean estimated response \((\mu_{R})\) and real orientation, \(x_{T}\) ; \(B = \mu_{R} - x_{T}\) . Using equation 4 and considering that the average target representation \((\mu_{T})\) should be unbiased and coincide with target \((\mu_{T} = x_{T})\) it follows that:  
+
+\[B = \mu_{R} - x_{T} = w(\mu_{1} + \mu_{2}) + \mu_{T} - 2w\mu_{T} - x_{T} = w(\mu_{1} + \mu_{2} - 2\mu_{T}) \quad (eq.5)\]
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+The term \(\mu_{1} + \mu_{2} - 2\mu_{T}\) can be rearranged as \(2((\mu_{1} + \mu_{2}) / 2 - \mu_{T})\) which is twice the distance between the average of the flanker representations, \((\mu_{1} + \mu_{2}) / 2\) , and the target representation \(\mu_{T}\) . For convenience we define:  
+
+\[d = (\mu_{1} + \mu_{2}) / 2 - \mu_{T} \quad (eq. 6)\]  
+
+so that Eqn. 5 becomes:  
+
+\[B = w(\mu_{1} + \mu_{2} - 2\mu_{T}) = 2wd \quad (eq. 7)\]  
+
+Variance of the linear combination of the flankers and target is itself a linear combination of the flanker and target variances \((\sigma_{F}^{2}\) and \(\sigma_{T}^{2}\) ) with the squared coefficients  
+
+\[S^{2} = w^{2}\sigma_{F}^{2} + w^{2}\sigma_{F}^{2} + (1 - 2w)^{2}\sigma_{T}^{2} \quad (eq. 8)\]  
+
+From Eqn 1, 7 and 8 it follows that RMSE can be written as:  
+
+\[\begin{array}{l}{E = 4w^{2}d^{2} + w^{2}\sigma_{F}^{2} + w^{2}\sigma_{F}^{2} + (1 - 2w)^{2}\sigma_{T}^{2}}\\ {= 4w^{2}d^{2} + w^{2}\sigma_{F}^{2} + w^{2}\sigma_{F}^{2} + (1 - 4w + 4w^{2})\sigma_{T}^{2}} \end{array} \quad (eq. 9)\]  
+
+Since RMSE is a function of second order of \(w\) , it is minimized when \(w = \frac{- b}{2a}\) , so the optimal weight \((w_{opt})\) is obtained at:  
+
+\[w_{opt} = -\frac{1}{2}\frac{-4\sigma_{T}^{2}}{4\sigma_{F}^{2} + 2\sigma_{F}^{2} + 4d^{2}} = \frac{\sigma_{T}^{2}}{2\sigma_{F}^{2} + \sigma_{F}^{2} + 2d^{2}} \quad (eq. 10)\]  
+
+This equation has much in common with that of all Bayesian- like integrations used in multisensory research and serial dependence: the weight depends directly on target variance \(\sigma_{T}^{2}\) , so targets of low reliability (inverse variance) benefit more from integration, resulting in higher weighting to the flankers. Increase in flanker variance \((\sigma_{F}^{2})\) has the opposite effect.  
+
+The term \(2d^{2}\) is fundamental for the signature function, as the weight of the flankers will decrease with angular difference between target and average flanker orientation. This is reminiscent of serial dependence effects, and ensures that contextual cues are used only if they are plausibly similar to the target \(^{24,17,25}\) . Importantly, the point that will ensure maximal weight of the flankers is when the target coincides with the average of the flankers (i.e. \(d^{2} = 0\) ).
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+Together the behaviour of Eqns 3 and 10 define the ideal observer behaviour. In order to accommodate suboptimal behaviour we introduce a scaling factor \((\alpha)\) which multiplies \(w_{opt}\) and sets the actual weight of the flankers:  
+
+\[R = \alpha w_{opt}F_1 + \alpha w_{opt}F_2 + (1 - 2\alpha w_{opt})T \quad [eq. 11]\]  
+
+## Causal Inference Model  
+
+An alternative model prescribes that the optimal blend of information can be obtained behaving as if the sources of information originated form one cause times the probability that two sources of information originate from the same cause<sup>26</sup>. Within this framework the maximal interaction between cues occurs when the two sources coincide, where the weight assigned of is the well known result known in sensory integration literature<sup>27,28</sup> (see also eq. 10):  
+
+\[w_{A}^{max} = \frac{\sigma_{B}^{2}}{\sigma_{A}^{2} + \sigma_{B}^{2}} \quad [eq. 12]\]  
+
+The probability of the two sources originating from a common cause can be calculated using Bayes' Theorem as demonstrated in<sup>26</sup>. Assuming gaussian probability distribution functions (with centres at \(\mu_{A}\) and \(\mu_{B}\) and variances \(\sigma_{A}^{2}\) and \(\sigma_{B}^{2}\) ), the solution is soluable analytically<sup>26</sup>:  
+
+\[p(A,B|C = 1)\propto \exp \left(-\frac{1}{2}\frac{(\mu_{A} - \mu_{B})^{2}\sigma_{B}^{2} + (\mu_{A} - \mu_{P})^{2}\sigma_{B}^{2} + (\mu_{B} - \mu_{P})^{2}\sigma_{A}^{2}}{\sigma_{A}^{2}\sigma_{B}^{2} + \sigma_{A}^{2}\sigma_{P}^{2} + \sigma_{B}^{2}\sigma_{P}^{2}}\right) \quad [eq. 13]\]  
+
+This is function of the variances of the two sources \((\sigma_{A}^{2}\) and \(\sigma_{B}^{2}\) ), the centres of the representations \((\mu_{A}\) and \(\mu_{B}\) ) and their distance, and the a- prior likelihood of there being one cause (itself gaussian and characterized by mean and variance \(\mu_{P}\) and \(\sigma_{P}^{2}\) ). If no prior knowledge is available \((\sigma_{P}^{2} \to \infty)\) Eqn 13 simplifies to  
+
+\[p(A,B|C = 1)\propto \exp \left(-\frac{1}{2}\frac{(\mu_{A} - \mu_{B})^{2}}{\sigma_{A}^{2} + \sigma_{B}^{2}}\right) \quad [eq. 14]\]  
+
+This is a gaussian peaking when the distribution of the two cues coincide \((\mu_{A} = \mu_{B})\) and falling off with a space constant related to the sum of their variances \((\sigma_{A}^{2} + \sigma_{B}^{2})\) .  
+
+In the specific case of our experiment we can map the two sources of information to the flanker compound (a gaussian with centre at \(\mu_{F} = (\mu_{1} + \mu_{2}) / 2\) , variance \(\sigma_{F}^{2} / 2\) ) and the
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+target (assumed gaussian with centre \(\mu_{T}\) , and variance \(\sigma_{T}^{2}\) . Putting together Eqns 12 and 14, the bias (difference between the response and the target) is given by:  
+
+\[B = w_{F}^{max}p(F,T|C = 1)(\mu_{F} - \mu_{T}) = \frac{\sigma_{F}^{2}}{\sigma_{F}^{2} + \sigma_{T}^{2}}\exp \left(-\frac{1}{2}\frac{(\mu_{F} - \mu_{T})^{2}}{\sigma_{F}^{2} + \sigma_{T}^{2}}\right)(\mu_{F} - \mu_{T}) \quad [eq 15]\]  
+
+Which is a derivative of gaussian as a function of flanker orientation \(\mu_{F}\)  
+
+It also follows that response scatter is minimized only when the system considers a common cause likely (Eqn 14), predicting U- shaped (gaussian) plots of Figures 2B and 5B.  
+
+Again, to allow for suboptimal behaviour we introduced two free parameters that regulate the amplitude of the dependency on the flankers \((\beta)\) and the breadth of the region of interaction \((y)\) so that the bias is:  
+
+\[B = \beta \frac{\sigma_{T}^{2}}{\sigma_{F}^{2} + \sigma_{T}^{2}}\exp \left(-\frac{1}{2}\frac{(\mu_{F} - \mu_{T})^{2}}{\gamma^{2}(\sigma_{F}^{2} + \sigma_{T}^{2})}\right)(\mu_{F} - \mu_{T}) \quad [eq 16]\]  
+
+Interestingly, comparable behaviour is obtained if, instead of constructing a system which multiplies probabilities as in \(^{26}\) , one considers a system that measures the similarity between two distributions via their point- by- point product of the distributions and takes either the peak or area under the distribution.  
+
+The product of gaussians is itself a gaussian, is centred at \((\frac{\mu_{B}\sigma_{A}^{2} + \mu_{A}\sigma_{B}^{2}}{\sigma_{A}^{2} + \sigma_{B}^{2}})\) , has variance \((\frac{\sigma_{A}^{2}\sigma_{B}^{2}}{\sigma_{A}^{2} + \sigma_{B}^{2}})\) and peak at:  
+
+\[\frac{1}{2\pi\sigma_{A}\sigma_{B}}\exp \left(-\frac{(\mu_{A} - \mu_{B})^{2}}{2(\sigma_{A}^{2} + \sigma_{B}^{2})}\right) \quad [eq. 17]\]  
+
+So the peak embeds the same behaviour of Eqn. 14. It is easy to demonstrate that also the area under the curve follows the same gaussian dependency on the distance between cues as the area of a gaussian is equivalent to the peak (Eqn. 16) times the standard deviation of the curve \((\sqrt{\frac{\sigma_{A}^{2}\sigma_{B}^{2}}{\sigma_{A}^{2} + \sigma_{B}^{2}}})\) and a constant factor \(1 / \sqrt{2\pi}\) all of which are constant once the distributions have known width and thus reduce to a scaling factor.  
+
+## Model Fitting  
+
+The predictions of the two modelling approaches are overlayed on the data of Figures 2 and 5 with dark and light colours. To minimize degrees of freedom we derived the values of
+
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+sensory reliability from the data of Figure 2b, assuming that the extreme points \((\pm 30^{\circ}\) and \(\pm 45^{\circ}\) ) give baseline data, not influenced by flanker integration: this is 17.1 for rounded targets (blue symbols), and 11.7 for elongated targets (red symbols).  
+
+We implemented the ideal observer model (Eqn. 11) with only a scaling constant \((\alpha)\) , which allows for sub- optimal behaviour. These fits are particularly good for the rounded targets (with largest effects), with \(\mathbb{R}^2\) of 0.97 and 0.74 (for bias and scatter), and 0.24 and 0.60 for elongated targets) and come about assuming \(\alpha = 0.57\) . One of the key features of the ideal observer model is that it reduces RMSE by leveraging on all available information. Thus it predicts the Global Integration of Figure 4, with centres of the Gaussian derivatives close to \(- 15^{\circ}\) . Besides capturing this key feature, the model also provides good quantitative fits to the data of Figure 5a with \(\mathbb{R}^2\) of 0.76 and average fits to those of Figure 5b 0.23 for bias and scatter respectively \((\alpha = 0.32)\) .  
+
+We used the same reliability values from Figure 2b to implement the "optimal causality gating model" \(^{26}\) , the derivative of gaussian function plotted with light colours in Figures 2 and 5. The sensory reliabilities fix both the maximal slope of the curve (see Eqn 12) and the width of the region of interaction (see Eqn 14). Assuming the same sensory precisions as above (17.1 and 11.7 for the two types of stimuli) maximal slopes should be 0.81 and 0.48 for the two conditions, larger than the real data. Also the widths (27.8 and 33.2) are larger than those predicted by Eqn 14 (19 and 16.8). For this reason we allowed two scaling factors, one enabling lower weighting of the context \((\beta)\) and the other modulating the width \((\gamma)\) . Setting \(\beta = 0.54\) and \(\gamma = 1.46\) led to good fits with \(\mathbb{R}^2 = 0.97\) and 0.89 for the low reliability target (bias and scatter curves), and 0.67 and 0.79 for the high reliability target \((\beta = 0.26\) and \(\gamma = 1.97\) ). As with the other model, the prediction in Experiment 2 is for large pooling of all available cues, thus the prediction is that of a centre at \(- 15^{\circ}\) . This model also provides good fits for response bias \((R^2 = 0.89)\) and acceptable fits for response scatter \((R^2 = 0.38, \beta = 0.62\) and \(\gamma = 1.37\) ).
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+
+## DISCUSSION  
+
+The results of this study suggest a novel interpretation of visual crowding: that it is a byproduct of efficient Bayesian processes, which lead to improved perceptual performance, minimizing production error. We tested and validated several key predictions of this idea. Firstly, crowding, measured as flanker- induced orientation bias, was greatest when targets had the weakest orientation signals (least reliability) and flankers had the strongest signals, as predicted from most models of optimal cue combination \(^{27,28}\) . The magnitude of the bias varied with the difference of target and flanker orientation, following the predicted nonlinear pattern, increasing to a maximum of around \(15^{\circ}\) , then falling off for larger orientation differences. Importantly, the interaction of the flankers and target was associated with a reduction in response scatter, which led to a reduction in total RMS error, an index of improved performance. Finally, the results suggest that the bias does not result from direct interactions with individual flankers, but from interaction with a representation of the average orientations of the two flankers. All these results were predicted by optimal feature combination principles, and quantitatively well modelled an ideal- observer model that minimizes reproduction errors.  
+
+These results are clearly difficult to reconcile with standard models of obligatory integration \(^{6,29}\) . Passive integration systems may be tweaked to explain the stronger effects for more elongated flankers (such as having more Fourier energy at that orientation), but cannot explain the fall off in crowding effects when the difference exceeds \(15^{\circ}\) . Any basic integrator would necessarily combine orientation energy of all angles, not only similar angles. On the other hand, the flexible integrator models proposed here (Eqns 10 and 15) predict both the pattern and the magnitude of the results. Furthermore, the final experiment suggests that this intelligent orientation- dependent integration is unlikely to occur directly within a higher order cell itself, as the orientation- dependent integration function aligns with the average of two disparate flankers, rather than with each individual flanker. This suggests that the integration is between the target and a broad representation that includes both flankers. Mechanisms operating directly between target and individual flankers (such as the proposed “local association field” \(^{30}\) ) are not consistent with the results of Figure 5, which shows that flankers are first combined with each other before exerting their effects on the target.
+
+<--- Page Split --->
+
+Combination of target and a broad representation of both flankers could be implemented in several ways. One physiologically plausible mechanism would be feedback from mid- level areas, such as V4, which have large receptive fields, integrating over a wide area. These cells could contain information of both flankers (as well as the target), which could be fed back to low levels (eg V1) to integrate flexibly with finer representations of the target. Within this framework the fine- grain target information is not lost, but combined with broad contextual information in an optimal manner to improve performance. This is analogous to the process of serial dependence, where representations of perceptual history (often termed Bayesian priors) are generated at mid- to high- levels of analysis, but feed back onto fairly low processing levels<sup>31</sup>. Similar processes could evoke crowding, integrating over space rather than time.  
+
+The predictions of the crowding behaviour derive from theoretical minimization of total RMS errors, explained in detail in the modelling section, but readily understood intuitively. There are two orthogonal sources of error, bias (average accuracy) and response scatter (precision), which combine by Pythagorean sum to yield total error. Thus although the contextual effects do lead to inaccuracies (biases), these are more than offset by the decrease in response scatter (Fig. 2C). Clearly, if the effects were to increase continuously with orientation, then the bias would become large, and offset the reduction in scatter, leading to increased error: integration is therefore efficient only over a limited range. Note that the efficiency- driven ideal model gives good fits simultaneous to both bias and scatter data with only one free parameter, a scaling factor. This comes out at around 0.57 for the main data and probably reflects other processes in orientation judgements that we did not control for, such as regression to the mean<sup>32,33</sup>.  
+
+The current experiment shows that under conditions of crowding, information about the target is not necessarily lost. This is consistent with a good deal of previous evidence (see reference<sup>34</sup> for review), including studies showing that it can affect the ensemble judgment<sup>5</sup>, can cause adaptation<sup>35</sup> and that crowding induced biases may not affect grasping<sup>36</sup>. Even more dramatic are the demonstrations that increasing flanker length<sup>37</sup> or adding additional flankers<sup>14</sup> can decrease or eliminate crowding. Our study employed simple well controlled stimuli to allow quantitative prediction and measurement of crowding- effects, similar to the studies with serial dependence studies. Thus they do not readily relate
+
+<--- Page Split --->
+
+to the clever uncrowding studies of Herzog and colleagues. However, it is not difficult to envisage extensions to the model incorporating grouping principles within the rules of integration, in the spirit of the general principles of our model: flexible, "intelligent" combination of signals, rather than a rigid integration via "rectify and sum" or similar rules<sup>10</sup>.  
+
+In summary, the current study suggests that crowding may be analogous to serial dependence, pointing to similar function and mechanisms. As serial dependence has been shown to exploit temporal redundancies to maximize performance, crowding may also reflect similar exploitation of redundancies over space. It is worth noting that while the rules governing crowding are flexible, leading to improved performance, crowding remains completely obligatory: no effort of will or deployment of attention can allow us to resolve the crowded objects, or to ignore the contextual effects of the orientated flankers. Indeed, while our proposed pooling process is flexible and "intelligent", it remains automatic, not subject to voluntary control. This is similar to many of the experience- driven perceptual illusions, such as the "hollow mask illusion"<sup>21</sup>: no effort of will can cause us to see the inside of a hollow mask as concave, we always see the convex face. However, while visual crowding remains an obligatory limitation to object recognition, we conclude that like the effects of temporal context and experience, it is best understood not as a defect or bottleneck of the system, but the consequence of efficient exploitation of spatial redundancies of the natural world.  
+
+## METHODS  
+
+## Participants  
+
+Fifteen healthy participants with normal or corrected- to- normal vision were recruited (aged 18- 55 years, mean age = 36, 7 females). Experimental procedures are in line with the declaration of Helsinki and approved by the local ethics committee (Commissione per l'Etica della Ricerca, University of Florence, 7 July 2020). Written informed consent was obtained from each participant, which included consent to process, preserve and publish the data in anonymous form.
+
+<--- Page Split --->
+
+## Stimuli  
+
+The stimuli, illustrated in Fig. 1A, were generated with Psychtoolbox for MATLAB (R2016b; MathWorks). They comprised an oval- shaped visual target flanked by oval- shaped upper and lower visual flankers, displayed 26 deg eccentric from the fixation point, with the target close to the horizontal meridian (vertical position was slightly varied from trial to trial to avoid pre- allocation of attention to the target) and flankers 5.5 deg away from the target. Both target and flankers were sketches of oval shapes, defined by 12 dark grey dots (diameter 0.3 deg, 1.4 deg inter- dots distant, 16.8 deg perimeter), presented against a uniform grey background. The target was orientated either \(+35^{\circ}\) or \(+55^{\circ}\) (clockwise) from the vertical, and flanker orientation randomly chosen in steps of \(5^{\circ}\) from \(- 45^{\circ}\) to \(+45^{\circ}\) with respect to the target orientation. The two flankers were 5.5 deg from target, leading to a Bouma ratio of 0.2. We manipulated the reliability of orientation information of target and flanker stimuli by using two different aspect ratios, 2.8 (axes 3.48 and 1.23 deg) and 1.4 (axes 3.19 and 2.28 deg), illustrated in Fig. 1A. The more elongated target was always associated with more rounded flankers, and vice versa. In each experimental session of the three experiments, the two target- flanker combinations were shown both kinds of stimuli in random order.  
+
+## Procedure  
+
+Stimuli were displayed on a linearized \(22^{\prime \prime}\) LCD monitor (resolution \(1920\times 1080\) pixels, refresh rate \(60\mathrm{Hz}\) ). Observers were positioned \(57~\mathrm{cm}\) from the monitor, in a quiet room with dim lighting, and maintained fixation on a small (0.35 deg) black central dot. After a random delay from the observer initiating the trial, the stimulus was displayed for \(167~\mathrm{ms}\) . Then a thin rotatable white bar \((0.05\times 5\) deg with a gaussian profile) was presented at the fixation point with random orientation, and observers matched its orientation to that of the target by mouse control. In the first two experiments, the orientation of the two flankers was yoked, while in the third, one flanker was always \(+15^{\circ}\) (clockwise) while the other varied from \(- 45^{\circ}\) to \(+45^{\circ}\) . In the second experiment, the target- flanker distance varied, being 5.5, 7.5, 11.0 and 16.6 deg, leading to Bouma ratios of 0.21, 0.27, 0.4, 0.6.  
+
+Ten observers participated in the first experiment, five in the second, thirteen in the third. They contributed for a total of 10699 trials for the first experiment, 14377 for the second (spread across the four flanker- target distances) and 16574 for the last.
+
+<--- Page Split --->
+
+## Data analysis  
+
+Responses occurred out from the range between 0.5 and 3 seconds after the stimulus offset were removed (for a total of \(15.9\%\) trials across the 3 experiments), as were responses with reproduction error greater than \(35^{\circ}\) ( \(6.9\%\) of trials).  
+
+For each target and relative orientation of the flanker, we calculated the average constant error (bias, positive meaning clockwise) and scatter. We then averaged the values for the two targets. Bias functions were fitted by a derivative of gaussian function, which can be considered to be a gaussian of width \(s\) multiplied by a straight line of slope a [or w], which can be considered the weighting given to the flankers: 1 means the flankers are weighted equally to the target. Bias is given by:  
+
+\[B = a\cdot (\theta -m)\exp \Big(-\frac{(\theta -m)^2}{s^2}\Big) + b \quad (eq. 18)\]  
+
+Where \(\theta\) is orientation difference, \(m\) the centre, and \(b\) the vertical offset of the function. \(a\) , \(b\) and \(m\) were free to vary.  
+
+Scatter \((S)\) was defined as the average root variance in each condition. The variation with orientation a gaussian function in the form:  
+
+\[S = a\cdot \exp \Big(-\frac{(\theta - m)^2}{s^2}\Big) + b \quad (eq. 19)\]  
+
+Where \(b\) is the baseline at high orientation differences and \(a\) is the amplitude of the Gaussian. As Bias and Scatter likely originate from the same process, we yoked the parameter \(s\) to best fit both curves.  
+
+## REFERENCES  
+
+1 Bouma, H. Interaction effects in parafoveal letter recognition. Nature 226, 177- 178, doi:10.1038/226177a0 (1970).  
+2 Bouma, H. Visual interference in the parafoveal recognition of initial and final letters of words. Vision Res 13, 767- 782, doi:10.1016/0042- 6989(73)90041- 2 (1973).  
+3 Pelli, D. G. & Tillman, K. A. The uncrowded window of object recognition. Nat Neurosci 11, 1129- 1135, doi:10.1038/nn.2187 (2008).  
+4 Strasburger, H., Rentschler, I. & Juttner, M. Peripheral vision and pattern recognition: a review. J Vis 11, 13, doi:10.1167/11.5.13 (2011).
+
+<--- Page Split --->
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+5 Whitney, D. & Levi, D. M. Visual crowding: a fundamental limit on conscious perception and object recognition. Trends Cogn Sci 15, 160- 168, doi:10.1016/j.tics.2011.02.005 (2011).6 Parkes, L., Lund, J., Angelucci, A., Solomon, J. A. & Morgan, M. Compulsory averaging of crowded orientation signals in human vision. Nat Neurosci 4, 739- 744, doi:10.1038/89532 (2001).7 Balas, B., Nakano, L. & Rosenholtz, R. A summary- statistic representation in peripheral vision explains visual crowding. J Vis 9, 13 11- 18, doi:10.1167/9.12.13 (2009).8 Levi, D. M. & Carney, T. Crowding in peripheral vision: why bigger is better. Curr Biol 19, 1988- 1993, doi:10.1016/j.cub.2009.09.056 (2009).9 Greenwood, J. A., Bex, P. J. & Dakin, S. C. Crowding changes appearance. Curr Biol 20, 496- 501, doi:10.1016/j.cub.2010.01.023 (2010).10 Freeman, J. & Simoncelli, E. P. Metamers of the ventral stream. Nat Neurosci 14, 1195- 1201, doi:10.1038/nn.2889 (2011).11 Nazir, T. A. Effects of lateral masking and spatial precueing on gap- resolution in central and peripheral vision. Vision Res 32, 771- 777, doi:10.1016/0042- 6989(92)90192- I (1992).12 Kooi, F. L., Toet, A., Tripathy, S. P. & Levi, D. M. The effect of similarity and duration on spatial interaction in peripheral vision. Spat Vis 8, 255- 279, doi:10.1163/156856894x00350 (1994).13 Kennedy, G. J. & Whitaker, D. The chromatic selectivity of visual crowding. J Vis 10, 15, doi:10.1167/10.6.15 (2010).14 Manassi, M., Sayim, B. & Herzog, M. H. When crowding of crowding leads to uncrowding. J Vis 13, 10, doi:10.1167/13.13.10 (2013).15 Levi, D. M. Crowding- an essential bottleneck for object recognition: a mini- review. Vision Res 48, 635- 654, doi:10.1016/j.visres.2007.12.009 (2008).16 Chopin, A. & Mamassian, P. Predictive properties of visual adaptation. Curr Biol 22, 622- 626, doi:10.1016/j.cub.2012.02.021 (2012).17 Cicchini, G. M., Anobile, G. & Burr, D. C. Compressive mapping of number to space reflects dynamic encoding mechanisms, not static logarithmic transform. Proc Natl Acad Sci U S A 111, 7867- 7872, doi:10.1073/pnas.1402785111 (2014).18 Fischer, J. & Whitney, D. Serial dependence in visual perception. Nat Neurosci 17, 738- 743, doi:10.1038/nn.3689 (2014).19 Pascucci, D. et al. Laws of concatenated perception: Vision goes for novelty, decisions for perseverance. PLoS Biol 17, e3000144, doi:10.1371/journal.pbio.3000144 (2019).20 Helmholtz, H. v. Handbuch der physiologischen Optik. (Voss, 1867).21 Gregory, R. L. Eye and brain; the psychology of seeing. (McGraw- Hill, 1966).22 Liberman, A., Fischer, J. & Whitney, D. Serial dependence in the perception of faces. Curr Biol 24, 2569- 2574, doi:10.1016/j.cub.2014.09.025 (2014).23 Taubert, J., Van der Burg, E. & Alais, D. Love at second sight: Sequential dependence of facial attractiveness in an on- line dating paradigm. Sci Rep 6, 22740, doi:10.1038/srep22740 (2016).24 Burr, D. & Cicchini, G. M. Vision: efficient adaptive coding. Curr Biol 24, R1096- 1098, doi:10.1016/j.cub.2014.10.002 (2014).
+
+<--- Page Split --->
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+25 Cicchini, G. M., Mikellidou, K. & Burr, D. C. The functional role of serial dependence. Proc Biol Sci 285, doi:10.1098/rspb.2018.1722 (2018).26 Kording, K. P. et al. Causal inference in multisensory perception. PLoS One 2, e943, doi:10.1371/journal.pone.0000943 (2007).27 Ernst, M. O. & Banks, M. S. Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415, 429- 433, doi:10.1038/415429a (2002).28 Alais, D. & Burr, D. The ventriloquist effect results from near- optimal bimodal integration. Curr Biol 14, 257- 262, doi:10.1016/j.cub.2004.01.029 (2004).29 Rosenholtz, R., Yu, D. & Keshvari, S. Challenges to pooling models of crowding: Implications for visual mechanisms. J Vis 19, 15, doi:10.1167/19.7.15 (2019).30 Field, D. J., Hayes, A. & Hess, R. F. Contour integration by the human visual system: evidence for a local "association field". Vision Res 33, 173- 193, doi:10.1016/0042- 6989(93)90156- q (1993).31 Cicchini, G. M., Benedetto, A. & Burr, D. C. Perceptual history propagates down to early levels of sensory analysis. Curr Biol 31, 1245- 1250 e1242, doi:10.1016/j.cub.2020.12.004 (2021).32 Jazayeri, M. & Shadlen, M. N. Temporal context calibrates interval timing. Nat Neurosci 13, 1020- 1026, doi:10.1038/nn.2590 (2010).33 Cicchini, G. M., Arrighi, R., Cecchetti, L., Giusti, M. & Burr, D. C. Optimal encoding of interval timing in expert percussionists. J Neurosci 32, 1056- 1060, doi:10.1523/JNEUROSCI.3411- 11.2012 (2012).34 Manassi, M. & Whitney, D. Multi- level Crowding and the Paradox of Object Recognition in Clutter. Curr Biol 28, R127- R133, doi:10.1016/j.cub.2017.12.051 (2018).35 He, S., Cavanagh, P. & Intriligator, J. Attentional resolution and the locus of visual awareness. Nature 383, 334- 337, doi:10.1038/383334a0 (1996).36 Bulakowski, P. F., Post, R. B. & Whitney, D. Visuomotor crowding: the resolution of grasping in cluttered scenes. Front Behav Neurosci 3, 49, doi:10.3389/neuro.08.049.2009 (2009).37 Banks, W. P., Larson, D. W. & Prinzmetal, W. Asymmetry of visual interference. Percept Psychophys 25, 447- 456, doi:10.3758/bf03213822 (1979).
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preprint/preprint__022065845b0149dd1bde836e14da3448f70c4603767a53533c6448baf134c8c3/preprint__022065845b0149dd1bde836e14da3448f70c4603767a53533c6448baf134c8c3_det.mmd

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+<|ref|>title<|/ref|><|det|>[[44, 106, 933, 175]]<|/det|>
+# Crowding results from optimal integration of visual targets with contextual information  
+
+<|ref|>text<|/ref|><|det|>[[44, 195, 712, 260]]<|/det|>
+Guido Marco CicchiniConsiglio Nazionale delle Ricerche https://orcid.org/0000- 0002- 3303- 0420  
+
+<|ref|>text<|/ref|><|det|>[[44, 263, 652, 330]]<|/det|>
+Giovanni D'ErricoCNR Neuroscience Institute https://orcid.org/0000- 0002- 0491- 581XDavid Burr (Dave@in.cnr.it)University of Florence https://orcid.org/0000- 0003- 1541- 8832  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 371, 101, 388]]<|/det|>
+## Article  
+
+<|ref|>title<|/ref|><|det|>[[44, 409, 135, 427]]<|/det|>
+# Keywords:  
+
+<|ref|>text<|/ref|><|det|>[[44, 446, 301, 465]]<|/det|>
+Posted Date: March 1st, 2022  
+
+<|ref|>text<|/ref|><|det|>[[44, 485, 473, 504]]<|/det|>
+DOI: https://doi.org/10.21203/rs.3.rs- 1296243/v1  
+
+<|ref|>text<|/ref|><|det|>[[44, 521, 910, 565]]<|/det|>
+License: ©  This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+<|ref|>text<|/ref|><|det|>[[44, 600, 914, 644]]<|/det|>
+Version of Record: A version of this preprint was published at Nature Communications on September 30th, 2022. See the published version at https://doi.org/10.1038/s41467- 022- 33508- 1.
+
+<--- Page Split --->
+<|ref|>title<|/ref|><|det|>[[155, 88, 847, 164]]<|/det|>
+# Crowding results from optimal integration of visual targets with contextual information  
+
+<|ref|>text<|/ref|><|det|>[[153, 221, 844, 244]]<|/det|>
+Guido Marco Cicchini<sup>1</sup>, Giovanni D'Errico<sup>1</sup> and David C. Burr<sup>1,2</sup>  
+
+<|ref|>text<|/ref|><|det|>[[150, 262, 805, 333]]<|/det|>
+1. Institute of Neuroscience, CNR, via Moruzzi, 1, 56124 – Pisa (ITALY)  
+2. Department of Neurosciences, Psychology, Drug Research and Child Health, University of Florence, viale Pieraccini, 6 – 50139 Firenze (ITALY)  
+
+<|ref|>sub_title<|/ref|><|det|>[[120, 376, 210, 392]]<|/det|>
+## ABSTRACT  
+
+<|ref|>text<|/ref|><|det|>[[118, 407, 870, 767]]<|/det|>
+Crowding is the inability to recognize peripheral objects in clutter, usually considered a fundamental low- level bottleneck to object recognition. Here we advance and test an alternative hypothesis, that crowding, like “serial dependence”, results from optimizing strategies that exploit redundancies in natural scenes. This notion leads to several testable predictions: (1) crowding should be greatest for unreliable targets and reliable flankers; (2) crowding- induced biases should be maximal when target and flankers have similar orientations, falling off for differences around \(20^{\circ}\) ; (3) flanker interference should be associated with higher precision in orientation judgements, leading to lower overall error rate; (4) effects should be maximal when the orientation of the target is near that of the average of the flankers, rather than to that of individual flankers. All these effects were verified, and well simulated with ideal- observer models that maximize performance. The results suggest that while crowding can impact strongly on object recognition, it is best understood not as a processing bottleneck, but as a consequence of efficient exploitation of the spatial redundancies of the natural world.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[120, 86, 255, 102]]<|/det|>
+## INTRODUCTION  
+
+<|ref|>text<|/ref|><|det|>[[118, 117, 874, 294]]<|/det|>
+Crowding is the inability to recognize and identify objects in clutter, despite their being clearly visible, and recognizable when presented in isolation<sup>1</sup> (see examples in Figure 1A). It is particularly elevated in the periphery, scaling linearly with eccentricity, such that the minimal spacing between targets and flanking elements for uncrowded vision is equal to about half the eccentricity (Bouma's law<sup>2</sup>). Crowding impacts on many important daily tasks, such as face recognition and reading (for reviews see<sup>3,4,5</sup>), to the extent it has been considered a major bottleneck to object recognition.  
+
+<|ref|>text<|/ref|><|det|>[[118, 307, 866, 510]]<|/det|>
+Most popular current models of crowding involve some form of compulsory pooling (or substitution) of targets with flankers. For example, Parkes and colleagues<sup>6</sup> showed that while the orientation of a single line cannot be determined when embedded in flankers, it does influence the perceived orientation of the ensemble: hence it is merged with the flankers, rather than suppressed. This is reinforced by several studies showing that the targets can take on characteristics of the flanker stimuli<sup>7- 9</sup>. Pelli and Tillmann<sup>3</sup> suggest that the compulsory integration occurs in higher cortical areas, such as V4, which have large receptive fields, appropriately sized to account for Bouma's law (see also<sup>10</sup>).  
+
+<|ref|>text<|/ref|><|det|>[[118, 523, 870, 727]]<|/det|>
+However, compulsory integration does not explain all the known facts about crowding. For example, flankers that are similar in size, colour or orientation cause more crowding than dissimilar ones<sup>11- 13</sup>. More difficult to explain are the recent demonstrations of Herzog and colleagues<sup>14</sup> of "uncrowding", where the addition of extra flanking stimuli around the flankers can reduce drastically their crowding effect, particularly if the extra flankers group with the original flankers to form coherent objects. These data do not fit easily with compulsory integration, even with appropriate linear filtering, which could in principle account for other effects, such as orientation or size selectivity.  
+
+<|ref|>text<|/ref|><|det|>[[118, 740, 877, 915]]<|/det|>
+Crowding has been studied for decades, and usually considered to be a defect in the system, "an essential bottleneck to object perception"<sup>15</sup>. Certainly, it impacts heavily on object recognition in tasks like or face recognition: but is it possible that it may reflect processes that are in principle advantageous to perception? Perception is strongly affected by contextual information, particularly temporal context, where recent and longer term perceptual history has been shown to exert a major influence on current perception<sup>16- 19</sup>. While the role of context and experience has been appreciated for some time<sup>20,21</sup>, it has
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[117, 84, 877, 260]]<|/det|>
+become particularly topical in recent years within the framework of Bayesian analysis. This approach has revealed an interesting phenomenon termed "serial dependence", where the appearance of many important attributes of a stimulus (including orientation, numerosity, facial identity, beauty etc) are biased towards previously viewed stimuli<sup>17,18,22,23</sup>. Counterintuitively, these consistent biases in perception have been shown to reflect an efficient perceptual strategy, exploiting temporal redundancies in natural viewing to reduce overall reproduction errors, despite the biases<sup>24,17,25</sup>.  
+
+<|ref|>text<|/ref|><|det|>[[117, 275, 875, 686]]<|/det|>
+Could crowding also be a consequence of efficient integration processes that exploit spatial (rather than temporal) redundancies to improve performance? We investigate this possibility by studying crowding with a similar paradigm used for serial dependence studies. If, like serial dependence, crowding is a by- product of efficient redundancy- reducing mechanisms, it should display several specific signature characteristics. One is that crowding- induced biases should be stronger for targets that are unreliably perceived, and for flankers that are reliably perceived. In addition, crowding should follow the signature pattern seen in serial dependence, highest when the orientations of target and flankers are similar, then steadily falling off. We verify these characteristics qualitatively and qualitatively, and show that crowding, while leading to biases, also improves overall performance. The results fit well with models simulating intelligent combination of signals from a small receptive field centred on the target with signals from a much larger integration region, following the same rules that govern serial dependence. On this view crowding should not be considered a defect, or bottleneck, in the system, but the unavoidable consequence of efficient exploitation of spatial redundancies of the natural world.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[133, 90, 850, 304]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[118, 333, 179, 346]]<|/det|>
+<center>Figure 1 </center>  
+
+<|ref|>text<|/ref|><|det|>[[118, 347, 878, 400]]<|/det|>
+a) Crowding is a visual phenomenon where items that can be easily identified in isolation are not identifiable if surrounded by similar items. The P and hand symbol on the right are difficult to recognize, while fixating the central red dots.  
+
+<|ref|>text<|/ref|><|det|>[[118, 393, 873, 473]]<|/det|>
+b) Stimuli employed in this experiment. Observers judged the orientation of a peripheral target (the central oval), which was flanked above and below by oval stimuli. Two conditions were tested: a rounded target with elongated flankers (Low reliability target, high reliability flankers, at left) or an elongated target with rounded ovals (at right). In the main condition the centre-to-centre distance of flankers and targets was 5.5 deg, and eccentricity 26 degrees, leading to a Bouma ratio of 0.21.  
+
+<|ref|>sub_title<|/ref|><|det|>[[119, 515, 195, 531]]<|/det|>
+## RESULTS  
+
+<|ref|>text<|/ref|><|det|>[[117, 546, 881, 855]]<|/det|>
+To test if visual crowding follows the rules of optimal integration, which well describe serial dependence \(^{18,25}\) , we measured crowding with an orientation reproduction task. Participants reproduced the orientation of oval stimuli, which were either elongated (aspect ratio 1: 2.8) or rounded (aspect 1: 1.4). Targets were presented \(26^{\circ}\) to the right of fixation, and vertically flanked by similar oval stimuli, elongated if the target was rounded, and vice versa (see Fig. 1B). The orientation of the target was either \(35^{\circ}\) or \(55^{\circ}\) (at random). The orientations of the two flankers were yoked together, and varied randomly over a range of \(\pm 45^{\circ}\) from target orientation. The clear prediction from the efficient integration model \(^{24}\) (see Eqn 10) is that the effects of crowding will be far stronger for the rounded targets and elongated flankers than vice versa. The reasons are explained formally the modelling section, but the intuition is that the rounded stimuli have less reliable orientation signals and therefore benefit more from integration with contextual information, especially if it is reliable.  
+
+<|ref|>text<|/ref|><|det|>[[119, 868, 875, 913]]<|/det|>
+Figure 2A shows the bias in target reproduction as a function of difference in flanker orientation. Clearly, the rounded targets show the strongest contextual effects of crowding,
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[117, 85, 870, 338]]<|/det|>
+with peak biases varying by up to \(\pm 5.1^{\circ}\) , compared with \(\pm 1.9^{\circ}\) for the elongated targets. Furthermore, the pattern of bias follows closely that predicted and observed in serial dependence studies \(^{25}\) , varying non- linearly with difference between target and flanker orientation, increasing to a maximum around \(\pm 20^{\circ}\) , then decreasing. These data are well fit by derivative of gaussian functions (eqn. 15, light- coloured lines), commonly used in serial dependence studies \(^{18}\) , and expected from a causal inference model (see modelling section \(^{26}\) ). The dark lines show the predictions of another Bayesian model (eqn. 10), which has also proven successful with serial dependence data \(^{17,25}\) . While the models are detailed later, it is worth noting that they are almost entirely anchored by data, down to a simple scaling factor, suggesting that the data are consistent with ideal behaviour.  
+
+<|ref|>image<|/ref|><|det|>[[131, 359, 880, 544]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[119, 569, 180, 582]]<|/det|>
+<center>Figure 2 </center>  
+
+<|ref|>text<|/ref|><|det|>[[118, 583, 880, 770]]<|/det|>
+a) Average response bias (response minus target orientation) as a function of the orientation of two identical flankers. Low reliability (rounded) targets in blue, high reliability (elongated) in red. Error bars show \(\pm 1\) SEM. Dark lines show predictions from an ideal-observer Bayesian model which scales the action of flankers according to their reliability and orientation difference (Eqn 10 of model section). Light blue and red curves show predictions for the causal inference model that doses flanker and target information according to their reliability and the probability of originating from a common cause (Eqn 15 of model section).  
+b) Response scatter as a function of the orientation of two identical flankers, together with model predictions. Colour coding as in A.  
+c) Response Scatter error plotted against bias errors for the two conditions. Dashed circles indicate regions with identical RMSE error (given by the Pythagorean sum of the two types of error). RMSE varies with orientation, and is least around \(0^{\circ}\) , when target and flankers coincide.  
+
+<|ref|>text<|/ref|><|det|>[[118, 808, 857, 905]]<|/det|>
+Another important prediction is that the contextual effects should improve performance. Figure 2B plots reproduction scatter (root- variance of reproduction trials) as a function of orientation difference. As expected, at all orientation differences, these are lower for the elongated than the rounded targets. However, for both targets, particularly the rounded
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[118, 84, 879, 261]]<|/det|>
+targets, the scatter decreased as the difference between target and flanker orientation decreased. Figure 2C plots scatter against bias, with points connected to follow the change in orientation. On this plot, total error (the Pythagorean sum of scatter and bias) is the radial distance from the origin. For the points with flanker orientation most distant from the target (near \(\pm 45^{\circ}\) ), the total error is around \(15^{\circ}\) . Between these extremes, total error falls off, despite the constant bias. When the flankers and targets have similar orientations, the error falls to around \(11^{\circ}\) , evidence that “crowding” improves overall performance.  
+
+<|ref|>text<|/ref|><|det|>[[117, 275, 880, 660]]<|/det|>
+If the effects shown in Figure 2 represent visual crowding, they should depend on critical spacing between target and flankers, and follow Bouma's law<sup>1</sup>. We therefore measured the effects as a function of target- flanker spacing, for 5 participants. Figure 3 shows the data for the rounded targets with elongated flankers (which show the strongest effects). For the two smallest spacings (5.5 and 7.5 deg), bias showed the characteristic S- shaped dependency on the orientation of the flankers. For the larger spacings (11.0 and 16.6 deg), however, the effect was much reduced and even inverted at 11 deg. As before, the curves are fit by a derivative of gaussian function (eqn 18), which is the product of a linear regression (illustrated by dashed line in Figure 3A) and a gaussian. The best fitting slope of this regression is an estimate of the weight given to the flankers when judging orientation. Figure 3B plots the fitted weight as a function of target- flanker spacing (lower abscissa), with the upper abscissa showing the Bouma constant, the distance between target and flanker centres divided by the eccentricity (26 deg). The weight drops from 0.5 to 0 for Bouma constants between 0.3 and 0.4, broadly in line with the literature, suggesting that the effects observed here relate to crowding.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[170, 130, 781, 350]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[119, 382, 179, 395]]<|/det|>
+<center>Figure 3 </center>  
+
+<|ref|>text<|/ref|><|det|>[[118, 395, 863, 475]]<|/det|>
+a) Response bias as function of flanker orientation for various target-flanker distances leading to four different Bouma ratios (distance between flanker and target centres divided by eccentricity. Data are fit with a derivative of gaussian function with free parameters (Eqn 18).  
+b) Weight of the flankers (maximal slope of the curves in panel A) as a function of the Bouma ratio (colour-code as before). Error bars show ±1 SEM.  
+
+<|ref|>text<|/ref|><|det|>[[118, 549, 866, 750]]<|/det|>
+The results so far show that integration is not obligatory, but depends on the reliability of both target and flankers, and on their orientation similarity. A remaining question is how the flankers integrate with the target: each separately, or after combination with each other. Figure 4 illustrates two possibilities (see also modelling section). One is feedforward model where the target integrates independently with low- level, high- resolution neural representations of each of the flankers. The other depicts integration with a broader representation including both flankers, potentially implemented through recurrent feedback.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[173, 88, 832, 364]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[119, 386, 180, 399]]<|/det|>
+<center>Figure 4 </center>  
+
+<|ref|>text<|/ref|><|det|>[[118, 400, 876, 540]]<|/det|>
+a) Rationale of Experiment 2. Flankers could either act independently on the target (as illustrated by purple arrows in top left panel), or first pooled into a larger RF, which in turn biases the target (illustrated by the large yellow circle and arrow in bottom left panel).  
+b) Predictions for the two hypotheses. If the flankers act independently, when one flanker is locked at \(+15^{\circ}\) and the other free to vary, the pattern should be similar to that of the main experiment (centre close to \(0^{\circ}\) ), but raised because of the action of the locked flanker (purple curve). If flankers are first integrated at a more global stage, maximal effect is expected when all the elements in the larger operator average \(0^{\circ}\) . Since one of the flankers is locked at \(+15^{\circ}\) , this occurs when the other flanker is \(-15^{\circ}\) , leading to a leftward shift of the curve of the main experiment (yellow curve)  
+
+<|ref|>text<|/ref|><|det|>[[118, 577, 881, 884]]<|/det|>
+To distinguish between these two plausible possibilities, we measured target bias with the orientation of the two flankers varying independently. Specifically, one flanker (randomly top or bottom) was always oriented \(+15^{\circ}\) from the target, while the other varied randomly over the range. The logic is that the gaussian function windowing the contextual effect should be centred where the orientations of target and context coincide. If the integration occurs directly between the target and individual flankers, then the maximum effects should occur when the variable flanker coincides with the target; on the other hand, if the integration is with a broader representation including both flankers, maximum integration should occur when the flanker mean is zero, which occurs when the variable flanker is \(-15^{\circ}\) . These predictions are illustrated in Figure 4B: note that the individual flanker effect also predicts the curve to be higher at all flanker orientations, as the fixed flanker will exert a constant effect at all orientations of the variable flanker.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[118, 85, 872, 288]]<|/det|>
+The results for the rounded targets with elongated flankers are shown figure 5A. The biases clearly follow the signature pattern, well fit by a derivative of gaussian function. The centre of the function is \(- 12.1^{\circ}\) , closer to the \(15^{\circ}\) predicted by integration with the average orientation of the flankers, than \(0^{\circ}\) predicted by the individual flanker model. The mean height of the function is \(0.5^{\circ}\) , close to that observed in the previous experiment \((- 0.9^{\circ})\) , while the individual-flanker integration model predicts a constant average bias \(4.7^{\circ}\) . Figure 5B shows the scatter for this experiment, which was reduced over the region of bias, well described by an inverted Gaussian.  
+
+<|ref|>image<|/ref|><|det|>[[133, 319, 880, 500]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[120, 535, 181, 549]]<|/det|>
+<center>Figure 5. </center>  
+
+<|ref|>text<|/ref|><|det|>[[118, 555, 870, 653]]<|/det|>
+a) Biasing errors as function of a single flanker orientation, while the other flanker was locked at \(+15^{\circ}\) . Colours and conventions as for Figure 2. Thick dark lines refer to the ideal observer model (Eqn 10), thick light blue lines to the causal inference model (Eqn 15). Thin dashed lines show best-fitting derivative of gaussian, with all parameters free to vary (Eqn 18).  
+b) Response Scatter as a function of the variable flanker orientation. Conventions as in panel a.  
+c) Histogram of the centres of the gaussian derivative for 1000 bootstrap fits.  
+
+<|ref|>text<|/ref|><|det|>[[118, 693, 875, 842]]<|/det|>
+To test significance, we bootstrapped the data 1000 times (sampling with replacement) and measured the centre of the gaussian derivative on each iteration. The results plotted in the histogram of Figure 5C show that on only 16 out of 1000 iterations (1.6%) was the centre closer to \(0^{\circ}\) (individual flanker prediction) than to \(- 15^{\circ}\) (joint-flanker prediction). This leads to a likelihood ratio (Bayes factor) of \(984 / 16 = 61.5\) , strong evidence in favour of the joint-flanker- integration model.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[119, 86, 225, 102]]<|/det|>
+## MODELLING  
+
+<|ref|>text<|/ref|><|det|>[[118, 117, 864, 268]]<|/det|>
+We propose two plausible models to explain the pattern of data. Both are motivated by principles of "optimal cue integration" commonly used in multi- sensory perception \(^{27,28}\) , which predict optimal combination of information from multiple sources after appropriate weighting to minimize overall root- mean- square error. The first is based on an ideal- observer model successfully used to model serial dependence \(^{17}\) , the second on a "causal- inference" model of multi- sensory integration \(^{26}\) . Both models predict well the data.  
+
+<|ref|>sub_title<|/ref|><|det|>[[119, 315, 245, 332]]<|/det|>
+## Ideal Observer  
+
+<|ref|>text<|/ref|><|det|>[[119, 347, 810, 393]]<|/det|>
+Total RMS error \((E)\) can be decomposed into bias \((B)\) and precision (scatter standard deviation: \(S\) ), whose squares sum to give total squared error:  
+
+<|ref|>equation<|/ref|><|det|>[[178, 405, 840, 428]]<|/det|>
+\[E = \sqrt{B^{2} + S^{2}} \quad (eq.1)\]  
+
+<|ref|>text<|/ref|><|det|>[[118, 442, 870, 514]]<|/det|>
+The ideal responses \((R)\) in a pooling model can be expressed as a linear weighted combination of internal representation of target \((T)\) and flankers \((F_{1}\) and \(F_{2}\) ), each weighted by \(w_{1}\) and \(w_{2}\) .  
+
+<|ref|>equation<|/ref|><|det|>[[178, 527, 840, 548]]<|/det|>
+\[R = w_{1}F_{1} + w_{2}F_{2} + (1 - w_{1} - w_{2})T \quad (eq.2)\]  
+
+<|ref|>text<|/ref|><|det|>[[118, 561, 877, 606]]<|/det|>
+As the two flankers of this study had the same aspect ratio they should be weighted equally, \((w_{1} = w_{2} = w)\) , so Eqn. 2 simplifies to:  
+
+<|ref|>equation<|/ref|><|det|>[[178, 620, 840, 641]]<|/det|>
+\[R = wF_{1} + wF_{2} + (1 - 2w)T \quad (eq.3)\]  
+
+<|ref|>text<|/ref|><|det|>[[118, 647, 872, 692]]<|/det|>
+The mean of the responses \((\mu_{R})\) is a simple linear combination of the means of flankers and target \((\mu_{1}, \mu_{2}\) and \(\mu_{T}\) ).  
+
+<|ref|>equation<|/ref|><|det|>[[178, 705, 840, 726]]<|/det|>
+\[\mu_{R} = w\mu_{1} + w\mu_{2} + (1 - 2w)\mu_{T} = w(\mu_{1} + \mu_{2}) + (1 - 2w)\mu_{T} \quad (eq.4)\]  
+
+<|ref|>text<|/ref|><|det|>[[118, 732, 872, 804]]<|/det|>
+Bias is the difference between the mean estimated response \((\mu_{R})\) and real orientation, \(x_{T}\) ; \(B = \mu_{R} - x_{T}\) . Using equation 4 and considering that the average target representation \((\mu_{T})\) should be unbiased and coincide with target \((\mu_{T} = x_{T})\) it follows that:  
+
+<|ref|>equation<|/ref|><|det|>[[178, 817, 840, 839]]<|/det|>
+\[B = \mu_{R} - x_{T} = w(\mu_{1} + \mu_{2}) + \mu_{T} - 2w\mu_{T} - x_{T} = w(\mu_{1} + \mu_{2} - 2\mu_{T}) \quad (eq.5)\]
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[118, 84, 866, 156]]<|/det|>
+The term \(\mu_{1} + \mu_{2} - 2\mu_{T}\) can be rearranged as \(2((\mu_{1} + \mu_{2}) / 2 - \mu_{T})\) which is twice the distance between the average of the flanker representations, \((\mu_{1} + \mu_{2}) / 2\) , and the target representation \(\mu_{T}\) . For convenience we define:  
+
+<|ref|>equation<|/ref|><|det|>[[179, 170, 840, 191]]<|/det|>
+\[d = (\mu_{1} + \mu_{2}) / 2 - \mu_{T} \quad (eq. 6)\]  
+
+<|ref|>text<|/ref|><|det|>[[118, 205, 320, 222]]<|/det|>
+so that Eqn. 5 becomes:  
+
+<|ref|>equation<|/ref|><|det|>[[179, 237, 840, 258]]<|/det|>
+\[B = w(\mu_{1} + \mu_{2} - 2\mu_{T}) = 2wd \quad (eq. 7)\]  
+
+<|ref|>text<|/ref|><|det|>[[118, 270, 877, 315]]<|/det|>
+Variance of the linear combination of the flankers and target is itself a linear combination of the flanker and target variances \((\sigma_{F}^{2}\) and \(\sigma_{T}^{2}\) ) with the squared coefficients  
+
+<|ref|>equation<|/ref|><|det|>[[179, 328, 840, 350]]<|/det|>
+\[S^{2} = w^{2}\sigma_{F}^{2} + w^{2}\sigma_{F}^{2} + (1 - 2w)^{2}\sigma_{T}^{2} \quad (eq. 8)\]  
+
+<|ref|>text<|/ref|><|det|>[[118, 363, 614, 382]]<|/det|>
+From Eqn 1, 7 and 8 it follows that RMSE can be written as:  
+
+<|ref|>equation<|/ref|><|det|>[[179, 394, 840, 450]]<|/det|>
+\[\begin{array}{l}{E = 4w^{2}d^{2} + w^{2}\sigma_{F}^{2} + w^{2}\sigma_{F}^{2} + (1 - 2w)^{2}\sigma_{T}^{2}}\\ {= 4w^{2}d^{2} + w^{2}\sigma_{F}^{2} + w^{2}\sigma_{F}^{2} + (1 - 4w + 4w^{2})\sigma_{T}^{2}} \end{array} \quad (eq. 9)\]  
+
+<|ref|>text<|/ref|><|det|>[[118, 462, 870, 514]]<|/det|>
+Since RMSE is a function of second order of \(w\) , it is minimized when \(w = \frac{- b}{2a}\) , so the optimal weight \((w_{opt})\) is obtained at:  
+
+<|ref|>equation<|/ref|><|det|>[[179, 528, 850, 561]]<|/det|>
+\[w_{opt} = -\frac{1}{2}\frac{-4\sigma_{T}^{2}}{4\sigma_{F}^{2} + 2\sigma_{F}^{2} + 4d^{2}} = \frac{\sigma_{T}^{2}}{2\sigma_{F}^{2} + \sigma_{F}^{2} + 2d^{2}} \quad (eq. 10)\]  
+
+<|ref|>text<|/ref|><|det|>[[118, 575, 877, 675]]<|/det|>
+This equation has much in common with that of all Bayesian- like integrations used in multisensory research and serial dependence: the weight depends directly on target variance \(\sigma_{T}^{2}\) , so targets of low reliability (inverse variance) benefit more from integration, resulting in higher weighting to the flankers. Increase in flanker variance \((\sigma_{F}^{2})\) has the opposite effect.  
+
+<|ref|>text<|/ref|><|det|>[[118, 686, 880, 837]]<|/det|>
+The term \(2d^{2}\) is fundamental for the signature function, as the weight of the flankers will decrease with angular difference between target and average flanker orientation. This is reminiscent of serial dependence effects, and ensures that contextual cues are used only if they are plausibly similar to the target \(^{24,17,25}\) . Importantly, the point that will ensure maximal weight of the flankers is when the target coincides with the average of the flankers (i.e. \(d^{2} = 0\) ).
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[118, 84, 875, 157]]<|/det|>
+Together the behaviour of Eqns 3 and 10 define the ideal observer behaviour. In order to accommodate suboptimal behaviour we introduce a scaling factor \((\alpha)\) which multiplies \(w_{opt}\) and sets the actual weight of the flankers:  
+
+<|ref|>equation<|/ref|><|det|>[[178, 171, 850, 192]]<|/det|>
+\[R = \alpha w_{opt}F_1 + \alpha w_{opt}F_2 + (1 - 2\alpha w_{opt})T \quad [eq. 11]\]  
+
+<|ref|>sub_title<|/ref|><|det|>[[119, 239, 320, 256]]<|/det|>
+## Causal Inference Model  
+
+<|ref|>text<|/ref|><|det|>[[117, 271, 880, 420]]<|/det|>
+An alternative model prescribes that the optimal blend of information can be obtained behaving as if the sources of information originated form one cause times the probability that two sources of information originate from the same cause<sup>26</sup>. Within this framework the maximal interaction between cues occurs when the two sources coincide, where the weight assigned of is the well known result known in sensory integration literature<sup>27,28</sup> (see also eq. 10):  
+
+<|ref|>equation<|/ref|><|det|>[[178, 433, 850, 469]]<|/det|>
+\[w_{A}^{max} = \frac{\sigma_{B}^{2}}{\sigma_{A}^{2} + \sigma_{B}^{2}} \quad [eq. 12]\]  
+
+<|ref|>text<|/ref|><|det|>[[118, 481, 880, 556]]<|/det|>
+The probability of the two sources originating from a common cause can be calculated using Bayes' Theorem as demonstrated in<sup>26</sup>. Assuming gaussian probability distribution functions (with centres at \(\mu_{A}\) and \(\mu_{B}\) and variances \(\sigma_{A}^{2}\) and \(\sigma_{B}^{2}\) ), the solution is soluable analytically<sup>26</sup>:  
+
+<|ref|>equation<|/ref|><|det|>[[178, 567, 850, 602]]<|/det|>
+\[p(A,B|C = 1)\propto \exp \left(-\frac{1}{2}\frac{(\mu_{A} - \mu_{B})^{2}\sigma_{B}^{2} + (\mu_{A} - \mu_{P})^{2}\sigma_{B}^{2} + (\mu_{B} - \mu_{P})^{2}\sigma_{A}^{2}}{\sigma_{A}^{2}\sigma_{B}^{2} + \sigma_{A}^{2}\sigma_{P}^{2} + \sigma_{B}^{2}\sigma_{P}^{2}}\right) \quad [eq. 13]\]  
+
+<|ref|>text<|/ref|><|det|>[[118, 614, 853, 715]]<|/det|>
+This is function of the variances of the two sources \((\sigma_{A}^{2}\) and \(\sigma_{B}^{2}\) ), the centres of the representations \((\mu_{A}\) and \(\mu_{B}\) ) and their distance, and the a- prior likelihood of there being one cause (itself gaussian and characterized by mean and variance \(\mu_{P}\) and \(\sigma_{P}^{2}\) ). If no prior knowledge is available \((\sigma_{P}^{2} \to \infty)\) Eqn 13 simplifies to  
+
+<|ref|>equation<|/ref|><|det|>[[178, 727, 850, 762]]<|/det|>
+\[p(A,B|C = 1)\propto \exp \left(-\frac{1}{2}\frac{(\mu_{A} - \mu_{B})^{2}}{\sigma_{A}^{2} + \sigma_{B}^{2}}\right) \quad [eq. 14]\]  
+
+<|ref|>text<|/ref|><|det|>[[118, 807, 846, 854]]<|/det|>
+This is a gaussian peaking when the distribution of the two cues coincide \((\mu_{A} = \mu_{B})\) and falling off with a space constant related to the sum of their variances \((\sigma_{A}^{2} + \sigma_{B}^{2})\) .  
+
+<|ref|>text<|/ref|><|det|>[[118, 867, 855, 913]]<|/det|>
+In the specific case of our experiment we can map the two sources of information to the flanker compound (a gaussian with centre at \(\mu_{F} = (\mu_{1} + \mu_{2}) / 2\) , variance \(\sigma_{F}^{2} / 2\) ) and the
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[118, 84, 875, 131]]<|/det|>
+target (assumed gaussian with centre \(\mu_{T}\) , and variance \(\sigma_{T}^{2}\) . Putting together Eqns 12 and 14, the bias (difference between the response and the target) is given by:  
+
+<|ref|>equation<|/ref|><|det|>[[120, 141, 848, 179]]<|/det|>
+\[B = w_{F}^{max}p(F,T|C = 1)(\mu_{F} - \mu_{T}) = \frac{\sigma_{F}^{2}}{\sigma_{F}^{2} + \sigma_{T}^{2}}\exp \left(-\frac{1}{2}\frac{(\mu_{F} - \mu_{T})^{2}}{\sigma_{F}^{2} + \sigma_{T}^{2}}\right)(\mu_{F} - \mu_{T}) \quad [eq 15]\]  
+
+<|ref|>text<|/ref|><|det|>[[118, 191, 703, 209]]<|/det|>
+Which is a derivative of gaussian as a function of flanker orientation \(\mu_{F}\)  
+
+<|ref|>text<|/ref|><|det|>[[118, 223, 880, 269]]<|/det|>
+It also follows that response scatter is minimized only when the system considers a common cause likely (Eqn 14), predicting U- shaped (gaussian) plots of Figures 2B and 5B.  
+
+<|ref|>text<|/ref|><|det|>[[118, 283, 867, 355]]<|/det|>
+Again, to allow for suboptimal behaviour we introduced two free parameters that regulate the amplitude of the dependency on the flankers \((\beta)\) and the breadth of the region of interaction \((y)\) so that the bias is:  
+
+<|ref|>equation<|/ref|><|det|>[[177, 366, 845, 402]]<|/det|>
+\[B = \beta \frac{\sigma_{T}^{2}}{\sigma_{F}^{2} + \sigma_{T}^{2}}\exp \left(-\frac{1}{2}\frac{(\mu_{F} - \mu_{T})^{2}}{\gamma^{2}(\sigma_{F}^{2} + \sigma_{T}^{2})}\right)(\mu_{F} - \mu_{T}) \quad [eq 16]\]  
+
+<|ref|>text<|/ref|><|det|>[[118, 414, 860, 511]]<|/det|>
+Interestingly, comparable behaviour is obtained if, instead of constructing a system which multiplies probabilities as in \(^{26}\) , one considers a system that measures the similarity between two distributions via their point- by- point product of the distributions and takes either the peak or area under the distribution.  
+
+<|ref|>text<|/ref|><|det|>[[118, 528, 868, 582]]<|/det|>
+The product of gaussians is itself a gaussian, is centred at \((\frac{\mu_{B}\sigma_{A}^{2} + \mu_{A}\sigma_{B}^{2}}{\sigma_{A}^{2} + \sigma_{B}^{2}})\) , has variance \((\frac{\sigma_{A}^{2}\sigma_{B}^{2}}{\sigma_{A}^{2} + \sigma_{B}^{2}})\) and peak at:  
+
+<|ref|>equation<|/ref|><|det|>[[177, 597, 850, 631]]<|/det|>
+\[\frac{1}{2\pi\sigma_{A}\sigma_{B}}\exp \left(-\frac{(\mu_{A} - \mu_{B})^{2}}{2(\sigma_{A}^{2} + \sigma_{B}^{2})}\right) \quad [eq. 17]\]  
+
+<|ref|>text<|/ref|><|det|>[[118, 644, 872, 789]]<|/det|>
+So the peak embeds the same behaviour of Eqn. 14. It is easy to demonstrate that also the area under the curve follows the same gaussian dependency on the distance between cues as the area of a gaussian is equivalent to the peak (Eqn. 16) times the standard deviation of the curve \((\sqrt{\frac{\sigma_{A}^{2}\sigma_{B}^{2}}{\sigma_{A}^{2} + \sigma_{B}^{2}}})\) and a constant factor \(1 / \sqrt{2\pi}\) all of which are constant once the distributions have known width and thus reduce to a scaling factor.  
+
+<|ref|>sub_title<|/ref|><|det|>[[119, 833, 236, 850]]<|/det|>
+## Model Fitting  
+
+<|ref|>text<|/ref|><|det|>[[118, 866, 877, 911]]<|/det|>
+The predictions of the two modelling approaches are overlayed on the data of Figures 2 and 5 with dark and light colours. To minimize degrees of freedom we derived the values of
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[119, 85, 860, 157]]<|/det|>
+sensory reliability from the data of Figure 2b, assuming that the extreme points \((\pm 30^{\circ}\) and \(\pm 45^{\circ}\) ) give baseline data, not influenced by flanker integration: this is 17.1 for rounded targets (blue symbols), and 11.7 for elongated targets (red symbols).  
+
+<|ref|>text<|/ref|><|det|>[[118, 170, 875, 399]]<|/det|>
+We implemented the ideal observer model (Eqn. 11) with only a scaling constant \((\alpha)\) , which allows for sub- optimal behaviour. These fits are particularly good for the rounded targets (with largest effects), with \(\mathbb{R}^2\) of 0.97 and 0.74 (for bias and scatter), and 0.24 and 0.60 for elongated targets) and come about assuming \(\alpha = 0.57\) . One of the key features of the ideal observer model is that it reduces RMSE by leveraging on all available information. Thus it predicts the Global Integration of Figure 4, with centres of the Gaussian derivatives close to \(- 15^{\circ}\) . Besides capturing this key feature, the model also provides good quantitative fits to the data of Figure 5a with \(\mathbb{R}^2\) of 0.76 and average fits to those of Figure 5b 0.23 for bias and scatter respectively \((\alpha = 0.32)\) .  
+
+<|ref|>text<|/ref|><|det|>[[117, 411, 880, 772]]<|/det|>
+We used the same reliability values from Figure 2b to implement the "optimal causality gating model" \(^{26}\) , the derivative of gaussian function plotted with light colours in Figures 2 and 5. The sensory reliabilities fix both the maximal slope of the curve (see Eqn 12) and the width of the region of interaction (see Eqn 14). Assuming the same sensory precisions as above (17.1 and 11.7 for the two types of stimuli) maximal slopes should be 0.81 and 0.48 for the two conditions, larger than the real data. Also the widths (27.8 and 33.2) are larger than those predicted by Eqn 14 (19 and 16.8). For this reason we allowed two scaling factors, one enabling lower weighting of the context \((\beta)\) and the other modulating the width \((\gamma)\) . Setting \(\beta = 0.54\) and \(\gamma = 1.46\) led to good fits with \(\mathbb{R}^2 = 0.97\) and 0.89 for the low reliability target (bias and scatter curves), and 0.67 and 0.79 for the high reliability target \((\beta = 0.26\) and \(\gamma = 1.97\) ). As with the other model, the prediction in Experiment 2 is for large pooling of all available cues, thus the prediction is that of a centre at \(- 15^{\circ}\) . This model also provides good fits for response bias \((R^2 = 0.89)\) and acceptable fits for response scatter \((R^2 = 0.38, \beta = 0.62\) and \(\gamma = 1.37\) ).
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[120, 87, 225, 103]]<|/det|>
+## DISCUSSION  
+
+<|ref|>text<|/ref|><|det|>[[118, 118, 878, 503]]<|/det|>
+The results of this study suggest a novel interpretation of visual crowding: that it is a byproduct of efficient Bayesian processes, which lead to improved perceptual performance, minimizing production error. We tested and validated several key predictions of this idea. Firstly, crowding, measured as flanker- induced orientation bias, was greatest when targets had the weakest orientation signals (least reliability) and flankers had the strongest signals, as predicted from most models of optimal cue combination \(^{27,28}\) . The magnitude of the bias varied with the difference of target and flanker orientation, following the predicted nonlinear pattern, increasing to a maximum of around \(15^{\circ}\) , then falling off for larger orientation differences. Importantly, the interaction of the flankers and target was associated with a reduction in response scatter, which led to a reduction in total RMS error, an index of improved performance. Finally, the results suggest that the bias does not result from direct interactions with individual flankers, but from interaction with a representation of the average orientations of the two flankers. All these results were predicted by optimal feature combination principles, and quantitatively well modelled an ideal- observer model that minimizes reproduction errors.  
+
+<|ref|>text<|/ref|><|det|>[[117, 515, 875, 902]]<|/det|>
+These results are clearly difficult to reconcile with standard models of obligatory integration \(^{6,29}\) . Passive integration systems may be tweaked to explain the stronger effects for more elongated flankers (such as having more Fourier energy at that orientation), but cannot explain the fall off in crowding effects when the difference exceeds \(15^{\circ}\) . Any basic integrator would necessarily combine orientation energy of all angles, not only similar angles. On the other hand, the flexible integrator models proposed here (Eqns 10 and 15) predict both the pattern and the magnitude of the results. Furthermore, the final experiment suggests that this intelligent orientation- dependent integration is unlikely to occur directly within a higher order cell itself, as the orientation- dependent integration function aligns with the average of two disparate flankers, rather than with each individual flanker. This suggests that the integration is between the target and a broad representation that includes both flankers. Mechanisms operating directly between target and individual flankers (such as the proposed “local association field” \(^{30}\) ) are not consistent with the results of Figure 5, which shows that flankers are first combined with each other before exerting their effects on the target.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[118, 84, 883, 365]]<|/det|>
+Combination of target and a broad representation of both flankers could be implemented in several ways. One physiologically plausible mechanism would be feedback from mid- level areas, such as V4, which have large receptive fields, integrating over a wide area. These cells could contain information of both flankers (as well as the target), which could be fed back to low levels (eg V1) to integrate flexibly with finer representations of the target. Within this framework the fine- grain target information is not lost, but combined with broad contextual information in an optimal manner to improve performance. This is analogous to the process of serial dependence, where representations of perceptual history (often termed Bayesian priors) are generated at mid- to high- levels of analysis, but feed back onto fairly low processing levels<sup>31</sup>. Similar processes could evoke crowding, integrating over space rather than time.  
+
+<|ref|>text<|/ref|><|det|>[[118, 378, 870, 686]]<|/det|>
+The predictions of the crowding behaviour derive from theoretical minimization of total RMS errors, explained in detail in the modelling section, but readily understood intuitively. There are two orthogonal sources of error, bias (average accuracy) and response scatter (precision), which combine by Pythagorean sum to yield total error. Thus although the contextual effects do lead to inaccuracies (biases), these are more than offset by the decrease in response scatter (Fig. 2C). Clearly, if the effects were to increase continuously with orientation, then the bias would become large, and offset the reduction in scatter, leading to increased error: integration is therefore efficient only over a limited range. Note that the efficiency- driven ideal model gives good fits simultaneous to both bias and scatter data with only one free parameter, a scaling factor. This comes out at around 0.57 for the main data and probably reflects other processes in orientation judgements that we did not control for, such as regression to the mean<sup>32,33</sup>.  
+
+<|ref|>text<|/ref|><|det|>[[118, 700, 881, 901]]<|/det|>
+The current experiment shows that under conditions of crowding, information about the target is not necessarily lost. This is consistent with a good deal of previous evidence (see reference<sup>34</sup> for review), including studies showing that it can affect the ensemble judgment<sup>5</sup>, can cause adaptation<sup>35</sup> and that crowding induced biases may not affect grasping<sup>36</sup>. Even more dramatic are the demonstrations that increasing flanker length<sup>37</sup> or adding additional flankers<sup>14</sup> can decrease or eliminate crowding. Our study employed simple well controlled stimuli to allow quantitative prediction and measurement of crowding- effects, similar to the studies with serial dependence studies. Thus they do not readily relate
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[118, 84, 844, 207]]<|/det|>
+to the clever uncrowding studies of Herzog and colleagues. However, it is not difficult to envisage extensions to the model incorporating grouping principles within the rules of integration, in the spirit of the general principles of our model: flexible, "intelligent" combination of signals, rather than a rigid integration via "rectify and sum" or similar rules<sup>10</sup>.  
+
+<|ref|>text<|/ref|><|det|>[[117, 223, 875, 606]]<|/det|>
+In summary, the current study suggests that crowding may be analogous to serial dependence, pointing to similar function and mechanisms. As serial dependence has been shown to exploit temporal redundancies to maximize performance, crowding may also reflect similar exploitation of redundancies over space. It is worth noting that while the rules governing crowding are flexible, leading to improved performance, crowding remains completely obligatory: no effort of will or deployment of attention can allow us to resolve the crowded objects, or to ignore the contextual effects of the orientated flankers. Indeed, while our proposed pooling process is flexible and "intelligent", it remains automatic, not subject to voluntary control. This is similar to many of the experience- driven perceptual illusions, such as the "hollow mask illusion"<sup>21</sup>: no effort of will can cause us to see the inside of a hollow mask as concave, we always see the convex face. However, while visual crowding remains an obligatory limitation to object recognition, we conclude that like the effects of temporal context and experience, it is best understood not as a defect or bottleneck of the system, but the consequence of efficient exploitation of spatial redundancies of the natural world.  
+
+<|ref|>sub_title<|/ref|><|det|>[[120, 690, 209, 705]]<|/det|>
+## METHODS  
+
+<|ref|>sub_title<|/ref|><|det|>[[120, 723, 223, 739]]<|/det|>
+## Participants  
+
+<|ref|>text<|/ref|><|det|>[[118, 754, 879, 903]]<|/det|>
+Fifteen healthy participants with normal or corrected- to- normal vision were recruited (aged 18- 55 years, mean age = 36, 7 females). Experimental procedures are in line with the declaration of Helsinki and approved by the local ethics committee (Commissione per l'Etica della Ricerca, University of Florence, 7 July 2020). Written informed consent was obtained from each participant, which included consent to process, preserve and publish the data in anonymous form.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[119, 87, 182, 102]]<|/det|>
+## Stimuli  
+
+<|ref|>text<|/ref|><|det|>[[117, 118, 876, 528]]<|/det|>
+The stimuli, illustrated in Fig. 1A, were generated with Psychtoolbox for MATLAB (R2016b; MathWorks). They comprised an oval- shaped visual target flanked by oval- shaped upper and lower visual flankers, displayed 26 deg eccentric from the fixation point, with the target close to the horizontal meridian (vertical position was slightly varied from trial to trial to avoid pre- allocation of attention to the target) and flankers 5.5 deg away from the target. Both target and flankers were sketches of oval shapes, defined by 12 dark grey dots (diameter 0.3 deg, 1.4 deg inter- dots distant, 16.8 deg perimeter), presented against a uniform grey background. The target was orientated either \(+35^{\circ}\) or \(+55^{\circ}\) (clockwise) from the vertical, and flanker orientation randomly chosen in steps of \(5^{\circ}\) from \(- 45^{\circ}\) to \(+45^{\circ}\) with respect to the target orientation. The two flankers were 5.5 deg from target, leading to a Bouma ratio of 0.2. We manipulated the reliability of orientation information of target and flanker stimuli by using two different aspect ratios, 2.8 (axes 3.48 and 1.23 deg) and 1.4 (axes 3.19 and 2.28 deg), illustrated in Fig. 1A. The more elongated target was always associated with more rounded flankers, and vice versa. In each experimental session of the three experiments, the two target- flanker combinations were shown both kinds of stimuli in random order.  
+
+<|ref|>sub_title<|/ref|><|det|>[[119, 544, 207, 559]]<|/det|>
+## Procedure  
+
+<|ref|>text<|/ref|><|det|>[[117, 575, 880, 830]]<|/det|>
+Stimuli were displayed on a linearized \(22^{\prime \prime}\) LCD monitor (resolution \(1920\times 1080\) pixels, refresh rate \(60\mathrm{Hz}\) ). Observers were positioned \(57~\mathrm{cm}\) from the monitor, in a quiet room with dim lighting, and maintained fixation on a small (0.35 deg) black central dot. After a random delay from the observer initiating the trial, the stimulus was displayed for \(167~\mathrm{ms}\) . Then a thin rotatable white bar \((0.05\times 5\) deg with a gaussian profile) was presented at the fixation point with random orientation, and observers matched its orientation to that of the target by mouse control. In the first two experiments, the orientation of the two flankers was yoked, while in the third, one flanker was always \(+15^{\circ}\) (clockwise) while the other varied from \(- 45^{\circ}\) to \(+45^{\circ}\) . In the second experiment, the target- flanker distance varied, being 5.5, 7.5, 11.0 and 16.6 deg, leading to Bouma ratios of 0.21, 0.27, 0.4, 0.6.  
+
+<|ref|>text<|/ref|><|det|>[[119, 844, 861, 915]]<|/det|>
+Ten observers participated in the first experiment, five in the second, thirteen in the third. They contributed for a total of 10699 trials for the first experiment, 14377 for the second (spread across the four flanker- target distances) and 16574 for the last.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[120, 87, 235, 103]]<|/det|>
+## Data analysis  
+
+<|ref|>text<|/ref|><|det|>[[119, 118, 878, 190]]<|/det|>
+Responses occurred out from the range between 0.5 and 3 seconds after the stimulus offset were removed (for a total of \(15.9\%\) trials across the 3 experiments), as were responses with reproduction error greater than \(35^{\circ}\) ( \(6.9\%\) of trials).  
+
+<|ref|>text<|/ref|><|det|>[[118, 204, 864, 352]]<|/det|>
+For each target and relative orientation of the flanker, we calculated the average constant error (bias, positive meaning clockwise) and scatter. We then averaged the values for the two targets. Bias functions were fitted by a derivative of gaussian function, which can be considered to be a gaussian of width \(s\) multiplied by a straight line of slope a [or w], which can be considered the weighting given to the flankers: 1 means the flankers are weighted equally to the target. Bias is given by:  
+
+<|ref|>equation<|/ref|><|det|>[[177, 366, 850, 399]]<|/det|>
+\[B = a\cdot (\theta -m)\exp \Big(-\frac{(\theta -m)^2}{s^2}\Big) + b \quad (eq. 18)\]  
+
+<|ref|>text<|/ref|><|det|>[[118, 410, 870, 455]]<|/det|>
+Where \(\theta\) is orientation difference, \(m\) the centre, and \(b\) the vertical offset of the function. \(a\) , \(b\) and \(m\) were free to vary.  
+
+<|ref|>text<|/ref|><|det|>[[118, 469, 855, 514]]<|/det|>
+Scatter \((S)\) was defined as the average root variance in each condition. The variation with orientation a gaussian function in the form:  
+
+<|ref|>equation<|/ref|><|det|>[[177, 527, 850, 559]]<|/det|>
+\[S = a\cdot \exp \Big(-\frac{(\theta - m)^2}{s^2}\Big) + b \quad (eq. 19)\]  
+
+<|ref|>text<|/ref|><|det|>[[118, 570, 812, 642]]<|/det|>
+Where \(b\) is the baseline at high orientation differences and \(a\) is the amplitude of the Gaussian. As Bias and Scatter likely originate from the same process, we yoked the parameter \(s\) to best fit both curves.  
+
+<|ref|>sub_title<|/ref|><|det|>[[119, 691, 228, 707]]<|/det|>
+## REFERENCES  
+
+<|ref|>text<|/ref|><|det|>[[118, 723, 875, 863]]<|/det|>
+1 Bouma, H. Interaction effects in parafoveal letter recognition. Nature 226, 177- 178, doi:10.1038/226177a0 (1970).  
+2 Bouma, H. Visual interference in the parafoveal recognition of initial and final letters of words. Vision Res 13, 767- 782, doi:10.1016/0042- 6989(73)90041- 2 (1973).  
+3 Pelli, D. G. & Tillman, K. A. The uncrowded window of object recognition. Nat Neurosci 11, 1129- 1135, doi:10.1038/nn.2187 (2008).  
+4 Strasburger, H., Rentschler, I. & Juttner, M. Peripheral vision and pattern recognition: a review. J Vis 11, 13, doi:10.1167/11.5.13 (2011).
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[115, 87, 870, 884]]<|/det|>
+5 Whitney, D. & Levi, D. M. Visual crowding: a fundamental limit on conscious perception and object recognition. Trends Cogn Sci 15, 160- 168, doi:10.1016/j.tics.2011.02.005 (2011).6 Parkes, L., Lund, J., Angelucci, A., Solomon, J. A. & Morgan, M. Compulsory averaging of crowded orientation signals in human vision. Nat Neurosci 4, 739- 744, doi:10.1038/89532 (2001).7 Balas, B., Nakano, L. & Rosenholtz, R. A summary- statistic representation in peripheral vision explains visual crowding. J Vis 9, 13 11- 18, doi:10.1167/9.12.13 (2009).8 Levi, D. M. & Carney, T. Crowding in peripheral vision: why bigger is better. Curr Biol 19, 1988- 1993, doi:10.1016/j.cub.2009.09.056 (2009).9 Greenwood, J. A., Bex, P. J. & Dakin, S. C. Crowding changes appearance. Curr Biol 20, 496- 501, doi:10.1016/j.cub.2010.01.023 (2010).10 Freeman, J. & Simoncelli, E. P. Metamers of the ventral stream. Nat Neurosci 14, 1195- 1201, doi:10.1038/nn.2889 (2011).11 Nazir, T. A. Effects of lateral masking and spatial precueing on gap- resolution in central and peripheral vision. Vision Res 32, 771- 777, doi:10.1016/0042- 6989(92)90192- I (1992).12 Kooi, F. L., Toet, A., Tripathy, S. P. & Levi, D. M. The effect of similarity and duration on spatial interaction in peripheral vision. Spat Vis 8, 255- 279, doi:10.1163/156856894x00350 (1994).13 Kennedy, G. J. & Whitaker, D. The chromatic selectivity of visual crowding. J Vis 10, 15, doi:10.1167/10.6.15 (2010).14 Manassi, M., Sayim, B. & Herzog, M. H. When crowding of crowding leads to uncrowding. J Vis 13, 10, doi:10.1167/13.13.10 (2013).15 Levi, D. M. Crowding- an essential bottleneck for object recognition: a mini- review. Vision Res 48, 635- 654, doi:10.1016/j.visres.2007.12.009 (2008).16 Chopin, A. & Mamassian, P. Predictive properties of visual adaptation. Curr Biol 22, 622- 626, doi:10.1016/j.cub.2012.02.021 (2012).17 Cicchini, G. M., Anobile, G. & Burr, D. C. Compressive mapping of number to space reflects dynamic encoding mechanisms, not static logarithmic transform. Proc Natl Acad Sci U S A 111, 7867- 7872, doi:10.1073/pnas.1402785111 (2014).18 Fischer, J. & Whitney, D. Serial dependence in visual perception. Nat Neurosci 17, 738- 743, doi:10.1038/nn.3689 (2014).19 Pascucci, D. et al. Laws of concatenated perception: Vision goes for novelty, decisions for perseverance. PLoS Biol 17, e3000144, doi:10.1371/journal.pbio.3000144 (2019).20 Helmholtz, H. v. Handbuch der physiologischen Optik. (Voss, 1867).21 Gregory, R. L. Eye and brain; the psychology of seeing. (McGraw- Hill, 1966).22 Liberman, A., Fischer, J. & Whitney, D. Serial dependence in the perception of faces. Curr Biol 24, 2569- 2574, doi:10.1016/j.cub.2014.09.025 (2014).23 Taubert, J., Van der Burg, E. & Alais, D. Love at second sight: Sequential dependence of facial attractiveness in an on- line dating paradigm. Sci Rep 6, 22740, doi:10.1038/srep22740 (2016).24 Burr, D. & Cicchini, G. M. Vision: efficient adaptive coding. Curr Biol 24, R1096- 1098, doi:10.1016/j.cub.2014.10.002 (2014).
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[115, 85, 868, 632]]<|/det|>
+25 Cicchini, G. M., Mikellidou, K. & Burr, D. C. The functional role of serial dependence. Proc Biol Sci 285, doi:10.1098/rspb.2018.1722 (2018).26 Kording, K. P. et al. Causal inference in multisensory perception. PLoS One 2, e943, doi:10.1371/journal.pone.0000943 (2007).27 Ernst, M. O. & Banks, M. S. Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415, 429- 433, doi:10.1038/415429a (2002).28 Alais, D. & Burr, D. The ventriloquist effect results from near- optimal bimodal integration. Curr Biol 14, 257- 262, doi:10.1016/j.cub.2004.01.029 (2004).29 Rosenholtz, R., Yu, D. & Keshvari, S. Challenges to pooling models of crowding: Implications for visual mechanisms. J Vis 19, 15, doi:10.1167/19.7.15 (2019).30 Field, D. J., Hayes, A. & Hess, R. F. Contour integration by the human visual system: evidence for a local "association field". Vision Res 33, 173- 193, doi:10.1016/0042- 6989(93)90156- q (1993).31 Cicchini, G. M., Benedetto, A. & Burr, D. C. Perceptual history propagates down to early levels of sensory analysis. Curr Biol 31, 1245- 1250 e1242, doi:10.1016/j.cub.2020.12.004 (2021).32 Jazayeri, M. & Shadlen, M. N. Temporal context calibrates interval timing. Nat Neurosci 13, 1020- 1026, doi:10.1038/nn.2590 (2010).33 Cicchini, G. M., Arrighi, R., Cecchetti, L., Giusti, M. & Burr, D. C. Optimal encoding of interval timing in expert percussionists. J Neurosci 32, 1056- 1060, doi:10.1523/JNEUROSCI.3411- 11.2012 (2012).34 Manassi, M. & Whitney, D. Multi- level Crowding and the Paradox of Object Recognition in Clutter. Curr Biol 28, R127- R133, doi:10.1016/j.cub.2017.12.051 (2018).35 He, S., Cavanagh, P. & Intriligator, J. Attentional resolution and the locus of visual awareness. Nature 383, 334- 337, doi:10.1038/383334a0 (1996).36 Bulakowski, P. F., Post, R. B. & Whitney, D. Visuomotor crowding: the resolution of grasping in cluttered scenes. Front Behav Neurosci 3, 49, doi:10.3389/neuro.08.049.2009 (2009).37 Banks, W. P., Larson, D. W. & Prinzmetal, W. Asymmetry of visual interference. Percept Psychophys 25, 447- 456, doi:10.3758/bf03213822 (1979).
+
+<--- Page Split --->

preprint/preprint__0226534d35058c645a89e4e44377c8b2e4f25f5dddefbf9c78c868b60659db7b/images_list.json

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+[
+  {
+    "type": "image",
+    "img_path": "images/Figure_1.jpg",
+    "caption": "Figure 1. Quantile Generalised Additive Models (qGAM) describing the relation between environmental factors and population density for 10-percentiles (dashed lines), 50-percentiles (solid lines), and 90-percentiles (doted lines). Here, only the six most limiting factors during the 22kaBP to 8kaBP are presented. Full explorations of evaluated variables presented in Supplementary material S1.",
+    "footnote": [],
+    "bbox": [
+      [
+        232,
+        85,
+        750,
+        581
+      ]
+    ],
+    "page_idx": 21
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_2.jpg",
+    "caption": "Figure 2. Convergence between current climatic conditions (hashed density plots) and paleoclimatic conditions at four different periods (coloured density plots). Paleoclimatic periods are Greenland Stadial 2, Greenland Interstadial 1, Greenland Stadial 1, and Holocene. As in Figure 1, only the six most limiting factors during the 22kaBP to 8kaBP are presented. Full explorations of evaluated variables presented in Supplementary material S2.",
+    "footnote": [],
+    "bbox": [
+      [
+        140,
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+        722
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+    ],
+    "page_idx": 22
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_3.jpg",
+    "caption": "Figure 3. Contrast between Europe wide mean population density (top panel), and trends in key environmental variables (bottom). Estimated average population density for all Europe based on a randomization approach (top panel) are compared to archaeological population proxy based on number of calibrated radiocarbon dates for Europe between 21 and 11kyBP based on \\(^{12}\\) summaries of the Radiocarbon Palaeolithic Europe Database v28 \\(^{86}\\) (red), and core area (cf. \\(^{23}\\) population density mean and upper/lower estimates based on the Cologne Protocol (blue). On the bottom panel, plotted variables are: Effective temperature, Minimum temperature of the Coldest Month, and Maximum temperature of the Warmest Month.",
+    "footnote": [],
+    "bbox": [
+      [
+        196,
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+    ],
+    "page_idx": 23
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_4.jpg",
+    "caption": "Figure 4. Estimated human population density and range (areas where population density \\(> 1\\) individual per \\(100\\mathrm{km}^2\\) ) (A-E) and factors limiting population density (F-J) across Europe for selected times during the 22ky to 8kyBP period. (A, F) Greenland Stadial 2; (B, G) Greenland Interstadial 1; (C, H) Greenland Stadial 1 warming terminations (D, I) Holocene initiation; (E, J) Mid-Holocene. Areas in grey scale represent the glacier extent as derived by PaleoMIST 85.",
+    "footnote": [],
+    "bbox": [
+      [
+        133,
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+    ],
+    "page_idx": 24
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_5.jpg",
+    "caption": "Figure 5. Proportion of the ice-free area of Europe where each variable was estimated to be the factor limiting population density (A); and estimated population size based on the mean environmental conditions for each century (B). In both panels, only the six variables with the highest percentages of cells where the variable is the limiting factor are presented.",
+    "footnote": [],
+    "bbox": [
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+    "page_idx": 25
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_unknown_0.jpg",
+    "caption": "Supplementary material S2. Overlap between current climatic conditions (hashed density plots) used for model building and paleoclimatic databases (coloured density plots) used to hindcast human population density for all 19-climatic variables used. Title acronyms as in Table 1.",
+    "footnote": [],
+    "bbox": [],
+    "page_idx": 26
+  }
+]

preprint/preprint__0226534d35058c645a89e4e44377c8b2e4f25f5dddefbf9c78c868b60659db7b/preprint__0226534d35058c645a89e4e44377c8b2e4f25f5dddefbf9c78c868b60659db7b.mmd

@@ -0,0 +1,270 @@
+
+# Changes in limiting factors for forager population dynamics in Europe across the Last Glacial-Interglacial Transition  
+
+Alejandro Ordonez ( \(\boxed{ \begin{array}{r l} \end{array} }\) alejandro.ordonez@bio.au.dk) Aarhus University Felix Riede Aarhus University  
+
+Article  
+
+Keywords:  
+
+Posted Date: December 22nd, 2021  
+
+DOI: https://doi.org/10.21203/rs.3.rs- 1173690/v1  
+
+License: © \(\circledast\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+Version of Record: A version of this preprint was published at Nature Communications on September 6th, 2022. See the published version at https://doi.org/10.1038/s41467- 022- 32750- x.
+
+<--- Page Split --->
+
+# Changes in limiting factors for forager population dynamics in Europe across the Last Glacial-Interglacial Transition  
+
+Alejandro Ordonez \(^{1,2,4}\) & Felix Riedel \(^{1,3}\)  
+
+1, Center for Biodiversity Dynamics in a Changing World, Aarhus University  
+
+2, Department of Biology, Aarhus University  
+
+3, Department of Archaeology and Heritage Studies, Aarhus University  
+
+4, Center for Sustainable Landscapes under Global Change, Aarhus University  
+
+## 8 Abstract  
+
+Population dynamics set the framework for human genetic and cultural evolution. For foragers, demographic and environmental changes correlate strongly, although the causal relations between different environmental variables and human responses through time and space likely varied. Building on the notion of limiting factors, namely that the scarcest resource regulates population size, we present a statistical approach to identify the dominant climatic constraints for hunter- gatherer population densities and then hindcast their changing dynamics in Europe for the period between 20kyBP to 8kyBP. Limiting factors shifted from temperature- related variables during the Pleistocene to a regional mosaic of limiting factors in the Holocene. This spatiotemporal variation suggests that hunter- gatherers needed to overcome very different adaptive challenges in different parts of Europe, and that these challenges vary over time. The signatures of these changing adaptations may be visible archaeologically. In addition, the spatial disaggregation of limiting factors from the Pleistocene to the Holocene coincides with and may partly explain the diversification of the cultural geography at this time.  
+
+## 23 Introduction  
+
+As the link between exogenous environmental factors and organismal physiology, demography is vital for understanding evolution, including cultural evolution \(^{1}\) . The relevance of past demography for understanding culture change in prehistory specifically has long been recognised \(^{2,3}\) . Demographic conditions impinge on cultural transmission \(^{4- 6}\) but are also clearly implicated in the boom- and- bust patterns of population fluctuations – including periodic
+
+<--- Page Split --->
+
+extirpations – suggested to have characterised the demographic histories of prehistoric foragers and incipient farmers in many regions \(^{7 - 10}\) . Numerous recent studies have focused on the drivers of population expansion to explain the pattern and timing of human colonisation using a variety of ecological comparative approaches \(^{11,12}\) (but see ref. \(^{13}\) for a discussion of points of concern of such approaches). Yet, as foragers have a high intrinsic growth rate, population increase is, in the absence of cultural or environmental constraints, the default demographic trajectory. Evidently, however, past populations did not grow substantially, making it particularly germane to understand the factors that curtailed population growth \(^{14,15}\) . The approach adopted here builds on the central theorem that population sizes would always be regulated by the scarcest resource: the limiting factor \(^{16}\) .  
+
+Foragers of the recent past persisted in a wide variety of environments, from the frigid Arctic to tropical rainforests. Each environment offered particular opportunities but also posed particular challenges. While several earlier studies have pointed at temperature or seasonality as key drivers of forager demography at global or continental scales \(^{17,18}\) , the specific factors that would have capped or even depressed population size are likely to have varied in both space and time. Only in understanding these limiting factors can we begin to conduct targeted investigations of how specific forager populations may have overcome them via either population- specific genetic adaptations or the sort of ‘extra- somatic adaptions’ \(^{19}\) that are so characteristic of human culture.  
+
+In this study, we focus specifically on forager palaeodemography in Europe from the Last Glacial Maximum (Greenland Stadial 2, GS2) to 8000 years before present (BP), a climatically volatile period also known as the Last Glacial- Interglacial Transition \(^{20}\) . Previous studies have identified broad patterns of population growth and expansion using different methods commonly used in ecological analyses \(^{12,21 - 23}\) . Correlations between temperature and overall population density have been identified, suggesting overall increases in energy availability as the key driver of the increase in human population size following the end of GS2 \(^{17}\) . However, regional population collapses have been suggested to have occurred asynchronously and in different places \(^{9,24}\) . This raises the question of which specific limiting factors acted on forager populations and how these limiting factors varied over space and time.  
+
+Like many related studies, we begin with the global ethnographic hunter- gatherer dataset originally assembled by Binford and now digitally available \(^{25,26}\) . We couple this to a suite of quantile Generalised Additive Models (qGAMs) to describe changes in maximum (90-
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+percentile) population density as a univariate function of environmental variables related to the effect of temperature and precipitation on available energy, annual variability, and productivity. We then use the downscaled centennial average- conditions of each predictor derived from a transient climatic simulation (CCSM3 SynTrace- 21 \(^{27}\) ) and the best performing univariate qGAM models to hindcast hunter- gatherer population densities between 20ky to 8kyBP. We define the limiting environmental factor as the variable predicting the lowest population density at a given place and time. This approach allows us to query the spatial dynamics of forager limiting factors across the Last Glacial- Interglacial Transition and derive specific hypotheses as to which selection pressures acted most strongly on different forager communities in Late Pleistocene and Early Holocene Europe.  
+
+Our analysis demonstrates that the dynamics on limiting factors for forager population densities showed marked differences in space and time. Temperature- related variables were the main limiting factors during the Pleistocene, whereas the Early Holocene was characterised by a regional mosaic of limiting factors. Furthermore, our model reveals geographic differences in the limiting factors between Fennoscandia, Southern, Central, and Eastern Europe. The spatiotemporal variation in limiting factors suggests that hunter- gatherers needed to overcome very different adaptive challenges in different parts of Europe across this period of climatic and environmental change.  
+
+## Results and discussion  
+
+The relation between the environmental factors explored here and population density assessed using qGAMs (Fig. 1) was negative for temperature seasonality, positive for effective temperature, winter/fall temperature, and unimodal for the warmest temperature. Seasonal, monthly, and extreme precipitation, and topographic heterogeneity showed an overall flat trend (supplementary figure S1), yet these also differed from a mean model as determined by the high deviance explained (Table 1).  
+
+No single environmental variable explained more than \(81\%\) of the population density variation among ethnographic foraging societies (Table 1). However, the performance of more complex multivariate models using machine learning approaches \(^{12}\) or Structural Equation Models \(^{11}\) that combine three or more variables perform only marginally better. The five environmental variables with the highest predictive accuracies (based on the deviance explained; Table 1) were Temperature of the Coldest Month; Temperature Seasonality; Winter Mean Temperature;
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+Effective Temperature and Mean Annual Temperature. These variables display high collinearity (Pearson correlations range between 0.83 and 0.96), suggesting that temperature overall best captures the effect of temperature minima and energy availability in relation to forager demography. Two of the variables (Temperature of the Coldest Month and Winter Mean Temperature) represent the effect of extreme cold conditions (= winter mortality) on demographic trends and/or ecological performance \(^{28,29}\) . The other two (Mean Annual Temperature, Effective Temperature, Temperature Seasonality) relate to overall energy availability \(^{28}\) . These factors are linked to higher environmental productivity and are expected to increase available resources, leading to higher population densities, as already suggested by a plethora of earlier studies \(^{30 - 32}\) . Other variables related to environmental productivity have lower predictive accuracy (explained deviances \(< 0.79\) , see Table 1) and lower collinearity with other variables related to energy availability.  
+
+Most seasonal temperature and precipitation variables showed some of the lowest explained deviances (Table 1), indicating that seasonal climatology most likely did not impose a direct limit on past forager populations densities in Late Pleistocene/Early Holocene Europe (contra \(^{18}\) ). Topographic complexity, a variable shown to influence population density in other studies \(^{11}\) , showed only above- average predictive accuracy (Table 1). Like seasonal temperature and precipitation, the topographic complexity effect on population density may be indirect and mediated by variables describing available resources or climate extremes.  
+
+Besides the well- known limitations of using foragers of the recent past for reconstructing prehistoric social and demographic conditions \(^{13}\) , the issue of model truncation and non- analogy of climatic conditions present themselves as major potential caveats. Climatic non- analogy here refers to the problem of projecting models beyond the domain for which they have been calibrated \(^{33 - 35}\) . Model truncation refers to the incomplete characterisation of hunter- gatherer populations' total climate space \(^{36 - 38}\) and has been a long noted limitation of ethnographic analogies for prehistoric foragers \(^{39}\) . However, it has also been shown that the dataset assembled by Binford is not critically biased in terms of forager niche space \(^{25}\) . Likewise, we do not see either truncation or severe non- analogy in a temporal context, as the climate space observed at different moments during the 21- to- 8kyBP period show broad overlaps with the climate space used to develop our qGAMs (Fig. 2; supplementary figure S2). This means that our models are not unduly extrapolating into environmental regions where there is no clear indication of how population density changes as a function of evaluated
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+climatic variables. By the same token, it is necessary to highlight that the distributions of some paleoclimatic conditions – including all those with the highest predictive values in our models – are skewed towards the lower end of contemporary values. This is especially pronounced for the Pleistocene and variables such as maximum temperatures (Fig. 2), affecting our inference power on changes in population densities at these extremes.  
+
+Our models indicate that the estimated human population size in Europe was the lowest at \(22\mathrm{kyBP}\) (\~294,000 individuals) and largest at \(8\mathrm{kyBP}\) (\~706,000 individuals). Also, based on our model, we show that at the warmest point of the Greenland Interstadial 1 (\~14.7kyBP; GI1), Europe's human population size estimated by our model was \~617,000 individuals; a number that decreased to \~607,000 individuals at the coldest point of the Greenland Stadial 1 (\~11.7kyBP; GS1). Overall occupied area (number of inhabited cells) was \(62.4\%\) of the region at the end of the GS2 (\~22kyBP). This number increased to \~98.8% during GI 1, decreased to \~97.7% during the GS 1, and reached the highest point (\~99.8%) by the mid- Holocene (\~8kyBP). Taken at face value, these values are gross overestimation of actual sustained forager land- use at this time. Forager land- use was evidently extensive, including largely empty spaces \(^{40}\) . By the same token, these numbers are in line with archaeologically derived trends of overall population growth and expansion during this time (red lines; Fig. 3A).  
+
+During the evaluated period, the mean population density in the inhabited area varied between 2.6 and 6.2 persons per \(100\mathrm{km}^2\) ( \(\mathrm{GS2} = 2.7\mathrm{p} / 100\mathrm{km}^2\) ; \(\mathrm{GI1} = 5.25\mathrm{p} / 100\mathrm{km}^2\) ; \(\mathrm{GS1} = 5.17\mathrm{p} / 100\mathrm{km}^2\) ; \(\mathrm{mHol} = 6\mathrm{p} / 100\mathrm{km}^2\) ; Fig. 3A). Although the temporal patterns in average population density derived from our limiting- factor analysis are similar to those of core area estimates by Bocquet- Appel, et al. \(^{21}\) (blue areas, Fig. 3A), these do not match numerically due to our focus on maximum population densities. Moreover, our population density estimates are consistent with those suggested by Tallavaara, et al. \(^{12}\) , and more recently Kavanagh, et al. \(^{11}\) .  
+
+The estimated pattern of human population density (Fig. 3) indicates a population expansion starting almost 3ky after the ice sheet began to recede from its maximum extent 22kabP. Evaluating the spatially explicit predictions of our model, we find that at the end of the GS2, hunter- gatherer societies in Europe extended as far north as central France, southern Germany and southern parts of modern- day Ukraine (Fig. 4A), a pattern that is consistent with archaeological evidence for the recolonisation of Europe \(^{41 - 44}\) . Our models also suggest that by the end of the GS2, a relatively large proportion of the European continent may have been at least sporadically inhabited (\~62%; Fig. 4A- B), with the Mediterranean region up to the north
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+of the Alps showing population densities up to 12 individuals/100 km \(^2\) . This restricted occurrence pattern is supported by the archaeological record \(^{40}\) . Furthermore, our model indicates a persistent southwest-northeast gradient of decreasing population densities in this southern region, with the most populated areas occurring in the Iberian Peninsula and the Mediterranean region (Fig. 4A- B). From this point, the recolonisation of the continent began at \(\sim 17\mathrm{kyBP}\) (Fig. 3), reaching almost all the way to Scandinavia by the start of GI1 \(\sim 14.7\mathrm{kyBP}\) , Fig. 4c). Earlier archaeological \(^{45,46}\) and modelling studies \(^{22}\) have already suggested that this colonisation was rapid but also that it proceeded in several steps where both climate and landforms served as barriers to expansion \(^{47}\) . Our results expand on this discussion by highlighting that different climate variables limited human dispersal for a given location and that these limits changed over time.  
+
+Using our limiting- factor approach, we improve our understanding of demographic mechanisms in Late Pleistocene and Early Holocene European hunter- gatherer societies by highlighting the spatiotemporal changes in the main factor restricting population density (Fig. 4F- T; and Fig. 5). Our modelled population density estimates can be linked to regional or local narratives or empirical tests of changes in occurrences and population sizes (e.g., refs. \(^{25,48}\) ). The changes in limiting factors suggested in our models can be divided into three periods. The first period spans from the termination of GS2 to the onset of interstadial warming at around \(15\mathrm{kyBP}\) . During this period, energy availability measured as effective temperature (ET) was the main factor limiting population density across most of the continent ( \(\sim 50\%\) of cells; Fig. 5A). Mean temperature of the warmest month (MWM) was also a strong limiting factor ( \(\sim 30\%\) of cells; Fig. 5). However, limitations imposed by winter temperatures, could be also considered as likely limiting factors based on estimates of average conditions at a continental scale (Fig. 5B). The range of experienced temperature conditions, represented by ET, can thus be seen as the major limiting factor shaping human population density in Europe between GS2 and the initiation of warming associated with GI1 (Fig. 5A). With temperature related variables as the overwhelming limiting factor during this period (Fig. 5B), it is likely that the emergence of sophisticated sewing techniques and pyrotechnology \(^{49}\) facilitated the persistence and even moderate expansion of populations at this time.  
+
+The second period covers the rapid warming (GI1) as well as cooling (GS1) events between \(14.7\mathrm{kyBP}\) to \(11.7\mathrm{kyBP}\) . During this period, the importance of ET steadily decreased, and mean temperature of the warmest month ( \(\sim 27\%\) of cells) and temperature seasonality ( \(\sim 23\%\) of cells)
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+became the main factors limiting population density (Fig. 5A). The decrease of ET as a limiting factor indicates that during this period of rapid change, it was not temperature but energy availability (due to the link between MWM and productivity) what determined human population density in Europe (Fig. 5B). Our models suggest that overall population densities increased (Fig. 3), although a temporary reduction associated with GS1 cooling is also clear.  
+
+The last period encompasses the Early Holocene from its onset at 11.7ky to 8kyBP. Here, temperature of the warmest month increased in importance as the main limiting factor ( \(\sim 50\%\) of cells; Fig. 5A), while the effect of ET became marginal (Fig. 5B). Also, temperature seasonality became a critical limiting factor in many regions (Fig. 4I, J). These patterns indicate a complete shift from experienced temperature conditions to available resources as the main limiting factor of European forager population densities during the Holocene. Such a shift is interesting as the Early Holocene also witnessed a significant reorganisation of forager socio- ecological systems towards more varied use of resources and more pronounced territoriality focused on spatial circumscribed and regionally available resources, and a widespread shift from immediate- return to delayed- return economies. This also aligns with the idea that decreasing territory sizes and more marked boundary formation directly relate to the spatiotemporal dynamics of resource availability \(^{50}\) .  
+
+The regional disaggregation of patterns in limiting factors shows strong differences between Fennoscandia, Southern, Central, and Eastern Europe (Fig. 4F- J). These patterns are persistent over time, with regional shifts linked to the main feature of temperature change. In Fennoscandia and the British Isles, effective temperature was the main limiting factor for most of the Late Pleistocene. This changed after the onset of the Holocene when seasonal temperatures and precipitation became the dominant limiting factor. In Eastern and Western Europe, effective temperature was the main limiting factor at the end of the GS2 but were replaced by Winter temperature and MWM at the onset of the GI1. During the GS1 and the early Holocene, the main limiting factors where MWM and TS. In southern Europe and especially in the Mediterranean, MWM was the main limiting factor throughout most of the GS2, after which precipitation became the dominant limiting factor.  
+
+Our analyses show that the main limiting factors that limited forager population densities across the Last Glacial- Interglacial Transition in Europe changed markedly over time (Fig. 5) and space (Fig. 4F- J). We can now return to the archaeological record with these insights, searching for material culture proxies that may have allowed these past communities to
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+overcome these particular limiting factors \(^{51 - 53}\) . While these may have related to water availability (= containers) in the Mediterranean, they are predicted to relate to temperature (= clothing or pyrotechnology) in higher latitudes. Where such technologies are absent in the archaeological record, we can also begin to think about population vulnerability to climatic factors at regional levels. Especially in higher latitudes, population fluctuations may have been pronounced at the sub- centennial scale, to the point of local population extirpations \(^{9,54}\) .  
+
+Finally, the marked shift in limiting factors at the onset of the Holocene may be indicative of a greater focus on resource access at a regional scale. The spatiotemporal dynamics of resource availability have a direct impact on land- use, mobility, territoriality, and the formation of information networks in foragers \(^{50,55}\) . In the Holocene, regional cultural signatures became more pronounced and borders between different cultural zones more strongly articulated. This itself may be seen as a response to the fundamental shift in limiting factors we have identified in our models.  
+
+Seeking correlations between environmental variables and past human population densities is not a new endeavour. Following recent calls for more theoretically- informed rather than mere statistical explorations of this relationship \(^{13}\) , we highlight that while the environment can be said to strongly constrain forager lifeways, precisely which aspects of the environment do so at any one place and time vary. Our approach offers a robust way to infer the hierarchy of limiting factors and hence provide a spatiotemporal hypothesis for major selection pressures acting on forager populations in the past.  
+
+Independent palaeodemographic estimates broadly support our models, but many questions remain. Climate models, for instance, only indirectly capture the interaction of human population dynamics with changes in biodiversity and ecosystem compositions. In addition, the match between modelled population densities and the field- validated presence of Late Pleistocene/Early Holocene populations is not equally robust everywhere. These deviations may stimulate targeted field- testing with the aim of assessing whether and why population densities periodically fell short of or exceeded modelled values. In conjunction with legacy data derived from archives and the literature, such fieldwork can also shed light on the specific strategies these past foragers employed to mitigate the risks posed by specific limiting factors.  
+
+Small- scale societies have a variety of adaptive options at their disposal (see ref. \(^{56}\) ), most of which can be captured through archaeological proxies \(^{57 - 59}\) . Our limiting factor model here
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+serves as an explicit spatiotemporal hypothesis of which risk mitigation measures should be in use at which time and place. The successful identification of these would throw significant new light on the resilience and adaptation – or lack of it – during this climatically and environmentally tumultuous time. Finally, the marked shifts in dominant limiting factors identified in our models map into the results of Late Pleistocene/Early Holocene Earth System tipping points recently discussed by ref. \(^{60}\) . It is likely that, just like analogues anthropogenic warming in the present, these periods of rapid and substantive climatic change would have created challenges for contemporaneous forager populations. In an effort to align archaeological perspectives on climate change with the quandaries of our time (cf. \(^{61}\) ), future research would be well- advised to focus on such periods of major systemic transitions.  
+
+## Methods  
+
+## Models of hunter-gatherers' population density  
+
+We use ethnographic data on terrestrially adapted mobile hunter- gatherers and their climatic space \(^{25}\) to construct a series of statistical models that predict hunter- gatherer population density based on one of 16 climatic predictors (see Table 1 for rezoning and source). While there are important caveats \(^{13}\) , this approach builds on multiple ethnographic studies showing a link between climate on the one hand and hunter- gatherer diet, mobility, and demography on the other \(^{55,62- 66}\) . This statistical connection is the basis of recent studies focused on building complex multivariate models of population dynamics \(^{11,12,67}\) . A benefit of our statistical approach is that it overcomes some significant limitations, such as lack of quantitative population size data based on the archaeological record itself or genetic data, each associated with their own limitations (as reviewed in refs. \(^{2,12}\) ).  
+
+We omitted four observation classes in the original ethnographic dataset in defining the association between hunter- gatherer population density and climatic predictors. First, we removed observations associated with food producers. Second, sedentary populations or those that reside at a single location for \(>1\) year. Third, populations using aquatic resources ( \(>30\%\) of their dietary protein comes from aquatic environments, as defined in \(^{68,69}\) ). Forth, we excluded all observations related to horse- riding populations. The filters employed here correspond to those used by Tallavaara, et al. \(^{12}\) to maximise the match between ethnographic data and the current knowledge of the highly mobile and overwhelmingly terrestrially oriented lifestyles of Late Pleistocene/early Holocene hunter- gatherers in Europe. The implemented
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+filters are less restrictive than those used by other studies that have sought to reconstruct forager population dynamics during this time \(^{70}\) and thus allow for a relatively large degree of behavioural variation. This is important given that increasing evidence of marine and lacustrine resource use is emerging for at least certain times and regions in Late Pleistocene Europe \(^{71- 73}\) , and that a marked diversification characterises the resource base of early Holocene foragers. Finally, these filters remove any population using external supplements to their hunter- gatherer lifestyle, resulting in a database including information on 127 populations.  
+
+We used climate data on historical averages (1970- 2000) for 19- climate variables (Table 1) to build our ethnography- based population density models. These were obtained from the Worldclim version 2.1 \(^{74}\) at a 10- ArcMin resolution. Importantly, we used Worldclim data instead of climatic variables directly available from the ethnographic dataset to ensure comparability between climatic variables not in the database (i.e., seasonal means). Equally importantly, this approach prevents any estimation biases due to differences between the data used to define climate- density relations and paleoclimatic surfaces (see the section estimating human populations density across the Pleistocene- Holocene transition below) used to estimate population density changes and limiting factors over time.  
+
+Initially, we model how population densities of hunter- gatherer communities change along current environmental gradients using Quantile Generalised Additive Models (qGAMS). Modelling such dynamics using qGAMs offers a transparent way to determine the non- linear changes in different percentiles of a response variable (= population densities) to one or multiple environmental variables. This approach is commonly used in the ecological literature to determine the likelihood of occurrence or abundance of a given species under a particular environmental regime \(^{75- 79}\) but has never before been applied to human palaeodemography. In contrast to previous studies evaluating past human population density changes, we do not consider the synergies between multiple climatic variables when describing the relation between population densities and climate. Instead, we focus on the individual effects of evaluated variables on the top 90- percentile of population densities to identify the most pronounced limiting factor that acted on palaeodemographic growth. The tendencies in population densities as a function of environmental variables were consistent for different percentiles (see supplementary figures S1).  
+
+The population density derived from the ethnographic data followed a log- normal distribution, so these were log- transformed for subsequent analyses, and a gaussian response distribution
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+was used in our qGAMs models. Annual, monthly, and seasonal precipitation variables were similarly transformed. The ability of each of the evaluated variables to predict hunter- gatherer population densities was determined using the mean deviance explained (1 - (Residual Deviance/Null Deviance)). These were calculated both for the whole dataset, and using a 1000- fold cross- validation approach (70% random sample for calibration and 30% for validation). All models and prediction accuracy estimates were implemented in R (version 3.6; 80) using the mgcv (version 1.8.24; 81) and qgam (version 1.3.2; 82) packages.  
+
+Estimating human populations density across the Pleistocene- Holocene transition  
+
+The monthly average temperature and annual precipitation values for Europe for the 21ky to 8kyBP period come from the CCSM3 SynTrace paleoclimate simulations 83. These were biased- corrected and downscaled to \(0.5^{\circ} \times 0.5^{\circ}\) following the methods described by Lorenz, et al. 84. The paleoclimatic simulation data used here was originally generated to evaluate changes in European and North American fossil pollen data and vegetation novelty since the Last Glacial Maximum 27. Source climate surfaces were aggregated to centennial means from the original decadal averages of monthly values.  
+
+Past hunter- gatherer population densities were then predicted for every 30ArcMin cell above sea level. For visualization we also show the areas covered by glaciers using the glacier extent shapefiles derived by PaleoMIST 85. To generate 90% percentile population density estimates for each variable/century combination, only those qGAM models parametrised using the ethnographic data and current climatic conditions with cross- validated deviances above 70% were projected into past climatic conditions. As our objective was to establish the climatic variable that imposed the strongest constraints on hunter- gatherer population density at any one time, we determined the variable estimating the lowest 90%- percentile population density for a given cell at each evaluated time- period to be the limiting factor (the scarcest resource that would then limit population size cf. 16). For each evaluated time- period, we summarised the proportion of the available land area (i.e., land area not covered by ice) where each of the assessed variables was determined to be the limiting factor.  
+
+We calculated the changes in the percentage of inhabited land area in Europe during the evaluated period by estimating the proportion of the inhabited area, here defined as the region where population densities were above 1 individual per \(100\mathrm{km}^2\) . To calculate human- population size in Europe during every century, we multiplied the predicted population density
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+in each cell by the land area of the corresponding cell to arrive at per cell population size. We then and summed these values to arrive at the total population size for each century.  
+
+Uncertainties in population density, size, occupied area, and limiting factor estimates were determined using a cross- validation approach, where model fitting was iterated 1000 times using a random sample (70%) of the ethnographic and climate data at each time step. Each model was used to hindcast populations densities, estimate the percentage of inhabited land area and human population size, and define the relevant limiting factor. Uncertainty in continental- scale estimates of population densities, occupied area and population size was determined using 95% confidence intervals. The variable selected as the limiting factor in most cross- validation folds was selected as the limiting factor.  
+
+## Validation of population density estimates  
+
+To assess the validity of our population density estimations, we use the International Union for Quaternary Science (INQUA) Radiocarbon Palaeolithic Europe Database v28 \(^{86}\) . Changes in the density of records are a useful continental- scale proxy- measurement of prehistoric population size changes and are increasingly used to describe prehistoric human population dynamics trends \(^{87 - 92}\) . We extracted proxy dates (based on \(^{14}\mathrm{C}\) dates) from the INQUA Radiocarbon Palaeolithic Europe Database, aggregating these to the closest 1000 years. Our goal is to determine the match between our qGAM derived populations densities and prehistoric population occupation derived from the frequencies of radiocarbon dates between 20kaBP and 10kaBP as done by Tallavaara, et al. \(^{12}\) . This approach allowed validating our hindcasted estimates of absolute prehistoric population density since our model is not archaeologically informed, avoiding any possible circularity between model development and validation.  
+
+We also used site- based estimates of population density as derived using the Cologne Protocol by Schmidt, et al. \(^{23}\) . We focus on estimates of extended interconnected socio- economic areas (Core Areas) for five unequal time bands between 25kaBP and 11.7KaBP. Although ultimately also based on Binford \(^{25}\) , these estimates present independently derived spatially implicit estimates of population density for the Late Palaeolithic in Europe.  
+
+## Data availability
+
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+375 The 'Binford' ethnographic database \(^{25}\) is available from the Database of Places, Language, Culture, and Environment (D- PLACE; https://d- place.org/about). Current and Late Quaternary environmental datasets are publicly available from the associated references. International Union for Quaternary Science (INQUA) Radiocarbon Palaeolithic Europe Database v28 is available from https://pandoradata.earth/am/dataset/radiocarbon-palaeolithic-europe-database-v28. Contemporary climate databases are available form the WorldClim project (https://www.worldclim.org), and late-Pleistocene climate sources are available at https://doi.org/10.6084/m9.figshare.c.4673120.v2.  
+
+## References  
+
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+
+## Acknowledgements  
+
+645 AO was supported by the AUFF Starting Grant (AUFF- F- 2018- 7- 8). FR's contribution is part of CLIOARCH, an ERC Consolidator Grant project that has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 817564).
+
+<--- Page Split --->
+
+## 649 Author contributions  
+
+650 AO: Conceptualization; Methodology; Formal analysis; Resources; writing - original draft, 651 writing - review & editing; Visualization.  
+
+652 FR: Conceptualization; Methodology; writing - original draft, writing - review & editing.
+
+<--- Page Split --->
+
+Table 1. Variables used to generate ethnographic based models of the effect of climate on hunter-gatherer population density and summary of cross-validated deviance explained for the evaluated variables. Estimates correspond to those of a 1000-fold cross-validation approach (1000 samples of 70% training and 30% testing observations) or the full dataset
+
+<table><tr><td>Variable name</td><td>Acronym</td><td>Units</td><td>How does the<br>variable<br>determine<br>population<br>density?</td><td>Cross-validated<br>Deviance<br>explained.<br>Mean [95% CI]</td><td>Full-dataset<br>Deviance<br>explained</td></tr><tr><td>Effective Temperature*</td><td>ET</td><td>C</td><td>Energy availability</td><td>0.774<br>[0.8-0.826]</td><td>0.792</td></tr><tr><td>Potential<br>Evapotranspiration**</td><td>PET</td><td>mm/yr</td><td>Energy availability</td><td>0.751<br>[0.8-0.81]</td><td>0.774</td></tr><tr><td>Mean Annual<br>Temperature</td><td>MAT</td><td>C</td><td>Energy availability</td><td>0.733<br>[0.7-0.777]</td><td>0.757</td></tr><tr><td>Mean temperature of the<br>Coldest Month</td><td>MCM</td><td>C</td><td>Extreme Events</td><td>0.798<br>[0.8-0.839]</td><td>0.812</td></tr><tr><td>Mean temperature of the<br>Warmest Month</td><td>MWM</td><td>C</td><td>Extreme Events</td><td>0.705<br>[0.7-0.762]</td><td>0.737</td></tr><tr><td>Temperature Seasonality</td><td>TSeson</td><td>C</td><td>Annual Variability</td><td>0.777<br>[0.8-0.839]</td><td>0.811</td></tr><tr><td>Spring Mean<br>Temperature</td><td>SpMT</td><td>C</td><td>Seasonal trends</td><td>0.789<br>[0.8-0.828]</td><td>0.804</td></tr><tr><td>Summer Mean<br>Temperature</td><td>SmMT</td><td>C</td><td>Seasonal trends</td><td>0.773<br>[0.8-0.817]</td><td>0.786</td></tr><tr><td>Fall Mean Temperature</td><td>FMT</td><td>C</td><td>Seasonal trends</td><td>0.725<br>[0.7-0.786]</td><td>0.750</td></tr><tr><td>Winter Mean<br>Temperature</td><td>WMT</td><td>C</td><td>Seasonal trends</td><td>0.765<br>[0.8-0.808]</td><td>0.782</td></tr><tr><td>Annual precipitation</td><td>PREC</td><td>mm/yr</td><td>Energy availability</td><td>0.701<br>[0.7-0.757]</td><td>0.712</td></tr><tr><td>Precipitation of the<br>Driest Month</td><td>PDM</td><td>mm/m<br>onth</td><td>Extreme Events</td><td>0.746<br>[0.7-0.793]</td><td>0.760</td></tr><tr><td>Precipitation of the<br>Wettest Month</td><td>PDM</td><td>mm/m<br>onth</td><td>Extreme Events</td><td>0.77<br>[0.8-0.804]</td><td>0.784</td></tr><tr><td>Precipitation Seasonality</td><td>PSeson</td><td>mm/m<br>onth</td><td>Annual Variability</td><td>0.737<br>[0.7-0.772]</td><td>0.748</td></tr><tr><td>Spring Precipitation</td><td>SpPREC</td><td>mm/m<br>onth</td><td>Seasonal trends</td><td>0.773<br>[0.8-0.814]</td><td>0.788</td></tr><tr><td>Summer Precipitation</td><td>SmPREC</td><td>mm/m<br>onth</td><td>Seasonal trends</td><td>0.753<br>[0.8-0.8]</td><td>0.779</td></tr><tr><td>Fall Precipitation</td><td>FPREC</td><td>mm/m<br>onth</td><td>Seasonal trends</td><td>0.7<br>[0.7-0.772]</td><td>0.711</td></tr><tr><td>Winter Precipitation</td><td>TPREC</td><td>mm/m<br>onth</td><td>Seasonal trends</td><td>0.774<br>[0.8-0.826]</td><td>0.792</td></tr><tr><td>Topographic<br>Ruggedness Index***</td><td></td><td>m</td><td>Habitat<br>Heterogeneity</td><td>0.751<br>[0.8-0.81]</td><td>0.774</td></tr><tr><td>654</td><td colspan="5">* Calculated following 25</td></tr><tr><td>655</td><td colspan="5">** Calculated following on 93.</td></tr><tr><td>656</td><td colspan="5">*** Calculated following 94</td></tr></table>
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+![](images/Figure_1.jpg)
+
+<center>Figure 1. Quantile Generalised Additive Models (qGAM) describing the relation between environmental factors and population density for 10-percentiles (dashed lines), 50-percentiles (solid lines), and 90-percentiles (doted lines). Here, only the six most limiting factors during the 22kaBP to 8kaBP are presented. Full explorations of evaluated variables presented in Supplementary material S1. </center>
+
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+![](images/Figure_2.jpg)
+
+<center>Figure 2. Convergence between current climatic conditions (hashed density plots) and paleoclimatic conditions at four different periods (coloured density plots). Paleoclimatic periods are Greenland Stadial 2, Greenland Interstadial 1, Greenland Stadial 1, and Holocene. As in Figure 1, only the six most limiting factors during the 22kaBP to 8kaBP are presented. Full explorations of evaluated variables presented in Supplementary material S2. </center>
+
+<--- Page Split --->
+![](images/Figure_3.jpg)
+
+<center>Figure 3. Contrast between Europe wide mean population density (top panel), and trends in key environmental variables (bottom). Estimated average population density for all Europe based on a randomization approach (top panel) are compared to archaeological population proxy based on number of calibrated radiocarbon dates for Europe between 21 and 11kyBP based on \(^{12}\) summaries of the Radiocarbon Palaeolithic Europe Database v28 \(^{86}\) (red), and core area (cf. \(^{23}\) population density mean and upper/lower estimates based on the Cologne Protocol (blue). On the bottom panel, plotted variables are: Effective temperature, Minimum temperature of the Coldest Month, and Maximum temperature of the Warmest Month. </center>
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+
+<center>Figure 4. Estimated human population density and range (areas where population density \(> 1\) individual per \(100\mathrm{km}^2\) ) (A-E) and factors limiting population density (F-J) across Europe for selected times during the 22ky to 8kyBP period. (A, F) Greenland Stadial 2; (B, G) Greenland Interstadial 1; (C, H) Greenland Stadial 1 warming terminations (D, I) Holocene initiation; (E, J) Mid-Holocene. Areas in grey scale represent the glacier extent as derived by PaleoMIST 85. </center>
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+
+<center>Figure 5. Proportion of the ice-free area of Europe where each variable was estimated to be the factor limiting population density (A); and estimated population size based on the mean environmental conditions for each century (B). In both panels, only the six variables with the highest percentages of cells where the variable is the limiting factor are presented. </center>
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+  
+
+Supplementary material S1 Quantile Generalised Additive Models (qGAM) describing the relation between the six most important environmental factors explored and population density for 10- percentiles (dashed lines), 50- percentiles (solid lines), and 90- percentiles (doted lines). Title acronyms as in Table 1.
+
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+![PLACEHOLDER_27_0]
+
+<center>Supplementary material S2. Overlap between current climatic conditions (hashed density plots) used for model building and paleoclimatic databases (coloured density plots) used to hindcast human population density for all 19-climatic variables used. Title acronyms as in Table 1. </center>
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preprint/preprint__02381398ad3c168dad4fb3121959ba38b9f694fc8f5d7aeae79ef0842f4b081a/preprint__02381398ad3c168dad4fb3121959ba38b9f694fc8f5d7aeae79ef0842f4b081a.mmd

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+
+# Reference-free and cost-effective automated cell type annotation with GPT-4 in single-cell RNA-seq analysis  
+
+Zhicheng Ji zhicheng.ji@duke.edu  
+
+Duke University https://orcid.org/0000- 0002- 9457- 4704  Wenpin Hou Columbia University https://orcid.org/0000- 0003- 0972- 2192  
+
+Brief Communication  
+
+Keywords:  
+
+Posted Date: May 2nd, 2023  
+
+DOI: https://doi.org/10.21203/rs.3.rs- 2824971/v1  
+
+License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+Additional Declarations: There is NO Competing Interest.  
+
+Version of Record: A version of this preprint was published at Nature Methods on March 25th, 2024. See the published version at https://doi.org/10.1038/s41592- 024- 02235- 4.
+
+<--- Page Split --->
+
+# Reference-free and cost-effective automated cell type annotation with GPT-4 in single-cell RNA-seq analysis  
+
+Wenpin Hou \(^{1,\dagger}\) and Zhicheng Ji \(^{2,\dagger}\)  
+
+\(^{1}\) Department of Biostatistics, The Mailman School of Public Health, Columbia University, New York City, NY, USA \(^{2}\) Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA. \(^{\dagger}\) Corresponding author. E- mail: wh2526@cumc.columbia.edu; zhicheng.ji@duke.edu  
+
+## ABSTRACT  
+
+Cell type annotation is an essential step in single- cell RNA- seq analysis. However, it is a time- consuming process that often requires expertise in collecting canonical marker genes and manually annotating cell types. Automated cell type annotation methods typically require the acquisition of high- quality reference datasets and the development of additional pipelines. We demonstrate that GPT- 4, a highly potent large language model, can automatically and accurately annotate cell types by utilizing marker gene information generated from standard single- cell RNA- seq analysis pipelines. Evaluated across hundreds of tissue types and cell types, GPT- 4 generates cell type annotations exhibiting strong concordance with manual annotations, and has the potential to considerably reduce the effort and expertise needed in cell type annotation.  
+
+## Main  
+
+In single- cell RNA- sequencing (scRNA- seq) analysis \(^{1,2}\) , cell type annotation is a fundamental step to elucidate cell population heterogeneity and understand the diverse functions of different cell populations within complex tissues. Standard single- cell analysis software, such as Seurat \(^{3}\) and Scanpy \(^{4}\) , routinely employ manual cell type annotation. These software tools assign single cells into clusters by cell clustering and conduct differential analysis to identify differentially expressed genes across cell clusters. Subsequently, a human expert compares canonical cell type markers with differential gene information to assign a cell type annotation to each cell cluster. This manual annotation approach requires prior knowledge of canonical cell type markers in the given tissues and is often laborious and time- consuming. Although several automated cell type annotation methods have been developed \(^{5 - 13}\) , manual cell type annotation using marker gene information remains widely used in scRNA- seq analysis \(^{14 - 28}\) .  
+
+Generative Pre- trained Transformers (GPT), including GPT- 3, ChatGPT, and GPT- 4, are large language models trained on massive amounts of data and capable of generating human- like text based on user- provided contexts. Recent studies have demonstrated the competitive performance of GPT models in answering biomedical questions \(^{29 - 32}\) . Thus, we hypothesize that GPT- 4, one of the most advanced GPT models, has the ability to accurately identify cell types using marker gene information. GPT- 4 will potentially transform the manual cell type annotation process into a semi- automated procedure, with optional help from human experts to fine- tune GPT- 4- generated annotations (Figure 1a). Compared to other automated cell type annotation methods that require building additional pipelines and collecting high- quality reference datasets, GPT- 4 offers cost- efficiency and seamless integration into existing single- cell analysis pipelines, such as Seurat \(^{3}\) and Scanpy \(^{4}\) . The vast amount of training data enables GPT- 4 to be applied across a wide variety of tissues and cell types, overcoming the limitations of other automated cell type annotation methods restricted to specific reference datasets. Additionally, the chatbot- like nature of GPT- 4 allows users to easily adjust annotation granularity and provide feedback for iterative answer improvement (Figure 1a- b) \(^{31}\) .  
+
+To validate the hypothesis, we systematically assessed GPT- 4's cell type annotation performance across five datasets, hundreds of tissue types and cell types, and in both human and mouse (Figure 2a). Computationally identified differential genes in four scRNA- seq datasets (Azimuth by HuBMAP \(^{22}\) , Human Cell Atlas (HCA) \(^{17}\) , Human Cell Landscape (HCL) \(^{19}\) , and Mouse Cell Atlas (MCA) \(^{18}\) ), and canonical marker genes identified through literature search in one dataset (literature) \(^{17}\) , were used as inputs to GPT- 4. Cell type annotation for HCL and MCA was performed and evaluated once by aggregating all tissues, similar to the original studies. In other studies, cell type annotation was performed and evaluated within each tissue. GPT- 4 was queried using prompts similar to Figure 1b, and its cell type annotations were compared to those provided by the original studies. The comparison results were classified as “fully match” if GPT- 4 and manual annotations refer to the same cell type, “partially match” if the two annotations refer to similar but distinct cell types (e.g., monocyte and macrophage), and “mismatch” if the two annotations refer to different cell types (e.g., T cell and macrophage). If the granularity of the manual annotation
+
+<--- Page Split --->
+
+exceeded GPT- 4 annotation, GPT- 4 was asked to give more specific annotations (Figure 1b). Figure 2b shows an example of evaluating GPT- 4 cell type annotations in a human prostate tissue literature search dataset. Supplementary Table 1 contains all cell type annotations generated manually or by GPT- 4 across different tissue types and datasets, as well as agreement between manual and GPT- 4 annotations.  
+
+The performance of cell type annotation can be affected by the number of top differential genes used as reference. So we first assessed whether the number of top differential genes would affect the performance of GPT- 4 cell type annotation. To facilitate comparison, we assigned agreement scores of 1, 0.5, and 0 to cases of "fully match", "partially match", and "mismatch" respectively, and calculated the average scores across cell types within a tissue or dataset. The comparison was only performed in HCA, HCL, and MCA datasets, as full lists of differential genes were available. Figure 2c shows that GPT- 4 has the best agreement with human annotation when using the top 10 differential genes, and using more differential genes may reduce agreement. A plausible explanation is that human experts may only rely on a small number of top differential genes if they already provide a clear cell type annotation. In subsequent analyses, we used GPT- 4 cell type annotation with the top 10 differential genes for HCA, HCL, and MCA datasets.  
+
+In almost all studies and tissues, GPT- 4 annotations fully or partially match manual annotations for at least \(75\%\) of cell types (Figure 2d), demonstrating GPT- 4's ability to generate cell type annotations comparable to those of human experts. The agreement is highest for marker genes identified through literature search, with GPT- 4 annotations fully matching manual annotations for approximately \(75\%\) of cell types. The agreement decreases in marker genes identified by differential analysis, which may be attributable to a lower proportion of canonical marker genes being identified as top differential genes. We then grouped cell types into major cell categories according to the manual cell type annotations (Figure 2e, Supplementary Table 1). The agreement between GPT- 4 and manual annotations is highest among cell categories that are more homogeneous (e.g., erythroid cells and adipocytes), and lowest among cell categories that are more heterogeneous (e.g., stromal cells).  
+
+The low agreement between GPT- 4 and manual annotations in some cell types does not necessarily imply that GPT- 4 annotation is incorrect. For instance, cell types classified as stromal cells include fibroblasts and osteoblasts, which express type I collagen genes, as well as chondrocytes, which express type II collagen genes. For cells manually annotated as stromal cells, GPT- 4 assigns cell type annotations with higher granularity (e.g., fibroblasts, osteoblasts, and chondrocytes), resulting in partial matches and a lower agreement. For cell types manually annotated as stromal cells, the type I collagen genes appear in the differential gene lists in \(80\%\) of cases annotated as fibroblast or osteoblast by GPT- 4 and in \(0\%\) of cases annotated as chondrocyte by GPT- 4 (Figure 2f). This agrees with prior knowledge and the pattern observed in cell types manually annotated as chondrocyte, fibroblast, and osteoblast (Figure 2f), suggesting that GPT- 4 provides more accurate cell type annotations than manual annotations for stromal cells.  
+
+We further tested the performance of GPT- 4 when dealing with more complicated situations in real data analysis (Figure 1c). We first tested GPT- 4's ability to identify a cell cluster representing a mixture of cell types, which may occur when a cluster contains a large number of doublets or has low- resolution cell clustering. We generated simulated datasets by combining canonical markers from two distinct cell types in half of the instances and using canonical markers from a single cell type in the other half (Methods). GPT- 4 discriminated between single and mixed cell types with an average accuracy of \(94\%\) (Figure 2g). We then tested GPT- 4's ability to identify new cell types with marker genes not documented by existing literature. We created simulation datasets using randomly selected genes as cell type markers in half of the cases and canonical markers from a single cell type in the other half (Methods). GPT- 4 is able to differentiate known and unknown cell types with an average accuracy of \(100\%\) (Figure 2h). We also tested the reproducibility of GPT- 4 annotations leveraging results in previous simulation studies (Methods). On average, GPT- 4 generated identical annotations for the same cell type markers in \(91.2\%\) of cases (Figure 2i), showing a high level of reproducibility. In conclusion, GPT- 4 exhibits robust performance across various scenarios encountered in real data analysis.  
+
+In conclusion, our findings demonstrate a high level of agreement between cell type annotations generated by GPT- 4 and by human experts. Remarkably, GPT- 4 exhibits higher accuracy in annotating specific cell types. GPT- 4 can be employed as a dependable tool for automated cell type annotation of single- cell RNA- seq data, substantially reducing the time and effort required for manual annotation.  
+
+## Methods  
+
+## Dataset collection  
+
+For the HuBMAP Azimuth project, manually annotated cell types and their marker genes were downloaded from the Azimuth website (https://azimuth.hubmapconsortium.org/). Azimuth provides cell type annotations for each tissue at different granularity levels. We selected the level of granularity with the fewest number of cell types, provided that there were more than 10 cell types within that level.  
+
+For HCA \(^{17}\) , HCL \(^{19}\) , and MCA \(^{18}\) , manually annotated cell types and corresponding differential gene lists were downloaded directly from the original studies. Lists of marker genes through literature search and the corresponding cell types were
+
+<--- Page Split --->
+
+downloaded from the HCA study \(^{17}\) , and only cell types with at least 5 marker genes were used.  
+
+## Gene set preparation and GPT-4 prompts  
+
+Before using GPT- 4 to identify cell types, one needs to first prepare a list of top differential genes for each cell cluster. For example, one can use the following R code to extract gene lists of top 10 differential genes obtained from the standard Seurat pipeline. In the extracted results, each row is a list of differential genes for one cell cluster, separated by '  
+
+# d is the differential gene table generated by Seurat ordered by p- values cat(tapply(d$gene,list(d$cluster),function(i) paste0(i[1:10],collapse='','))sep='\n')  
+
+The gene lists used in this study were prepared using customized code.  
+
+GPT- 4 was accessed by visiting the ChatGPT website (https://chat.openai.com/). The "Mar 23" version of GPT- 4 was used for this study. The following words were pasted on top of the differential gene lists and used as the initial prompt for GPT- 4. The word "prostate" in the following prompt was replaced with the appropriate tissue names when annotating cell types for each tissue.  
+
+Identify cell types of human prostate cells using the following markers. Identify one cell type for each row. Only provide the cell type name.  
+
+GPT- 4 returned a list of cell type names for each query. The following prompt was used to increase the granularity of cell type annotations when needed.  
+
+Be more specific  
+
+To annotate cell clusters that could be a mixture of multiple cell types, the following words are added to the prompt.  
+
+Some could be a mixture of multiple cell types.  
+
+To annotate cell clusters that cannot be characterized by known cell type markers and are potentially new cell types, the following words are added to the prompt  
+
+Some could be unknown cell types.  
+
+Finally, the following prompt can be used to convert the list of cell type annotations generated by GPT- 4 into R code that directly creates a vector of cell type names in R.  
+
+Use "', " to concatenate all results into a single sentence. Put "c(" in front of the sentence and "')" after the sentence  
+
+## Simulation studies and reproducibility  
+
+To generate simulation datasets of unknown cell types, we compiled a list of all human genes using the Bioconductor org.Hs.eg.db package \(^{33}\) . In each simulation iteration, ten simulated unknown cell types were generated. The marker genes for each unknown cell type were produced by combining ten randomly selected human genes. Additionally, we included the literature- based cell type markers of ten randomly chosen human breast cell types as negative controls of known cell types, similar to the previous simulation study. This entire simulation process was repeated five times. Subsequently, GPT- 4 was queried using these simulated marker gene lists, and its performance in differentiating between mixed and single cell types was assessed.  
+
+To generate simulation datasets of unknown cell types, we compiled a list of all human genes using the Bioconductor org.Hs.eg.db package \(^{33}\) . In each simulation iteration, ten simulated unknown cell types were generated. The marker genes for each unknown cell type were produced by combining ten randomly selected human genes. Additionally, we included the literature- based cell type markers of ten randomly chosen human breast cell types as negative controls of known cell types, similar to the previous simulation study. This entire simulation process was repeated five times. Subsequently, GPT- 4 was queried using these simulated marker gene lists, and its performance in distinguishing between known and unknown cell types was assessed.  
+
+We assessed the reproducibility of GPT- 4 responses by leveraging the repeated querying of GPT- 4 with identical marker gene lists of the same negative control cell types in both simulation studies. For each cell type, reproducibility is defined as the proportion of instances in which GPT- 4 generates the most prevalent cell type annotation. For instance, in the case of vascular endothelial cells, GPT- 4 produces "endothelial cells" 8 times and "blood vascular endothelial cells" once. Consequently, the most prevalent cell type annotation is "endothelial cells," and the reproducibility is calculated as \(\frac{8}{9} = 0.89\) .
+
+<--- Page Split --->
+
+## Acknowledgments  
+
+Z.J. was supported by the National Institutes of Health under Award Number 1U54AG075936- 01. The manuscript was polished by GPT- 4.  
+
+## Author contributions  
+
+All authors conceived the study, conducted the analysis, and wrote the manuscript.  
+
+## Competing interests  
+
+All authors declare no competing interests.
+
+<--- Page Split --->
+![](images/Figure_1.jpg)
+
+<center>Figure 1. a, Diagram comparing cell type annotations by human experts, GPT-4, and other automated methods. b, An example showing GPT-4 prompts and answers for annotating human prostate cells with increasing granularity. c, An example showing GPT-4 prompts and answers for annotating single cell types (first two cell types), mixed cell types (third cell type), and new cell types (fourth cell type). </center>
+
+<--- Page Split --->
+
+
+<table><tr><td>a</td><td>Dataset</td><td>Species</td><td>Number of tissues</td><td>Number of cell types</td><td>Gene list source</td></tr><tr><td rowspan="5"></td><td>Azimuth</td><td>Human</td><td>11</td><td>276</td><td>Differential analysis</td></tr><tr><td>Human Cell Atlas (HCA)</td><td>Human</td><td>7</td><td>72</td><td>Differential analysis</td></tr><tr><td>Human Cell Landscape (HCL)</td><td>Human</td><td>60*</td><td>101</td><td>Differential analysis</td></tr><tr><td>literature (from HCA)</td><td>Human</td><td>7</td><td>30</td><td>Literature search</td></tr><tr><td>Mouse Cell Atlas (MCA)</td><td>Mouse</td><td>51*</td><td>65</td><td>Differential analysis</td></tr></table>
+
+\\* Cell type annotations were done by aggregating across tissues in the original studies  
+
+<table><tr><td>Manual annotation</td><td>GPT-4 answer</td><td>Agreement</td></tr><tr><td>Adipocyte</td><td>Adipocytes</td><td>Full</td></tr><tr><td>B cell_memory</td><td>B cells</td><td>Partial</td></tr><tr><td>Fibroblast</td><td>Fibroblasts</td><td>Full</td></tr><tr><td>Luminal Epithelial</td><td>Luminal epithelial cells</td><td>Full</td></tr><tr><td>Lymphatic Endothelial</td><td>Lymphatic endothelial</td><td>Full</td></tr><tr><td>Macrophage</td><td>Macrophages</td><td>Full</td></tr><tr><td>Mast Cell</td><td>Mast cells</td><td>Full</td></tr><tr><td>Pericyte</td><td>Pericytes</td><td>Full</td></tr><tr><td>Smooth Muscle</td><td>Smooth muscle cells</td><td>Full</td></tr><tr><td>T cell</td><td>T cells</td><td>Full</td></tr><tr><td>Vascular Endothelial</td><td>Endothelial cells</td><td>Partial</td></tr></table>  
+
+![](images/Figure_2.jpg)
+
+<center>Figure 2. Evaluation of cell type annotation by GPT-4. a, Datasets included in this study b, Agreement between original and GPT-4 annotations in identifying cell types of human prostate cells. c, Averaged agreement score (y-axis) and the number of top differential genes (x-axis) in HCA, HCL, and MCA datasets. d, Proportion of cell types with different levels of agreement in each study and tissue. Averaged agreement scores are shown as black dots. e, Proportion of cell types with different levels of agreement in each cell category. Averaged agreement scores are shown as black dots. f, Proportion of cell types that include type I collagen gene in the differential gene lists. The cell types are either classified as stromal cells by manual annotations and fibroblast, osteoblast, or chondrocyte by GPT-4 annotations, or classified as fibroblast, osteoblast, or chondrocyte by manual annotations. g, Proportion of cases where GPT-4 correctly identifies mixed and single cell types. Each dot represents one round of simulation. h, Proportion of cases where GPT-4 correctly identifies known and unknown cell types. Each dot represents one round of simulation. i, Reproducibility of GPT-4 annotations. Each dot represents one cell type. </center>
+
+<--- Page Split --->
+
+## References
+
+<--- Page Split --->
+
+27. Zeisel, A. et al. Cell types in the mouse cortex and hippocampus revealed by single-cell rna-seq. Science 347, 1138–1142 (2015).  
+
+28. Zou, Z. et al. A single-cell transcriptomic atlas of human skin aging. Dev. cell 56, 383–397 (2021).  
+
+29. Kung, T. H. et al. Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. PLoS digital health 2, e0000198 (2023).  
+
+30. Hou, W. & Ji, Z. Geneturing tests gpt models in genomics. bioRxiv 2023–03 (2023).  
+
+31. Shue, E. et al. Empowering beginners in bioinformatics with chatgpt. bioRxiv 2023–03 (2023).  
+
+32. Duong, D. & Solomon, B. D. Analysis of large-language model versus human performance for genetics questions. medRxiv 2023–01 (2023).  
+
+33. Gentleman, R. C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome biology 5, 1–16 (2004).
+
+<--- Page Split --->
+
+## Supplementary Files  
+
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+- supptable1.csv
+
+<--- Page Split --->

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+<|ref|>title<|/ref|><|det|>[[44, 108, 904, 209]]<|/det|>
+# Reference-free and cost-effective automated cell type annotation with GPT-4 in single-cell RNA-seq analysis  
+
+<|ref|>text<|/ref|><|det|>[[44, 230, 280, 277]]<|/det|>
+Zhicheng Ji zhicheng.ji@duke.edu  
+
+<|ref|>text<|/ref|><|det|>[[44, 303, 590, 370]]<|/det|>
+Duke University https://orcid.org/0000- 0002- 9457- 4704  Wenpin Hou Columbia University https://orcid.org/0000- 0003- 0972- 2192  
+
+<|ref|>text<|/ref|><|det|>[[44, 410, 230, 429]]<|/det|>
+Brief Communication  
+
+<|ref|>text<|/ref|><|det|>[[44, 448, 136, 466]]<|/det|>
+Keywords:  
+
+<|ref|>text<|/ref|><|det|>[[44, 486, 291, 505]]<|/det|>
+Posted Date: May 2nd, 2023  
+
+<|ref|>text<|/ref|><|det|>[[44, 524, 475, 544]]<|/det|>
+DOI: https://doi.org/10.21203/rs.3.rs- 2824971/v1  
+
+<|ref|>text<|/ref|><|det|>[[44, 561, 910, 604]]<|/det|>
+License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+<|ref|>text<|/ref|><|det|>[[44, 622, 530, 642]]<|/det|>
+Additional Declarations: There is NO Competing Interest.  
+
+<|ref|>text<|/ref|><|det|>[[42, 678, 943, 721]]<|/det|>
+Version of Record: A version of this preprint was published at Nature Methods on March 25th, 2024. See the published version at https://doi.org/10.1038/s41592- 024- 02235- 4.
+
+<--- Page Split --->
+<|ref|>title<|/ref|><|det|>[[90, 74, 907, 137]]<|/det|>
+# Reference-free and cost-effective automated cell type annotation with GPT-4 in single-cell RNA-seq analysis  
+
+<|ref|>text<|/ref|><|det|>[[90, 147, 395, 168]]<|/det|>
+Wenpin Hou \(^{1,\dagger}\) and Zhicheng Ji \(^{2,\dagger}\)  
+
+<|ref|>text<|/ref|><|det|>[[90, 183, 901, 231]]<|/det|>
+\(^{1}\) Department of Biostatistics, The Mailman School of Public Health, Columbia University, New York City, NY, USA \(^{2}\) Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA. \(^{\dagger}\) Corresponding author. E- mail: wh2526@cumc.columbia.edu; zhicheng.ji@duke.edu  
+
+<|ref|>sub_title<|/ref|><|det|>[[90, 254, 198, 272]]<|/det|>
+## ABSTRACT  
+
+<|ref|>text<|/ref|><|det|>[[94, 303, 904, 401]]<|/det|>
+Cell type annotation is an essential step in single- cell RNA- seq analysis. However, it is a time- consuming process that often requires expertise in collecting canonical marker genes and manually annotating cell types. Automated cell type annotation methods typically require the acquisition of high- quality reference datasets and the development of additional pipelines. We demonstrate that GPT- 4, a highly potent large language model, can automatically and accurately annotate cell types by utilizing marker gene information generated from standard single- cell RNA- seq analysis pipelines. Evaluated across hundreds of tissue types and cell types, GPT- 4 generates cell type annotations exhibiting strong concordance with manual annotations, and has the potential to considerably reduce the effort and expertise needed in cell type annotation.  
+
+<|ref|>sub_title<|/ref|><|det|>[[90, 437, 137, 454]]<|/det|>
+## Main  
+
+<|ref|>text<|/ref|><|det|>[[90, 463, 908, 600]]<|/det|>
+In single- cell RNA- sequencing (scRNA- seq) analysis \(^{1,2}\) , cell type annotation is a fundamental step to elucidate cell population heterogeneity and understand the diverse functions of different cell populations within complex tissues. Standard single- cell analysis software, such as Seurat \(^{3}\) and Scanpy \(^{4}\) , routinely employ manual cell type annotation. These software tools assign single cells into clusters by cell clustering and conduct differential analysis to identify differentially expressed genes across cell clusters. Subsequently, a human expert compares canonical cell type markers with differential gene information to assign a cell type annotation to each cell cluster. This manual annotation approach requires prior knowledge of canonical cell type markers in the given tissues and is often laborious and time- consuming. Although several automated cell type annotation methods have been developed \(^{5 - 13}\) , manual cell type annotation using marker gene information remains widely used in scRNA- seq analysis \(^{14 - 28}\) .  
+
+<|ref|>text<|/ref|><|det|>[[90, 601, 908, 770]]<|/det|>
+Generative Pre- trained Transformers (GPT), including GPT- 3, ChatGPT, and GPT- 4, are large language models trained on massive amounts of data and capable of generating human- like text based on user- provided contexts. Recent studies have demonstrated the competitive performance of GPT models in answering biomedical questions \(^{29 - 32}\) . Thus, we hypothesize that GPT- 4, one of the most advanced GPT models, has the ability to accurately identify cell types using marker gene information. GPT- 4 will potentially transform the manual cell type annotation process into a semi- automated procedure, with optional help from human experts to fine- tune GPT- 4- generated annotations (Figure 1a). Compared to other automated cell type annotation methods that require building additional pipelines and collecting high- quality reference datasets, GPT- 4 offers cost- efficiency and seamless integration into existing single- cell analysis pipelines, such as Seurat \(^{3}\) and Scanpy \(^{4}\) . The vast amount of training data enables GPT- 4 to be applied across a wide variety of tissues and cell types, overcoming the limitations of other automated cell type annotation methods restricted to specific reference datasets. Additionally, the chatbot- like nature of GPT- 4 allows users to easily adjust annotation granularity and provide feedback for iterative answer improvement (Figure 1a- b) \(^{31}\) .  
+
+<|ref|>text<|/ref|><|det|>[[90, 770, 908, 921]]<|/det|>
+To validate the hypothesis, we systematically assessed GPT- 4's cell type annotation performance across five datasets, hundreds of tissue types and cell types, and in both human and mouse (Figure 2a). Computationally identified differential genes in four scRNA- seq datasets (Azimuth by HuBMAP \(^{22}\) , Human Cell Atlas (HCA) \(^{17}\) , Human Cell Landscape (HCL) \(^{19}\) , and Mouse Cell Atlas (MCA) \(^{18}\) ), and canonical marker genes identified through literature search in one dataset (literature) \(^{17}\) , were used as inputs to GPT- 4. Cell type annotation for HCL and MCA was performed and evaluated once by aggregating all tissues, similar to the original studies. In other studies, cell type annotation was performed and evaluated within each tissue. GPT- 4 was queried using prompts similar to Figure 1b, and its cell type annotations were compared to those provided by the original studies. The comparison results were classified as “fully match” if GPT- 4 and manual annotations refer to the same cell type, “partially match” if the two annotations refer to similar but distinct cell types (e.g., monocyte and macrophage), and “mismatch” if the two annotations refer to different cell types (e.g., T cell and macrophage). If the granularity of the manual annotation
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[90, 79, 908, 140]]<|/det|>
+exceeded GPT- 4 annotation, GPT- 4 was asked to give more specific annotations (Figure 1b). Figure 2b shows an example of evaluating GPT- 4 cell type annotations in a human prostate tissue literature search dataset. Supplementary Table 1 contains all cell type annotations generated manually or by GPT- 4 across different tissue types and datasets, as well as agreement between manual and GPT- 4 annotations.  
+
+<|ref|>text<|/ref|><|det|>[[90, 141, 908, 276]]<|/det|>
+The performance of cell type annotation can be affected by the number of top differential genes used as reference. So we first assessed whether the number of top differential genes would affect the performance of GPT- 4 cell type annotation. To facilitate comparison, we assigned agreement scores of 1, 0.5, and 0 to cases of "fully match", "partially match", and "mismatch" respectively, and calculated the average scores across cell types within a tissue or dataset. The comparison was only performed in HCA, HCL, and MCA datasets, as full lists of differential genes were available. Figure 2c shows that GPT- 4 has the best agreement with human annotation when using the top 10 differential genes, and using more differential genes may reduce agreement. A plausible explanation is that human experts may only rely on a small number of top differential genes if they already provide a clear cell type annotation. In subsequent analyses, we used GPT- 4 cell type annotation with the top 10 differential genes for HCA, HCL, and MCA datasets.  
+
+<|ref|>text<|/ref|><|det|>[[90, 276, 908, 397]]<|/det|>
+In almost all studies and tissues, GPT- 4 annotations fully or partially match manual annotations for at least \(75\%\) of cell types (Figure 2d), demonstrating GPT- 4's ability to generate cell type annotations comparable to those of human experts. The agreement is highest for marker genes identified through literature search, with GPT- 4 annotations fully matching manual annotations for approximately \(75\%\) of cell types. The agreement decreases in marker genes identified by differential analysis, which may be attributable to a lower proportion of canonical marker genes being identified as top differential genes. We then grouped cell types into major cell categories according to the manual cell type annotations (Figure 2e, Supplementary Table 1). The agreement between GPT- 4 and manual annotations is highest among cell categories that are more homogeneous (e.g., erythroid cells and adipocytes), and lowest among cell categories that are more heterogeneous (e.g., stromal cells).  
+
+<|ref|>text<|/ref|><|det|>[[90, 397, 908, 533]]<|/det|>
+The low agreement between GPT- 4 and manual annotations in some cell types does not necessarily imply that GPT- 4 annotation is incorrect. For instance, cell types classified as stromal cells include fibroblasts and osteoblasts, which express type I collagen genes, as well as chondrocytes, which express type II collagen genes. For cells manually annotated as stromal cells, GPT- 4 assigns cell type annotations with higher granularity (e.g., fibroblasts, osteoblasts, and chondrocytes), resulting in partial matches and a lower agreement. For cell types manually annotated as stromal cells, the type I collagen genes appear in the differential gene lists in \(80\%\) of cases annotated as fibroblast or osteoblast by GPT- 4 and in \(0\%\) of cases annotated as chondrocyte by GPT- 4 (Figure 2f). This agrees with prior knowledge and the pattern observed in cell types manually annotated as chondrocyte, fibroblast, and osteoblast (Figure 2f), suggesting that GPT- 4 provides more accurate cell type annotations than manual annotations for stromal cells.  
+
+<|ref|>text<|/ref|><|det|>[[90, 533, 908, 713]]<|/det|>
+We further tested the performance of GPT- 4 when dealing with more complicated situations in real data analysis (Figure 1c). We first tested GPT- 4's ability to identify a cell cluster representing a mixture of cell types, which may occur when a cluster contains a large number of doublets or has low- resolution cell clustering. We generated simulated datasets by combining canonical markers from two distinct cell types in half of the instances and using canonical markers from a single cell type in the other half (Methods). GPT- 4 discriminated between single and mixed cell types with an average accuracy of \(94\%\) (Figure 2g). We then tested GPT- 4's ability to identify new cell types with marker genes not documented by existing literature. We created simulation datasets using randomly selected genes as cell type markers in half of the cases and canonical markers from a single cell type in the other half (Methods). GPT- 4 is able to differentiate known and unknown cell types with an average accuracy of \(100\%\) (Figure 2h). We also tested the reproducibility of GPT- 4 annotations leveraging results in previous simulation studies (Methods). On average, GPT- 4 generated identical annotations for the same cell type markers in \(91.2\%\) of cases (Figure 2i), showing a high level of reproducibility. In conclusion, GPT- 4 exhibits robust performance across various scenarios encountered in real data analysis.  
+
+<|ref|>text<|/ref|><|det|>[[90, 714, 907, 774]]<|/det|>
+In conclusion, our findings demonstrate a high level of agreement between cell type annotations generated by GPT- 4 and by human experts. Remarkably, GPT- 4 exhibits higher accuracy in annotating specific cell types. GPT- 4 can be employed as a dependable tool for automated cell type annotation of single- cell RNA- seq data, substantially reducing the time and effort required for manual annotation.  
+
+<|ref|>sub_title<|/ref|><|det|>[[90, 789, 172, 806]]<|/det|>
+## Methods  
+
+<|ref|>sub_title<|/ref|><|det|>[[90, 815, 232, 829]]<|/det|>
+## Dataset collection  
+
+<|ref|>text<|/ref|><|det|>[[90, 831, 907, 891]]<|/det|>
+For the HuBMAP Azimuth project, manually annotated cell types and their marker genes were downloaded from the Azimuth website (https://azimuth.hubmapconsortium.org/). Azimuth provides cell type annotations for each tissue at different granularity levels. We selected the level of granularity with the fewest number of cell types, provided that there were more than 10 cell types within that level.  
+
+<|ref|>text<|/ref|><|det|>[[90, 891, 904, 921]]<|/det|>
+For HCA \(^{17}\) , HCL \(^{19}\) , and MCA \(^{18}\) , manually annotated cell types and corresponding differential gene lists were downloaded directly from the original studies. Lists of marker genes through literature search and the corresponding cell types were
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[90, 78, 711, 95]]<|/det|>
+downloaded from the HCA study \(^{17}\) , and only cell types with at least 5 marker genes were used.  
+
+<|ref|>sub_title<|/ref|><|det|>[[90, 110, 409, 126]]<|/det|>
+## Gene set preparation and GPT-4 prompts  
+
+<|ref|>text<|/ref|><|det|>[[90, 127, 910, 173]]<|/det|>
+Before using GPT- 4 to identify cell types, one needs to first prepare a list of top differential genes for each cell cluster. For example, one can use the following R code to extract gene lists of top 10 differential genes obtained from the standard Seurat pipeline. In the extracted results, each row is a list of differential genes for one cell cluster, separated by '  
+
+<|ref|>text<|/ref|><|det|>[[90, 185, 919, 216]]<|/det|>
+# d is the differential gene table generated by Seurat ordered by p- values cat(tapply(d$gene,list(d$cluster),function(i) paste0(i[1:10],collapse='','))sep='\n')  
+
+<|ref|>text<|/ref|><|det|>[[110, 226, 577, 241]]<|/det|>
+The gene lists used in this study were prepared using customized code.  
+
+<|ref|>text<|/ref|><|det|>[[90, 241, 910, 301]]<|/det|>
+GPT- 4 was accessed by visiting the ChatGPT website (https://chat.openai.com/). The "Mar 23" version of GPT- 4 was used for this study. The following words were pasted on top of the differential gene lists and used as the initial prompt for GPT- 4. The word "prostate" in the following prompt was replaced with the appropriate tissue names when annotating cell types for each tissue.  
+
+<|ref|>text<|/ref|><|det|>[[90, 314, 792, 344]]<|/det|>
+Identify cell types of human prostate cells using the following markers. Identify one cell type for each row. Only provide the cell type name.  
+
+<|ref|>text<|/ref|><|det|>[[90, 354, 907, 385]]<|/det|>
+GPT- 4 returned a list of cell type names for each query. The following prompt was used to increase the granularity of cell type annotations when needed.  
+
+<|ref|>text<|/ref|><|det|>[[90, 397, 249, 411]]<|/det|>
+Be more specific  
+
+<|ref|>text<|/ref|><|det|>[[101, 422, 870, 438]]<|/det|>
+To annotate cell clusters that could be a mixture of multiple cell types, the following words are added to the prompt.  
+
+<|ref|>text<|/ref|><|det|>[[90, 451, 549, 466]]<|/det|>
+Some could be a mixture of multiple cell types.  
+
+<|ref|>text<|/ref|><|det|>[[90, 477, 907, 508]]<|/det|>
+To annotate cell clusters that cannot be characterized by known cell type markers and are potentially new cell types, the following words are added to the prompt  
+
+<|ref|>text<|/ref|><|det|>[[90, 520, 411, 535]]<|/det|>
+Some could be unknown cell types.  
+
+<|ref|>text<|/ref|><|det|>[[90, 545, 907, 576]]<|/det|>
+Finally, the following prompt can be used to convert the list of cell type annotations generated by GPT- 4 into R code that directly creates a vector of cell type names in R.  
+
+<|ref|>text<|/ref|><|det|>[[90, 588, 697, 618]]<|/det|>
+Use "', " to concatenate all results into a single sentence. Put "c(" in front of the sentence and "')" after the sentence  
+
+<|ref|>sub_title<|/ref|><|det|>[[90, 632, 387, 648]]<|/det|>
+## Simulation studies and reproducibility  
+
+<|ref|>text<|/ref|><|det|>[[90, 649, 909, 740]]<|/det|>
+To generate simulation datasets of unknown cell types, we compiled a list of all human genes using the Bioconductor org.Hs.eg.db package \(^{33}\) . In each simulation iteration, ten simulated unknown cell types were generated. The marker genes for each unknown cell type were produced by combining ten randomly selected human genes. Additionally, we included the literature- based cell type markers of ten randomly chosen human breast cell types as negative controls of known cell types, similar to the previous simulation study. This entire simulation process was repeated five times. Subsequently, GPT- 4 was queried using these simulated marker gene lists, and its performance in differentiating between mixed and single cell types was assessed.  
+
+<|ref|>text<|/ref|><|det|>[[90, 741, 909, 845]]<|/det|>
+To generate simulation datasets of unknown cell types, we compiled a list of all human genes using the Bioconductor org.Hs.eg.db package \(^{33}\) . In each simulation iteration, ten simulated unknown cell types were generated. The marker genes for each unknown cell type were produced by combining ten randomly selected human genes. Additionally, we included the literature- based cell type markers of ten randomly chosen human breast cell types as negative controls of known cell types, similar to the previous simulation study. This entire simulation process was repeated five times. Subsequently, GPT- 4 was queried using these simulated marker gene lists, and its performance in distinguishing between known and unknown cell types was assessed.  
+
+<|ref|>text<|/ref|><|det|>[[90, 846, 909, 922]]<|/det|>
+We assessed the reproducibility of GPT- 4 responses by leveraging the repeated querying of GPT- 4 with identical marker gene lists of the same negative control cell types in both simulation studies. For each cell type, reproducibility is defined as the proportion of instances in which GPT- 4 generates the most prevalent cell type annotation. For instance, in the case of vascular endothelial cells, GPT- 4 produces "endothelial cells" 8 times and "blood vascular endothelial cells" once. Consequently, the most prevalent cell type annotation is "endothelial cells," and the reproducibility is calculated as \(\frac{8}{9} = 0.89\) .
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[90, 77, 264, 95]]<|/det|>
+## Acknowledgments  
+
+<|ref|>text<|/ref|><|det|>[[90, 101, 905, 131]]<|/det|>
+Z.J. was supported by the National Institutes of Health under Award Number 1U54AG075936- 01. The manuscript was polished by GPT- 4.  
+
+<|ref|>sub_title<|/ref|><|det|>[[90, 148, 285, 166]]<|/det|>
+## Author contributions  
+
+<|ref|>text<|/ref|><|det|>[[90, 173, 628, 188]]<|/det|>
+All authors conceived the study, conducted the analysis, and wrote the manuscript.  
+
+<|ref|>sub_title<|/ref|><|det|>[[90, 205, 281, 222]]<|/det|>
+## Competing interests  
+
+<|ref|>text<|/ref|><|det|>[[90, 230, 370, 244]]<|/det|>
+All authors declare no competing interests.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[98, 270, 900, 690]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[88, 703, 896, 746]]<|/det|>
+<center>Figure 1. a, Diagram comparing cell type annotations by human experts, GPT-4, and other automated methods. b, An example showing GPT-4 prompts and answers for annotating human prostate cells with increasing granularity. c, An example showing GPT-4 prompts and answers for annotating single cell types (first two cell types), mixed cell types (third cell type), and new cell types (fourth cell type). </center>
+
+<--- Page Split --->
+<|ref|>table<|/ref|><|det|>[[115, 115, 900, 234]]<|/det|>
+
+<table><tr><td>a</td><td>Dataset</td><td>Species</td><td>Number of tissues</td><td>Number of cell types</td><td>Gene list source</td></tr><tr><td rowspan="5"></td><td>Azimuth</td><td>Human</td><td>11</td><td>276</td><td>Differential analysis</td></tr><tr><td>Human Cell Atlas (HCA)</td><td>Human</td><td>7</td><td>72</td><td>Differential analysis</td></tr><tr><td>Human Cell Landscape (HCL)</td><td>Human</td><td>60*</td><td>101</td><td>Differential analysis</td></tr><tr><td>literature (from HCA)</td><td>Human</td><td>7</td><td>30</td><td>Literature search</td></tr><tr><td>Mouse Cell Atlas (MCA)</td><td>Mouse</td><td>51*</td><td>65</td><td>Differential analysis</td></tr></table>
+
+<|ref|>table_footnote<|/ref|><|det|>[[198, 235, 806, 250]]<|/det|>
+\\* Cell type annotations were done by aggregating across tissues in the original studies  
+
+<|ref|>table<|/ref|><|det|>[[115, 258, 530, 437]]<|/det|>
+
+<table><tr><td>Manual annotation</td><td>GPT-4 answer</td><td>Agreement</td></tr><tr><td>Adipocyte</td><td>Adipocytes</td><td>Full</td></tr><tr><td>B cell_memory</td><td>B cells</td><td>Partial</td></tr><tr><td>Fibroblast</td><td>Fibroblasts</td><td>Full</td></tr><tr><td>Luminal Epithelial</td><td>Luminal epithelial cells</td><td>Full</td></tr><tr><td>Lymphatic Endothelial</td><td>Lymphatic endothelial</td><td>Full</td></tr><tr><td>Macrophage</td><td>Macrophages</td><td>Full</td></tr><tr><td>Mast Cell</td><td>Mast cells</td><td>Full</td></tr><tr><td>Pericyte</td><td>Pericytes</td><td>Full</td></tr><tr><td>Smooth Muscle</td><td>Smooth muscle cells</td><td>Full</td></tr><tr><td>T cell</td><td>T cells</td><td>Full</td></tr><tr><td>Vascular Endothelial</td><td>Endothelial cells</td><td>Partial</td></tr></table>  
+
+<|ref|>image<|/ref|><|det|>[[114, 440, 911, 765]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[88, 775, 911, 903]]<|/det|>
+<center>Figure 2. Evaluation of cell type annotation by GPT-4. a, Datasets included in this study b, Agreement between original and GPT-4 annotations in identifying cell types of human prostate cells. c, Averaged agreement score (y-axis) and the number of top differential genes (x-axis) in HCA, HCL, and MCA datasets. d, Proportion of cell types with different levels of agreement in each study and tissue. Averaged agreement scores are shown as black dots. e, Proportion of cell types with different levels of agreement in each cell category. Averaged agreement scores are shown as black dots. f, Proportion of cell types that include type I collagen gene in the differential gene lists. The cell types are either classified as stromal cells by manual annotations and fibroblast, osteoblast, or chondrocyte by GPT-4 annotations, or classified as fibroblast, osteoblast, or chondrocyte by manual annotations. g, Proportion of cases where GPT-4 correctly identifies mixed and single cell types. Each dot represents one round of simulation. h, Proportion of cases where GPT-4 correctly identifies known and unknown cell types. Each dot represents one round of simulation. i, Reproducibility of GPT-4 annotations. Each dot represents one cell type. </center>
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[92, 77, 199, 95]]<|/det|>
+## References
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[87, 78, 909, 110]]<|/det|>
+27. Zeisel, A. et al. Cell types in the mouse cortex and hippocampus revealed by single-cell rna-seq. Science 347, 1138–1142 (2015).  
+
+<|ref|>text<|/ref|><|det|>[[90, 113, 770, 130]]<|/det|>
+28. Zou, Z. et al. A single-cell transcriptomic atlas of human skin aging. Dev. cell 56, 383–397 (2021).  
+
+<|ref|>text<|/ref|><|det|>[[88, 133, 904, 165]]<|/det|>
+29. Kung, T. H. et al. Performance of chatgpt on usmle: Potential for ai-assisted medical education using large language models. PLoS digital health 2, e0000198 (2023).  
+
+<|ref|>text<|/ref|><|det|>[[88, 169, 671, 186]]<|/det|>
+30. Hou, W. & Ji, Z. Geneturing tests gpt models in genomics. bioRxiv 2023–03 (2023).  
+
+<|ref|>text<|/ref|><|det|>[[88, 189, 742, 206]]<|/det|>
+31. Shue, E. et al. Empowering beginners in bioinformatics with chatgpt. bioRxiv 2023–03 (2023).  
+
+<|ref|>text<|/ref|><|det|>[[88, 210, 905, 241]]<|/det|>
+32. Duong, D. & Solomon, B. D. Analysis of large-language model versus human performance for genetics questions. medRxiv 2023–01 (2023).  
+
+<|ref|>text<|/ref|><|det|>[[88, 245, 905, 277]]<|/det|>
+33. Gentleman, R. C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome biology 5, 1–16 (2004).
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[44, 43, 311, 70]]<|/det|>
+## Supplementary Files  
+
+<|ref|>text<|/ref|><|det|>[[44, 93, 765, 112]]<|/det|>
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+<|ref|>text<|/ref|><|det|>[[61, 131, 216, 150]]<|/det|>
+- supptable1.csv
+
+<--- Page Split --->

preprint/preprint__025de565043ff8e7db332a9d49b5794afb4f1a967efe3413a21f6a9dbeddf7e4/preprint__025de565043ff8e7db332a9d49b5794afb4f1a967efe3413a21f6a9dbeddf7e4_det.mmd

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+<|ref|>title<|/ref|><|det|>[[44, 107, 890, 208]]<|/det|>
+# Unequal Emissions, Unequal Impacts: How High-Income Groups Disproportionately Contribute to Climate Extremes Worldwide  
+
+<|ref|>text<|/ref|><|det|>[[44, 230, 377, 276]]<|/det|>
+Sarah Schoengart sarah.schoengart@hu- berlin.de  
+
+<|ref|>text<|/ref|><|det|>[[44, 304, 925, 512]]<|/det|>
+HU Berlin Zebedee Nicholls University of Melbourne https://orcid.org/0000- 0002- 4767- 2723 Roman Hoffmann International Institute for Applied Systems Analysis (IIASA) https://orcid.org/0000- 0003- 3512- 1737 Setu Pelz International Institute for Applied Systems Analysis https://orcid.org/0000- 0002- 3528- 8679 Carl- Friedrich Schleussner IIASA https://orcid.org/0000- 0001- 8471- 848X  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 550, 103, 567]]<|/det|>
+## Article  
+
+<|ref|>text<|/ref|><|det|>[[44, 587, 497, 606]]<|/det|>
+Keywords: Attribution, Inequality, Injustice, Extremes  
+
+<|ref|>text<|/ref|><|det|>[[44, 625, 350, 644]]<|/det|>
+Posted Date: November 27th, 2024  
+
+<|ref|>text<|/ref|><|det|>[[44, 663, 475, 682]]<|/det|>
+DOI: https://doi.org/10.21203/rs.3.rs- 5417521/v1  
+
+<|ref|>text<|/ref|><|det|>[[44, 700, 914, 742]]<|/det|>
+License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+<|ref|>text<|/ref|><|det|>[[44, 761, 535, 780]]<|/det|>
+Additional Declarations: There is NO Competing Interest.  
+
+<|ref|>text<|/ref|><|det|>[[42, 816, 940, 858]]<|/det|>
+Version of Record: A version of this preprint was published at Nature Climate Change on May 7th, 2025. See the published version at https://doi.org/10.1038/s41558- 025- 02325- x.
+
+<--- Page Split --->
+<|ref|>title<|/ref|><|det|>[[92, 85, 787, 111]]<|/det|>
+# Unequal Emissions, Unequal Impacts: How  
+
+<|ref|>title<|/ref|><|det|>[[92, 115, 737, 142]]<|/det|>
+# High-Income Groups Disproportionately  
+
+<|ref|>title<|/ref|><|det|>[[92, 147, 775, 174]]<|/det|>
+# Contribute to Climate Extremes Worldwide  
+
+<|ref|>text<|/ref|><|det|>[[92, 183, 830, 220]]<|/det|>
+Sarah Schöngart<sup>1,2</sup>, Zebedee Nicholls<sup>1,3,4</sup>, Roman Hoffmann<sup>1</sup>, Setu Pelz<sup>1</sup>, and Carl- Friedrich Schleussner<sup>1,2</sup>  
+
+<|ref|>text<|/ref|><|det|>[[92, 231, 883, 312]]<|/det|>
+<sup>1</sup>International Institute for Applied Systems Analysis (IIASA), Schloβplatz 1, 2361 Laxenburg, Austria  <sup>2</sup>IRIThesys, Humboldt- Universität zu Berlin, Friedrichstrasse 191, 10117 Berlin, Germany  <sup>3</sup>Climate Resource, Melbourne, Australia  <sup>4</sup>School of Geography, Earth and Atmospheric Sciences, The University of Melbourne, Melbourne, Australia  
+
+<|ref|>text<|/ref|><|det|>[[92, 323, 280, 355]]<|/det|>
+Corresponding author: Sarah Schöngart<sup>1</sup>  
+
+<|ref|>text<|/ref|><|det|>[[92, 360, 516, 376]]<|/det|>
+Email address: sarah.schoengart@climateanalytics.org  
+
+<|ref|>sub_title<|/ref|><|det|>[[92, 397, 222, 415]]<|/det|>
+## ABSTRACT  
+
+<|ref|>text<|/ref|><|det|>[[114, 428, 875, 624]]<|/det|>
+One of the fundamental injustices of climate change is that those least responsible often bear the brunt of its impacts. This injustice persists not only between countries but also on the individual level within societies. Here, we assess how greenhouse gas emissions from consumption and investments attributable to the wealthiest population groups from 1990 to 2019 have influenced present- day (2020) global mean temperature levels as well as monthly temperature and potential drought extremes. We combine emission inequality data with an emulator- based modelling framework, enabling a systematic attribution of changes in regional extremes worldwide. We find that the top \(10\%\) wealthiest individuals globally contributed about 6.5 times the global average to global warming \((0.40^{\circ}\mathrm{C} \pm 0.16^{\circ}\mathrm{C})\) , the top \(1\%\) even 20 times the average \((0.12^{\circ}\mathrm{C} \pm 0.05^{\circ}\mathrm{C})\) . These disproportionate contributions further amplify for extreme events, with the top \(10\%\) contributing about 7 times more to the emergence of 1- in- 100 year heat and potential drought events than the global average \((11.5 \pm 3.9\) and \(4.7 \pm 2.8\) additional occurrences), the top \(1\%\) 25 times more \((4.0 \pm 1.3\) and \(1.7 \pm 0.9\) additional occurrences). Emissions from the wealthiest \(10\%\) in the United States and China, the two largest greenhouse gas emitters, are associated with a two- to three- fold increase in the frequency of heat and drought extremes across vulnerable regions. Quantifying the relationship between wealth disparities and climate change impacts can assist the discourse on climate equity and justice.  
+
+<|ref|>text<|/ref|><|det|>[[115, 643, 488, 658]]<|/det|>
+Keywords: Attribution; Inequality; Injustice; Extremes  
+
+<|ref|>sub_title<|/ref|><|det|>[[92, 690, 287, 707]]<|/det|>
+## 1 INTRODUCTION  
+
+<|ref|>text<|/ref|><|det|>[[92, 714, 884, 880]]<|/det|>
+Over the past two decades, extreme events attributable to climate change led to an annual average of 143 billion USD in damages<sup>[1]</sup>. How these costs could and should be covered - both between and within countries - is subject to debate<sup>[2]</sup>. Central to this debate is the stark disparity between those responsible for emissions and those affected by their impacts. The wealthiest \(10\%\) of the global population accounted for nearly half of global emissions in 2019 through private consumption and investments, whereas the poorest \(50\%\) contributed only one- tenth of global emissions<sup>[3]</sup>. At the same time, regions with low historic emissions and income levels are typically more frequently and severely exposed to climate impacts<sup>[4,5]</sup> with limited resources for adaptation<sup>[6]</sup>. This cause- and- effect injustice is widely acknowledged<sup>[7]</sup>, yet, an quantification of how carbon inequality translates into unequal accountability for the resulting global temperature levels and extreme climate events is missing. Given the role of non- \(\mathrm{CO}_{2}\) greenhouse gases (GHGs), such as methane, in recent warming, a modelling rather than a metric- based approach is required to accurately assess the warming contributions of different emitters<sup>[8]</sup>.  
+
+<|ref|>text<|/ref|><|det|>[[92, 896, 884, 912]]<|/det|>
+In this study, we combine wealth- based carbon inequality assessments<sup>[3]</sup>, with an emulator- based climate mod
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[115, 88, 880, 269]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[114, 279, 876, 355]]<|/det|>
+<center>Figure 1. |Overview modelling framework. Counterfactual emissions are converted into GMT using the simple climate model MAGICC and subsequently translated into grid-point level realisations of climatic variables using the ESM emulator MESMER-M-TP. a, Counterfactual CO2 emission pathways. Historic emissions (blue) without contribution of a selected emitter group post 1990 (orange). b, GMT levels for historic and counterfactual emission pathways. c, Reference, present-day and counterfactual temperature distributions at a single grid-cell. </center>  
+
+<|ref|>text<|/ref|><|det|>[[88, 380, 883, 472]]<|/det|>
+29 elling framework[9] to systematically attribute changes in global mean temperature (GMT) levels and grid- point- level climate extremes to emissions from different wealth groups. We use the Model for the Assessment of the Greenhouse Gas Induced Climate Change (MAGICC)[10], a simple climate model, in conjunction with the Modular Earth System Model Emulator for Monthly Temperature and Precipitation (MESMER- M- TP)[11], a model that is able to generate large ensembles of spatially explicit monthly temperature and precipitation data which closely resembles that of complex Earth System Models (ESMs) at a fraction of the cost.  
+
+<|ref|>text<|/ref|><|det|>[[88, 485, 884, 578]]<|/det|>
+We use attribution science frameworks to link human- induced GHG emissions to changes in the frequency and intensity of individual extreme events[12]. Originally, these frameworks were developed to attribute changes to total human emissions[13,14], but they are increasingly applied to individual emitters, such as companies or countries[15,16]. When attributing impacts among multiple emitters, various approaches exist, each yielding different outcomes and being equally justifiable[17,18]. Here, we assess the changes in the characteristics of extreme events but for the emissions of a specific emitter group[15,18].  
+
+<|ref|>text<|/ref|><|det|>[[88, 592, 884, 760]]<|/det|>
+We generate counterfactual emission pathways by subtracting the 1990- 2019 emissions of specific emitter groups, namely the wealthiest \(10\% /1\% /0.1\%\) globally, as well as in the US, the EU27, India, and China (Fig. 1 Panel b). Emissions data, drawn from[3], include emissions from domestic consumption, public and private investments, and trade; emissions are attributed to consumers, except, emissions from capital formation in production sectors are attributed to firm owners[19]. Emissions are reported as a basket of GHGs, aggregated with Global Warming Potential 100. We then convert these counterfactual pathways into GMT levels and gridded climatic variables (panel a in Fig. 1), allowing us to compare the 2020 climate against the hypothetical 2020 climate state that we would observe, if these groups had not emitted. Specifically, we attribute GMT levels and changes in the probability and intensity of monthly temperature and potential drought extremes (Fig. 1 Panel c), using the Standardised Precipitation Evapotranspiration Index computed over 3 month periods (SPEI- 3)[20]. For illustration, we also assess counterfactual warming outcomes based on rescaling global emissions according to the per capita profile of individual income percentiles.  
+
+<|ref|>text<|/ref|><|det|>[[88, 774, 883, 820]]<|/det|>
+Our analysis attributes climate impacts to wealthy emitters and compares these to impacts from global average per capita emissions. However, we do not assess what would constitute fair or just emissions for these groups, nor do we assign direct responsibility for the resulting impacts.  
+
+<|ref|>sub_title<|/ref|><|det|>[[88, 838, 227, 855]]<|/det|>
+## 2 RESULTS  
+
+<|ref|>sub_title<|/ref|><|det|>[[88, 864, 564, 880]]<|/det|>
+### 2.1 Inequality in Attributed Global Warming Contributions  
+
+<|ref|>text<|/ref|><|det|>[[88, 881, 883, 911]]<|/det|>
+Our modelling framework depicts natural variability and uncertainty in the global response to emission changes (see Methods). Unless mentioned otherwise, we provide median results and the \((5^{\mathrm{th}} - 95^{\mathrm{th}})\) confidence interval. As
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[118, 88, 883, 578]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[114, 585, 884, 800]]<|/det|>
+<center>Figure 2. |Attributed 1990-2020 GMT increase by emitter group. a, GMT increase attributed to global top \(10\% /1\% /0.1\%\) (orange/teal/pink) and actual GMT increase over 1990-2020 (grey). Hatched areas indicate the warming for each group based on an equal per capita contribution to warming. Climate Inequality Factors (CIFs) indicating the group's contribution to global warming relative to the average contribution are given above the bars. Vertical lines represent \(5^{\mathrm{th}}\) - \(95^{\mathrm{th}}\) quantile ranges from natural variability and uncertainty in the global temperature response to emission changes. Circles highlight median values from sensitivity analysis, see Methods (lower circle: \(\mathrm{CO_2}\) -based emissions; upper circle: non- \(\mathrm{CO_2}\) -based emissions). b, Hypothetical GMT increase from 1990-2020 if everyone emitted like the given income groups, with \(5^{\mathrm{th}}\) - \(95^{\mathrm{th}}\) uncertainty ranges shown by vertical lines. c-d, Regional breakdown of the global top \(10\% /1\%\) over time. e, Same as Panel a but for the regional top \(10\% /1\% /0.1\%\) (orange, teal, pink) in the US, the EU27, India and China. Grey bar highlights the GMT increase attributable to the region as a whole. Two CIFs are given: the lighter (darker and lower) value is relative to the country's equal share (actual emissions) and measures global (regional) inequality. f-g, Income thresholds of the regional top \(10\% /1\%\) ranked against global income levels. Values below (above) bold grey line indicate regional top \(10\% /1\%\) are wealthier (poorer) than global top \(10\% /1\%\) . </center>  
+
+<|ref|>text<|/ref|><|det|>[[90, 832, 883, 878]]<|/det|>
+our database provides only basket emissions, we derived the main results assuming emissions for each GHG scale proportionally with the globally aggregated emissions (see Methods). We explore the sensitivity to this assumption in the Supplementary 5.2.  
+
+<|ref|>text<|/ref|><|det|>[[90, 880, 883, 911]]<|/det|>
+GMT has risen by \(0.61^{\circ}\mathrm{C}(\pm 0.24^{\circ}\mathrm{C})\) between 1990 and 2020. We find that about \(65\% (0.40^{\circ}\mathrm{C}\pm 0.16^{\circ}\mathrm{C})\) of this increase is attributable to the global top \(10\%\) , \(20\% (0.12^{\circ}\mathrm{C}\pm 0.05^{\circ}\mathrm{C})\) to the top 1 and \(8\% (0.05^{\circ}\mathrm{C}\pm 0.02^{\circ}\mathrm{C})\) to the
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[92, 90, 883, 152]]<|/det|>
+67 top \(0.1\%\) (see Fig. 2 Panel a and Table S2). These warming responsibilities are higher (by about one fifth) than the respective group's contributions to aggregated GHG basket emissions (see Table S1), underscoring the importance of using a climate model to assess warming contributions and the potential for non- linearities in such attribution [21] (see also Supplementary Material 5.2).  
+
+<|ref|>text<|/ref|><|det|>[[112, 152, 883, 227]]<|/det|>
+To put these numbers into perspective, we compute equal shares by scaling the total GMT increase according to the group's share of the global population (e.g. the global top \(10\%\) 's equal share are \(10\%\) of the full \(0.61^{\circ}\mathrm{C}\) increase). We then derive Climate Inequality Factors (CIFs) as the group's actual contribution to global warming relative to their equal share. CIFs increase from 6.5 for the top 10 to 20 (77) for the top 1 (0.1), indicating an amplification of climate inequality with increasing wealth.  
+
+<|ref|>text<|/ref|><|det|>[[112, 227, 883, 289]]<|/det|>
+The full depth of the disproportion in contributions to GMT level becomes tangible when rescaling global emissions according to the per capita profile of global income groups (Fig. 2 Panel b). If the entire world population had emitted like the bottom \(50\%\) , there would have been minimal additional warming since 1990. However, if the entire world population had emitted like the top \(10\% /1\% /0.1\%\) , the GMT increase since 1990 would have been \(2.9 / 6.7 / 12.2^{\circ}\mathrm{C}\) .  
+
+<|ref|>text<|/ref|><|det|>[[112, 289, 883, 395]]<|/det|>
+Accounting from 1990, the top emitters primarily come from the world's highest emitting countries: the US, China, India and the EU27 (Fig. 2 Panels c- d). Shares of the wealthy have been shifting over the past 30 years: while more than \(27\%\) of the global top \(10\%\) were European in 1990, this number has dropped to \(19\%\) in 2019; and while less than \(1\%\) of the global top \(10\%\) came from China in 1990, the share has grown to \(13\%\) in 2019. While our focus is on wealthy individuals from the world's largest economies, we note that those from smaller countries also contribute disproportionately, with within country inequalities being even more pronounced in countries in Sub- Saharan Africa and the Middle East and North Africa (MENA) region [3].  
+
+<|ref|>text<|/ref|><|det|>[[112, 408, 883, 484]]<|/det|>
+We also assess contributions of emitters from selected regions (US, EU27, China, India). Income levels from regional top emitters deviate from their global counter parts, i.e. the top \(10\% /1\%\) in the US and the EU27 (India and China) are wealthier (poorer) than the globally wealthiest \(10\% /1\%\) (Fig. 2 f and g). In the US, the regional top \(10\% /1\%\) belong to the globally wealthiest \(1 - 2\% /0.1 - 0.2\%\) . In China, the top \(10\%\) were among the globally wealthiest \(37\%\) (13%) in 1990 (2019).  
+
+<|ref|>text<|/ref|><|det|>[[112, 485, 883, 606]]<|/det|>
+Attributed GMT shares by regional emitter groups combine within- and between- region inequality. In the US/EU27, the top \(10\%\) contribute 3.1/2.8 times more to global warming than the average citizen, but 17/8 times more than the global average. For the US, the top \(10\%\) 's contribution alone exceeds the entire country's equal share. This relative inequality increases with increasing wealth: the top \(1\%\) in the US/EU27 contribute 53/21 times their equal shares, and the top \(0.1\%\) contribute 190/64 times their equal shares. In China, where the overall CIF is near 1, the top \(10\% /1\% /0.1\%\) emit 4, 13, and 50 times their equal shares, showing even greater regional influence from societal elites. Similarly, in India, where the national CIF is 0.3 - implying the countries per capita average emissions are below the global average -, the top \(10\% /1\% /0.1\%\) emit 1.2, 4 and 10 times above the global average.  
+
+<|ref|>sub_title<|/ref|><|det|>[[112, 621, 557, 637]]<|/det|>
+### 2.2 Major Disparities in Attributable Extremes Worldwide  
+
+<|ref|>text<|/ref|><|det|>[[112, 638, 883, 745]]<|/det|>
+Throughout the year, regional increases in the occurrence frequency of 1- in- 100 year heat and potential drought extremes are attributable to the emissions of the global top \(10\%\) (Fig 3 Panel a). For heat extremes, changes are most (least) pronounced in August (February), where \(11.5 \pm 3.9\) ( \(3.5 \pm 1.2\) ) additional events are attributable to the global top \(10\%\) . Similarly, attributable potential drought extremes are highest in August and September ( \(4.7 \pm 2.8\) and \(4.8 \pm 2.4\) ) additional events) and lowest in February (negligible increases). These changes refer to the global median over land that is predominantly located on the Northern Hemisphere, and Southern Hemisphere locations may see up to 30 addition attributable events in February (see also Supplementary Fig. S3).  
+
+<|ref|>text<|/ref|><|det|>[[112, 745, 882, 775]]<|/det|>
+Given the intra- annual distribution of impacts, we focus on extremes in August and present results for other months in Supplementary 5.4.  
+
+<|ref|>text<|/ref|><|det|>[[112, 776, 883, 866]]<|/det|>
+In August, the total increase in heat (potential droughts) events since 1990 is \(15.7 \pm 5.8\) ( \(6.5 \pm 4.1\) ). The top \(10\%\) contribute 7.3 (7.0) times the global average to this, while the top \(1\%\) contribute 25.5 (25.6) times the average (see Fig. 3 Panel c and d). In addition, the intensities of 1- in- 100 year August extreme heat (potential drought) events increased by \(0.86^{\circ}\mathrm{C} \pm 0.16^{\circ}\mathrm{C}\) ( \(0.21 \pm 0.54\) ) since 1990. \(0.56^{\circ}\mathrm{C} \pm 0.08^{\circ}\mathrm{C}\) ( \(0.13 \pm 0.06\) ) of this increase is attributable to the top \(10\%\) suggesting their contribution is 6.7 (6.3) times higher than global average S6- S10. Similarly, \(0.18^{\circ}\mathrm{C} \pm 0.02^{\circ}\mathrm{C}\) ( \(0.03 \pm 0.02\) ) are attributable to the top \(1\%\) which represents 21.0 (16.4) times the global average.  
+
+<|ref|>text<|/ref|><|det|>[[112, 880, 883, 911]]<|/det|>
+While these results hold true for the global median, there is a strong spatial disparity in attributable changes at the grid- cell level with some regions being more severely impacted (see Fig. 3 and Supplementary Fig. S3- S10). For
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[118, 170, 880, 686]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[113, 703, 884, 823]]<|/det|>
+<center>Figure 3. | Frequency change of 1-in-100 year heat and drought events attributable to global top 10. a, Distribution of attributed extreme events across grid-cells (boxes: median and inter-quartile range, whiskers: min-max range over inliers, circles: outliers). Heat (potential drought) highlighted in red (blue) with qualitative colour shades. b, Spatial distribution attributed extreme events in August. Red (blue) areas are dominated by heat (drought) events, purple nuances indicate increases in both. c, Additional occurrences of August heat extremes by region (highlighted on map). Total increase in events between 1990 and 2020 (grey) and shares attributable to top 10 (1) in orange (teal). Climate Inequality Factors indicating the group’s contribution to extremes relative to the average contribution are given above the bars. Vertical lines correspond to \(5^{\text{th}}\) - \(95^{\text{th}}\) uncertainty ranges. d, Same as c but for potential drought conditions. </center>
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[115, 90, 882, 166]]<|/det|>
+example, in South East Asia \(28.7(\pm 5.8)\) heat events and in West Southern Africa \(18.0(\pm 8.5)\) potential drought events are attributable to the top \(10\%\) . In particular, regions that have disproportionately contributed to the emissions of the top \(10\%\) , for example the EU27 and the US (see Fig. 2), face relatively smaller increases compared to regions that have contributed very little (e.g., Western North America and West&Central Europe compared to Northern South America in Fig. 3 Panels b and c).  
+
+<|ref|>text<|/ref|><|det|>[[115, 167, 882, 226]]<|/det|>
+Notable are the relatively small (attributable) impacts over India and parts of China and the simultaneously high inter- model disagreement. This is inconsistent with the increasing number of climate- related disasters India is already facing [22]. We attribute this behaviour to the effects of air pollution in our training dataset and discuss this in the Supplementary 5.3.  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 239, 784, 255]]<|/det|>
+### 2.3 Transboundary Impacts Attributable to Affluent Groups in High-emitting Countries  
+
+<|ref|>text<|/ref|><|det|>[[115, 255, 882, 435]]<|/det|>
+The inequality in warming contributions from affluent groups in high- emitting regions exceed the inequality in their global counter- parts (see Fig. 2). This disparity also appears at the grid- cell level: for example, in the global median, emissions from the top \(10\%\) ( \(1\%\) ) in the US are associated with \(1.5 \pm 0.46\) ( \(0.5 \pm 0.17\) ) additional 1- in- 100 year heat events in August. This impact represents 21 (70) times the global average contribution and about three times the global top \(10\%\) 's ( \(1\%\) 's) relative contribution (see Fig. 3). For potential droughts, the top \(10\%\) ( \(1\%\) ) in the US contribute \(0.7 \pm 0.31\) ( \(0.17 \pm 0.08\) ) additional extreme events, which equates to 33 (88) times the global average. These effects are unevenly distributed across regions. For instance, in heat- vulnerable areas such as Northern South America and South East Africa, emissions from the top \(10\%\) in China (the US) are linked to \(3.0 \pm 1.0\) ( \(2.7 \pm 1.0\) ) additional occurrences of extreme heat (Fig. 4). Similarly, in drought- prone areas like Northeastern South America and West and Central Asia, \(2.7 \pm 1.0\) additional events are attributable to the top \(10\%\) in both China and the US. In most regions (we highlight Southeastern Africa and West and Central Asia in Fig. 4), emissions from regional top emitters significantly elevate risks of both heat and drought extremes.  
+
+<|ref|>text<|/ref|><|det|>[[115, 437, 882, 676]]<|/det|>
+Attributing changes in extreme events to country specific wealthy emitter groups becomes increasingly challenging as emitter groups decrease in per capita size, meaning their cumulative emissions decrease even if their relative emission contributions increase. The lower bound ( \(5^{\text{th}}\) quantile) of changes in the frequency of extreme heat and drought events attributable to the global top \(10\%\) is greater than zero at \(94\%\) of locations. This numbers drops to \(62\%\) for the top \(10\%\) in China with an additional \(34\%\) ( \(2\%\) ) of locations where only changes in heat (potential drought) are robust; and it further drops to \(18\%\) for the top \(10\%\) in India with an additional \(39\%\) ( \(6\%\) ) of locations where only changes in heat (potential drought) are robust. For small cumulative emissions, robust signals extend mainly from the equatorial zone to the subtropics and are less robust from the temperate zone polewards. This implies changes in the global median of 1- in- 100 year potential drought events attributable to the top \(10\%\) in India are largely obscured by natural variability (even within our emulator framework that allows far bigger ensembles than traditional methods), while there is still a robust signal in vulnerable regions. For example, in South Eastern Africa and West and Central Asia even the lower bound estimate for additional potential heat and drought events attributable to the top \(10\%\) in India entails a \(50\%\) increase in occurrence frequency compared to pre- industrial. This again highlights the spatial disparity in attributable impacts and shows that for vulnerable areas even seemingly small increments in emissions result in considerable impacts. The induced risks depend on the definition of extremes, with tail risks becoming increasingly pronounced as event rarity increases (see Supplementary Fig. S11- S14).  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 690, 444, 707]]<|/det|>
+## 3 DISCUSSION AND CONCLUSION  
+
+<|ref|>text<|/ref|><|det|>[[115, 714, 882, 880]]<|/det|>
+This study introduces a framework to attribute local extreme events to individual emitters, linking wealth- based emissions to shifts in global mean temperature (GMT) and regional extremes in heat and potential drought. We find that the globally wealthiest \(10\%\) contributed 6.5 times more to global warming than the average, with the top \(1\%\) and \(0.1\%\) contributing 20 and 76 times more, respectively. This imbalance is more pronounced at the grid- cell level: the globally wealthiest \(10\%\) and \(1\%\) contributed more than 7 and 25 times to the frequency increase of pre- industrial 1- in- 100 year heat and potential drought extremes in August than the global average. These wealthiest groups are mostly located in high- emitting countries like the US, the EU27, and China, which are less affected by local climate impacts. Therefore, their emissions are associated with significant transboundary effects, with the wealthiest \(10\%\) within the US and China contributing to at least two additional extreme heat and drought events in vulnerable regions, such as South East Asia, South East Africa, and Northern South America. This climate injustice underscores the need for addressing inequality in policy discussions [23].  
+
+<|ref|>text<|/ref|><|det|>[[115, 897, 882, 911]]<|/det|>
+From a mitigation perspective, our findings suggest affluent groups are crucial in reducing their own carbon footprints
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[130, 95, 860, 319]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[113, 348, 882, 424]]<|/det|>
+<center>Figure 4. |Frequency increase of 1-in-100 year August extremes in selected regions attributable to regional top 10. Left: additional number of additional occurrences of heat extremes in selected regions that are attributable to the top \(10\%\) of emitters in China (dark red), US (light red), EU27 (gold) and India (blue). Right: Same as on the left but for potential droughts. Wider bars indicate more events are attributable to a given emitter group. Value on bar indicates additional number of events over the course of 100 years. </center>  
+
+<|ref|>text<|/ref|><|det|>[[113, 456, 882, 563]]<|/det|>
+and in supporting global climate action [24]. International climate agreements are typically based on production- side accounting, while our findings reinforce the need to explore mitigation strategies that also target consumption- side emissions [25,26], in particular those related to wealth, as proposed for example in ref. [3]. Additionally, our sensitivity analysis underscores the critical role of methane \((\mathrm{CH}_4)\) emissions in near- term warming and calls for new research to disentangle emissions from different wealth groups at the level of individual gases. Reducing \(\mathrm{CH}_4\) emissions in line with Paris Agreement compatible pathways can yield immediate reductions in global temperatures and climate extremes [27].  
+
+<|ref|>text<|/ref|><|det|>[[112, 577, 882, 805]]<|/det|>
+From an adaptation and loss- and- damage perspective, quantifying individual contributions to climate impacts can inform financial mechanisms such as the Loss and Damage fund and domestic climate financing structures [28]. Although our framework could, in theory, aid in estimating emissions- based financial obligations, it is bound by conceptual challenges and value judgments in approach and implementation. First, our attribution relies on consumption- based carbon data, allocating emissions between consumers and shareholders through shared ownership; second, we employ the but for attribution method. All quantitative estimates are therefore tied to these assumptions. For instance, our focus on consumption- based emissions contrasts with production- based approaches [15,29]. Additionally, there are various ways to attribute capital- related emissions between consumers and shareholders [19]. Balancing these methods is essential to avoid double- counting and to address ethical and legal issues around responsibility. Additionally, the non- linear relationship between global and local emission responses complicates attribution, as emission removal sequences can lead to varying outcomes [18]. Addressing these challenges is key to developing a unified approach to attribution, which can support robust policy decisions. Accordingly, our analysis does not assign responsibility for resulting climate impacts, nor does it determine fair emission levels for any income group. Such determinations require an integrated view of fairness, justice, and socio- economic factors [23,30], with different reference points for societies at varying levels of development.  
+
+<|ref|>text<|/ref|><|det|>[[112, 819, 882, 895]]<|/det|>
+From a technical viewpoint, our analysis is limited by the lack of data on how GHG emission composition varies with income and wealth. This limits the accuracy of our results, as emission composition strongly affects attribution. In addition, our drought indicator is based solely on temperature and precipitation, which may lead to overestimations in drought risks [31]. Finally, since our analysis is based on modelled data, it is subject to errors due to computational deviations from observations [32,33].
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[85, 90, 883, 136]]<|/det|>
+In conclusion, our findings underscore the contribution of seemingly small amounts of emissions to changes in regional extremes. Advancing frameworks for attributing emissions to individual emitters can inform global climate action and address climate inequalities.  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 161, 400, 179]]<|/det|>
+## 4 MATERIALS AND METHODS  
+
+<|ref|>text<|/ref|><|det|>[[115, 187, 883, 383]]<|/det|>
+We quantify intensity and frequency changes in temperature and potential drought extremes attributable to specific emitter groups. The methodological framework relies on three steps (Fig. 1): first, we construct counterfactual emissions pathways, i.e. emission pathways with and without the emissions of selected population groups; second, we translate emissions into gridded temperature, precipitation and potential drought data via a chain of computationally efficient emulators; and third, we build on the framework of extreme event attribution to quantify changes in the grid- point level distributions of the climatic variables. We rely on the Standardised Precipitation Evapotranspiration Index computed over a 3 month period (SPEI- 3) to identify potential droughts [20]. The SPEI- 3 can be computed from precipitation and potential evapotranspiration (PET) data. Ideally, PET is estimated via the Penman- Monteith equation [34,35] which takes temperatures, radiation, wind speed and humidity into account. Given our emulation framework only depicts temperature and precipitation, we rely on the Thornthwaite method to compute PET from temperature data only [36]. However, PET estimates via Thornthwaite are prone to overestimations in terms of magnitude and temporal trends [31]. While this approach is practical, it is only an approximation of true drought conditions, which is why we refer to our drought indicator as potential droughts.  
+
+<|ref|>text<|/ref|><|det|>[[113, 396, 883, 760]]<|/det|>
+Counterfactual emission pathways. We assess what our climate today would look like if the wealthiest \(10\% /1\% /0.1\%\) globally, as well as in the US, EU27, India and China had not contributed to global emissions between 1990 and 2019. We follow Nicholls et al. [37] to construct a timeseries of historic baseline emissions from 1850- 2019 resolved by gas. Next, we remove emitter- specific contributions from these baseline emissions (Fig. 1). To this end, we rely on a dataset of consumption- based carbon dioxide equivalent \(\mathrm{CO_2 - e}\) ) emissions by country and income decile between 1990- 2019 [3]. The estimates relate to all emissions except emissions from agriculture, forestry and other land- use (AFOLU). Our analysis requires us to make assumptions about how to disaggregate the reported basket emissions into individual gases. We focus on decomposing emissions into \(\mathrm{CO_2}\) , nitrogen oxide \(\mathrm{(N_2O)}\) and methane \(\mathrm{(CH_4)}\) . These three gases make up \(98.7\%\) of total global greenhouse gas emissions (excluding AFOLU) [38]. The composition of production- side GHG emissions varies strongly by country, ranging from primarily \(\mathrm{CO_2}\) - based emissions (e.g. Singapore), to almost equal shares between \(\mathrm{CO_2}\) and \(\mathrm{CH_4 / N_2O}\) (e.g. Qatar) and, particularly in least developed countries, primarily \(\mathrm{CH_4 / N_2O}\) (e.g. Chad) [39]. The carbon inequality dataset from [3] employs input- output tables that re- distribute production- side emissions to consumers across countries. About half of global methane emissions are embodied in global trade with household consumption dominating the final demand category [40]. Given these considerations and a lack of alternative data, we chose to apply the same decomposition assumptions across countries and emitter groups. For our central estimate, we assume that emissions for each GHG scale proportionally with the globally aggregated emissions. To test the sensitivity to this assumption we provide two extreme cases in which the wealthy emitters 1) solely emit carbon \(\mathrm{CO_2}\) - case) or 2) solely emit \(\mathrm{CH_4}\) and \(\mathrm{N_2O}\) (non- \(\mathrm{CO_2}\) - case). Note that in the \(2^{\mathrm{nd}}\) case, the emissions associated with the global top \(10\%\) are larger than the total global \(\mathrm{CH_4}\) and \(\mathrm{N_2O}\) emissions combined and we remove the excessive emissions from the \(\mathrm{CO_2}\) timeseries (converting to \(\mathrm{CO_2}\) - e using GWP100). We find that the disaggregation into dominant non- \(\mathrm{CO_2}\) GHGs, \(\mathrm{CH_4}\) and \(\mathrm{N_2O}\) , has a strong impact on the results (Fig. 2 Panels a&e). In the \(\mathrm{CO_2}\) - case the inequality in contributions to GMT levels persists, but is slightly below the inequality in GHG basket emissions. For the non- \(\mathrm{CO_2}\) - case, warming inequality is strongly amplified. This is expected given the near- term warming potential of non- \(\mathrm{CO_2}\) GHGs (see Supplementary 5.2).  
+
+<|ref|>text<|/ref|><|det|>[[115, 775, 883, 911]]<|/det|>
+Emulator- based Modelling Approach. We transform counterfactual emissions into grid- point level distributions of temperature and precipitation and subsequently compute the SPEI- 3 indicator as a potential drought measure from the emulated data. The emulation consists of two steps: first, converting emissions into GMT; and second translating GMT into grid- point level monthly mean temperature and precipitation distributions (Fig. 1). The first translation step is carried out with the Model for the Assessment of the Greenhouse Gas induced Climate Change (MAGICC) [10,41,41]. MAGICC is a simple, computationally efficient climate model for global climate indicators. Our temperature outcomes are calculated with MAGICC v7.5 in a probabilistic setting that reflects the assessed uncertainty ranges from the IPCC's Sixth Assessment Report see [42]. We generate 600 GMT trajectories for each scenario. The second translation steps is carried out using the Modular Earth System Model Emulator for Monthly Temperature and Precipitation
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[86, 90, 883, 211]]<|/det|>
+(MESMER- M- TP)[111]. MESMER- M- TP combines parametric approaches and stochastic sampling to approximate the behaviour of individual climate models. For any climate model, the emulator can be calibrated on a small set of actual climate model data and then generates gridded temperature and precipitation data that statistically resemble the climate model data. Here, we calibrate MESMER- M- TP on 24 different models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) (see Supplementary Table S3). Subsequently, we convert each GMT trajectory into a single gridded timeseries of temperature and precipitation. We compute the SPEI- 3 indicator following[20] and rely on the gamma distribution for normalisation. This leaves us with a dataset containing 3 variables x 600 realisations x 2,652 grid- points x 170 years x 12 months for each scenario.  
+
+<|ref|>text<|/ref|><|det|>[[86, 225, 883, 347]]<|/det|>
+Attribution Framework. Traditional attribution studies typically aim at understanding how climate change altered the statistics of a specified observed extreme. Our study deviates from this approach. We are interested in understanding the extent to which changes in a broad class of historic extremes can be related to emissions from specific emitter groups. Therefore, we use the framework for event attribution (EA) as a guideline[14] but modify it according to our research questions. Most importantly, our analysis fully relies on modelled data meaning we are not taking observational data into account. Hence, the EA framework reduces to three essential steps: first, we define extreme events; second, we perform an analysis using emulated (climate model) data and last, we synthesise the hazards into an attribution statement.  
+
+<|ref|>text<|/ref|><|det|>[[86, 347, 883, 377]]<|/det|>
+Extreme event definition. We define extreme events relative to the reference period 1850- 1900 and focus on 1- in- 50, 1- in- 100 and 1- in- 10,000 year (unprecedented) events.  
+
+<|ref|>text<|/ref|><|det|>[[86, 377, 883, 468]]<|/det|>
+Climate model analysis and Hazard synthesis. We use the modelled distribution of climatic variables over the reference period at each grid- point to derive grid- point specific intensity thresholds for our defined events. To assess frequency changes, we count how many times the reference intensity threshold is exceeded in a present- day (2020) climate and in a counterfactual 2020 climate and attribute the difference to a specific emitter group. Similarly, to quantify intensity changes, we assess how hot (dry) a specific extreme event would be in present- day climate and in the counterfactual climate and attribute the difference in thresholds.  
+
+<|ref|>sub_title<|/ref|><|det|>[[86, 483, 367, 500]]<|/det|>
+## AUTHOR CONTRIBUTIONS  
+
+<|ref|>text<|/ref|><|det|>[[86, 507, 883, 537]]<|/det|>
+All authors conceived the study. S.S. performed the data analysis and wrote the manuscript with contributions from all authors. All authors have read and agreed to the published version of the manuscript.  
+
+<|ref|>sub_title<|/ref|><|det|>[[86, 552, 303, 569]]<|/det|>
+## DATA AVAILABILITY  
+
+<|ref|>text<|/ref|><|det|>[[86, 577, 883, 653]]<|/det|>
+The data generated for this study is available at[43]. The results can be reproduced using public data records. The starting point of our analysis are timeseries of \(\mathrm{CO_2}\) - e per capita emissions from[3]. We further use historic emissions available through[37] compiled from[39,44- 52]. We rely on MAGICC v7.5[10,41,41] to translate our input data into GMT levels. We then rely on MESMER- M- TPv0.1.0[53,54] to generate a large- ensemble of temperature and precipitation data.  
+
+<|ref|>sub_title<|/ref|><|det|>[[86, 669, 328, 686]]<|/det|>
+## ACKNOWLEDGMENTS  
+
+<|ref|>text<|/ref|><|det|>[[86, 693, 883, 785]]<|/det|>
+S.S. acknowledges support by the German Federal Environmental Foundation (DBU). S.S. and C.F.S. acknowledge funds by European Union's Horizon 2020 Research and Innovation Programme under Grant No. 101003687 (PROVIDE). Z.N. acknowledges support from the European Union's Horizon 2020 Research and Innovation Funding Programme (Grant No. 101003536, Earth System Models for the Future, ESM2025). R.H. acknowledges funding by the European Union's Horizon Europe Programme under Grant Agreement No. 101094551 (SPES). The authors furthermore gratefully acknowledge funding from IIASA and the National Member Organizations that support the institute.  
+
+<|ref|>sub_title<|/ref|><|det|>[[86, 799, 363, 816]]<|/det|>
+## CONFLICTS OF INTEREST  
+
+<|ref|>text<|/ref|><|det|>[[86, 824, 390, 838]]<|/det|>
+The authors declare no conflict of interest.  
+
+<|ref|>sub_title<|/ref|><|det|>[[86, 853, 250, 870]]<|/det|>
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[74] Martin Schupfner, Karl- Hermann Wieners, Fabian Wachsmann, Sebastian Milinski, Christian Steger, Matthias Bittner, Johann Jungclaus, Barbara Früh, Klaus Pankatz, Marco Giorgetta, Christian Reick, Stephanie Legutke, Monika Esch, Veronika Gayler, Helmuth Haak, Philipp de Vrese, Thomas Raddatz, Thorsten Mauritsen, Jin- Song von Storch, Jörg Behrens, Victor Brovkin, Martin Claussen, Traute Crueger, Irina Fast, Stephanie Fiedler, Stefan Hagemann, Cathy Hohenegger, Thomas Jahns, Silvia Kloster, Stefan Kinne, Gitta Lasslop, Luis Kornblueh, Jochem Marotzke, Daniela Matei, Katharina Meraner, Uwe Mikolajewicz, Kameswarrao Modali, Wolfgang Müller, Julia Nabel, Dirk Notz, Karsten Peters- von Gehlen, Robert Pincus, Holger Pohlmann, Julia Pongratz, Sebastian Rast, Hauke Schmidt, Reiner Schnur, Uwe Schulzweida, Katharina Six, Bjorn Stevens, Aiko Voigt, and Erich Roeckner. DKRZ MPI- ESM1.2- LR model output prepared for CMIP6 scenarioMIP, 2021. URL https://doi.org/10.22033/ESGF/CMIP6.15349. [75] Seiji Yukimoto, Tsuyoshi Koshiro, Hideaki Kawai, Naga Oshima, Kohei Yoshida, Shogo Urakawa, Hiroyuki Tsujino, Makoto Deushi, Taichu Tanaka, Masahiro Hosaka, Hiromasa Yoshimura, Eiki Shindo, Ryo Mizuta, Masayoshi Ishii, Atsushi Obata, and Yukimasa Adachi. MRI MRI- ESM2.0 model output prepared for CMIP6 scenarioMIP, 2019. URL https://doi.org/10.22033/ESGF/CMIP6.638. [76] Jian Cao. NUIST NESMv3 model output prepared for CMIP6 scenarioMIP, 2019. URL https://doi.org/10.22033/ESGF/CMIP6.2027. [77] Oyvind Seland, Mats Bentsen, Dirk Jan Leo Olivie, Thomas Toniazzo, Ada Gjermundsen, Lise Seland Graff, Jens Boldingh Debernard, Alok Kumar Gupta, Yanchun He, Alf Kirkevåg, Jörg Schwinger, Jerry Tjiputra, Kjetil Schanke Aas, Ingo Bethke, Yuanchao Fan, Jan Griesfeller, Alf Grini, Chuncheng Guo, Mehmet Ilicak, Inger Helene Hafshal Karset, Oskar Andreas Landgren, Johan Liakka, Kine Onsum Moseid, Aleksi Nummelin, Clemens Spensberger, Hui Tang, Zhongshi Zhang, Christoph Heinze, Trond Iversen, and Michael Schulz. NCC NorESM2- LM model output prepared for CMIP6 scenarioMIP, 2019. URL https://doi.org/10.22033/ESGF/CMIP6.604. [78] Mats Bentsen, Dirk Jan Leo Olivie, Oyvind Seland, Thomas Toniazzo, Ada Gjermundsen, Lise Seland Graff, Jens Boldingh Debernard, Alok Kumar Gupta, Yanchun He, Alf Kirkevåg, Jörg Schwinger, Jerry Tjiputra, Kjetil Schanke Aas, Ingo Bethke, Yuanchao Fan, Jan Griesfeller, Alf Grini, Chuncheng Guo, Mehmet Ilicak, Inger Helene Hafshal Karset, Oskar Andreas Landgren, Johan Liakka, Kine Onsum Moseid, Aleksi Nummelin, Clemens Spensberger, Hui Tang, Zhongshi Zhang, Christoph Heinze, Trond Iversen, and Michael Schulz. NCC NorESM2- MM model output prepared for CMIP6 scenarioMIP, 2019. URL https://doi.org/10.22033/ESGF/CMIP6.608. [79] Peter Good, Alistair Sellar, Yongming Tang, Steve Rumbold, Rich Ellis, Douglas Kelley, Till Kuhlbrodt, and Jeremy Walton. MOHC UKESM1.0- LL model output prepared for CMIP6 scenarioMIP, 2019. URL https://doi.org/10.22033/ESGF/CMIP6.1567. [80] Shabtai Cohen and Gerald Stanhill. Chapter 29 - widespread surface solar radiation changes and their effects: Dimming and brightening. In Trevor M. Letcher, editor, Climate Change (Second Edition), pages 491- 511. Elsevier, Boston, second edition edition, 2016. ISBN 978- 0- 444- 63524- 2. doi: https://doi.org/10.1016/ B978- 0- 444- 63524- 2.00029- 4. URL https://www.sciencedirect.com/science/article/pii/
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+<--- Page Split --->
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+B9780444635242000294.  
+
+<|ref|>text<|/ref|><|det|>[[85, 106, 884, 180]]<|/det|>
+[81] Aolin Jia, Shunlin Liang, Dongdong Wang, Bo Jiang, and Xiaotong Zhang. Air pollution slows down surface warming over the tibetan plateau. Atmospheric Chemistry and Physics, 20(2):881- 899, 2020.  [82] Zhili Wang, Lei Lin, Yangyang Xu, Huizheng Che, Xiaoye Zhang, Hua Zhang, Wenjie Dong, Chense Wang, Ke Gui, and Bing Xie. Incorrect asian aerosols affecting the attribution and projection of regional climate change in cmip6 models. npj Climate and Atmospheric Science, 4(1):2, 2021.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[43, 43, 312, 71]]<|/det|>
+## Supplementary Files  
+
+<|ref|>text<|/ref|><|det|>[[43, 93, 768, 113]]<|/det|>
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+<|ref|>text<|/ref|><|det|>[[59, 131, 378, 230]]<|/det|>
+- supplement.pdf- AttributedGMT.csv- processedextremesfrequency.csv- processedextremesintensity.csv
+
+<--- Page Split --->

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preprint/preprint__03d68cf8e193cb35c9be6144c0f8c2e40f012dc558e3da2c1ab952dea9dac6d5/preprint__03d68cf8e193cb35c9be6144c0f8c2e40f012dc558e3da2c1ab952dea9dac6d5.mmd

@@ -0,0 +1,289 @@
+
+# Mn-inlaid antiphase boundaries in perovskite structure  
+
+Lingyan Wang 1.y.wang@mail.xjtu.edu.cn  
+
+Electronic Materials Research Laboratory, Key Laboratory of the Ministry of Education, School of Electronic Science and Engineering, Xi'an Jiaotong University  
+
+Chao Li Xi'an Jiaotong University  
+
+Liqiang Xu  
+
+Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University  
+
+Xuerong Ren  
+
+Electronic Materials Research Laboratory, Key Laboratory of the Ministry of Education, School of Electronic Science and Engineering, Xi'an Jiaotong University  
+
+Fangzhou Yao  
+
+Wuzhen Laboratory/Center of Advanced Ceramic Materials and Devices, Yangtze Delta Region Institute of Tsinghua University  
+
+Jiangbo Lu School of Physics and Information Technology, Shaanxi Normal University  
+
+Dong Wang Xi'an Jiaotong University  
+
+zhongshuai Liang Xi'an Jiaotong University  
+
+Ping Huang Xi'an Jiaotong University https://orcid.org/0000- 0002- 5295- 8216  
+
+Shengqiang Wu  
+
+School of Materials Science and Engineering, Peking University  
+
+Hongmei Jing  
+
+School of Physics and Information Technology, Shaanxi Normal University  
+
+Yijun Zhang  
+
+Electronic Materials Research Laboratory, Key Laboratory of the Ministry of Education, School of Electronic Science and Engineering, Xi'an Jiaotong University  
+
+Guohua Dong  
+
+Xi'an Jiaotong University https://orcid.org/0000- 0002- 5484- 5442
+
+<--- Page Split --->
+
+Haixia Liu Xi'an Jiaotong University  
+
+Chuansheng Ma Instrumental Analysis Center, Xi'an Jiaotong University  
+
+Yinong Lyu Nanjing Tech University  
+
+Xiaoyong Wei  
+
+Electronic Materials Research Laboratory, Key Laboratory of the Ministry of Education and International Center for Dielectric Research, Xi'an Jiao Tong University  
+
+Wei Ren  
+
+Xi'an Jiaotong University School of Electronic and Information Engineering  
+
+Ke Wang  
+
+State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, P. R. China https://orcid.org/0000- 0001- 9840- 2427  
+
+Zuo-Guang Ye  
+
+Simon Fraser University https://orcid.org/0000- 0003- 2378- 7304  
+
+Feng Chen  
+
+High Magnetic Field Laboratory, Chinese Academy of Sciences  
+
+## Article  
+
+Keywords:  
+
+Posted Date: February 5th, 2024  
+
+DOI: https://doi.org/10.21203/rs.3.rs- 3884985/v1  
+
+License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+Additional Declarations: There is NO Competing Interest.  
+
+Version of Record: A version of this preprint was published at Nature Communications on August 7th, 2024. See the published version at https://doi.org/10.1038/s41467- 024- 51024- 2.
+
+<--- Page Split --->
+
+## Mn-inlaid antiphase boundaries in perovskite structure  
+
+High- performance perovskite ferroelectrics are central to various electro- mechanical functional devices \(^{1,2}\) . However, the use of toxic Pb- based ferroelectrics in high- end applications is being limited due to environmental concerns and the related legislations \(^{3}\) . As an eco- friendly alternative, lead- free perovskite potassium sodium niobate (K \(_4\) Na \(_1\) - \(_3\) NbO \(_3\) , KNN)- based perovskite ferroelectrics materials have been intensively studied since the discovery of a large piezoelectric coefficient \(d_{33}\) value of 416 pC/N for KNN- based ceramics by Saito et al. \(^{4 - 6}\) . With advancements in the ferroelectric performances of the KNN- based ceramics and single crystals \(^{7 - 10}\) , a great deal of efforts have also been made to prepare the KNN- based thin films by various deposition techniques \(^{11 - 14}\) . However, the volatilization of the alkali ions has been identified as a major issue in obtaining high- quality KNN- based thin films. The inevitable loss of potassium and sodium elements during the vapor or chemical solution- based depositions changes the stoichiometry, resulting in the formation of undesired alkali- deficient secondary phases and defects. Consequently, KNN- based films exhibit high electrical conduction and poor ferroelectric response \(^{15}\) . Previous strategies focused on chemical composition adjustment to compensate for the elemental volatilization, and construction of morphotropic or polytropic phase boundaries to enhance the dielectric and ferroelectric properties of KNN- based thin films \(^{16 - 19}\) . Recently, some other methods, such as interface effects, flexible substrates, and defects engineering have been explored to modulate lattice strain, displacement polarization and electronic structure \(^{11,12,20 - 23}\) . All those efforts have led to a gradual improvement in the overall ferroelectric performances, which, however, still remain significantly inferior to Pb- based thin films and thereby are far from being suitable for practical applications in new micro electro- mechanical devices, ferroelectric field- effect transistors, nonvolatile memories, electro- optic devices, etc. \(^{1,24,25}\) .  
+
+Material properties are strongly influenced by the microstructure. In terms of crystal structure, the above- mentioned attempts primarily focused on distorting or tilting the lattices within the basic perovskite framework, which has given rise to limited effects in improving the ferroelectric properties. It has been observed that the physical constraints of underlying substrate and the large unit cell of oxide perovskite structure can lead to the generation and propagation of out- of- phase boundary defects through the entire thickness of the film, especially with special deposition processes \(^{12,26}\) . These charged out- of- phase boundaries, originated from the alkali- deficiency, have been identified and found to play an important role in the piezoelectric performances of NaNbO \(_3\) and KNN thin films \(^{12}\) . In this work, we also harnessed these inherent characteristics (alkali deficiency and out- of- phase boundaries) to create a unique nanocolumnar structure, and then incorporated another element at the boundaries between the KNN nanocolumns to form a new perovskite derivative structure. Specifically, the nanocolumnar KNN- based thin film consists of perovskite KNN nanocolumns that are interspersed with Mn- inlaid antiphase boundaries. Atomic resolution images revealed that the thickness of perovskite KNN slabs varied from a few to tens of nanometers, while the coherent antiphase boundaries consisted of one or two atomic layers enriched with Mn. This strategic incorporation of Mn not only helped to balance the charges originated from alkali ions deficiency, leading to a reduced leakage current, but also induced a noticeable out- of- plane tensile strain in the KNN nanocolumns. This strain promoted a high degree of tetragonality, resulting in a remarkable improvement in ferroelectric polarization, with a high twice remanent polarization value of \(114 \mu \mathrm{C} / \mathrm{cm}^2\) .  
+
+A ceramic target of \(\mathrm{K}_{0.5}\mathrm{Na}_{0.5}\mathrm{NbO}_{3}\) with \(2 \mathrm{wt\% MnO}_2\) addition (KNN- M) was used for deposition of the KNN- M films on \(\mathrm{La}_{0.07}\mathrm{Sr}_{0.93}\mathrm{SnO}_3\) (LSSO)- coated STO (001) substrates by
+
+<--- Page Split --->
+
+pulsed laser deposition (PLD) method. High- resolution X- ray diffraction (HRXRD) techniques were employed to assess the crystalline quality of the KNN- M films. Fig. 1a shows the X- ray diffraction (XRD) \(\theta - 2\theta\) pattern of a representative KNN- M film. Only the (00l) \((l = 1, 2, 3)\) reflection peaks of KNN- M, LSSO and STO are observed, indicating that the film is epitaxially grown along the \(c\) - axis direction with a single phase. The rocking curves shown in Fig. 1b exhibit a full- width- at- half- maximum of approximately \(0.182^{\circ}\) for KNN- M (002) and \(0.093^{\circ}\) for LSSO (002), demonstrating a high crystallinity of the films. To examine the epitaxial relationship and the strain state of the samples, X- ray reciprocal space mappings (RSMs) around the symmetric (002) and asymmetric \((\overline{1} 03)\) reflections of the film are shown in Figs. 1c and d respectively. The discrete and clear spots in Figs. 1c and d confirm the orientation relationship between the (001) films and the (001) STO substrate. As displayed in Fig. 1d, the \(Q_{x}\) value of the KNN- M \((\overline{1} 03)\) RSM spot obviously deviates from that of STO and LSSO, suggesting a relaxation of the lattice mismatch strain between the film and substrate. Based on the RSM results, the out- of- plane lattice parameter " \(c\) " and in- plane lattice parameters " \(a\) " or " \(b\) " of the KNN- M films were calculated to be \(4.02 \mathring{\mathrm{A}}\) and \(3.95 \mathring{\mathrm{A}}\) , respectively.  
+
+Fig. 1e displays a high angle annular dark field (HAADF) scanning transmission electron microscopy (STEM) image of the KNN- M thin film, recorded along the [010] direction. It is evident that the KNN- M thin film consists of vertically aligned nanocolumns in the out- of- plane direction, rather than a homogeneous and smooth cross section (Fig.1e). X- ray energy dispersive spectroscopy (EDS) analysis confirms the presence of Mn enrichment at the column boundaries along the out- of- plane direction, as seen in Supplementary Fig. 1. Additionally, the selected area electron diffraction (SAED) pattern of the KNN- M thin films along the [010] zone axis, as shown in Fig 1f, reveals classic single crystal diffraction spots in the out- of- plane direction, while the streaks along the in- plane direction are attributed to the "shape effect". This effect arises from the principle that a small thickness in real space corresponds to a large length in reciprocal space, and vice versa. Thus, the reciprocal diffraction pattern further supports the presence of nanocolumns in the specimens, which is consistent with the HAADF image in real space. Atomic resolution HAADF images reveal a series of nanocolumn grains exhibiting the classic single crystal phase perovskite structure, as shown in Fig. 1g. However, it is observed that the lattices of neighboring perovskite KNN nanocolumns undergo a relative shift by half a unit cell length at the phase boundaries along the out- of- plane direction, indicated by the red triangle in Fig.1g.  
+
+Due to the overlap of the nanocolumnar structure in the cross- sectional observation direction, indicated by the brace in Fig. 1g and Supplementary Fig. 2, we studied the microstructure using a plan- view sample without substrate. We found that the KNN- M films present the 'Tetris- like' microstructure consisting of dense nanocolumns with sizes ranging from a few to tens of nanometers. Through annular bright field (ABF) imaging, we observed that the nanocolumns predominantly exhibit atomic- scale linear dark contrast along both the [001] and [010] directions, as shown in Fig. 1h and Supplementary Fig. 3. The corresponding EDS composition analysis reveals that the nanocolumn areas are composed of K, Na, Mn, Nb and O elements, with apparent Mn enrichment observed in some linear dark contrast areas, as shown in Fig. 1i and Supplementary Fig. 4. Electron energy loss spectroscopy (EELS) analysis indicates that the Mn ions mainly exhibit bivalence (Mn \(^{2 + }\) ) (Supplementary Fig. 5). In addition, atomic resolution HAADF imaging reveal that the lattices of adjacent perovskite KNN nanocolumns are coherent, but undergo a half- unit cell shift relative to each other across the atomic- scale antiphase boundaries with Mn enrichment, as marked within the yellow rectangle in Fig. 1j. At the ends of these antiphase boundaries, the lattice misregistry of 1/2 unit cell is gradually reduced to zero
+
+<--- Page Split --->
+
+with continuous atom deficiency along the boundaries and no more Mn element could be detected with EDS analysis, as shown in the red rectangles in Fig. 1j and Supplementary Fig. 6.  
+
+![](images/Figure_1.jpg)
+
+<center>Fig. 1 | Crystalline structure and microstructure of the KNN-M thin film grown on a LSSO-buffered STO (001) substrate. a, XRD \(\theta -2\theta\) pattern. b, Rocking curves around STO (002), LSSO (002) and KNN-M (002). c, d, Reciprocal space maps around the STO (002) and (f03) reflections. e, Cross-sectional low magnification HAADF image. f, SEAD pattern of the KNN-M thin film in the out-of-plane direction. g, Atomic resolution HAADF image of the KNN-M thin film. h, Planar-view low-magnification ABF image. i, Corresponding EDS mapping of Mn element for the KNN-M thin film. j, Plan-view atomic resolution HAADF image of the KNN-M thin film. The scale bars in (g) and (j) are 2 nm. </center>
+
+<--- Page Split --->
+
+Furthermore, we have identified two different atomic configurations for the Mn- inlaid antiphase boundaries, as illustrated in Fig. 2. Fig. 2a displays an atomic resolution HAADF image of one type of antiphase boundary structure (referred to as Type- I). In this image, a coherent antiphase feature is observed, where the A- site lattice of one KNN nanocolumn runs into the B- site lattice of the neighboring nanocolumn across a single atomic column. This single atomic column exhibits a brighter contrast than the A- site K/Na atomic columns, but a darker contrast compared to the B- site Nb/O atom columns. Line- scans of image intensity were performed along the single atomic column layer. The line profile of intensity reveals similar atomic column intensities, as shown in Fig. 2b. Based on the uniform image contrasts and corresponding line profiles, it can be inferred that the atomic columns in the Type- I antiphase boundaries have similar chemical compositions. The intensity of the atomic column in HAADF image is approximately proportional to the square of the atomic number \((Z^{2})^{27}\) , making the HAADF images suitable for composition analysis. However, due to the much smaller scattering cross- section of oxygen, the light element oxygen cannot be observed in the HAADF image. Therefore, we also obtained atomic resolution integrated differential phase contrast (iDPC) images to visualize the distribution of all atoms, as shown in Fig. 2c. The iDPC image reveals only one atomic column at the antiphase boundary plane, and no separate oxygen atom columns are observed in the plan- view direction. In addition, we observed that the single atomic layer at the antiphase boundaries can gradually translate to a "Ruddlesden- Popper" like double atomic layer with in- situ beam irradiation in STEM mode, and vice versa (Fig. 2d, Supplementary Fig. 7 and Supplementary Fig. 8).  
+
+We performed the EDS mapping to study the atomic resolution composition distribution in Type- I antiphase boundary regions, as shown in Fig. 2e. The color- coded elemental maps of K, Na, O, Mn and Nb are presented in Figs. 2f- j, respectively. It is evident that all the elements are generally distributed uniformly in the KNN- based nanocolumns. However, the atomic columns at the antiphase boundary layer are primarily composed of the Mn and O elements, with a trace amount of K/Na. The composite map of Nb and Mn, as shown in Fig. 2k, illustrates the relative positions of the Nb and Mn atoms. Based on the elemental distribution maps, we find that Mn occupies the A- site of the perovskite lattice. Due to the structural changes at the boundaries during in- situ beam irradiation, as shown in Fig. 2d and Supplementary Fig. 7, Mn enrichments is observed along the boundary, but the location of Mn signals at the antiphase boundaries does not precisely correspond to the atomic- scale image from the EDS analysis. By analyzing the atomic resolution images in both the planar- view and cross- sectional directions (Supplementary Fig. 2) and conducting composition analysis, we have depicted the schematic structure of Type- I antiphase boundaries in Fig. 2l. This structure consists of nanocolumn perovskite KNN sheets alternating with a single Mn/O sheet running along the \(c\) axis direction and the neighboring perovskite slabs relatively shifting by half a unit cell length in both the in- plane and out- of- plane directions. At Type- I antiphase boundaries, the lattice site corresponds to the A- site of one KNN nanocolumn and the O- site of another KNN nanocolumn. Therefore, both the Mn and O atoms can randomly occupy the atom site at the boundaries theoretically.  
+
+The other antiphase boundary (Type- II) is illustrated in Fig. 2m, where the two adjacent KNN phases coherently transit across a double atom layer, and the distance between the neighboring Nb atomic columns at the boundaries is approximately 3/2- unit cells. Notably, we observed distinct contrast variation for the atom columns at the transition layers, which differ from the uniform contrast of the atom columns at Type- I antiphase boundaries. The line profile of intensity also exhibits noticeable difference for the atomic column along one row of transition atomic columns in Fig. 2m, as shown in Fig. 2n. The observed variations in image contrast or line profiles can be attributed to composition differences in each atomic column at Type- II
+
+<--- Page Split --->
+
+antiphase boundaries. Through atomic resolution composition analysis, we determined that the atom columns with bright contrast primarily consist of Nb atoms, while the dark atom columns are composed of K/Na/Mn (Supplementary Fig. 9). Additionally, we also observed the presence of Mn enrichment at the boundaries. However, the signals are noticeably weaker compared to the distinct single Mn- rich layer observed at Type- I antiphase boundaries (Supplementary Fig. 10). We also acquired the iDPC images for Type- II antiphase boundaries (Fig. 2o), which reveal separated O atom columns that can bond with neighboring Nb atoms to form oxygen octahedra at the boundaries. Furthermore, we found that Type- II antiphase boundaries remained stable under beam irradiation. Based on the aforementioned experimental results, we constructed the schematic structural model of Type- II antiphase boundaries, as depicted in Fig. 2p.  
+
+For comparison, we also prepared the undoped KNN thin films with a similar deposition process. From cross- sectional view, serials of nanocolumns can also be observed (Supplementary Fig. 11a). Atomic resolution HAADF images revealed no noticeable lattice shifts between two adjacent nanocolumns in the out- of- plane direction (Supplementary Fig. 11b). However, the plan- view image displayed a high density of stacking faults due to the K/Na deficiency (Supplementary Fig. 11c). This deficiency resulted in a high leakage current and prevented the display of ferroelectricity at room temperature \(^{12,14}\) .
+
+<--- Page Split --->
+![](images/Figure_2.jpg)
+
+<center>Fig.2 | In-plane atomic structure of Mn-inlaid antiphase boundaries in the KNN-M thin films. a, In-plane atomic resolution HAADF image of Type-I antiphase boundaries. b, Line-profile of the single antiphase atomic columns along the direction of the antiphase boundary in (A). c, iDPC image of Type-I antiphase boundaries. d, HAADF image of Type-I antiphase boundaries with in-situ electron irradiations. e, In-plane HAADF image of the KNN-M thin film containing Type-I antiphase boundaries. f-j, Corresponding color-coded atomic resolution EDS mapping of the K, Na, O, Mn and Nb elements, respectively. K, Composite elemental map with Nb (in red) and Mn (in green). I, Schematic structural models of Type-I Mn-inlaid antiphase boundaries, where the red, purple, green, and cyanine colors represent the O, K/Na, Mn and Nb elements, respectively. m, In-plane atomic resolution HAADF image of Type-II antiphase boundaries. n, Line-profile of the single antiphase atomic columns along the direction of the transition boundary in (m). o, iDPC image of Type-II antiphase boundaries. p, Schematic structural model of Type-II antiphase boundaries, where the red, purple, green, and cyanine colors represent the O, K/Na, Mn and Nb elements, respectively. The scale bar is 1nm. </center>
+
+<--- Page Split --->
+
+We examined the ferroelectric polarization of this film and found that the out-of-plane phase image of the as-grown film without dc bias exhibits a remarkable homogeneity across the layer with a weak contrast, as shown in Fig. 3a, indicating an ordered out-of-plane polarization component. This result is also attested to the excellent epitaxial quality of the films. The out-of-plane piezoelectric force microscope (PFM) phase can switch to the opposite direction under a voltage of - 8 V. When a positive dc bias of \(+8\mathrm{V}\) was applied, the PFM phase switched to the same direction as the as-grown film, which confirms that the out-of-plane polarization of film is directed uniformly toward the bottom of the film. Additionally, well-saturated ferroelectric polarization-electric field \((P - E)\) hysteresis loops of the KNN- M film were displayed at room temperature (Fig. 3b). The film demonstrated a remarkable enhancement in remanent polarization \((P_{r})\) with the \(2P_{r}\) value reaching \(114\mu \mathrm {C}/\mathrm {cm}^{2}\) when subjected to an applied electric field of \(606\mathrm {kV/cm}\) . Furthermore, at the temperature of \(80\mathrm {K}\) , no significant variation in \(P_{r}\) was observed, but the breakdown field strength increased to \(2700\mathrm {kV/cm}\) , as shown in Fig. 3c. Notably, the \(2P_{r}\) value of the KNN- M thin film is larger than the values so far reported for most KNN- based films \((7.1-64.9\mu \mathrm {C}/\mathrm {cm}^{2})\) , as shown in Fig. 3d. Moreover, the \(P_{r}\) value is comparable to that of typical PZT ferroelectric thin films \({}^{24}\) .  
+
+![](images/Figure_3.jpg)
+
+<center>Fig. 3 | Ferroelectric properties of the KNN-M thin films. a, Out-of-plane polarization switching of the KNN-M film captured using PFM imaging technique, under a dc bias of \(\pm 8\mathrm {V}\) . b, Room-temperature polarization–electric field \((P - E)\) hysteresis loops displayed under different electric fields. c, \(P - E\) hysteresis loops under different electric field for the KNN-M thin films at a low-temperature of \(80\mathrm {K}\) . d, Comparison of the polarization \((2P_{r})\) of the present KNN-M films with previously reported values of the KNN-based films. </center>
+
+<--- Page Split --->
+
+Based on the microstructure analysis and the ferroelectric properties, we delve into the mechanisms behind the formation of nanocolumnar structure with Mn- inlaid antiphase boundaries and enhancement of ferroelectric polarization. In the case of pure KNN thin films without Mn doping, the unique deposition process led to the formation of vertical nanocolumnar structures aligned along the out- of- plane direction. Through atomic resolution investigation of microstructure, it was observed that the KNN nanocolumns maintained the normal perovskite structure. Significant K/Na deficiencies primarily occurred at the boundaries, resulting in lattice rearrangement and formation of charged out- of- phase boundaries. These crystallographic out- of- phase boundaries, commonly found in epitaxial perovskite films, tend to propagate throughout the entire thickness of the film. Consequently, nanocolumnar KNN thin films exhibited large leakage currents, as depicted in Supplementary Fig. 12 and Ref. 12. However, consistent with previous studies \(^{11,16,19}\) , our experimental results showed the addition of a small concentration of Mn into KNN film was very effective in reducing the leakage current density. Due to the smaller radius of Mn ion than those of the A- site \(\mathrm{K^{+} / Na^{+}}\) ions, the solid solubility of Mn is low in KNN lattice. When doping Mn into the KNN films, the Mn ions are believed to occupy a small number of A- site vacancies, which were formed due to the volatilization of the \(\mathrm{K^{+} / Na^{+}}\) ions in the KNN nanocolumns, according to the atomic structural imaging and composition analysis. Considering its primary valence, \(\mathrm{Mn^{2 + }}\) can act as a donor- dopant on the A- site of the perovskite lattice. However, in order to balance the charge, the occupancy of \(\mathrm{Mn^{2 + }}\) on the A- site vacancies was only partial, with every other A- site occupied. As a result, partial A- site deficiency still existed in the KNN nanocolumns \(^{18}\) . On the contrary, the high density of Mn accumulated at the phase boundaries to form atomic- scale Mn- rich layer, which could compensate the serious K/Na deficiencies at the boundaries. In the case of Type- I antiphase boundaries, both cation \(\mathrm{Mn^{2 + }}\) and anion \(\mathrm{O^{2 - }}\) can occupy the atomic site at the single antiphase boundary layer. By adjusting the ratio of Mn/O, we can achieve charge balance in optimum experimental conditions (Supplementary Fig. 13). For Type- II antiphase boundaries, which resemble the Ruddlesden- Popper structure, separate atomic columns of cations and anion \(\mathrm{O^{2 - }}\) were observed. The cation atomic sites at these boundaries can be occupied by the \(\mathrm{Nb^{5 + }}\) , \(\mathrm{Mn^{2 + }}\) , \(\mathrm{K^{+}}\) and \(\mathrm{Na^{+}}\) ions. The ratios among these four cations could also be adjusted experimentally to maintain charge balance (Supplementary Fig. 14). Consequently, the leakage currents can be significantly reduced, which is crucial for the ferroelectric functionality of the thin film. In our study, we investigated a series of Mn- doped samples with the Mn concentration ranging from 1 to \(5\mathrm{wt}\%\) . It was observed that the sample with a low Mn doping content, such as \(1\mathrm{wt}\%\) , exhibited a vertical nanocolumns structure, but the K/Na deficiencies were not fully compensated (Supplementary Fig. 15). As the doping level increased to \(5\%\) , the nanoscale Mn enriched phases were observed, which disrupted the epitaxial growth of the thin films, as shown in Supplementary Fig. 16.  
+
+More importantly, the specific atomic configurations at the Mn- inlaid antiphase boundaries induce noticeable change in the lattice structure compared to the KNN nanocolumns. The high- density boundaries are organized in an ordered manner on a nanoscale and extend vertically in the film, resulting in the generation of apparent lattice strains. These strains also impact the neighboring KNN nanocolumns, as evidenced by the ADF images (Supplementary Fig. 17, Supplementary Fig. 18), where the brighter image contrast indicates the presence of apparent lattice strains near the Mn- inlaid antiphase boundaries. It is well- known that lattice strain can significantly influence the ferroelectric polarization of thin films. We examined the relative lattice strain and the evolution of lattice parameters using the geometry phase analysis method. Fig. 4a displays an ADF image of the KNN- M thin film, revealing the presence of two types of boundaries. The corresponding relative lattice strain are plotted in Fig. 4b \((\epsilon_{xx})\) , Fig. 4c \((\epsilon_{yy})\) and
+
+<--- Page Split --->
+
+Fig. 4d \((\epsilon_{xy})\) , respectively. It can be seen that the boundaries exhibit apparent relative lattice strains, mainly perpendicular to their orientation with no observable relative strains parallel to the boundaries (Supplementary Fig. 19). The increased atomic spacing at the boundaries, indicative of tensile strain, is accompanied by the compressive strain in the KNN nanocolumns. It is worth noting that a strain in one direction typically induces an opposite strain component in the other direction to achieve overall relaxation \(^{28}\) . Consequently, the in-plane compressive strain induces an out-of-plane tensile strain, as confirmed by the XRD results with a larger \(c\) than \(a\) and \(b\) in the KNN lattice, which is advantageous for achieving high ferroelectric polarization \(^{7}\) .  
+
+The displacement of the center polar B- site cations relative to the corner A- site cations \((\delta_{\mathrm{B - A}})\) was used as a measure of the local polarization. Fig. 4e displays a colored arrow map of \(\delta_{\mathrm{B - A}}\) , representing the orientation and magnitude of the polarization. The film exhibited apparent displacement polarization, with the largest polarization observed near the boundaries. The different strains present at each type of boundary resulted in the formation of multidomain structures with a mixture of various orientations of polarizations. To investigate the impact of boundary strain on ferroelectric polarization, we conducted phase field simulations. The phenomenological model and relative lattice strain distributions used in these simulations were obtained from Fig. 4a- d, as shown in Supplementary Fig. 20. Specifically, we examined the different lattice strains, namely \(\epsilon_{\mathrm{local}}(0, 0.1, \text{and} 0.2)\) , induced by the Mn accumulated boundary, to elucidate their effects. Our simulations employed the orthorhombic phase with the [110]- oriented polarization of KNN at room temperature \(^{16}\) , as illustrated in Supplementary Fig. 21, which showcases the detailed microstructural evolution. As the applied external electric field increased, the polarizations transitioned from the orthorhombic phase (represented by different colors in Fig. 4f) to the tetragonal phase (indicated by light blue color in Fig. 4g). Importantly, the lattice strains around the boundaries induced larger polarization in both in- plane and out- of- plane directions, as shown in Fig. 4f and Supplementary Fig. 22. The simulated ferroelectric loop demonstrated an increase in both the coercive electric field strength \((E_{\mathrm{c}})\) and ferroelectric polarization in the out- of- plane directions with the increase in lattice strain (see Fig. 4h and Supplementary Fig. 21a). Thus, the Mn- inlaid antiphase boundaries offer a new structural framework for enhancing the ferroelectric polarization of lead- free ferroelectric thin films.  
+
+In summary, we prepared and observed the new Mn- inlaid antiphase boundaries structure in perovskites. The high- density vertical Mn- inlaid antiphase boundaries show the positivity effect on charge balance, coherent transition to the neighboring KNN nanocolumns and the induction of large strain for the nanocolumnar KNN lattice in the entire KNN- M thin films. Therefore, the ever higher ferroelectric polarizations have been observed for the KNN- based thin films. The Mn- inlaid antiphase boundaries also give a new possible structure frame to modulate the various properties of perovskites.
+
+<--- Page Split --->
+![](images/Figure_4.jpg)
+
+<center>Fig. 4 | Strains and atom displacement polarizations in the KNN-M thin film. a, Planar-view atomic-resolution HAADF image of the KNN-M thin film. b-d, Maps depicting the relative lattice strains: (b) \(\epsilon_{xx}\) (in-plane strain), (c) \(\epsilon_{yy}\) (out-of-plane strain) and (d) \(\epsilon_{xy}\) (shear strain). e, Colored arrows map of polarizations \((\partial_{Nb-KNa})\) , indicating the polarization orientation of the KNN nanocolumns in (a). f, Polarization of KNN-M obtained from phase-field simulation. g, Mapping of the piezoelectric response of the KNN-M film under an applied electric field obtained from phase-field simulation. h, Calculated P-E curves with different local strains. </center>  
+
+## References  
+
+1 Martin, L. W. & Rappe, A. M. Thin- film ferroelectric materials and their applications. Nature Reviews Materials 2, 16087, doi:10.1038/natrevmats.2016.87 (2016).  2 Yang, Q. et al. Ferroelectricity in layered bismuth oxide down to 1 nanometer. Science 379, 1218- 1224, doi:10.1126/science.abm5134 (2023).  3 Zhang, S., Malič, B., Li, J.- F. & Rödel, J. Lead- free ferroelectric materials: Prospective applications. J. Mater. Res. 36, 985- 995, doi:10.1557/s43578- 021- 00180- y (2021).  4 Saito, Y. et al. Lead- free piezoceramics. Nature 432, 84- 87, doi:10.1038/nature03028 (2004).  5 Wu, J., Xiao, D. & Zhu, J. Potassium- Sodium Niobate Lead- Free Piezoelectric Materials: Past, Present, and Future of Phase Boundaries. Chem. Rev. 115, 2559- 2595, doi:10.1021/cr5006809 (2015).  6 Zhang, N., Zheng, T. & Wu, J. Lead- Free (K,Na)NbO3- Based Materials: Preparation Techniques and Piezoelectricity. ACS Omega 5, 3099- 3107, doi:10.1021/acsomega.9b03658 (2020).  7 Gao, X. et al. The mechanism for the enhanced piezoelectricity in multi- elements doped (K,Na)NbO3 ceramics. Nature Communications 12, 881, doi:10.1038/s41467- 021- 21202- 7 (2021).  8 Xu, K. et al. Superior Piezoelectric Properties in Potassium- Sodium Niobate Lead- Free Ceramics. Adv. Mater. 28, 8519- 8523, doi:10.1002/adma.201601859 (2016).
+
+<--- Page Split --->
+
+9 Zhu, B. et al. New Potassium Sodium Niobate Single Crystal with Thickness-independent High-performance for Photoacoustic Angiography of Atherosclerotic Lesion. Sci. Rep. 6, 39679, doi:10.1038/srep39679 (2016). 10 Yin, F. et al. Transparent Lead-Free Ferroelectric (K,Na)NbO Single Crystal with Giant Second Harmonic Generation and Wide Mid- Infrared Transparency Window. Advanced Optical Materials 10, 2201721, doi:10.1002/adom.202201721 (2022). 11 Xu, L. et al. Robust Ferroelectric Properties in (K,Na)NbO3- Based Lead- Free Films via a Self- Assembled Nanocomposite Approach. ACS Appl. Mat. Interfaces 12, 4616- 4624, doi:10.1021/acsami.9b20311 (2020). 10 12 Waqar, M. et al. Origin of giant electric- field- induced strain in faulted alkali niobate films. Nature Communications 13, 3922, doi:10.1038/s41467- 022- 31630- 8 (2022). 13 Li, C. et al. A Novel Multiple Interface Structure with the Segregation of Dopants in Lead- Free Ferroelectric (K0.5Na0.5)NbO3 Thin Films. Advanced Materials Interfaces 5, 1700972, doi:10.1002/admi.201700972 (2018). 15 Liu, H. et al. Giant piezoelectricity in oxide thin films with nanopillar structure. Science 369, 292- 297, doi:10.1126/science.abb3209 (2020). 15 Cho, C.- R. & Grishin, A. Background Oxygen Effects on Pulsed Laser Deposited Na0.5K0.5NbO Films: From Superparaelectric State to Ferroelectricity. J. Appl. Phys. 87, 4439- 4448, doi:10.1063/1.373089 (2000). 20 Seog, H. J. et al. Recent Progress in Potassium Sodium Niobate Lead- free Thin Films. Journal of the Korean Physical Society 72, 1467- 1483, doi:10.3938/jkps.72.1467 (2018). 17 Chen, W., Zhao, J., Wang, L., Ren, W. & Liu, M. Lead- free piezoelectric KNN- BZ- BNT films with a vertical morphotropic phase boundary. AIP Advances 5, 077190, doi:10.1063/1.4928095 (2015). 25 Rubio- Marcos, F. et al. Effect of MnO doping on the structure, microstructure and electrical properties of the (K,Na,Li)(Nb,Ta,Sb)O3 lead- free piezoceramics. J. Alloys Compd. 509, 8804- 8811, doi:https://doi.org/10.1016/j.jallcom.2011.06.080 (2011). 19 Won, S. S. et al. Lead- free Mn- doped (K0.5,Na0.5)NbO3 piezoelectric thin films for MEMS- based vibrational energy harvester applications. Appl. Phys. Lett. 108, 30 doi:10.1063/1.4953623 (2016). 20 Li, C. et al. Atomic Resolution Interfacial Structure of Lead- free Ferroelectric K0.5Na0.5NbO3 Thin films Deposited on SrTiO3. Sci. Rep. 6, 37788, doi:10.1038/srep37788 (2016). 21 Luo, J. et al. Monoclinic (K,Na)NbO3 Ferroelectric Phase in Epitaxial Films. Advanced Electronic Materials 3, 1700226, doi:10.1002/aelm.201700226 (2017). 22 Cheng, Y.- Y.- S. et al. All- Inorganic Flexible (K, Na)NbO3- Based Lead- Free Piezoelectric Thin Films Spin- Coated on Metallic Foils. ACS Appl. Mat. Interfaces 13, 39633- 39640, doi:10.1021/acsami.1c11418 (2021). 23 Shiraishi, T. et al. Ferroelectric and piezoelectric properties of KNbO3 films deposited on flexible organic substrate by hydrothermal method. Jpn. J. Appl. Phys. 53, 09PA10, doi:10.7567/JJAP.53.09PA10 (2014). 24 Nishino, R., Fujita, T. C., Kagawa, F. & Kawasaki, M. Evolution of ferroelectricity in ultrathin PbTiO3 films as revealed by electric double layer gating. Sci. Rep. 10, 10864, doi:10.1038/s41598- 020- 67580- 8 (2020). 45 Dahl, O., Grepstad, J. K. & Tybell, T. Polarization direction and stability in ferroelectric lead titanate thin films. J. Appl. Phys. 106, 084104, doi:10.1063/1.3240331 (2009). 26 Zurbuchen, M. A. et al. Morphology, structure, and nucleation of out- of- phase boundaries (OPBs) in epitaxial films of layered oxides. J. Mater. Res. 22, 1439- 1471,
+
+<--- Page Split --->
+
+doi:10.1557/JMR.2007.0198 (2007).  
+
+27 Pennycook, S. J. Z- contrast stem for materials science. Ultramicroscopy 30, 58- 69, doi:10.1016/0304- 3991(89)90173- 3 (1989).  
+
+28 Zhao, J. et al. Strained hybrid perovskite thin films and their impact on the intrinsic stability of perovskite solar cells. Science Advances 3, eaa05616, doi:10.1126/sciadv.aa05616 (2017).  
+
+1. L. W. Martin, A. M. Rappe, Thin-film ferroelectric materials and their applications. Nature Reviews Materials 2, 16087 (2016). doi: 10.1038/natrevmat.2016.87.  
+
+2. Q. Yang, J. Hu, Y.-W. Fang, Y. Jia, R. Yang, S. Deng, Y. Lu, O. Dieguez, L. Fan, D. Zheng, X. Zhang, Y. Dong, Z. Luo, Z. Wang, H. Wang, M. Sui, X. Xing, J. Chen, J. Tian, L. Zhang, Ferroelectricity in layered bismuth oxide down to 1 nanometer. Science 379, 1218-1224 (2023). doi: doi:10.1126/science.abm5134.  
+
+3. S. Zhang, B. Malič, J.-F. Li, J. Rödel, Lead-free ferroelectric materials: Prospective applications. J. Mater. Res. 36, 985-995 (2021). doi: 10.1557/s43578-021-00180-y.  
+
+4. Y. Saito, H. Takao, T. Tani, T. Nonoyama, K. Takatori, T. Homma, T. Nagaya, M. Nakamura, Lead-free piezoceramics. Nature 432, 84-87 (2004). doi: 10.1038/nature03028.  
+
+5. J. Wu, D. Xiao, J. Zhu, Potassium-Sodium Niobate Lead-Free Piezoelectric Materials: Past, Present, and Future of Phase Boundaries. Chem. Rev. 115, 2559-2595 (2015). doi: 10.1021/cr5006809.  
+
+6. N. Zhang, T. Zheng, J. Wu, Lead-Free (K,Na)NbO₃-Based Materials: Preparation Techniques and Piezoelectricity. ACS Omega 5, 3099-3107 (2020). doi: 10.1021/acsomega.9b03658.  
+
+7. X. Gao, Z. Cheng, Z. Chen, Y. Liu, X. Meng, X. Zhang, J. Wang, Q. Guo, B. Li, H. Sun, Q. Gu, H. Hao, Q. Shen, J. Wu, X. Liao, S. P. Ringer, H. Liu, L. Zhang, W. Chen, F. Li, S. Zhang, The mechanism for the enhanced piezoelectricity in multi-elements doped (K,Na)NbO₃ ceramics. Nature Communications 12, 881 (2021). doi: 10.1038/s41467-021-21202-7.  
+
+8. K. Xu, J. Li, X. Lv, J. Wu, X. Zhang, D. Xiao, J. Zhu, Superior Piezoelectric Properties in Potassium-Sodium Niobate Lead-Free Ceramics. Adv. Mater. 28, 8519-8523 (2016). doi: 10.1002/adma.201601859.  
+
+9. B. Zhu, Y. Zhu, J. Yang, J. Ou-Yang, X. Yang, Y. Li, W. Wei, New Potassium Sodium Niobate Single Crystal with Thickness-independent High-performance for Photoacoustic Angiography of Atherosclerotic Lesion. Sci. Rep. 6, 39679 (2016). doi: 10.1038/srep39679.  
+
+10. F. Yin, L. Liu, M. Zhu, J. Lv, X. Guan, J. Zhang, N. Lin, X. Fu, Z. Jia, X. Tao, Transparent Lead-Free Ferroelectric (K,Na)NbO₃ Single Crystal with Giant Second Harmonic Generation and Wide Mid-Infrared Transparency Window. Advanced Optical Materials 10, 2201721 (2022). doi: 10.1002/adom.202201721.  
+
+11. L. Xu, F. Chen, F. Jin, H. Huang, L. Qu, K. Zhang, Z. Zhang, G. Gao, Y. Lu, F. Zhang, K. Wang, C. Ma, W. Wu, Robust Ferroelectric Properties in (K,Na)NbO₃-Based Lead-Free Films via a Self-Assembled Nanocomposite Approach. ACS Appl. Mat. Interfaces 12, 4616-4624 (2020). doi: 10.1021/acsami.9b20311.  
+
+12. M. Waqar, H. Wu, K. P. Ong, H. Liu, C. Li, P. Yang, W. Zang, W. H. Liew, C. Diao, S. Xi, D. J. Singh, Q. He, K. Yao, S. J. Pennycook, J. Wang, Origin of giant electric-field-induced strain in faulted alkali niobate films. Nature Communications 13, 3922 (2022). doi: 10.1038/s41467-022-31630-8.  
+
+13. C. Li, L. Wang, W. Chen, L. Lu, H. Nan, D. Wang, Y. Zhang, Y. Yang, C.-L. Jia, A Novel
+
+<--- Page Split --->
+
+Multiple Interface Structure with the Segregation of Dopants in Lead- Free Ferroelectric \(\mathrm{(K_{0.5}Na_{0.5})NbO_{3}}\) Thin Films. Advanced Materials Interfaces 5, 1700972 (2018). doi: 10.1002/admi.201700972. 14. H. Liu, H. Wu, K. P. Ong, T. Yang, P. Yang, P. K. Das, X. Chi, Y. Zhang, C. Diao, W. K. A. Wong, E. P. Chew, Y. F. Chen, C. K. I. Tan, A. Rusydi, M. B. H. Breese, D. J. Singh, L.- Q. Chen, S. J. Pennycook, K. Yao, Giant piezoelectricity in oxide thin films with nanopillar structure. Science 369, 292- 297 (2020). doi: 10.1126/science.abb3209. 15. C.- R. Cho, A. Grishin, Background Oxygen Effects on Pulsed Laser Deposited \(\mathrm{Na_{0.5}K_{0.5}NbO_{3}}\) Films: From Superparaelectric State to Ferroelectricity. J. Appl. Phys. 87, 4439- 4448 (2000). doi: 10.1063/1.373089. 16. H. J. Seog, A. Ullah, C. W. Ahn, I. W. Kim, S. Y. Lee, J. Park, H. J. Lee, S. S. Won, S.- H. Kim, Recent Progress in Potassium Sodium Niobate Lead- free Thin Films. Journal of the Korean Physical Society 72, 1467- 1483 (2018). doi: 10.3938/jkps.72.1467. 17. W. Chen, J. Zhao, L. Wang, W. Ren, M. Liu, Lead- free piezoelectric KNN- BZ- BNT films with a vertical morphotropic phase boundary. AIP Advances 5, 077190 (2015). doi: 10.1063/1.4928095. 18. F. Rubio- Marcos, P. Marchet, X. Vendrell, J. J. Romero, F. Remondiere, L. Mestres, J. F. Fernandez, Effect of MnO doping on the structure, microstructure and electrical properties of the \(\mathrm{(K,Na,Li)(Nb,Ta,Sb)O_{3}}\) lead- free piezoceramics. J. Alloys Compd. 509, 8804- 8811 (2011). doi: https://doi.org/10.1016/j.jallcom.2011.06.080. 19. S. S. Won, J. Lee, V. Venugopal, D.- J. Kim, J. Lee, I. W. Kim, A. I. Kingon, S.- H. Kim, Lead- free Mn- doped \(\mathrm{(K_{0.5}Na_{0.5})NbO_{3}}\) piezoelectric thin films for MEMS- based vibrational energy harvester applications. Appl. Phys. Lett. 108, (2016). doi: 10.1063/1.4953623. 25. C. Li, L. Wang, Z. Wang, Y. Yang, W. Ren, G. Yang, Atomic Resolution Interfacial Structure of Lead- free Ferroelectric \(\mathrm{K_{0.5}Na_{0.5}NbO_{3}}\) Thin films Deposited on SrTiO3. Sci. Rep. 6, 37788 (2016). doi: 10.1038/srep37788. 21. J. Luo, W. Sun, Z. Zhou, H.- Y. Lee, K. Wang, F. Zhu, Y. Bai, Z. J. Wang, J.- F. Li, Monoclinic \(\mathrm{(K,Na)NbO_{3}}\) Ferroelectric Phase in Epitaxial Films. Advanced Electronic Materials 3, 1700226 (2017). doi: 10.1002/aelm.201700226. 22. Y.- Y.- S. Cheng, L. Liu, Y. Huang, L. Shu, Y.- X. Liu, L. Wei, J.- F. Li, All- Inorganic Flexible (K, Na)NbO3- Based Lead- Free Piezoelectric Thin Films Spin- Coated on Metallic Foils. ACS Appl. Mat. Interfaces 13, 39633- 39640 (2021). doi: 10.1021/acsami.1c11418. 23. T. Shiraishi, N. Kaneko, M. Kurosawa, H. Uchida, T. Hirayama, H. Funakubo, Ferroelectric and piezoelectric properties of KNbO3 films deposited on flexible organic substrate by hydrothermal method. Jpn. J. Appl. Phys. 53, 09PA10 (2014). doi: 10.7567/JJAP.53.09PA10. 24. R. Nishino, T. C. Fujita, F. Kagawa, M. Kawasaki, Evolution of ferroelectricity in ultrathin PbTiO3 films as revealed by electric double layer gating. Sci. Rep. 10, 10864 (2020). doi: 10.1038/s41598- 020- 67580- 8. 25. O. Dahl, J. K. Grepstad, T. Tybell, Polarization direction and stability in ferroelectric lead titanate thin films. J. Appl. Phys. 106, 084104 (2009). doi: 10.1063/1.3240331. 26. M. A. Zurbuchen, W. Tian, X. Q. Pan, D. Fong, S. K. Streiffer, M. E. Hawley, J. Lettieri, Y. Jia, G. Asayama, S. J. Fulk, D. J. Comstock, S. Knapp, A. H. Carim, D. G. Schlom, Morphology, structure, and nucleation of out- of- phase boundaries (OPBs) in epitaxial films of layered oxides. J. Mater. Res. 22, 1439- 1471 (2007). doi: 10.1557/JMR.2007.0198.
+
+<--- Page Split --->
+
+27. S. J. Pennycook, Z-contrast stem for materials science. Ultramicroscopy 30, 58-69 (1989). doi: 10.1016/0304-3991(89)90173-3.  
+28. J. Zhao, Y. Deng, H. Wei, X. Zheng, Z. Yu, Y. Shao, J. E. Shield, J. Huang, Strained hybrid perovskite thin films and their impact on the intrinsic stability of perovskite solar cells. Science Advances 3, eaao5616 (2017). doi: 10.1126/sciadv.aao5616.  
+
+## Methods  
+
+## The ceramic targets synthesis  
+
+The ceramic targets synthesisThe ceramic targets of \((\mathrm{K,Na}) \mathrm{NbO}_3 - 0 / 1 / 2 / 5 \mathrm{wt\% Mn}\) (KNN-M) were fabricated using the conventional solid-state reaction method. High-purity materials, including \(\mathrm{K}_2\mathrm{CO}_3(99.0\%)\) , \(\mathrm{Na}_2\mathrm{CO}_3(99.8\%)\) , \(\mathrm{Nb}_2\mathrm{O}_5(99.9\%)\) and \(\mathrm{MnO}_2(98.8\%)\) , were obtained from Sinopharm Chemical Reagent Beijing Co. Ltd. Initially, the raw materials were accurately weighed according to the stoichiometric ratio of \((\mathrm{K}_0.5\mathrm{Na}_0.5)\mathrm{NbO}_3\) and then homogenized in a planetary mill for 24h using ethyl alcohol as the medium. After calcination at \(750^{\circ}\mathrm{C}\) for 4h, the resulting powder mixtures were milled, dried, and sieved. Subsequently, additional \(\mathrm{MnO}_2\) was incorporated into the powders and homogenized for another 24 h in the planetary mill. The resulting powder mixtures were then dried and sieved. Next, the mixed KNN-Mn powders were compacted into disks by uniaxial pressing at a pressure of \(150\mathrm{MPa}\) for \(2\mathrm{min}\) . Finally, the KNN-Mn pellets, with a diameter of \(30\mathrm{mm}\) , were sintered at \(1120^{\circ}\mathrm{C}\) for \(4\mathrm{h}\) to prepare the target  
+
+## Thin Films Growth  
+
+Thin Films GrowthThe KNN-M films were deposited onto the (001) SrTiO\(_3\) (STO) substrate buffered with \(\mathrm{La}_{0.07}\mathrm{Sr}_{0.93}\mathrm{SnO}_3\) (LSSO) layer using the PLD technique with a \(248\mathrm{nm}\) KrF excimer laser. The deposition process involved maintaining the substrate temperature at \(735^{\circ}\mathrm{C}\) and the \(\mathrm{O}_2\) pressure at \(15\mathrm{Pa}\) for the LSSO layer, followed by deposition of the KNN-film rat \(700^{\circ}\mathrm{C}\) and \(30\mathrm{Pa}\) . The target-substrate distance was set at \(5\mathrm{cm}\) , and the laser energy were kept at \(2\mathrm{J / cm}^2\) . The thicknesses of the KNN-M and LSSO were approximately \(300\mathrm{nm}\) and \(30\mathrm{nm}\) , respectively. After deposition, each film underwent in-situ annealing for 15 minutes before being cooled down to room temperature.  
+
+## X-ray diffraction  
+
+X-ray diffractionThe crystalline phase of films was characterized by high-resolution X-ray diffraction (XRD) using Cu Kα1 radiation. The samples were mounted in the diffractometer (PANalytical, X'Pert), for linear scans, rocking curves, and reciprocal space mappings (RSM) at room temperature.  
+
+## Transmission Electron Microscopy (TEM)  
+
+Transmission Electron Microscopy (TEM)The specimens were prepared using both the Tripod method (ALLIED Multiprep) and focused ion beam (FIB, Thermofisher Helios UX5) for the cross-section and plan-view STEM observation. TEM characterizations, low magnification STEM imaging, and EDS composition analysis were performed using the Thermofisher Talos F200X. Atomic resolution HAADF and iDPC images were acquired using the Thermofisher Titan Themis Z. Atom resolution HAADF, ADF image and EDS mapping were obtained using the Thermofisher Titan cubed Themis G2 300. The acquisition of EELS data was carried out using Gatan electron energy loss spectroscopy (EELS) on both the FEI Titan G2 80- 300 and JEOL ARM300.
+
+<--- Page Split --->
+
+## Electrical Testing  
+
+Electrical TestingPolarization versus electric field \((P - E)\) hysteresis loops were measured through the ferroelectric test system (TF Analyzer 2000E, Germany) at \(1\mathrm{kHz}\) in room temperature. The \(P - E\) loops at liquid nitrogen temperature of \(80\mathrm{K}\) were obtained with Cryogenic Probe Station supplied by Lake Shore Company. The ferroelectric domains and polarization reversal behaviors were characterized by piezoresponse force microscopy (PFM, Asylum Cypher).  
+
+## The calculation of the displacements in ADF images  
+
+A standard peak finding algorithm is employed for the ADF image, which is based on fitting two- dimensional Gaussian functions to the intensity maxima. This algorithm allows us to determine the position and brightness of each column. Using these data, we can calculate the offcenter ion displacements between the Nb column and the center of the unit cell. The center of the unit cell is determined by the average coordinate of the four K/Na atomic columns. We use the following formula to calculate the displacements: \(\mathbf{u}_{ij} = \mathbf{r}_{ij} - 1 / 4\) \((R_{ij} + (i + 1) + (j + 1) + (i + 1)(j + 1))\) . Here, \(\mathrm{i} / \mathrm{j}\) indicate the row/column number of each atom column, \(\mathbf{r}_{\mathrm{ij}}\) indicates the position of Nb atomic columns, and \(\mathrm{R_{ij}}\) indicates the position of K/Na atomic columns.  
+
+## Phase-Field Simulations  
+
+A single crystal considering Cubic \((C)\) to Orthorhombic (O) ferroelectric transition with Mninlaid antiphase boundaries has been carried out in phase- field simulations. The total free energy of the ferroelectric system can be described as (29- 32):  
+
+\[F = \int_{\nu}(f_{\mathrm{bulk}} + f_{\mathrm{grad}} + f_{\mathrm{couple}})dV + \int_{\nu}(f_{\mathrm{elas}} + f_{\mathrm{elec}})dV \quad (1)\]  
+
+where \(f_{\mathrm{bulk}}\) represents the bulk free energy density,  
+
+\[f_{\mathrm{bulk}} = \alpha_{1}(P_{1}^{2} + P_{2}^{2} + P_{3}^{2}) - \alpha_{11}(P_{1}^{2} + P_{2}^{2} + P_{3}^{3})^{2} + \alpha_{12}(P_{1}^{2}P_{2}^{2} + P_{2}^{2}P_{3}^{2} + P_{1}^{2}P_{3}^{2})\] \[\qquad +\alpha_{112}(P_{1}^{4}P_{2}^{2} + P_{2}^{4}P_{3}^{2} + P_{1}^{4}P_{3}^{2} + P_{1}^{2}P_{2}^{4} + P_{2}^{2}P_{3}^{4} + P_{1}^{2}P_{3}^{4}) + \alpha_{113}(P_{1}^{2}P_{2}^{2}P_{3}^{2})\] \[\qquad +\alpha_{111}(P_{1}^{2} + P_{2}^{2} + P_{3}^{3})^{3}\]  
+
+where \(\alpha_{ij}\) is the coefficient and depends on concentration \(c\) and temperature \(T\)  
+
+\(f_{\mathrm{grad}}\) represents the gradient energy density,  
+
+\[f_{\mathrm{grad}} = \frac{1}{2} G_{11}((P_{1,1})^{2} + (P_{1,2})^{2} + (P_{1,3})^{2} + (P_{2,1})^{2} + (P_{2,2})^{2} + (P_{2,3})^{2} + (P_{3,1})^{2} + (P_{3,2})^{2} + (P_{3,3})^{2}) \quad (3)\]  
+
+where \(G_{11}\) is the gradient energy coefficient. \(f_{\mathrm{couple}}\) represents the couple effect caused by lattice strain \(\epsilon_{\mathrm{local}}(33)\)  
+
+\[f_{\mathrm{couple}} = -(q_{11}\epsilon_{11} + q_{12}\epsilon_{22} + q_{12}\epsilon_{33})P_{1}^{2} - (q_{11}\epsilon_{22} + q_{12}\epsilon_{11} + q_{12}\epsilon_{33})P_{2}^{2}\] \[-(q_{11}\epsilon_{33} + q_{12}\epsilon_{11} + q_{12}\epsilon_{22})P_{3}^{2} - 2q_{44}(\epsilon_{12}P_{1}P_{2} + \epsilon_{23}P_{2}P_{3} + \epsilon_{13}P_{1}P_{3})~,\]  
+
+where \(q_{11} = C_{11}Q_{11} + 2C_{12}Q_{12}\) , \(q_{12} = C_{11}Q_{12} + C_{12}(Q_{11} + Q_{12})\) , \(q_{44} = 2C_{44}Q_{44}\) , \(C_{11}\) , \(C_{12}\) , and \(C_{44}\) is the elastic constants in Voigt's notation and \(Q_{\mathrm{ij}}\) is the electrostrictive coefficients. \(f_{\mathrm{elas}}\) is the long- range elastic interaction energy densities and \(f_{\mathrm{elec}}\) is the electrostatic interaction energy densities.  
+
+\(f_{\mathrm{elas}} = \frac{1}{2} c_{ijkl}e_{ij}e_{kl} = \frac{1}{2} c_{ijkl}(\epsilon_{ij} - \epsilon_{ij}^{0})(\epsilon_{kl} - \epsilon_{kl}^{0})\) , where \(c_{ijkl}\) is the elastic constant tensor, \(\epsilon_{ij}\) the total strain, \(\epsilon^{0}_{kl}\) the electrostrictive stress- free strain, i.e., \(\epsilon^{0}_{kl} = Q_{ijkl}P_{k}P_{l}\) . \(f_{\mathrm{elec}} = f_{\mathrm{dipole}} + f_{\mathrm{depola}} + f_{\mathrm{appl}}\) , where \(f_{\mathrm{dipole}}\) is the dipole- dipole interaction caused by polarization, \(f_{\mathrm{depola}}\) the depolarization energy density and \(f_{\mathrm{appl}}\) the energy density caused by applied electric field. The dimensionless parameters used in our simulations:  
+
+\[\alpha_{1} = -0.1134, \alpha_{11} = -2.2896, \alpha_{12} = -7.5572, \alpha_{111} = 12.94, \alpha_{112} = 1.776, \alpha_{123} = 144.6.\]
+
+<--- Page Split --->
+
+\[C_{11} = 1780, C_{12} = 964, C_{44} = 1220, Q_{11} = 0.1, Q_{12} = -0.034, Q_{44} = 0.029.\]  
+
+The temporal evolution of the spontaneous polarization field \((P)\) can be obtained by solving the time dependent Ginzburg Landau (TDGL) equation: \(\frac{dP_{i}(x,t)}{dt} = - M\frac{\delta F}{\delta P_{i}(X,t)}, i = 1,2,3\) , where \(M\) is the kinetic coefficient, \(F\) is the total free energy, and \(t\) is time.  
+
+## Data availability  
+
+The data that support the findings of this study are available from the corresponding authors upon request.  
+
+29. D. Wang, X. Ke, Y. Wang, J. Gao, Y. Wang, L. Zhang, S. Yang, X. Ren, Phase diagram of polar states in doped ferroelectric systems. Phys. Rev. B 86, 054120 (2012). doi: 10.1103/PhysRevB.86.054120.  
+30. S. Semenovskaya, A. G. Khachaturyan, Development of ferroelectric mixed states in a random field of static defects. J. Appl. Phys. 83, 5125-5136 (1998). doi: 10.1063/1.367330.  
+31. Y. L. Li, L. E. Cross, L. Q. Chen, A phenomenological thermodynamic potential for BaTiO₃ single crystals. J. Appl. Phys. 98, (2005). doi: 10.1063/1.2042528.  
+32. L. Zhang, H. Wang, D. Wang, M. Guo, X. Lou, D. Wang, A New Strategy for Large Dynamic Piezoelectric Responses in Lead-Free Ferroelectrics: The Relaxor/Morphotropic Phase Boundary Crossover. Adv. Funct. Mater. 30, 2004641 (2020). doi: 10.1002/adfm.202004641.  
+33. Y. L. Li, S. Y. Hu, Z. K. Liu, L. Q. Chen, Effect of substrate constraint on the stability and evolution of ferroelectric domain structures in thin films. Acta Mater. 50, 395-411 (2002). doi: 10.1016/S1359-6454(01)00360-3.
+
+<--- Page Split --->
+
+## Supplementary Files  
+
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+SupplementaryInformation2. pdf
+
+<--- Page Split --->

preprint/preprint__03d68cf8e193cb35c9be6144c0f8c2e40f012dc558e3da2c1ab952dea9dac6d5/preprint__03d68cf8e193cb35c9be6144c0f8c2e40f012dc558e3da2c1ab952dea9dac6d5_det.mmd

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+<|ref|>title<|/ref|><|det|>[[44, 106, 825, 175]]<|/det|>
+# Mn-inlaid antiphase boundaries in perovskite structure  
+
+<|ref|>text<|/ref|><|det|>[[44, 195, 333, 240]]<|/det|>
+Lingyan Wang 1.y.wang@mail.xjtu.edu.cn  
+
+<|ref|>text<|/ref|><|det|>[[44, 267, 891, 311]]<|/det|>
+Electronic Materials Research Laboratory, Key Laboratory of the Ministry of Education, School of Electronic Science and Engineering, Xi'an Jiaotong University  
+
+<|ref|>text<|/ref|><|det|>[[44, 316, 272, 357]]<|/det|>
+Chao Li Xi'an Jiaotong University  
+
+<|ref|>text<|/ref|><|det|>[[44, 363, 141, 381]]<|/det|>
+Liqiang Xu  
+
+<|ref|>text<|/ref|><|det|>[[44, 384, 896, 428]]<|/det|>
+Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University  
+
+<|ref|>text<|/ref|><|det|>[[44, 432, 157, 450]]<|/det|>
+Xuerong Ren  
+
+<|ref|>text<|/ref|><|det|>[[44, 453, 893, 497]]<|/det|>
+Electronic Materials Research Laboratory, Key Laboratory of the Ministry of Education, School of Electronic Science and Engineering, Xi'an Jiaotong University  
+
+<|ref|>text<|/ref|><|det|>[[44, 501, 168, 519]]<|/det|>
+Fangzhou Yao  
+
+<|ref|>text<|/ref|><|det|>[[44, 522, 950, 565]]<|/det|>
+Wuzhen Laboratory/Center of Advanced Ceramic Materials and Devices, Yangtze Delta Region Institute of Tsinghua University  
+
+<|ref|>text<|/ref|><|det|>[[44, 570, 698, 612]]<|/det|>
+Jiangbo Lu School of Physics and Information Technology, Shaanxi Normal University  
+
+<|ref|>text<|/ref|><|det|>[[44, 617, 272, 657]]<|/det|>
+Dong Wang Xi'an Jiaotong University  
+
+<|ref|>text<|/ref|><|det|>[[44, 662, 272, 702]]<|/det|>
+zhongshuai Liang Xi'an Jiaotong University  
+
+<|ref|>text<|/ref|><|det|>[[44, 708, 630, 750]]<|/det|>
+Ping Huang Xi'an Jiaotong University https://orcid.org/0000- 0002- 5295- 8216  
+
+<|ref|>text<|/ref|><|det|>[[44, 755, 184, 774]]<|/det|>
+Shengqiang Wu  
+
+<|ref|>text<|/ref|><|det|>[[44, 777, 603, 797]]<|/det|>
+School of Materials Science and Engineering, Peking University  
+
+<|ref|>text<|/ref|><|det|>[[44, 802, 168, 820]]<|/det|>
+Hongmei Jing  
+
+<|ref|>text<|/ref|><|det|>[[44, 823, 700, 842]]<|/det|>
+School of Physics and Information Technology, Shaanxi Normal University  
+
+<|ref|>text<|/ref|><|det|>[[44, 847, 150, 866]]<|/det|>
+Yijun Zhang  
+
+<|ref|>text<|/ref|><|det|>[[44, 869, 890, 911]]<|/det|>
+Electronic Materials Research Laboratory, Key Laboratory of the Ministry of Education, School of Electronic Science and Engineering, Xi'an Jiaotong University  
+
+<|ref|>text<|/ref|><|det|>[[44, 916, 166, 934]]<|/det|>
+Guohua Dong  
+
+<|ref|>text<|/ref|><|det|>[[50, 937, 630, 957]]<|/det|>
+Xi'an Jiaotong University https://orcid.org/0000- 0002- 5484- 5442
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[44, 42, 272, 84]]<|/det|>
+Haixia Liu Xi'an Jiaotong University  
+
+<|ref|>text<|/ref|><|det|>[[44, 90, 530, 131]]<|/det|>
+Chuansheng Ma Instrumental Analysis Center, Xi'an Jiaotong University  
+
+<|ref|>text<|/ref|><|det|>[[44, 137, 264, 177]]<|/det|>
+Yinong Lyu Nanjing Tech University  
+
+<|ref|>text<|/ref|><|det|>[[44, 183, 164, 201]]<|/det|>
+Xiaoyong Wei  
+
+<|ref|>text<|/ref|><|det|>[[44, 204, 950, 246]]<|/det|>
+Electronic Materials Research Laboratory, Key Laboratory of the Ministry of Education and International Center for Dielectric Research, Xi'an Jiao Tong University  
+
+<|ref|>text<|/ref|><|det|>[[44, 251, 118, 268]]<|/det|>
+Wei Ren  
+
+<|ref|>text<|/ref|><|det|>[[44, 272, 702, 293]]<|/det|>
+Xi'an Jiaotong University School of Electronic and Information Engineering  
+
+<|ref|>text<|/ref|><|det|>[[44, 298, 125, 316]]<|/det|>
+Ke Wang  
+
+<|ref|>text<|/ref|><|det|>[[44, 319, 925, 361]]<|/det|>
+State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, P. R. China https://orcid.org/0000- 0001- 9840- 2427  
+
+<|ref|>text<|/ref|><|det|>[[44, 366, 168, 384]]<|/det|>
+Zuo-Guang Ye  
+
+<|ref|>text<|/ref|><|det|>[[50, 387, 622, 407]]<|/det|>
+Simon Fraser University https://orcid.org/0000- 0003- 2378- 7304  
+
+<|ref|>text<|/ref|><|det|>[[44, 412, 138, 430]]<|/det|>
+Feng Chen  
+
+<|ref|>text<|/ref|><|det|>[[50, 434, 601, 454]]<|/det|>
+High Magnetic Field Laboratory, Chinese Academy of Sciences  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 495, 103, 512]]<|/det|>
+## Article  
+
+<|ref|>text<|/ref|><|det|>[[44, 533, 137, 551]]<|/det|>
+Keywords:  
+
+<|ref|>text<|/ref|><|det|>[[44, 570, 325, 590]]<|/det|>
+Posted Date: February 5th, 2024  
+
+<|ref|>text<|/ref|><|det|>[[44, 608, 475, 628]]<|/det|>
+DOI: https://doi.org/10.21203/rs.3.rs- 3884985/v1  
+
+<|ref|>text<|/ref|><|det|>[[44, 645, 916, 689]]<|/det|>
+License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+<|ref|>text<|/ref|><|det|>[[44, 707, 535, 727]]<|/det|>
+Additional Declarations: There is NO Competing Interest.  
+
+<|ref|>text<|/ref|><|det|>[[42, 762, 925, 806]]<|/det|>
+Version of Record: A version of this preprint was published at Nature Communications on August 7th, 2024. See the published version at https://doi.org/10.1038/s41467- 024- 51024- 2.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[226, 89, 769, 110]]<|/det|>
+## Mn-inlaid antiphase boundaries in perovskite structure  
+
+<|ref|>text<|/ref|><|det|>[[112, 133, 883, 515]]<|/det|>
+High- performance perovskite ferroelectrics are central to various electro- mechanical functional devices \(^{1,2}\) . However, the use of toxic Pb- based ferroelectrics in high- end applications is being limited due to environmental concerns and the related legislations \(^{3}\) . As an eco- friendly alternative, lead- free perovskite potassium sodium niobate (K \(_4\) Na \(_1\) - \(_3\) NbO \(_3\) , KNN)- based perovskite ferroelectrics materials have been intensively studied since the discovery of a large piezoelectric coefficient \(d_{33}\) value of 416 pC/N for KNN- based ceramics by Saito et al. \(^{4 - 6}\) . With advancements in the ferroelectric performances of the KNN- based ceramics and single crystals \(^{7 - 10}\) , a great deal of efforts have also been made to prepare the KNN- based thin films by various deposition techniques \(^{11 - 14}\) . However, the volatilization of the alkali ions has been identified as a major issue in obtaining high- quality KNN- based thin films. The inevitable loss of potassium and sodium elements during the vapor or chemical solution- based depositions changes the stoichiometry, resulting in the formation of undesired alkali- deficient secondary phases and defects. Consequently, KNN- based films exhibit high electrical conduction and poor ferroelectric response \(^{15}\) . Previous strategies focused on chemical composition adjustment to compensate for the elemental volatilization, and construction of morphotropic or polytropic phase boundaries to enhance the dielectric and ferroelectric properties of KNN- based thin films \(^{16 - 19}\) . Recently, some other methods, such as interface effects, flexible substrates, and defects engineering have been explored to modulate lattice strain, displacement polarization and electronic structure \(^{11,12,20 - 23}\) . All those efforts have led to a gradual improvement in the overall ferroelectric performances, which, however, still remain significantly inferior to Pb- based thin films and thereby are far from being suitable for practical applications in new micro electro- mechanical devices, ferroelectric field- effect transistors, nonvolatile memories, electro- optic devices, etc. \(^{1,24,25}\) .  
+
+<|ref|>text<|/ref|><|det|>[[112, 517, 883, 865]]<|/det|>
+Material properties are strongly influenced by the microstructure. In terms of crystal structure, the above- mentioned attempts primarily focused on distorting or tilting the lattices within the basic perovskite framework, which has given rise to limited effects in improving the ferroelectric properties. It has been observed that the physical constraints of underlying substrate and the large unit cell of oxide perovskite structure can lead to the generation and propagation of out- of- phase boundary defects through the entire thickness of the film, especially with special deposition processes \(^{12,26}\) . These charged out- of- phase boundaries, originated from the alkali- deficiency, have been identified and found to play an important role in the piezoelectric performances of NaNbO \(_3\) and KNN thin films \(^{12}\) . In this work, we also harnessed these inherent characteristics (alkali deficiency and out- of- phase boundaries) to create a unique nanocolumnar structure, and then incorporated another element at the boundaries between the KNN nanocolumns to form a new perovskite derivative structure. Specifically, the nanocolumnar KNN- based thin film consists of perovskite KNN nanocolumns that are interspersed with Mn- inlaid antiphase boundaries. Atomic resolution images revealed that the thickness of perovskite KNN slabs varied from a few to tens of nanometers, while the coherent antiphase boundaries consisted of one or two atomic layers enriched with Mn. This strategic incorporation of Mn not only helped to balance the charges originated from alkali ions deficiency, leading to a reduced leakage current, but also induced a noticeable out- of- plane tensile strain in the KNN nanocolumns. This strain promoted a high degree of tetragonality, resulting in a remarkable improvement in ferroelectric polarization, with a high twice remanent polarization value of \(114 \mu \mathrm{C} / \mathrm{cm}^2\) .  
+
+<|ref|>text<|/ref|><|det|>[[112, 866, 883, 900]]<|/det|>
+A ceramic target of \(\mathrm{K}_{0.5}\mathrm{Na}_{0.5}\mathrm{NbO}_{3}\) with \(2 \mathrm{wt\% MnO}_2\) addition (KNN- M) was used for deposition of the KNN- M films on \(\mathrm{La}_{0.07}\mathrm{Sr}_{0.93}\mathrm{SnO}_3\) (LSSO)- coated STO (001) substrates by
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 80, 882, 343]]<|/det|>
+pulsed laser deposition (PLD) method. High- resolution X- ray diffraction (HRXRD) techniques were employed to assess the crystalline quality of the KNN- M films. Fig. 1a shows the X- ray diffraction (XRD) \(\theta - 2\theta\) pattern of a representative KNN- M film. Only the (00l) \((l = 1, 2, 3)\) reflection peaks of KNN- M, LSSO and STO are observed, indicating that the film is epitaxially grown along the \(c\) - axis direction with a single phase. The rocking curves shown in Fig. 1b exhibit a full- width- at- half- maximum of approximately \(0.182^{\circ}\) for KNN- M (002) and \(0.093^{\circ}\) for LSSO (002), demonstrating a high crystallinity of the films. To examine the epitaxial relationship and the strain state of the samples, X- ray reciprocal space mappings (RSMs) around the symmetric (002) and asymmetric \((\overline{1} 03)\) reflections of the film are shown in Figs. 1c and d respectively. The discrete and clear spots in Figs. 1c and d confirm the orientation relationship between the (001) films and the (001) STO substrate. As displayed in Fig. 1d, the \(Q_{x}\) value of the KNN- M \((\overline{1} 03)\) RSM spot obviously deviates from that of STO and LSSO, suggesting a relaxation of the lattice mismatch strain between the film and substrate. Based on the RSM results, the out- of- plane lattice parameter " \(c\) " and in- plane lattice parameters " \(a\) " or " \(b\) " of the KNN- M films were calculated to be \(4.02 \mathring{\mathrm{A}}\) and \(3.95 \mathring{\mathrm{A}}\) , respectively.  
+
+<|ref|>text<|/ref|><|det|>[[113, 343, 882, 639]]<|/det|>
+Fig. 1e displays a high angle annular dark field (HAADF) scanning transmission electron microscopy (STEM) image of the KNN- M thin film, recorded along the [010] direction. It is evident that the KNN- M thin film consists of vertically aligned nanocolumns in the out- of- plane direction, rather than a homogeneous and smooth cross section (Fig.1e). X- ray energy dispersive spectroscopy (EDS) analysis confirms the presence of Mn enrichment at the column boundaries along the out- of- plane direction, as seen in Supplementary Fig. 1. Additionally, the selected area electron diffraction (SAED) pattern of the KNN- M thin films along the [010] zone axis, as shown in Fig 1f, reveals classic single crystal diffraction spots in the out- of- plane direction, while the streaks along the in- plane direction are attributed to the "shape effect". This effect arises from the principle that a small thickness in real space corresponds to a large length in reciprocal space, and vice versa. Thus, the reciprocal diffraction pattern further supports the presence of nanocolumns in the specimens, which is consistent with the HAADF image in real space. Atomic resolution HAADF images reveal a series of nanocolumn grains exhibiting the classic single crystal phase perovskite structure, as shown in Fig. 1g. However, it is observed that the lattices of neighboring perovskite KNN nanocolumns undergo a relative shift by half a unit cell length at the phase boundaries along the out- of- plane direction, indicated by the red triangle in Fig.1g.  
+
+<|ref|>text<|/ref|><|det|>[[113, 639, 883, 899]]<|/det|>
+Due to the overlap of the nanocolumnar structure in the cross- sectional observation direction, indicated by the brace in Fig. 1g and Supplementary Fig. 2, we studied the microstructure using a plan- view sample without substrate. We found that the KNN- M films present the 'Tetris- like' microstructure consisting of dense nanocolumns with sizes ranging from a few to tens of nanometers. Through annular bright field (ABF) imaging, we observed that the nanocolumns predominantly exhibit atomic- scale linear dark contrast along both the [001] and [010] directions, as shown in Fig. 1h and Supplementary Fig. 3. The corresponding EDS composition analysis reveals that the nanocolumn areas are composed of K, Na, Mn, Nb and O elements, with apparent Mn enrichment observed in some linear dark contrast areas, as shown in Fig. 1i and Supplementary Fig. 4. Electron energy loss spectroscopy (EELS) analysis indicates that the Mn ions mainly exhibit bivalence (Mn \(^{2 + }\) ) (Supplementary Fig. 5). In addition, atomic resolution HAADF imaging reveal that the lattices of adjacent perovskite KNN nanocolumns are coherent, but undergo a half- unit cell shift relative to each other across the atomic- scale antiphase boundaries with Mn enrichment, as marked within the yellow rectangle in Fig. 1j. At the ends of these antiphase boundaries, the lattice misregistry of 1/2 unit cell is gradually reduced to zero
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 80, 883, 116]]<|/det|>
+with continuous atom deficiency along the boundaries and no more Mn element could be detected with EDS analysis, as shown in the red rectangles in Fig. 1j and Supplementary Fig. 6.  
+
+<|ref|>image<|/ref|><|det|>[[130, 128, 863, 744]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[113, 750, 883, 880]]<|/det|>
+<center>Fig. 1 | Crystalline structure and microstructure of the KNN-M thin film grown on a LSSO-buffered STO (001) substrate. a, XRD \(\theta -2\theta\) pattern. b, Rocking curves around STO (002), LSSO (002) and KNN-M (002). c, d, Reciprocal space maps around the STO (002) and (f03) reflections. e, Cross-sectional low magnification HAADF image. f, SEAD pattern of the KNN-M thin film in the out-of-plane direction. g, Atomic resolution HAADF image of the KNN-M thin film. h, Planar-view low-magnification ABF image. i, Corresponding EDS mapping of Mn element for the KNN-M thin film. j, Plan-view atomic resolution HAADF image of the KNN-M thin film. The scale bars in (g) and (j) are 2 nm. </center>
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 80, 882, 448]]<|/det|>
+Furthermore, we have identified two different atomic configurations for the Mn- inlaid antiphase boundaries, as illustrated in Fig. 2. Fig. 2a displays an atomic resolution HAADF image of one type of antiphase boundary structure (referred to as Type- I). In this image, a coherent antiphase feature is observed, where the A- site lattice of one KNN nanocolumn runs into the B- site lattice of the neighboring nanocolumn across a single atomic column. This single atomic column exhibits a brighter contrast than the A- site K/Na atomic columns, but a darker contrast compared to the B- site Nb/O atom columns. Line- scans of image intensity were performed along the single atomic column layer. The line profile of intensity reveals similar atomic column intensities, as shown in Fig. 2b. Based on the uniform image contrasts and corresponding line profiles, it can be inferred that the atomic columns in the Type- I antiphase boundaries have similar chemical compositions. The intensity of the atomic column in HAADF image is approximately proportional to the square of the atomic number \((Z^{2})^{27}\) , making the HAADF images suitable for composition analysis. However, due to the much smaller scattering cross- section of oxygen, the light element oxygen cannot be observed in the HAADF image. Therefore, we also obtained atomic resolution integrated differential phase contrast (iDPC) images to visualize the distribution of all atoms, as shown in Fig. 2c. The iDPC image reveals only one atomic column at the antiphase boundary plane, and no separate oxygen atom columns are observed in the plan- view direction. In addition, we observed that the single atomic layer at the antiphase boundaries can gradually translate to a "Ruddlesden- Popper" like double atomic layer with in- situ beam irradiation in STEM mode, and vice versa (Fig. 2d, Supplementary Fig. 7 and Supplementary Fig. 8).  
+
+<|ref|>text<|/ref|><|det|>[[113, 448, 882, 779]]<|/det|>
+We performed the EDS mapping to study the atomic resolution composition distribution in Type- I antiphase boundary regions, as shown in Fig. 2e. The color- coded elemental maps of K, Na, O, Mn and Nb are presented in Figs. 2f- j, respectively. It is evident that all the elements are generally distributed uniformly in the KNN- based nanocolumns. However, the atomic columns at the antiphase boundary layer are primarily composed of the Mn and O elements, with a trace amount of K/Na. The composite map of Nb and Mn, as shown in Fig. 2k, illustrates the relative positions of the Nb and Mn atoms. Based on the elemental distribution maps, we find that Mn occupies the A- site of the perovskite lattice. Due to the structural changes at the boundaries during in- situ beam irradiation, as shown in Fig. 2d and Supplementary Fig. 7, Mn enrichments is observed along the boundary, but the location of Mn signals at the antiphase boundaries does not precisely correspond to the atomic- scale image from the EDS analysis. By analyzing the atomic resolution images in both the planar- view and cross- sectional directions (Supplementary Fig. 2) and conducting composition analysis, we have depicted the schematic structure of Type- I antiphase boundaries in Fig. 2l. This structure consists of nanocolumn perovskite KNN sheets alternating with a single Mn/O sheet running along the \(c\) axis direction and the neighboring perovskite slabs relatively shifting by half a unit cell length in both the in- plane and out- of- plane directions. At Type- I antiphase boundaries, the lattice site corresponds to the A- site of one KNN nanocolumn and the O- site of another KNN nanocolumn. Therefore, both the Mn and O atoms can randomly occupy the atom site at the boundaries theoretically.  
+
+<|ref|>text<|/ref|><|det|>[[114, 778, 882, 917]]<|/det|>
+The other antiphase boundary (Type- II) is illustrated in Fig. 2m, where the two adjacent KNN phases coherently transit across a double atom layer, and the distance between the neighboring Nb atomic columns at the boundaries is approximately 3/2- unit cells. Notably, we observed distinct contrast variation for the atom columns at the transition layers, which differ from the uniform contrast of the atom columns at Type- I antiphase boundaries. The line profile of intensity also exhibits noticeable difference for the atomic column along one row of transition atomic columns in Fig. 2m, as shown in Fig. 2n. The observed variations in image contrast or line profiles can be attributed to composition differences in each atomic column at Type- II
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 80, 882, 255]]<|/det|>
+antiphase boundaries. Through atomic resolution composition analysis, we determined that the atom columns with bright contrast primarily consist of Nb atoms, while the dark atom columns are composed of K/Na/Mn (Supplementary Fig. 9). Additionally, we also observed the presence of Mn enrichment at the boundaries. However, the signals are noticeably weaker compared to the distinct single Mn- rich layer observed at Type- I antiphase boundaries (Supplementary Fig. 10). We also acquired the iDPC images for Type- II antiphase boundaries (Fig. 2o), which reveal separated O atom columns that can bond with neighboring Nb atoms to form oxygen octahedra at the boundaries. Furthermore, we found that Type- II antiphase boundaries remained stable under beam irradiation. Based on the aforementioned experimental results, we constructed the schematic structural model of Type- II antiphase boundaries, as depicted in Fig. 2p.  
+
+<|ref|>text<|/ref|><|det|>[[114, 255, 882, 378]]<|/det|>
+For comparison, we also prepared the undoped KNN thin films with a similar deposition process. From cross- sectional view, serials of nanocolumns can also be observed (Supplementary Fig. 11a). Atomic resolution HAADF images revealed no noticeable lattice shifts between two adjacent nanocolumns in the out- of- plane direction (Supplementary Fig. 11b). However, the plan- view image displayed a high density of stacking faults due to the K/Na deficiency (Supplementary Fig. 11c). This deficiency resulted in a high leakage current and prevented the display of ferroelectricity at room temperature \(^{12,14}\) .
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[132, 85, 867, 666]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[113, 672, 883, 880]]<|/det|>
+<center>Fig.2 | In-plane atomic structure of Mn-inlaid antiphase boundaries in the KNN-M thin films. a, In-plane atomic resolution HAADF image of Type-I antiphase boundaries. b, Line-profile of the single antiphase atomic columns along the direction of the antiphase boundary in (A). c, iDPC image of Type-I antiphase boundaries. d, HAADF image of Type-I antiphase boundaries with in-situ electron irradiations. e, In-plane HAADF image of the KNN-M thin film containing Type-I antiphase boundaries. f-j, Corresponding color-coded atomic resolution EDS mapping of the K, Na, O, Mn and Nb elements, respectively. K, Composite elemental map with Nb (in red) and Mn (in green). I, Schematic structural models of Type-I Mn-inlaid antiphase boundaries, where the red, purple, green, and cyanine colors represent the O, K/Na, Mn and Nb elements, respectively. m, In-plane atomic resolution HAADF image of Type-II antiphase boundaries. n, Line-profile of the single antiphase atomic columns along the direction of the transition boundary in (m). o, iDPC image of Type-II antiphase boundaries. p, Schematic structural model of Type-II antiphase boundaries, where the red, purple, green, and cyanine colors represent the O, K/Na, Mn and Nb elements, respectively. The scale bar is 1nm. </center>
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 80, 883, 360]]<|/det|>
+We examined the ferroelectric polarization of this film and found that the out-of-plane phase image of the as-grown film without dc bias exhibits a remarkable homogeneity across the layer with a weak contrast, as shown in Fig. 3a, indicating an ordered out-of-plane polarization component. This result is also attested to the excellent epitaxial quality of the films. The out-of-plane piezoelectric force microscope (PFM) phase can switch to the opposite direction under a voltage of - 8 V. When a positive dc bias of \(+8\mathrm{V}\) was applied, the PFM phase switched to the same direction as the as-grown film, which confirms that the out-of-plane polarization of film is directed uniformly toward the bottom of the film. Additionally, well-saturated ferroelectric polarization-electric field \((P - E)\) hysteresis loops of the KNN- M film were displayed at room temperature (Fig. 3b). The film demonstrated a remarkable enhancement in remanent polarization \((P_{r})\) with the \(2P_{r}\) value reaching \(114\mu \mathrm {C}/\mathrm {cm}^{2}\) when subjected to an applied electric field of \(606\mathrm {kV/cm}\) . Furthermore, at the temperature of \(80\mathrm {K}\) , no significant variation in \(P_{r}\) was observed, but the breakdown field strength increased to \(2700\mathrm {kV/cm}\) , as shown in Fig. 3c. Notably, the \(2P_{r}\) value of the KNN- M thin film is larger than the values so far reported for most KNN- based films \((7.1-64.9\mu \mathrm {C}/\mathrm {cm}^{2})\) , as shown in Fig. 3d. Moreover, the \(P_{r}\) value is comparable to that of typical PZT ferroelectric thin films \({}^{24}\) .  
+
+<|ref|>image<|/ref|><|det|>[[140, 370, 857, 808]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[113, 818, 883, 915]]<|/det|>
+<center>Fig. 3 | Ferroelectric properties of the KNN-M thin films. a, Out-of-plane polarization switching of the KNN-M film captured using PFM imaging technique, under a dc bias of \(\pm 8\mathrm {V}\) . b, Room-temperature polarization–electric field \((P - E)\) hysteresis loops displayed under different electric fields. c, \(P - E\) hysteresis loops under different electric field for the KNN-M thin films at a low-temperature of \(80\mathrm {K}\) . d, Comparison of the polarization \((2P_{r})\) of the present KNN-M films with previously reported values of the KNN-based films. </center>
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 95, 883, 725]]<|/det|>
+Based on the microstructure analysis and the ferroelectric properties, we delve into the mechanisms behind the formation of nanocolumnar structure with Mn- inlaid antiphase boundaries and enhancement of ferroelectric polarization. In the case of pure KNN thin films without Mn doping, the unique deposition process led to the formation of vertical nanocolumnar structures aligned along the out- of- plane direction. Through atomic resolution investigation of microstructure, it was observed that the KNN nanocolumns maintained the normal perovskite structure. Significant K/Na deficiencies primarily occurred at the boundaries, resulting in lattice rearrangement and formation of charged out- of- phase boundaries. These crystallographic out- of- phase boundaries, commonly found in epitaxial perovskite films, tend to propagate throughout the entire thickness of the film. Consequently, nanocolumnar KNN thin films exhibited large leakage currents, as depicted in Supplementary Fig. 12 and Ref. 12. However, consistent with previous studies \(^{11,16,19}\) , our experimental results showed the addition of a small concentration of Mn into KNN film was very effective in reducing the leakage current density. Due to the smaller radius of Mn ion than those of the A- site \(\mathrm{K^{+} / Na^{+}}\) ions, the solid solubility of Mn is low in KNN lattice. When doping Mn into the KNN films, the Mn ions are believed to occupy a small number of A- site vacancies, which were formed due to the volatilization of the \(\mathrm{K^{+} / Na^{+}}\) ions in the KNN nanocolumns, according to the atomic structural imaging and composition analysis. Considering its primary valence, \(\mathrm{Mn^{2 + }}\) can act as a donor- dopant on the A- site of the perovskite lattice. However, in order to balance the charge, the occupancy of \(\mathrm{Mn^{2 + }}\) on the A- site vacancies was only partial, with every other A- site occupied. As a result, partial A- site deficiency still existed in the KNN nanocolumns \(^{18}\) . On the contrary, the high density of Mn accumulated at the phase boundaries to form atomic- scale Mn- rich layer, which could compensate the serious K/Na deficiencies at the boundaries. In the case of Type- I antiphase boundaries, both cation \(\mathrm{Mn^{2 + }}\) and anion \(\mathrm{O^{2 - }}\) can occupy the atomic site at the single antiphase boundary layer. By adjusting the ratio of Mn/O, we can achieve charge balance in optimum experimental conditions (Supplementary Fig. 13). For Type- II antiphase boundaries, which resemble the Ruddlesden- Popper structure, separate atomic columns of cations and anion \(\mathrm{O^{2 - }}\) were observed. The cation atomic sites at these boundaries can be occupied by the \(\mathrm{Nb^{5 + }}\) , \(\mathrm{Mn^{2 + }}\) , \(\mathrm{K^{+}}\) and \(\mathrm{Na^{+}}\) ions. The ratios among these four cations could also be adjusted experimentally to maintain charge balance (Supplementary Fig. 14). Consequently, the leakage currents can be significantly reduced, which is crucial for the ferroelectric functionality of the thin film. In our study, we investigated a series of Mn- doped samples with the Mn concentration ranging from 1 to \(5\mathrm{wt}\%\) . It was observed that the sample with a low Mn doping content, such as \(1\mathrm{wt}\%\) , exhibited a vertical nanocolumns structure, but the K/Na deficiencies were not fully compensated (Supplementary Fig. 15). As the doping level increased to \(5\%\) , the nanoscale Mn enriched phases were observed, which disrupted the epitaxial growth of the thin films, as shown in Supplementary Fig. 16.  
+
+<|ref|>text<|/ref|><|det|>[[115, 723, 882, 916]]<|/det|>
+More importantly, the specific atomic configurations at the Mn- inlaid antiphase boundaries induce noticeable change in the lattice structure compared to the KNN nanocolumns. The high- density boundaries are organized in an ordered manner on a nanoscale and extend vertically in the film, resulting in the generation of apparent lattice strains. These strains also impact the neighboring KNN nanocolumns, as evidenced by the ADF images (Supplementary Fig. 17, Supplementary Fig. 18), where the brighter image contrast indicates the presence of apparent lattice strains near the Mn- inlaid antiphase boundaries. It is well- known that lattice strain can significantly influence the ferroelectric polarization of thin films. We examined the relative lattice strain and the evolution of lattice parameters using the geometry phase analysis method. Fig. 4a displays an ADF image of the KNN- M thin film, revealing the presence of two types of boundaries. The corresponding relative lattice strain are plotted in Fig. 4b \((\epsilon_{xx})\) , Fig. 4c \((\epsilon_{yy})\) and
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 80, 882, 220]]<|/det|>
+Fig. 4d \((\epsilon_{xy})\) , respectively. It can be seen that the boundaries exhibit apparent relative lattice strains, mainly perpendicular to their orientation with no observable relative strains parallel to the boundaries (Supplementary Fig. 19). The increased atomic spacing at the boundaries, indicative of tensile strain, is accompanied by the compressive strain in the KNN nanocolumns. It is worth noting that a strain in one direction typically induces an opposite strain component in the other direction to achieve overall relaxation \(^{28}\) . Consequently, the in-plane compressive strain induces an out-of-plane tensile strain, as confirmed by the XRD results with a larger \(c\) than \(a\) and \(b\) in the KNN lattice, which is advantageous for achieving high ferroelectric polarization \(^{7}\) .  
+
+<|ref|>text<|/ref|><|det|>[[113, 222, 882, 585]]<|/det|>
+The displacement of the center polar B- site cations relative to the corner A- site cations \((\delta_{\mathrm{B - A}})\) was used as a measure of the local polarization. Fig. 4e displays a colored arrow map of \(\delta_{\mathrm{B - A}}\) , representing the orientation and magnitude of the polarization. The film exhibited apparent displacement polarization, with the largest polarization observed near the boundaries. The different strains present at each type of boundary resulted in the formation of multidomain structures with a mixture of various orientations of polarizations. To investigate the impact of boundary strain on ferroelectric polarization, we conducted phase field simulations. The phenomenological model and relative lattice strain distributions used in these simulations were obtained from Fig. 4a- d, as shown in Supplementary Fig. 20. Specifically, we examined the different lattice strains, namely \(\epsilon_{\mathrm{local}}(0, 0.1, \text{and} 0.2)\) , induced by the Mn accumulated boundary, to elucidate their effects. Our simulations employed the orthorhombic phase with the [110]- oriented polarization of KNN at room temperature \(^{16}\) , as illustrated in Supplementary Fig. 21, which showcases the detailed microstructural evolution. As the applied external electric field increased, the polarizations transitioned from the orthorhombic phase (represented by different colors in Fig. 4f) to the tetragonal phase (indicated by light blue color in Fig. 4g). Importantly, the lattice strains around the boundaries induced larger polarization in both in- plane and out- of- plane directions, as shown in Fig. 4f and Supplementary Fig. 22. The simulated ferroelectric loop demonstrated an increase in both the coercive electric field strength \((E_{\mathrm{c}})\) and ferroelectric polarization in the out- of- plane directions with the increase in lattice strain (see Fig. 4h and Supplementary Fig. 21a). Thus, the Mn- inlaid antiphase boundaries offer a new structural framework for enhancing the ferroelectric polarization of lead- free ferroelectric thin films.  
+
+<|ref|>text<|/ref|><|det|>[[115, 586, 882, 708]]<|/det|>
+In summary, we prepared and observed the new Mn- inlaid antiphase boundaries structure in perovskites. The high- density vertical Mn- inlaid antiphase boundaries show the positivity effect on charge balance, coherent transition to the neighboring KNN nanocolumns and the induction of large strain for the nanocolumnar KNN lattice in the entire KNN- M thin films. Therefore, the ever higher ferroelectric polarizations have been observed for the KNN- based thin films. The Mn- inlaid antiphase boundaries also give a new possible structure frame to modulate the various properties of perovskites.
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[128, 85, 876, 430]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[113, 437, 883, 550]]<|/det|>
+<center>Fig. 4 | Strains and atom displacement polarizations in the KNN-M thin film. a, Planar-view atomic-resolution HAADF image of the KNN-M thin film. b-d, Maps depicting the relative lattice strains: (b) \(\epsilon_{xx}\) (in-plane strain), (c) \(\epsilon_{yy}\) (out-of-plane strain) and (d) \(\epsilon_{xy}\) (shear strain). e, Colored arrows map of polarizations \((\partial_{Nb-KNa})\) , indicating the polarization orientation of the KNN nanocolumns in (a). f, Polarization of KNN-M obtained from phase-field simulation. g, Mapping of the piezoelectric response of the KNN-M film under an applied electric field obtained from phase-field simulation. h, Calculated P-E curves with different local strains. </center>  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 558, 208, 574]]<|/det|>
+## References  
+
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+<|ref|>text<|/ref|><|det|>[[115, 572, 882, 625]]<|/det|>
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+
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+
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+10. F. Yin, L. Liu, M. Zhu, J. Lv, X. Guan, J. Zhang, N. Lin, X. Fu, Z. Jia, X. Tao, Transparent Lead-Free Ferroelectric (K,Na)NbO₃ Single Crystal with Giant Second Harmonic Generation and Wide Mid-Infrared Transparency Window. Advanced Optical Materials 10, 2201721 (2022). doi: 10.1002/adom.202201721.  
+
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+11. L. Xu, F. Chen, F. Jin, H. Huang, L. Qu, K. Zhang, Z. Zhang, G. Gao, Y. Lu, F. Zhang, K. Wang, C. Ma, W. Wu, Robust Ferroelectric Properties in (K,Na)NbO₃-Based Lead-Free Films via a Self-Assembled Nanocomposite Approach. ACS Appl. Mat. Interfaces 12, 4616-4624 (2020). doi: 10.1021/acsami.9b20311.  
+
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+12. M. Waqar, H. Wu, K. P. Ong, H. Liu, C. Li, P. Yang, W. Zang, W. H. Liew, C. Diao, S. Xi, D. J. Singh, Q. He, K. Yao, S. J. Pennycook, J. Wang, Origin of giant electric-field-induced strain in faulted alkali niobate films. Nature Communications 13, 3922 (2022). doi: 10.1038/s41467-022-31630-8.  
+
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+13. C. Li, L. Wang, W. Chen, L. Lu, H. Nan, D. Wang, Y. Zhang, Y. Yang, C.-L. Jia, A Novel
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[56, 70, 886, 920]]<|/det|>
+Multiple Interface Structure with the Segregation of Dopants in Lead- Free Ferroelectric \(\mathrm{(K_{0.5}Na_{0.5})NbO_{3}}\) Thin Films. Advanced Materials Interfaces 5, 1700972 (2018). doi: 10.1002/admi.201700972. 14. H. Liu, H. Wu, K. P. Ong, T. Yang, P. Yang, P. K. Das, X. Chi, Y. Zhang, C. Diao, W. K. A. Wong, E. P. Chew, Y. F. Chen, C. K. I. Tan, A. Rusydi, M. B. H. Breese, D. J. Singh, L.- Q. Chen, S. J. Pennycook, K. Yao, Giant piezoelectricity in oxide thin films with nanopillar structure. Science 369, 292- 297 (2020). doi: 10.1126/science.abb3209. 15. C.- R. Cho, A. Grishin, Background Oxygen Effects on Pulsed Laser Deposited \(\mathrm{Na_{0.5}K_{0.5}NbO_{3}}\) Films: From Superparaelectric State to Ferroelectricity. J. Appl. Phys. 87, 4439- 4448 (2000). doi: 10.1063/1.373089. 16. H. J. Seog, A. Ullah, C. W. Ahn, I. W. Kim, S. Y. Lee, J. Park, H. J. Lee, S. S. Won, S.- H. Kim, Recent Progress in Potassium Sodium Niobate Lead- free Thin Films. Journal of the Korean Physical Society 72, 1467- 1483 (2018). doi: 10.3938/jkps.72.1467. 17. W. Chen, J. Zhao, L. Wang, W. Ren, M. Liu, Lead- free piezoelectric KNN- BZ- BNT films with a vertical morphotropic phase boundary. AIP Advances 5, 077190 (2015). doi: 10.1063/1.4928095. 18. F. Rubio- Marcos, P. Marchet, X. Vendrell, J. J. Romero, F. Remondiere, L. Mestres, J. F. Fernandez, Effect of MnO doping on the structure, microstructure and electrical properties of the \(\mathrm{(K,Na,Li)(Nb,Ta,Sb)O_{3}}\) lead- free piezoceramics. J. Alloys Compd. 509, 8804- 8811 (2011). doi: https://doi.org/10.1016/j.jallcom.2011.06.080. 19. S. S. Won, J. Lee, V. Venugopal, D.- J. Kim, J. Lee, I. W. Kim, A. I. Kingon, S.- H. Kim, Lead- free Mn- doped \(\mathrm{(K_{0.5}Na_{0.5})NbO_{3}}\) piezoelectric thin films for MEMS- based vibrational energy harvester applications. Appl. Phys. Lett. 108, (2016). doi: 10.1063/1.4953623. 25. C. Li, L. Wang, Z. Wang, Y. Yang, W. Ren, G. Yang, Atomic Resolution Interfacial Structure of Lead- free Ferroelectric \(\mathrm{K_{0.5}Na_{0.5}NbO_{3}}\) Thin films Deposited on SrTiO3. Sci. Rep. 6, 37788 (2016). doi: 10.1038/srep37788. 21. J. Luo, W. Sun, Z. Zhou, H.- Y. Lee, K. Wang, F. Zhu, Y. Bai, Z. J. Wang, J.- F. Li, Monoclinic \(\mathrm{(K,Na)NbO_{3}}\) Ferroelectric Phase in Epitaxial Films. Advanced Electronic Materials 3, 1700226 (2017). doi: 10.1002/aelm.201700226. 22. Y.- Y.- S. Cheng, L. Liu, Y. Huang, L. Shu, Y.- X. Liu, L. Wei, J.- F. Li, All- Inorganic Flexible (K, Na)NbO3- Based Lead- Free Piezoelectric Thin Films Spin- Coated on Metallic Foils. ACS Appl. Mat. Interfaces 13, 39633- 39640 (2021). doi: 10.1021/acsami.1c11418. 23. T. Shiraishi, N. Kaneko, M. Kurosawa, H. Uchida, T. Hirayama, H. Funakubo, Ferroelectric and piezoelectric properties of KNbO3 films deposited on flexible organic substrate by hydrothermal method. Jpn. J. Appl. Phys. 53, 09PA10 (2014). doi: 10.7567/JJAP.53.09PA10. 24. R. Nishino, T. C. Fujita, F. Kagawa, M. Kawasaki, Evolution of ferroelectricity in ultrathin PbTiO3 films as revealed by electric double layer gating. Sci. Rep. 10, 10864 (2020). doi: 10.1038/s41598- 020- 67580- 8. 25. O. Dahl, J. K. Grepstad, T. Tybell, Polarization direction and stability in ferroelectric lead titanate thin films. J. Appl. Phys. 106, 084104 (2009). doi: 10.1063/1.3240331. 26. M. A. Zurbuchen, W. Tian, X. Q. Pan, D. Fong, S. K. Streiffer, M. E. Hawley, J. Lettieri, Y. Jia, G. Asayama, S. J. Fulk, D. J. Comstock, S. Knapp, A. H. Carim, D. G. Schlom, Morphology, structure, and nucleation of out- of- phase boundaries (OPBs) in epitaxial films of layered oxides. J. Mater. Res. 22, 1439- 1471 (2007). doi: 10.1557/JMR.2007.0198.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[113, 80, 883, 170]]<|/det|>
+27. S. J. Pennycook, Z-contrast stem for materials science. Ultramicroscopy 30, 58-69 (1989). doi: 10.1016/0304-3991(89)90173-3.  
+28. J. Zhao, Y. Deng, H. Wei, X. Zheng, Z. Yu, Y. Shao, J. E. Shield, J. Huang, Strained hybrid perovskite thin films and their impact on the intrinsic stability of perovskite solar cells. Science Advances 3, eaao5616 (2017). doi: 10.1126/sciadv.aao5616.  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 201, 190, 217]]<|/det|>
+## Methods  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 244, 364, 260]]<|/det|>
+## The ceramic targets synthesis  
+
+<|ref|>text<|/ref|><|det|>[[115, 260, 882, 452]]<|/det|>
+The ceramic targets synthesisThe ceramic targets of \((\mathrm{K,Na}) \mathrm{NbO}_3 - 0 / 1 / 2 / 5 \mathrm{wt\% Mn}\) (KNN-M) were fabricated using the conventional solid-state reaction method. High-purity materials, including \(\mathrm{K}_2\mathrm{CO}_3(99.0\%)\) , \(\mathrm{Na}_2\mathrm{CO}_3(99.8\%)\) , \(\mathrm{Nb}_2\mathrm{O}_5(99.9\%)\) and \(\mathrm{MnO}_2(98.8\%)\) , were obtained from Sinopharm Chemical Reagent Beijing Co. Ltd. Initially, the raw materials were accurately weighed according to the stoichiometric ratio of \((\mathrm{K}_0.5\mathrm{Na}_0.5)\mathrm{NbO}_3\) and then homogenized in a planetary mill for 24h using ethyl alcohol as the medium. After calcination at \(750^{\circ}\mathrm{C}\) for 4h, the resulting powder mixtures were milled, dried, and sieved. Subsequently, additional \(\mathrm{MnO}_2\) was incorporated into the powders and homogenized for another 24 h in the planetary mill. The resulting powder mixtures were then dried and sieved. Next, the mixed KNN-Mn powders were compacted into disks by uniaxial pressing at a pressure of \(150\mathrm{MPa}\) for \(2\mathrm{min}\) . Finally, the KNN-Mn pellets, with a diameter of \(30\mathrm{mm}\) , were sintered at \(1120^{\circ}\mathrm{C}\) for \(4\mathrm{h}\) to prepare the target  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 470, 280, 486]]<|/det|>
+## Thin Films Growth  
+
+<|ref|>text<|/ref|><|det|>[[115, 486, 882, 626]]<|/det|>
+Thin Films GrowthThe KNN-M films were deposited onto the (001) SrTiO\(_3\) (STO) substrate buffered with \(\mathrm{La}_{0.07}\mathrm{Sr}_{0.93}\mathrm{SnO}_3\) (LSSO) layer using the PLD technique with a \(248\mathrm{nm}\) KrF excimer laser. The deposition process involved maintaining the substrate temperature at \(735^{\circ}\mathrm{C}\) and the \(\mathrm{O}_2\) pressure at \(15\mathrm{Pa}\) for the LSSO layer, followed by deposition of the KNN-film rat \(700^{\circ}\mathrm{C}\) and \(30\mathrm{Pa}\) . The target-substrate distance was set at \(5\mathrm{cm}\) , and the laser energy were kept at \(2\mathrm{J / cm}^2\) . The thicknesses of the KNN-M and LSSO were approximately \(300\mathrm{nm}\) and \(30\mathrm{nm}\) , respectively. After deposition, each film underwent in-situ annealing for 15 minutes before being cooled down to room temperature.  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 644, 260, 660]]<|/det|>
+## X-ray diffraction  
+
+<|ref|>text<|/ref|><|det|>[[115, 661, 882, 713]]<|/det|>
+X-ray diffractionThe crystalline phase of films was characterized by high-resolution X-ray diffraction (XRD) using Cu Kα1 radiation. The samples were mounted in the diffractometer (PANalytical, X'Pert), for linear scans, rocking curves, and reciprocal space mappings (RSM) at room temperature.  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 731, 472, 748]]<|/det|>
+## Transmission Electron Microscopy (TEM)  
+
+<|ref|>text<|/ref|><|det|>[[115, 748, 882, 888]]<|/det|>
+Transmission Electron Microscopy (TEM)The specimens were prepared using both the Tripod method (ALLIED Multiprep) and focused ion beam (FIB, Thermofisher Helios UX5) for the cross-section and plan-view STEM observation. TEM characterizations, low magnification STEM imaging, and EDS composition analysis were performed using the Thermofisher Talos F200X. Atomic resolution HAADF and iDPC images were acquired using the Thermofisher Titan Themis Z. Atom resolution HAADF, ADF image and EDS mapping were obtained using the Thermofisher Titan cubed Themis G2 300. The acquisition of EELS data was carried out using Gatan electron energy loss spectroscopy (EELS) on both the FEI Titan G2 80- 300 and JEOL ARM300.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[115, 81, 265, 98]]<|/det|>
+## Electrical Testing  
+
+<|ref|>text<|/ref|><|det|>[[115, 99, 882, 187]]<|/det|>
+Electrical TestingPolarization versus electric field \((P - E)\) hysteresis loops were measured through the ferroelectric test system (TF Analyzer 2000E, Germany) at \(1\mathrm{kHz}\) in room temperature. The \(P - E\) loops at liquid nitrogen temperature of \(80\mathrm{K}\) were obtained with Cryogenic Probe Station supplied by Lake Shore Company. The ferroelectric domains and polarization reversal behaviors were characterized by piezoresponse force microscopy (PFM, Asylum Cypher).  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 203, 550, 220]]<|/det|>
+## The calculation of the displacements in ADF images  
+
+<|ref|>text<|/ref|><|det|>[[115, 220, 882, 360]]<|/det|>
+A standard peak finding algorithm is employed for the ADF image, which is based on fitting two- dimensional Gaussian functions to the intensity maxima. This algorithm allows us to determine the position and brightness of each column. Using these data, we can calculate the offcenter ion displacements between the Nb column and the center of the unit cell. The center of the unit cell is determined by the average coordinate of the four K/Na atomic columns. We use the following formula to calculate the displacements: \(\mathbf{u}_{ij} = \mathbf{r}_{ij} - 1 / 4\) \((R_{ij} + (i + 1) + (j + 1) + (i + 1)(j + 1))\) . Here, \(\mathrm{i} / \mathrm{j}\) indicate the row/column number of each atom column, \(\mathbf{r}_{\mathrm{ij}}\) indicates the position of Nb atomic columns, and \(\mathrm{R_{ij}}\) indicates the position of K/Na atomic columns.  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 367, 320, 384]]<|/det|>
+## Phase-Field Simulations  
+
+<|ref|>text<|/ref|><|det|>[[115, 391, 882, 444]]<|/det|>
+A single crystal considering Cubic \((C)\) to Orthorhombic (O) ferroelectric transition with Mninlaid antiphase boundaries has been carried out in phase- field simulations. The total free energy of the ferroelectric system can be described as (29- 32):  
+
+<|ref|>equation<|/ref|><|det|>[[115, 443, 610, 470]]<|/det|>
+\[F = \int_{\nu}(f_{\mathrm{bulk}} + f_{\mathrm{grad}} + f_{\mathrm{couple}})dV + \int_{\nu}(f_{\mathrm{elas}} + f_{\mathrm{elec}})dV \quad (1)\]  
+
+<|ref|>text<|/ref|><|det|>[[115, 472, 518, 490]]<|/det|>
+where \(f_{\mathrm{bulk}}\) represents the bulk free energy density,  
+
+<|ref|>equation<|/ref|><|det|>[[115, 490, 725, 560]]<|/det|>
+\[f_{\mathrm{bulk}} = \alpha_{1}(P_{1}^{2} + P_{2}^{2} + P_{3}^{2}) - \alpha_{11}(P_{1}^{2} + P_{2}^{2} + P_{3}^{3})^{2} + \alpha_{12}(P_{1}^{2}P_{2}^{2} + P_{2}^{2}P_{3}^{2} + P_{1}^{2}P_{3}^{2})\] \[\qquad +\alpha_{112}(P_{1}^{4}P_{2}^{2} + P_{2}^{4}P_{3}^{2} + P_{1}^{4}P_{3}^{2} + P_{1}^{2}P_{2}^{4} + P_{2}^{2}P_{3}^{4} + P_{1}^{2}P_{3}^{4}) + \alpha_{113}(P_{1}^{2}P_{2}^{2}P_{3}^{2})\] \[\qquad +\alpha_{111}(P_{1}^{2} + P_{2}^{2} + P_{3}^{3})^{3}\]  
+
+<|ref|>text<|/ref|><|det|>[[115, 561, 728, 578]]<|/det|>
+where \(\alpha_{ij}\) is the coefficient and depends on concentration \(c\) and temperature \(T\)  
+
+<|ref|>text<|/ref|><|det|>[[115, 582, 470, 599]]<|/det|>
+\(f_{\mathrm{grad}}\) represents the gradient energy density,  
+
+<|ref|>equation<|/ref|><|det|>[[115, 600, 765, 635]]<|/det|>
+\[f_{\mathrm{grad}} = \frac{1}{2} G_{11}((P_{1,1})^{2} + (P_{1,2})^{2} + (P_{1,3})^{2} + (P_{2,1})^{2} + (P_{2,2})^{2} + (P_{2,3})^{2} + (P_{3,1})^{2} + (P_{3,2})^{2} + (P_{3,3})^{2}) \quad (3)\]  
+
+<|ref|>text<|/ref|><|det|>[[115, 636, 881, 671]]<|/det|>
+where \(G_{11}\) is the gradient energy coefficient. \(f_{\mathrm{couple}}\) represents the couple effect caused by lattice strain \(\epsilon_{\mathrm{local}}(33)\)  
+
+<|ref|>equation<|/ref|><|det|>[[115, 672, 593, 718]]<|/det|>
+\[f_{\mathrm{couple}} = -(q_{11}\epsilon_{11} + q_{12}\epsilon_{22} + q_{12}\epsilon_{33})P_{1}^{2} - (q_{11}\epsilon_{22} + q_{12}\epsilon_{11} + q_{12}\epsilon_{33})P_{2}^{2}\] \[-(q_{11}\epsilon_{33} + q_{12}\epsilon_{11} + q_{12}\epsilon_{22})P_{3}^{2} - 2q_{44}(\epsilon_{12}P_{1}P_{2} + \epsilon_{23}P_{2}P_{3} + \epsilon_{13}P_{1}P_{3})~,\]  
+
+<|ref|>text<|/ref|><|det|>[[115, 720, 882, 777]]<|/det|>
+where \(q_{11} = C_{11}Q_{11} + 2C_{12}Q_{12}\) , \(q_{12} = C_{11}Q_{12} + C_{12}(Q_{11} + Q_{12})\) , \(q_{44} = 2C_{44}Q_{44}\) , \(C_{11}\) , \(C_{12}\) , and \(C_{44}\) is the elastic constants in Voigt's notation and \(Q_{\mathrm{ij}}\) is the electrostrictive coefficients. \(f_{\mathrm{elas}}\) is the long- range elastic interaction energy densities and \(f_{\mathrm{elec}}\) is the electrostatic interaction energy densities.  
+
+<|ref|>text<|/ref|><|det|>[[115, 781, 882, 881]]<|/det|>
+\(f_{\mathrm{elas}} = \frac{1}{2} c_{ijkl}e_{ij}e_{kl} = \frac{1}{2} c_{ijkl}(\epsilon_{ij} - \epsilon_{ij}^{0})(\epsilon_{kl} - \epsilon_{kl}^{0})\) , where \(c_{ijkl}\) is the elastic constant tensor, \(\epsilon_{ij}\) the total strain, \(\epsilon^{0}_{kl}\) the electrostrictive stress- free strain, i.e., \(\epsilon^{0}_{kl} = Q_{ijkl}P_{k}P_{l}\) . \(f_{\mathrm{elec}} = f_{\mathrm{dipole}} + f_{\mathrm{depola}} + f_{\mathrm{appl}}\) , where \(f_{\mathrm{dipole}}\) is the dipole- dipole interaction caused by polarization, \(f_{\mathrm{depola}}\) the depolarization energy density and \(f_{\mathrm{appl}}\) the energy density caused by applied electric field. The dimensionless parameters used in our simulations:  
+
+<|ref|>equation<|/ref|><|det|>[[135, 881, 685, 900]]<|/det|>
+\[\alpha_{1} = -0.1134, \alpha_{11} = -2.2896, \alpha_{12} = -7.5572, \alpha_{111} = 12.94, \alpha_{112} = 1.776, \alpha_{123} = 144.6.\]
+
+<--- Page Split --->
+<|ref|>equation<|/ref|><|det|>[[135, 80, 644, 99]]<|/det|>
+\[C_{11} = 1780, C_{12} = 964, C_{44} = 1220, Q_{11} = 0.1, Q_{12} = -0.034, Q_{44} = 0.029.\]  
+
+<|ref|>text<|/ref|><|det|>[[114, 105, 883, 185]]<|/det|>
+The temporal evolution of the spontaneous polarization field \((P)\) can be obtained by solving the time dependent Ginzburg Landau (TDGL) equation: \(\frac{dP_{i}(x,t)}{dt} = - M\frac{\delta F}{\delta P_{i}(X,t)}, i = 1,2,3\) , where \(M\) is the kinetic coefficient, \(F\) is the total free energy, and \(t\) is time.  
+
+<|ref|>sub_title<|/ref|><|det|>[[115, 216, 256, 234]]<|/det|>
+## Data availability  
+
+<|ref|>text<|/ref|><|det|>[[115, 241, 883, 277]]<|/det|>
+The data that support the findings of this study are available from the corresponding authors upon request.  
+
+<|ref|>text<|/ref|><|det|>[[112, 300, 884, 565]]<|/det|>
+29. D. Wang, X. Ke, Y. Wang, J. Gao, Y. Wang, L. Zhang, S. Yang, X. Ren, Phase diagram of polar states in doped ferroelectric systems. Phys. Rev. B 86, 054120 (2012). doi: 10.1103/PhysRevB.86.054120.  
+30. S. Semenovskaya, A. G. Khachaturyan, Development of ferroelectric mixed states in a random field of static defects. J. Appl. Phys. 83, 5125-5136 (1998). doi: 10.1063/1.367330.  
+31. Y. L. Li, L. E. Cross, L. Q. Chen, A phenomenological thermodynamic potential for BaTiO₃ single crystals. J. Appl. Phys. 98, (2005). doi: 10.1063/1.2042528.  
+32. L. Zhang, H. Wang, D. Wang, M. Guo, X. Lou, D. Wang, A New Strategy for Large Dynamic Piezoelectric Responses in Lead-Free Ferroelectrics: The Relaxor/Morphotropic Phase Boundary Crossover. Adv. Funct. Mater. 30, 2004641 (2020). doi: 10.1002/adfm.202004641.  
+33. Y. L. Li, S. Y. Hu, Z. K. Liu, L. Q. Chen, Effect of substrate constraint on the stability and evolution of ferroelectric domain structures in thin films. Acta Mater. 50, 395-411 (2002). doi: 10.1016/S1359-6454(01)00360-3.
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[43, 42, 312, 70]]<|/det|>
+## Supplementary Files  
+
+<|ref|>text<|/ref|><|det|>[[43, 92, 768, 113]]<|/det|>
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+<|ref|>text<|/ref|><|det|>[[60, 130, 365, 150]]<|/det|>
+SupplementaryInformation2. pdf
+
+<--- Page Split --->

preprint/preprint__03e6375d91ba2acc0d93fa9c97f15a5154400d7aed2f4249391cfd96e1971e8e/images_list.json

@@ -0,0 +1,77 @@
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+    "type": "image",
+    "img_path": "images/Figure_1.jpg",
+    "caption": "Figure 1",
+    "footnote": [],
+    "bbox": [
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+    "page_idx": 11
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_2.jpg",
+    "caption": "Figure 2",
+    "footnote": [],
+    "bbox": [
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+        75,
+        925,
+        685
+      ]
+    ],
+    "page_idx": 12
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_3.jpg",
+    "caption": "Figure 3",
+    "footnote": [],
+    "bbox": [
+      [
+        78,
+        65,
+        900,
+        580
+      ]
+    ],
+    "page_idx": 13
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_4.jpg",
+    "caption": "Figure 4",
+    "footnote": [],
+    "bbox": [
+      [
+        72,
+        66,
+        911,
+        440
+      ]
+    ],
+    "page_idx": 14
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_5.jpg",
+    "caption": "Figure 5",
+    "footnote": [],
+    "bbox": [
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preprint/preprint__0417a97ecc0d13717181d9623733f3148d5ba9e7ac583590ea0cf0862739b60a/images_list.json

@@ -0,0 +1,122 @@
+[
+  {
+    "type": "image",
+    "img_path": "images/Figure_1.jpg",
+    "caption": "FIG. 1. Three different structural states of 2D vortices/rotors - hyperuniformity for Euler point vortices (A) and QG rotors/surface rotors (B), (C) phase enrichment induced by circulation differences where green (black) represents vortices of high (low) circulation, and (D) crystallization arising from hydrosteric interactions. The insets of (A), (B) and (C) show the structure factor, \\(S(q)\\) . In (A) and (B) \\(S(q)\\) decays to zero at small \\(q\\) , indicating that the distribution is hyperuniform. In (C) the structure factor shows the six distinct peaks of a hexagonal lattice.",
+    "footnote": [],
+    "bbox": [
+      [
+        168,
+        68,
+        836,
+        205
+      ]
+    ],
+    "page_idx": 2
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_2.jpg",
+    "caption": "FIG. 2. (A) A representation of a membrane rotor — a disk rotating due to a torque \\(\\tau\\) in the plane of the membrane. (B) The velocity field due to a membrane rotor (solid line) which scales as a point vortex \\(v \\sim 1 / r\\) at small distances (dotted), \\(r / \\lambda \\ll 1\\) , transitioning to a QG behavior at large distances \\(v \\sim 1 / r^2\\) (dashed). (C) Contour dynamics of an ellipse with radii ratios \\(r_l / r_s \\leq 3\\) , where \\(r_l\\) ( \\(r_s\\) ) is the major (minor) axis. Starting from the same contour, the dynamics differ according to the radius relative to the SD length. Blue is in the limit \\(r_l \\ll \\lambda\\) . In this limit the ellipse is rotating as a rigid body, as predicted by Kelvin (38) for an elliptic patch in an Euler fluid. Black is in the limit \\(r_l \\gg \\lambda\\) , no longer conserving its shape since the large distance flow is in the quasigeostrophic regime. (D) 200 point membrane rotors, blue is the initial random configuration, black is the final configuration. Solid line shows typical trajectory of an individual vortex. Note that the area did not change considerably since the system of vortices is self-bounding. (E) the relative error in \\(\\mathcal{H}\\) and \\(M\\) over a few cycle times, \\(t_c\\) .",
+    "footnote": [],
+    "bbox": [
+      [
+        125,
+        66,
+        896,
+        188
+      ]
+    ],
+    "page_idx": 3
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_3.jpg",
+    "caption": "FIG. 3. Hyperuniformity in ensembles of point vortices and rotors. (A) Snapshots of 10,000 point vortices initially (left) and at steady-state (right). Insets show the structure factor, \\(S(\\mathbf{q})\\) showing a distinct cavity at steady-state. (B) Angular average of the structure factor shown in A, in a log-log scale with solid line showing a \\(q^{1.3}\\) scaling. Error bars are standard deviation over 10 well separated timesteps. Inset shows the structure factor of the rotors shown in (C) with increasing hue corresponding to increased concentration \\(\\phi = (0.14, 0.24, 0.37, 0.54)\\) . Solid line is the same \\(\\alpha \\sim 1.3\\) scaling. (C) Steady state configurations of 2,000 membrane rotors with the corresponding structure factors, showing a transition from disordered hyperuniformity to a hexagonal lattice. (D) A plot of the returnity measuring the deviation of particle \\(i\\) at position \\(r_i\\) from its position at the previous cycle, \\(returnity = \\Delta r_i(t_{\\mathrm{cyc}}) / R\\) , where \\(R\\) is the initial radius of the ensemble. The cycle time, \\(t_{\\mathrm{cyc}}\\) , is defined at steady state as the distance between two adjacent minima of the function \\(f = \\sum_i^N \\Delta r_i(\\Delta t)\\) , where \\(\\Delta t\\) is the time difference. Color scheme is from blue to yellow with increasing deviation.",
+    "footnote": [],
+    "bbox": [
+      [
+        125,
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+        876,
+        494
+      ]
+    ],
+    "page_idx": 4
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_4.jpg",
+    "caption": "FIG. 4. Two populations of vortices with different circulations showing phase enrichment, \\(\\Gamma_{l} = 2\\pi\\) in black and \\(\\Gamma_{h} = 256\\pi\\) in green. (A) Steady-state configuration for ten thousand point vortices of a circulation ratio \\(\\gamma = \\Gamma_{h} / \\Gamma_{l} = 128\\) . Each inset shows a close-up view of one of the populations within the same physical region. (B) Density of the configuration in (A), \\(\\rho (r)\\) , averaged over angle as a function of distance from the center. Note how density fluctuations are suppressed for the high circulation vortices, as is more clearly observed by the averaged structure factor, \\(S(q)\\) , in (C), where the solid green line shows a \\(\\sim q^{1.3}\\) power law. (D) The second moment for \\(N = 10,000\\) vortices. Plotted separately for the high (in green) and low (in black) vortices at steady state as a function of \\(\\gamma\\) (i.e. increasing \\(\\Gamma_{h}\\) ). (E) LOSSLESS compression for the two populations showing an increase (decrease) in file size (an estimate of entropy) for the low (high) circulation vortices over a couple of cycles. In blue is the file size for the total system. Solid line is a moving average, time is normalized by an average cycle time \\(t_{c}\\) .",
+    "footnote": [],
+    "bbox": [
+      [
+        123,
+        75,
+        874,
+        325
+      ]
+    ],
+    "page_idx": 5
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_1.jpg",
+    "caption": "Figure 1",
+    "footnote": [],
+    "bbox": [
+      [
+        55,
+        100,
+        940,
+        285
+      ]
+    ],
+    "page_idx": 8
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_2.jpg",
+    "caption": "Figure 2",
+    "footnote": [],
+    "bbox": [
+      [
+        58,
+        510,
+        947,
+        650
+      ]
+    ],
+    "page_idx": 8
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_3.jpg",
+    "caption": "Figure 3",
+    "footnote": [],
+    "bbox": [
+      [
+        55,
+        102,
+        940,
+        608
+      ]
+    ],
+    "page_idx": 9
+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_4.jpg",
+    "caption": "Figure 4",
+    "footnote": [],
+    "bbox": [
+      [
+        55,
+        50,
+        940,
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+      ]
+    ],
+    "page_idx": 10
+  }
+]

preprint/preprint__0417a97ecc0d13717181d9623733f3148d5ba9e7ac583590ea0cf0862739b60a/preprint__0417a97ecc0d13717181d9623733f3148d5ba9e7ac583590ea0cf0862739b60a_det.mmd

@@ -0,0 +1,238 @@
+<|ref|>title<|/ref|><|det|>[[44, 108, 888, 175]]<|/det|>
+# Hyperuniformity and phase enrichment in vortex and rotor assemblies  
+
+<|ref|>text<|/ref|><|det|>[[44, 195, 574, 238]]<|/det|>
+Naomi Oppenheimer ( naomiop@gmail.com ) Tel Aviv University https://orcid.org/0000- 0002- 8212- 3404  
+
+<|ref|>text<|/ref|><|det|>[[44, 243, 588, 383]]<|/det|>
+David Stein Flatiron Institute Matan Yah Ben Zion New York University https://orcid.org/0000- 0002- 9876- 787X Michael Shelley Flatiron Institute https://orcid.org/0000- 0002- 4835- 0339  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 418, 102, 436]]<|/det|>
+## Article  
+
+<|ref|>text<|/ref|><|det|>[[44, 455, 877, 499]]<|/det|>
+Keywords: Particle Ensembles, Two- dimensional Fluid, Spontaneous Self- assembly, Hamiltonian Structure, Topological Defects  
+
+<|ref|>text<|/ref|><|det|>[[44, 515, 296, 536]]<|/det|>
+Posted Date: April 26th, 2021  
+
+<|ref|>text<|/ref|><|det|>[[44, 553, 463, 574]]<|/det|>
+DOI: https://doi.org/10.21203/rs.3.rs- 385285/v1  
+
+<|ref|>text<|/ref|><|det|>[[44, 591, 910, 635]]<|/det|>
+License: ©  This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License  
+
+<|ref|>text<|/ref|><|det|>[[42, 669, 945, 713]]<|/det|>
+Version of Record: A version of this preprint was published at Nature Communications on February 10th, 2022. See the published version at https://doi.org/10.1038/s41467- 022- 28375- 9.
+
+<--- Page Split --->
+<|ref|>title<|/ref|><|det|>[[160, 63, 841, 81]]<|/det|>
+# Hyperuniformity and phase enrichment in vortex and rotor assemblies  
+
+<|ref|>text<|/ref|><|det|>[[87, 93, 920, 181]]<|/det|>
+Naomi Oppenheimer, \(^{1,*}\) David B. Stein, \(^{2}\) Matan Yah Ben Zion, \(^{3}\) and Michael J. Shelley \(^{2,4,}\) \(^{1}\) School of Physics, and the Center for Physics and Chemistry of Living Systems, Tel Aviv University, Tel Aviv 6997801, Israel \(^{2}\) Center for Computational Biology, Flatiron Institute, New York, NY 10010, USA \(^{3}\) Laboratoire Gulliver, UMR CNRS 7083, ESPCI Paris, PSL Research University, 75005 Paris, France \(^{4}\) Courant Institute, New York University, New York, NY 10012, USA(Dated: April 13, 2021)  
+
+<|ref|>text<|/ref|><|det|>[[174, 188, 830, 360]]<|/det|>
+Ensembles of particles rotating in a two- dimensional fluid can exhibit chaotic dynamics yet develop signatures of hidden order. Such "rotors" are found in the natural world spanning vastly disparate length scales — from the rotor proteins in cellular membranes to models of atmospheric dynamics. Here we show that an initially random distribution of either ideal vortices in an inviscid fluid, or driven rotors in a viscous membrane, spontaneously self assembles. Despite arising from drastically different physics, these systems share a Hamiltonian structure that sets geometrical conservation laws resulting in distinct structural states. We find that the rotationally invariant interactions isotropically suppress long wavelength fluctuations — a hallmark of a disordered hyperuniform material. With increasing area fraction, the system orders into a hexagonal lattice. In mixtures of two co- rotating populations, the stronger population will gain order from the other and both will become phase enriched. Finally, we show that classical 2D point vortex systems arise as exact limits of the experimentally accessible microscopic membrane rotors, yielding a new system through which to study topological defects.  
+
+<|ref|>text<|/ref|><|det|>[[86, 384, 487, 558]]<|/det|>
+Two- dimensional (or nearly so) fluid flows show rich and complex vortical dynamics. These can arise from flow interactions with boundaries (1, 2), the inverse cascades of 2D turbulence (3- 5), from Coriolis force dominated atmospheric flows (6), and from quantization effects in super fluid He- II (7, 8). Point vortices have long been staples for the modeling of such inertially dominated inviscid flows. Kirchoff (9) was the first to describe point vortices using a Hamiltonian framework and his work was extended by many others [e.g. (10- 13)], notably, Onsager (14) in his statistical mechanics treatment of 2D turbulence as clouds of point vortices.  
+
+<|ref|>text<|/ref|><|det|>[[86, 559, 487, 775]]<|/det|>
+Remarkably, structurally identical Hamiltonian and moment constraints can arise in the microscopic viscously- dominated realm from a strict balance of dissipation with drive on immersed rotating objects. These objects include models of interacting transmembrane ATP- synthase "rotor- proteins" (15- 17), and the planar interactions of rotors — microscopic particles driven to rotate by an external torque (18, 19). We refer to such systems as BDD systems, as in balanced drive and dissipation. In modeling rotational BDD systems other physical effects may also come into play, such as steric interactions, that can yield interesting complexities (17). Interacting assemblies of driven- to- rotate particles has become an area of intensifying interest in the active matter community (18- 26)  
+
+<|ref|>text<|/ref|><|det|>[[86, 776, 487, 848]]<|/det|>
+Here we study both point vortices and a BDD rotor system of rotationally- driven microscopic particles — membrane rotors — immersed in a flat membrane. We show that in both systems, their Hamiltonian conservation laws lead to distinct structural states — hyperuniformity, phase enrichment and crystallization (see Fig. 1), not yet observed for either system. We use the Hamiltonian to derive a bound for spatial correlations requiring hyperuniformity. We demonstrate numerically that rotational dynamics robustly self- assembles particles into a disordered hyperuniform 2D material; This self- assembly is insensitive to the details of the hydrodynamic interactions, steric repulsion, or the presence of impurities in the form of different rotation rates. At steady state, the long wavelength configuration is characterized by an isotropically vanishing structure factor, \(S(\mathbf{q} \to 0) \to 0\) (where \(\mathbf{q}\) is the wavevector), leading to an isotropic band- gap (27- 29).  
+
+<|ref|>text<|/ref|><|det|>[[516, 384, 916, 573]]<|/det|>
+mity, phase enrichment and crystallization (see Fig. 1), not yet observed for either system. We use the Hamiltonian to derive a bound for spatial correlations requiring hyperuniformity. We demonstrate numerically that rotational dynamics robustly self- assembles particles into a disordered hyperuniform 2D material; This self- assembly is insensitive to the details of the hydrodynamic interactions, steric repulsion, or the presence of impurities in the form of different rotation rates. At steady state, the long wavelength configuration is characterized by an isotropically vanishing structure factor, \(S(\mathbf{q} \to 0) \to 0\) (where \(\mathbf{q}\) is the wavevector), leading to an isotropic band- gap (27- 29).  
+
+<|ref|>text<|/ref|><|det|>[[516, 574, 916, 730]]<|/det|>
+In classical mechanics, symmetries of the Hamiltonian \(\mathcal{H}\) restrict the phase- space of the conjugate variables, position and momentum. However, in 2D point vortex or BDD point rotor systems, the conjugate variables are the actual spatial coordinates of the ensemble \(\{x_{i}\}\) and \(\{y_{i}\}\) . The conservation laws are therefore geometrical in nature, bounding the proximity and distribution of the particles. For both point vortices and membrane rotors, as well as for a myriad of other 2D rotating systems (18- 21, 24, 30), the dynamics are dictated by Hamilton's equations,  
+
+<|ref|>equation<|/ref|><|det|>[[668, 737, 914, 755]]<|/det|>
+\[\Gamma_{i}\mathbf{v}_{1} = \partial_{i}^{\perp}\mathcal{H}, \quad (1)\]  
+
+<|ref|>text<|/ref|><|det|>[[516, 763, 916, 836]]<|/det|>
+where \(\partial_{i}^{\perp} = (\partial y_{i}, - \partial x_{i})\) , \(\mathbf{v}_{1}\) is the velocity of rotor \(i\) , and \(\Gamma_{i}\) is the circulation (proportional to the magnitude of the torque for rotors). Our finding, as we will show, is that the spatial arrangements of point vortices, as measured by \(S(\mathbf{q})\) , are dictated by the Hamiltonian,  
+
+<|ref|>equation<|/ref|><|det|>[[617, 842, 914, 877]]<|/det|>
+\[\mathcal{H}[\rho (\mathbf{r})] = \frac{N\Gamma^{2}}{4\pi}\int \mathrm{d}\mathbf{q}\frac{S(\mathbf{q})}{q^{2}}. \quad (2)\]  
+
+<|ref|>text<|/ref|><|det|>[[516, 883, 916, 911]]<|/det|>
+To derive Eq. 2 and to find the Hamiltonian of \(N\) particles, we first describe the flow due to a single vortex in
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[168, 68, 836, 205]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[85, 225, 919, 293]]<|/det|>
+<center>FIG. 1. Three different structural states of 2D vortices/rotors - hyperuniformity for Euler point vortices (A) and QG rotors/surface rotors (B), (C) phase enrichment induced by circulation differences where green (black) represents vortices of high (low) circulation, and (D) crystallization arising from hydrosteric interactions. The insets of (A), (B) and (C) show the structure factor, \(S(q)\) . In (A) and (B) \(S(q)\) decays to zero at small \(q\) , indicating that the distribution is hyperuniform. In (C) the structure factor shows the six distinct peaks of a hexagonal lattice. </center>  
+
+<|ref|>text<|/ref|><|det|>[[86, 320, 487, 450]]<|/det|>
+an ideal Euler fluid and show its equivalence to a point rotor in a viscous membrane. We then use the linearity of the equations to extend the result to the many- body case. An ideal point vortex is given by a singular vorticity, \(\omega = \nabla \times \mathbf{v} = \delta (\mathbf{r})\) . A 2D incompressible fluid can be described using a stream function \(\Psi\) such that the velocity, \(\mathbf{v}\) , is given by \(\mathbf{v} = \partial^{\perp}\Psi\) . This equation, combined with the equation above gives, \(\Psi = - \frac{1}{2\pi}\log r\) (12). The flow, \(\mathbf{v}(r)\) , therefore, scales as \(1 / r\) , where \(r = |\mathbf{r}|\) .  
+
+<|ref|>text<|/ref|><|det|>[[86, 450, 487, 552]]<|/det|>
+We switch now to a point rotor in a viscous membrane, driven by an external torque \(\tau\) . Following Saffman and Delbruck's seminal work (31), and many others that followed (32- 34), we assume that the membrane is incompressible \((\nabla \cdot \mathbf{v} = 0)\) , and that inertia is negligible. Under these assumptions, the Stokes momentum conservation equation for the membrane reads,  
+
+<|ref|>equation<|/ref|><|det|>[[140, 559, 485, 597]]<|/det|>
+\[0 = \eta_{2D}\nabla^{2}\mathbf{v} + \eta_{3D}\frac{\partial\mathbf{u}^{\pm}}{\partial z}\bigg|_{z = 0^{\pm}} + \tau \partial^{\perp}\delta (\mathbf{r}), \quad (3)\]  
+
+<|ref|>text<|/ref|><|det|>[[86, 607, 487, 760]]<|/det|>
+where \(\mathbf{v}\) is the 2D velocity in the plane of the membrane, \(\mathbf{u}^{\pm}\) is the 3D flow in the outer fluids, \(\eta_{2D}\) is the 2D viscosity, and \(\eta_{3D}\) is the viscosity of the outer fluids. The second term on the right hand side is the surface shear stress of the outer fluids, and the third term is the force due to a rotating point object. There is no pressure contribution when the motion is purely rotational. This equation is coupled to the equations of the outer fluids. It is easy to solve the above equations using a 2D Fourier Transform \((\tilde{F} (\mathbf{q}) = \int_{-\infty}^{\infty}\int_{-\infty}^{\infty}F(\mathbf{r})e^{-i\mathbf{q}\cdot \mathbf{r}}d^{2}r)\) , giving:  
+
+<|ref|>equation<|/ref|><|det|>[[156, 768, 485, 802]]<|/det|>
+\[\tilde{\mathbf{v}} (\mathbf{q}) = \Gamma \partial^{\perp}\tilde{\Psi} \quad ; \quad \tilde{\Psi} = \frac{1}{q(q + \lambda^{-1})}, \quad (4)\]  
+
+<|ref|>text<|/ref|><|det|>[[86, 812, 487, 912]]<|/det|>
+where \(\Gamma = \tau /\eta_{2D}\) , and \(\lambda = \eta_{2D} / 2\eta_{3D}\) is the Saffman Delbruck length. At small distances ( \(r \ll \lambda\) ) momentum travels in the plane of the membrane. At large distances ( \(r \gg \lambda\) ) momentum travels through the outer fluid as well (35, 36). In real space \(\Psi (\mathbf{r}) = 1 / 4(H_{0}(r / \lambda) - Y_{0}(r / \lambda))\) , where \(H_{0}\) and \(Y_{0}\) are zeroth order Struve function and Bessel function of the second kind respectively.  
+
+<|ref|>text<|/ref|><|det|>[[513, 320, 916, 596]]<|/det|>
+In the limit of small distances, \(r \ll \lambda\) , the stream function is, \(\Psi \approx - \frac{1}{2\pi}\log r\) , i.e. exactly the same as for an ideal point vortex. In the opposite limit, \(r \gg \lambda\) , the stream function becomes \(\Psi = \frac{1}{2\pi r}\) as in quasigeostrophic (QG) flows — atmospheric or oceanic flows coming from gradients in pressure coupled to the Coriolis force (37), or driven rotors on the surface of a fluid (22). A membrane rotor, therefore, transitions from a point vortex for Euler at small distances to that of QG flow at large distances. The velocity is given by derivatives of \(\Psi\) and is thus proportional to \(1 / r\) ( \(1 / r^{2}\) ) in the limit of small (large) distances (see Fig. 2B). For simplicity, we work primarily in the limit of small distances, \(r \ll \lambda\) , since in this limit the dynamics in a membrane converge with those of point vortices (many results still apply to the more general case as shown in the SI). In what follows, we will use "point vortices" when there are only hydrodynamic interactions and "rotors" when the particles have steric interactions in addition to hydrodynamic ones.  
+
+<|ref|>text<|/ref|><|det|>[[513, 607, 916, 912]]<|/det|>
+The dynamics of \(N\) point vortices follows from the Hamiltonian \(\mathcal{H} = \frac{1}{2}\sum_{i \neq j}\Gamma_{i}\Gamma_{j}\Psi (|\mathbf{r}_{i} - \mathbf{r}_{j}|)\) , where \(\Gamma_{i}\) is the circulation of vortex \(i\) (in a membrane \(\Gamma_{i} = \tau_{i} / \eta_{2D}\) ). The Hamiltonian depends on the conjugate variables \(\mathbf{r}_{i} = (x_{i}, y_{i})\) , [normalized by the circulation \(\sqrt{|\Gamma_{i}|} \operatorname{sgn}(\Gamma_{i})\) ], i.e. the positions of the vortices (12). The symmetries of the Hamiltonian correspond to conservation laws (39). In this case, we have symmetries with respect to translation in time, space, and rotation, corresponding to conservation of the Hamiltonian itself, and of the first and second moments of the distribution, \(\mathbf{L} = \sum_{i}\Gamma_{i}\mathbf{r}_{i} (= \mathbf{0}\) wlog), and \(M = \sum_{i,j}\Gamma_{i}r_{i}^{2}\) . Thus, the initial area cannot change dramatically, particles cannot drift to infinity since the second moment is fixed, nor can they collapse to a point since the Hamiltonian is conserved. These properties are readily observed in simulations. Figure 2D shows typical trajectories of 200 membrane rotors. The initial distribution is random in a predefined finite area, and the dynamics are chaotic (40). The final configuration occupies nearly the same region of space as the initial configuration does, and the conservation laws hold
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[125, 66, 896, 188]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[84, 201, 919, 323]]<|/det|>
+<center>FIG. 2. (A) A representation of a membrane rotor — a disk rotating due to a torque \(\tau\) in the plane of the membrane. (B) The velocity field due to a membrane rotor (solid line) which scales as a point vortex \(v \sim 1 / r\) at small distances (dotted), \(r / \lambda \ll 1\) , transitioning to a QG behavior at large distances \(v \sim 1 / r^2\) (dashed). (C) Contour dynamics of an ellipse with radii ratios \(r_l / r_s \leq 3\) , where \(r_l\) ( \(r_s\) ) is the major (minor) axis. Starting from the same contour, the dynamics differ according to the radius relative to the SD length. Blue is in the limit \(r_l \ll \lambda\) . In this limit the ellipse is rotating as a rigid body, as predicted by Kelvin (38) for an elliptic patch in an Euler fluid. Black is in the limit \(r_l \gg \lambda\) , no longer conserving its shape since the large distance flow is in the quasigeostrophic regime. (D) 200 point membrane rotors, blue is the initial random configuration, black is the final configuration. Solid line shows typical trajectory of an individual vortex. Note that the area did not change considerably since the system of vortices is self-bounding. (E) the relative error in \(\mathcal{H}\) and \(M\) over a few cycle times, \(t_c\) . </center>  
+
+<|ref|>text<|/ref|><|det|>[[85, 350, 487, 394]]<|/det|>
+to high precision in our simulations, as shown in Fig. 2E. This self confining property of vortex dynamics has further consequences, as we now show.  
+
+<|ref|>text<|/ref|><|det|>[[86, 394, 488, 669]]<|/det|>
+Hyperuniformity. Hyperuniformity is the suppression of density- density fluctuations at small wavenumbers (or correspondingly, at large distances) (41- 43). Disordered hyperuniformity can emerge due to short ranged interactions such as those that arise in sheared suspensions (30, 44, 45), jammed materials (46), and for spinning particles (47). Here we will show hyperuniformity emerging from long ranged interactions, similar to its emergence in sedimentation of irregular objects (48). A good way to characterize hyperuniformity is the structure factor, defined as \(S(\mathbf{q}) = N^{- 1}|\tilde{\rho} (\mathbf{q})|^2\) , where \(\rho (\mathbf{r}) = \sum_i \delta (\mathbf{r} - \mathbf{r}_i)\) is the coarse grained density. In a hyperuniform material, \(S(q)\) goes to zero as a power law at small wavenumbers. We argue that point vortices must be hyperuniform due to the conservation of the Hamiltonian. For a density of rotors, the Hamiltonian is given by \(\mathcal{H}[\rho (\mathbf{r})] \sim \frac{\Gamma^2}{2} \int \mathrm{d}\mathbf{r} \int \mathrm{d}\mathbf{r}' \rho (\mathbf{r}) \rho (\mathbf{r}') \psi (|\mathbf{r} - \mathbf{r}'|)\) . Using the convolution theorem, we find a general relation between the Hamiltonian and the structure factor  
+
+<|ref|>equation<|/ref|><|det|>[[172, 675, 487, 710]]<|/det|>
+\[\mathcal{H}[\rho (\mathbf{r})] = \frac{N\Gamma^2}{4\pi} \int \mathrm{d}\mathbf{q} S(\mathbf{q}) \tilde{\Psi} (\mathbf{q}). \quad (5)\]  
+
+<|ref|>text<|/ref|><|det|>[[86, 721, 488, 840]]<|/det|>
+In the case of point vortices, \(\tilde{\Psi} (\mathbf{q}) = 1 / q^2\) , which gives Eq. 2. For the integral of Eq. 2 to converge in 2D, \(S(\mathbf{q}) \sim q^\alpha\) near the origin, and we must have \(\alpha > 0\) . In other words, an ensemble of point vortices is hyperuniform (a similar calculation in the QG limit, where \(\tilde{\Psi} = \lambda /q\) , yields \(\alpha > - 1\) ). Figures 3B and 4C, show an apparent \(\alpha \sim 1.3\) scaling for point vortices, consistent with the above argument.  
+
+<|ref|>text<|/ref|><|det|>[[86, 841, 488, 912]]<|/det|>
+Using simulations we show that a set of \(N\) vortices, uniformly distributed within a radius \(R\) , evolves to a disordered steady- state with a hidden order visible to the naked eye (compare Figures 3A left and right). We quantitatively characterize the system in steady- state in three  
+
+<|ref|>text<|/ref|><|det|>[[515, 350, 917, 740]]<|/det|>
+ways: (1) The structure factor. At steady- state \(S(\mathbf{q})\) shows a distinct cavity, at \(q \approx 0\) , \(S(\mathbf{q}) \to 0\) , for both points vortices (Fig. 3A) and rotors (Fig. 3C). All simulations produce a hyperuniform arrangement. (2) Perturbations. We demonstrate that hyperuniformity is robust under different perturbations, be it in the form of numerical errors, repulsive interactions, or impurities (in the next section). For point vortices, the steady state appears later and later as the timestep is decreased, suggesting that perturbations are necessary for convergence, here very small but persistent timestepping errors (49). Adding steric interactions, hyperuniformity appears on a timescale that is independent of the timestep. Moreover, with steric interactions, as the area fraction \(\phi\) of the particles is increased, the system transitions from disordered hyperuniform, to an ordered hyperuniform hexagonal lattice at \(\phi \sim 0.5\) , as can be seen in Fig. 3C. The inset of Fig. 3B shows the averaged structure factor where at intermediate area fractions we see Percus- Yevick type features for the structure factor of disks (50). (3) The returnity. We observe that at late times the ensemble of point vortices rotates almost as a rigid body and each particle goes back to its position at the previous cycle. We measure particle deviations by what we term the "returnity" (see Fig. 3D for details). The system may seem to have reached an absorbing state, but the motion of vortices over many cycles is still chaotic.  
+
+<|ref|>text<|/ref|><|det|>[[515, 768, 917, 912]]<|/det|>
+Rotation induced phase enrichment. We now show that for mixed populations of fast and slow rotating particles, there is phase enrichment of both populations and hyperuniformity of the fast ones. Consider a mixture of two equally numbered populations ( \(\rho_l = \rho_h\) at \(t = 0\) ) initially placed within the same radius \(R\) . \(\rho_l\) rotates slowly with \(\Gamma_l \ll \Gamma_h\) , where \(\Gamma_h\) is the circulation of the second population. Figure 4A shows long- time simulation results for 10,000 point vortices. The two populations behave very differently. The fast vortices remain in
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[125, 70, 876, 494]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[84, 504, 919, 641]]<|/det|>
+<center>FIG. 3. Hyperuniformity in ensembles of point vortices and rotors. (A) Snapshots of 10,000 point vortices initially (left) and at steady-state (right). Insets show the structure factor, \(S(\mathbf{q})\) showing a distinct cavity at steady-state. (B) Angular average of the structure factor shown in A, in a log-log scale with solid line showing a \(q^{1.3}\) scaling. Error bars are standard deviation over 10 well separated timesteps. Inset shows the structure factor of the rotors shown in (C) with increasing hue corresponding to increased concentration \(\phi = (0.14, 0.24, 0.37, 0.54)\) . Solid line is the same \(\alpha \sim 1.3\) scaling. (C) Steady state configurations of 2,000 membrane rotors with the corresponding structure factors, showing a transition from disordered hyperuniformity to a hexagonal lattice. (D) A plot of the returnity measuring the deviation of particle \(i\) at position \(r_i\) from its position at the previous cycle, \(returnity = \Delta r_i(t_{\mathrm{cyc}}) / R\) , where \(R\) is the initial radius of the ensemble. The cycle time, \(t_{\mathrm{cyc}}\) , is defined at steady state as the distance between two adjacent minima of the function \(f = \sum_i^N \Delta r_i(\Delta t)\) , where \(\Delta t\) is the time difference. Color scheme is from blue to yellow with increasing deviation. </center>  
+
+<|ref|>text<|/ref|><|det|>[[86, 680, 487, 827]]<|/det|>
+a disk of only slightly smaller size than their initial area (Fig. 4B). The slow particle distribution shows a significant expansion. In addition, there is a striking difference when comparing the independently computed structure factors of these two populations, the fast vortices are hyperuniform with the same scaling as before, \(S(q) \sim q^{1.3}\) , whereas the slow ones show no signs of hyperuniformity (Fig. 4C). This difference is dramatic enough to be visible in a cursory examination of the separate distributions; see Fig. 4A.  
+
+<|ref|>text<|/ref|><|det|>[[86, 840, 487, 911]]<|/det|>
+Using a heuristic model, we show that the conservation laws allow two solutions at steady- state. In one solution, the two populations remain confined to a circle of the same radius. In the second solution, the radius of the slower population expands, while the radius of the faster  
+
+<|ref|>text<|/ref|><|det|>[[515, 682, 917, 899]]<|/det|>
+population contracts. We then show that the segregated solution is the one that maximizes the number of states in the system. For simplicity, we assume that the final steady states are uniform (not true for the slow vortices as is clear from Fig. 4B). There are two possible solutions where \(\mathcal{H}\) and \(M\) are conserved — in the first, the initial radius, \(R\) , does not change; in the second, the radius of the fast vortices slightly decreases to \(R_h\) , allowing the slow vortices to expand to a larger radius \(R_l\) given by \(R_l^2 = (\gamma + 1)R^2 - R_h^2 \gamma\) , where \(\gamma = \Gamma_h / \Gamma_l\) (see Fig. 4D). Linearly expanding in \(1 / \gamma\) , we find that \(R_h \simeq R(1 - \beta / \gamma)\) for the high circulation vortices, where \(\beta\) is a positive prefactor of order 1. The slow vortices asymptote to \(R_l \simeq R\sqrt{1 + 2\beta} + O(1 / \gamma)\) . The simulation results indicate that the outer radius indeed asymptotes to a larger valued
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[123, 75, 874, 325]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[85, 338, 919, 460]]<|/det|>
+<center>FIG. 4. Two populations of vortices with different circulations showing phase enrichment, \(\Gamma_{l} = 2\pi\) in black and \(\Gamma_{h} = 256\pi\) in green. (A) Steady-state configuration for ten thousand point vortices of a circulation ratio \(\gamma = \Gamma_{h} / \Gamma_{l} = 128\) . Each inset shows a close-up view of one of the populations within the same physical region. (B) Density of the configuration in (A), \(\rho (r)\) , averaged over angle as a function of distance from the center. Note how density fluctuations are suppressed for the high circulation vortices, as is more clearly observed by the averaged structure factor, \(S(q)\) , in (C), where the solid green line shows a \(\sim q^{1.3}\) power law. (D) The second moment for \(N = 10,000\) vortices. Plotted separately for the high (in green) and low (in black) vortices at steady state as a function of \(\gamma\) (i.e. increasing \(\Gamma_{h}\) ). (E) LOSSLESS compression for the two populations showing an increase (decrease) in file size (an estimate of entropy) for the low (high) circulation vortices over a couple of cycles. In blue is the file size for the total system. Solid line is a moving average, time is normalized by an average cycle time \(t_{c}\) . </center>  
+
+<|ref|>text<|/ref|><|det|>[[86, 487, 488, 516]]<|/det|>
+constant as \(\gamma\) increases and does not increase indefinitely (see Fig. 4D and SI).  
+
+<|ref|>text<|/ref|><|det|>[[86, 517, 488, 737]]<|/det|>
+A solution with two different radii is therefore possible and is indeed observed at large circulation ratios. Such a solution is favored entropically since it maximizes the available states. Asymptotically at large \(\gamma\) , the main entropical contribution is volumetric, \(\Delta S_{\mathrm{volume}} = 2N \log (R_{\mathrm{final}} / R_{\mathrm{initial}})\) . Since the high circulation vortices hardly change radius, \(R_{h} \xrightarrow{\gamma \to \infty} R\) , the change in entropy is coming mainly from the expansion of the low circulation vortices and is given by \(\Delta S_{\mathrm{total}} \sim N \log (1 + 2\beta) > 0\) . Coupling the two populations allows one population to expand where before it was bounded (51). The situation is analogous to depletion interactions, where the net entropy of a system increases by condensing the large particles allowing for the small particles to explore a larger volume (52).  
+
+<|ref|>text<|/ref|><|det|>[[86, 738, 488, 825]]<|/det|>
+A simple way to estimate the entropy in a system is by using LOSSLESS compression, as suggested by Refs. (53, 54). Compressing plots of particle positions in a system of 10,000 point vortices with circulation ratio \(\Gamma_{h} / \Gamma_{l} = 128\) shows an increase in file size for \(\rho_{l}\) and a decrease for \(\rho_{h}\) , while the combined system is increasing, see Fig. 4E.  
+
+<|ref|>text<|/ref|><|det|>[[86, 827, 488, 911]]<|/det|>
+Discussion. We have shown that driven particles in a membrane or a soap film, as well as point vortices in an ideal 2D fluid, have geometrical conservation laws which limit their distribution. These conservation laws dictate different possible structural states — namely hyperuniformity and phase enrichment. We have shown that hy  
+
+<|ref|>text<|/ref|><|det|>[[515, 486, 917, 631]]<|/det|>
+peruniformity is robust to several forms of perturbations whether arising due to numerical errors, steric interactions, or impurities in the form of low circulation vortices. For rotors with steric interactions, the unbounded ensemble crystallizes into a hexagonal lattice when the area fraction \(\phi \gtrsim 0.5\) (see also (17)). We have limited the discussion to membrane rotors and vortices, but the results hold for other settings in which mass is conserved in the 2D plane, e.g. particles at the surface of a fluid (see SI).  
+
+<|ref|>text<|/ref|><|det|>[[515, 634, 917, 836]]<|/det|>
+What is especially interesting about our particular BDD system is its potential for experimental realizability, its moment and Hamiltonian structure, and that its near- field interactions (i.e. below the Saffman- Delbruck length) are identical to those of Euler point vortices. Further, the far- field interactions of membrane rotors are identical to those of point vortices of the semiquasigeostrophic equations (37, 55, 56) used to model atmospheric flows. Thus, to observe the interesting dynamical features we describe, one does not need to go to the atmospheric scale, or cool a fluid to near- zero temperature. In principle, one can simply observe microscopic particles on a soap film, in smectic films, a membrane, or even at the surface of a fluid (19, 22, 57, 58).  
+
+<|ref|>text<|/ref|><|det|>[[515, 839, 917, 911]]<|/det|>
+Methods. Simulations. Simulations were performed in Python. Random initial configurations within the unit disk were found by rejection sampling (points in the unit rectangle were sampled uniformly, transformed to the rectangle \([- 1,1]^{2}\) , and those with \(r > 1\) were discarded). The initial Hamiltonian \(H_{0}\) is computed at \(t = 0\) , and the relative error \(\epsilon (t) = |H_{t} - H_{0}| / H_{0}\) is
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[86, 67, 488, 237]]<|/det|>
+monitored as a measure of fidelity. For simulations of rotors (i.e. with steric repulsion), a 5th order explicit Runge- Kutta method based on the Dormand- Prince scheme (59) with a fixed timestep size of \(\delta t = 10^{- 7}\) was used. Long integration times were required for simulations of point vortices, and for these simulations an explicit eighth- order adaptive method based on the Dormand- Prince scheme (60, 61) was used, with both relative and absolute tolerances set to \(10^{- 6}\) . The specific implementation of the scheme used was the \(DOP853\) method of scipy.integrate (62). For simulations of 10,000 point vortices with \(\Gamma = 2\pi\) , \(\epsilon (t) < 1.6 \times 10^{- 3}\) up to \(t \approx 16,000\) cycles, while for simulations with 5,000 vortices with \(\Gamma = 2\pi\) and 5,000 vortices with \(\Gamma = 256\pi\) , \(\epsilon (t) < 5 \cdot 10^{- 3}\) up to \(t \approx 10\) cycles. Time is normalized by the average cycle time, \(t_c \approx 4\pi^2 R^2 / \sum_i \Gamma_i\) , where \(R\) is the initial radius.  
+
+<|ref|>text<|/ref|><|det|>[[86, 237, 488, 320]]<|/det|>
+Steric interactions were taken as the repulsive part of a harmonic potential, i.e. for two particles whose centers are distance \(r_i\) apart, \(F = - kr_{ij}\) if \(r_{ij} < 2a\) and zero otherwise. The use of a harmonic potential, rather than a sharp step function for hard core particles, provided improved numerical stability and convergence. A large \(k\) value was chosen to ensure no overlap between particles, \(k = 1 \cdot 10^6\) , for particles of size \(a = 0.01\) .  
+
+<|ref|>text<|/ref|><|det|>[[86, 320, 488, 369]]<|/det|>
+Structure factor. To accurately compute the structure factor \(S(\mathbf{q})\) we use a type- 1 non- uniform fast- Fourier transform (63). Explicitly, points are restricted to a windowing region which is confined entirely within the unit disk. The frequencies \(\tilde{\rho} (\mathbf{q})\) are com  
+
+<|ref|>text<|/ref|><|det|>[[515, 67, 917, 163]]<|/det|>
+puted for the first 512 modes in each direction, and the average value (i.e. \(\tilde{\rho} (0)\) ) is set to 0. This results in structure factors in the plane, such as those shown in Fig. 3. Except in those cases where crystallization occurs, these structure factors are azimuthally isotropic. To summarize this information, the angular average over the structure factor was calculated by slicing the result to 1000 equal bins between \(q_{\mathrm{min}}\) and \(q_{\mathrm{max}}\) and taking the mean of the results that fell within each slice.  
+
+<|ref|>text<|/ref|><|det|>[[515, 163, 917, 237]]<|/det|>
+Compression. A plot of the positions of the point vortices was compressed using PNG with AGG backend. Each vortex was plotted by a single pixel. The total size of the plots was kept fixed in time. The figure size was chosen to minimize overlap between neighboring vortices but maintaining a computationally accessible file size.  
+
+<|ref|>text<|/ref|><|det|>[[515, 237, 917, 366]]<|/det|>
+Acknowledgment We thank Haim Diamant for insightful discussions regarding the emergence of hyperuniformity from the conservation laws, to Martin Lenz for suggesting a simple heuristic model of the phase enrichment, and to Enkeleida Lushi. N.O. acknowledges supported by the Israel Science Foundation (grant No. 1752/20). M.J.S. acknowledges support by the National Science Foundation under Awards Nos. DMR- 1420073 (NYU MRSEC), DMS- 1620331, and DMR- 2004469.  
+
+<|ref|>text<|/ref|><|det|>[[85, 420, 488, 912]]<|/det|>
+[1] R. King, Ocean Engineering 4, 141 (1977). [2] M. J. Shelley and J. Zhang, Annual Review of Fluid Mechanics 43, 449 (2011). [3] R. Fjortoft, Tellus 5, 225 (1953). [4] R. H. Kraichnan, Physics of Fluids 10, 1417 (1967). [5] D. Bernard, G. Boffetta, A. Celani, and G. Falkovich, Nature Physics 2, 124 (2006). [6] R. P. Behringer, S. D. Meyers, and H. L. Swinney, Physics of Fluids A: Fluid Dynamics 3, 1243 (1991). [7] A. Abrikosov, Sov. Phys. - JETP (Engl. Transl.); (United States) (1957). [8] M. R. Matthews, B. P. Anderson, P. C. Haljan, D. S. Hall, C. E. Wieman, and E. A. Cornell, Physical Review Letters 83, 2498 (1999), arXiv:9908209 [cond- mat]. [9] G. Kirchhoff, Vorlesungen über mathematische physik: mechanik, Vol. 1 (BG Teubner, 1876). [10] H. Aref, Annual Review of Fluid Mechanics 15, 345 (1983). [11] C. C. Lin, Proceedings of the National Academy of Sciences 27, 570 (1941). [12] P. K. Newton, The N- Vortex Problem, Applied Mathematical Sciences, Vol. 145 (Springer New York, New York, NY, 2001). [13] A. Bogatskiy and P. Wiegmann, Physical Review Letters 122, 214505 (2019), arXiv:1812.00763. [14] L. Onsager, Il Nuovo Cimento 6, 279 (1949). [15] P. Lenz, J.- F. Joanny, F. Julicher, and J. Prost, Physical Review Letters 91, 108104 (2003). [16] P. Lenz, J.- F. Joanny, F. Julicher, and J. Prost, The European Physical Journal E 13, 379 (2004). [17] N. Oppenheimer, D. B. Stein, and M. J. Shelley, Phys. Rev. Lett. 123, 148101 (2019), arXiv:1903.00940. [18] B. a. Grzybowski, H. a. Stone, and G. M. Whitesides, Nature 405, 1033 (2000). [19] V. Soni, E. S. Bililign, S. Magkiriadou, S. Sacanna, D. Bartolo, M. J. Shelley, and W. T. M. Irvine, Nature Physics (2019).  
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+[20] N. H. P. Nguyen, D. Klotsa, M. Engel, and S. C. Glotzer, Physical Review Letters 112, 075701 (2014). [21] E. Lushi, H. Wioland, and R. E. Goldstein, Proceedings of the National Academy of Sciences 111, 9733 (2014), arXiv:1407.3633. [22] K. Yeo, E. Lushi, and P. M. Vlahovska, Physical Review Letters 114, 188301 (2015). [23] E. Lushi and P. M. Vlahovska, Journal of Nonlinear Science 25, 1111 (2015). [24] Y. Goto and H. Tanaka, Nature Communications 6, 5994 (2015). [25] Z. Shen and J. S. Lintuvuori, Physical Review Letters 125, 228002 (2020), arXiv:2007.16142. [26] E. S. Bililign, F. B. Usabiaga, Y. A. Ganan, V. Soni, S. Magkiriadou, M. J. Shelley, D. Bartolo, and W. T. M. Irvine, 1 (2021), arXiv:2102.03263. [27] S. John, Phys. Rev. Lett. 58, 2486 (1987). [28] E. Yablonovitch, Phys. Rev. Lett. 58, 2059 (1987). [29] W. Man, M. Florescu, E. P. Williamson, Y. He, S. R. Hashemizad, B. Y. C. Leung, D. R. Liner, S. Torquato, P. M. Chaikin, and P. J. Steinhardt, Proceedings of the National Academy of Sciences 110, 15886 (2013). [30] J. H. Weijs, R. Jeanneret, R. Dreyfus, and D. Bartolo, Phys. Rev. Lett. 115, 1 (2015). [31] P. G. Saffman and M. Delbruck, Proceedings of the National Academy of Sciences 72, 3111 (1975). [32] A. J. Levine, T. B. Liverpool, and F. C. MacKintosh, Physical Review E 69, 021503 (2004). [33] K. Seki, S. Mogre, and S. Komura, Physical Review E 89, 022713 (2014). [34] B. A. Camley and F. L. H. Brown, Physical Review Letters 105, 148102 (2010), arXiv:1105.4898. [35] N. Oppenheimer and H. Diamant, Biophysical Journal 96, 3041 (2009), arXiv:0809.4163. [36] N. Oppenheimer and H. A. Stone, Biophysical Journal 113, 440 (2017). [37] I. M. Held, R. T. Pierrehubert, S. T. Garner, and K. L.
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+Swanson, J. Fluid Mech. 282, 1 (1995). [38] W. Thomson, The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science 10, 155 (1880). [39] E. Noether, Nachr D Konig Gesellschaft D Wiss Zu Gottingen Mathphys Klasse (1918). [40] H. Aref and N. Pomphrey, Proc. Natl. Acad. Sci. 380, 359 (1982). [41] S. Torquato, Physical Review E 94, 022122 (2016). [42] D. Hexner and D. Levine, Physical Review Letters 114, 110602 (2015). [43] G. Ariel and H. Diamant, Physical Review E 102, 022110 (2020), arXiv:2004.10588. [44] S. Wilken, R. E. Guerra, D. J. Pine, and P. M. Chaikin, arXiv, 0 (2020), arXiv:2002.04499. [45] J. Wang, J. M. Schwarz, and J. D. Paulsen, Nature Communications 9, 2836 (2018), arXiv:1711.06731. [46] S. Torquato, Physics Reports 745, 1 (2018). [47] Q.- L. Lei and R. Ni, Proceedings of the National Academy of Sciences 116, 22983 (2019), arXiv:1904.07514. [48] T. Goldfriend, H. Diamant, and T. A. Witten, Physical Review Letters 118, 158005 (2017), arXiv:1612.08632. [49] W. Dai and M. J. Shelley, Physics of Fluids A: Fluid Dynamics 5, 2131 (1993). [50] J. K. Percus and G. J. Yevick, Physical Review 110, 1 (1958). [51] On a side note, as Onsager first suggested [Onsager1949], in a bound system, configurational entropy must have a maximum. Entropy therefore decreases beyond a critical energy, and the system has a negative temperature. Negative temperature manifests itself as an increase in order with an increase in the energy. When coupling two vortical systems of negative temperature, there is a tendency  
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+to segregate. [52] M. Kardar, Statistical Physics of Particles (Cambridge University Press, Cambridge, 2007). [53] S. Martiniani, P. M. Chaikin, and D. Levine, Physical Review X 9, 011031 (2019), arXiv:1708.04993. [54] R. Avinery, M. Kornreich, and R. Beck, Physical Review Letters 123, 178102 (2019), arXiv:1709.10164. [55] G. Falkovich, Journal of Physics A: Mathematical and Theoretical 42, 123001 (2009). [56] D. Córdoba, C. Fefferman, and J. L. Rodrigo, Proc. Natal. Acad. Sci. 101, 2687 (2004). [57] Z. H. Nguyen, M. Atkinson, C. S. Park, J. Maclennan, M. Glaser, and N. Clark, Physical Review Letters 105, 268304 (2010). [58] G. Lumay, N. Obara, F. Weyer, and N. Vandewalle, Soft Matter 9, 2420 (2013). [59] J. R. Dormand and P. J. Prince, Journal of computational and applied mathematics 6, 19 (1980). [60] P. J. Prince and J. R. Dormand, Journal of computational and applied mathematics 7, 67 (1981). [61] E. Hairer, S. P. Norsett, and G. Wanner, Solving Ordinary Differential Equations I (2nd Revised. Ed.): Nons- tiff Problems (Springer- Verlag, Berlin, Heidelberg, 1993). [62] P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, S. J. van der Walt, M. Brett, J. Wilson, K. J. Millman, N. Mayorov, A. R. J. Nelson, E. Jones, R. Kern, E. Larson, C. J. Carey, I. Polat, Y. Feng, E. W. Moore, J. VanderPlas, D. Laxalde, J. Perktold, R. Cimrman, I. Henriksen, E. A. Quintero, C. R. Harris, A. M. Archibald, A. H. Ribeiro, F. Pedregosa, P. van Mulbregt, and SciPy 1.0 Contributors, Nature Methods 17, 261 (2020). [63] A. H. Barnett, J. Magland, and L. af Klinteberg, SIAM Journal on Scientific Computing 41, C479 (2019).
+
+<--- Page Split --->
+<|ref|>sub_title<|/ref|><|det|>[[44, 44, 143, 70]]<|/det|>
+## Figures  
+
+<|ref|>image<|/ref|><|det|>[[55, 100, 940, 285]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[42, 323, 116, 343]]<|/det|>
+<center>Figure 1 </center>  
+
+<|ref|>text<|/ref|><|det|>[[41, 364, 951, 499]]<|/det|>
+Three different structural states of 2D vortices/rotors - hyperuniformity for Euler point vortices (A) and QG ro- tors/surface rotors (B), (C) phase enrichment induced by circulation differences where green (black) represents vortices of high (low) circulation, and (D) crystallization arising from hydrosteric interactions. The insets of (A), (B) and (C) show the structure factor, S(q). In (A) and (B) S(q) decays to zero at small q, indicating that the distribution is hyperuniform. In (C) the structure factor shows the six distinct peaks of a hexagonal lattice.  
+
+<|ref|>image<|/ref|><|det|>[[58, 510, 947, 650]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[42, 679, 117, 699]]<|/det|>
+<center>Figure 2 </center>  
+
+<|ref|>text<|/ref|><|det|>[[40, 720, 955, 923]]<|/det|>
+(A) A representation of a membrane rotor - a disk rotating due to a torque \(\tau\) in the plane of the membrane. (B) The velocity field due to a membrane rotor (solid line) which scales as a point vortex \(\nu \mathbb{Q} /1 / r\) at small distances (dotted), \(r / \lambda \ll 1\) , transitioning to a QG behavior at large distances \(\nu \mathbb{Q} /1 / r2\) (dashed). (C) Contour dynamics of an ellipse with radii ratios \(\mathrm{rl} / \mathrm{rs} \leq 3\) , where \(\mathrm{rl}\) (rs) is the major (minor) axis. Starting from the same contour, the dynamics differ according to the radius relative to the SD length. Blue is in the limit \(\mathrm{rl} \ll \lambda\) . In this limit the ellipse is rotating as a rigid body, as predicted by Kelvin (38) for an elliptic patch in an Euler fluid. Black is in the limit \(\mathrm{rl} \gg \lambda\) , no longer conserving its shape since the large distance flow is in the quasigeostrophic regime. (D) 200 point membrane rotors, blue is the initial random configuration, black is the final configuration. Solid line shows typical trajectory of an individual vortex.
+
+<--- Page Split --->
+<|ref|>text<|/ref|><|det|>[[42, 45, 907, 88]]<|/det|>
+Note that the area did not change considerably since the system of vortices is self- bounding. (E) the relative error in H and M over a few cycle times, tc.  
+
+<|ref|>image<|/ref|><|det|>[[55, 102, 940, 608]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[42, 633, 115, 653]]<|/det|>
+<center>Figure 3 </center>  
+
+<|ref|>text<|/ref|><|det|>[[42, 676, 905, 718]]<|/det|>
+Hyperuniformity in ensembles of point vortices and rotors. Please see manuscript .pdf for full figure caption
+
+<--- Page Split --->
+<|ref|>image<|/ref|><|det|>[[55, 50, 940, 352]]<|/det|>
+<|ref|>image_caption<|/ref|><|det|>[[42, 382, 117, 401]]<|/det|>
+<center>Figure 4 </center>  
+
+<|ref|>text<|/ref|><|det|>[[40, 424, 958, 673]]<|/det|>
+Two populations of vortices with different circulations showing phase enrichment, \(\Gamma 1 = 2\pi\) in black and \(\Gamma h = 256\pi\) in green. (A) Steady- state configuration for ten thousand point vortices of a circulation ratio \(\gamma = \Gamma h / \Gamma 1 = 128\) . Each inset shows a close- up view of one of the populations within the same physical region. (B) Density of the configuration in (A), \(\rho (r)\) , averaged over angle as a function of distance from the center. Note how density fluctuations are suppressed for the high circulation vortices, as is more clearly observed by the averaged structure factor, S(q), in (C), where the solid green line shows a \(\mathbb{Q}1.3\) power law. (D) The second moment for \(\mathrm{N} = 10,000\) vortices. Plotted separately for the high (in green) and low (in black) vortices at steady state as a function of \(\gamma\) (i.e. increasing \(\Gamma h\) ). (E) LOSSLESS compression for the two populations showing an increase (decrease) in file size (an estimate of entropy) for the low (high) circulation vortices over a couple of cycles. In blue is the file size for the total system. Solid line is a moving average, time is normalized by an average cycle time tc.  
+
+<|ref|>sub_title<|/ref|><|det|>[[44, 695, 311, 722]]<|/det|>
+## Supplementary Files  
+
+<|ref|>text<|/ref|><|det|>[[44, 746, 764, 767]]<|/det|>
+This is a list of supplementary files associated with this preprint. Click to download.  
+
+<|ref|>text<|/ref|><|det|>[[61, 785, 137, 803]]<|/det|>
+- SI.pdf
+
+<--- Page Split --->

preprint/preprint__042a95bb963a3fded623677d176d398a6df7372697fadd494fd320558dfef397/images_list.json

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+    "caption": "Fig. 2 Tumor response dynamics were recapitulated by modeling. a. Schematic plot of tumor growth model. b. Box plots of model parameters \\(Kd\\) , \\(F\\) and \\(Kg\\) across organs. Significance was calculated using Kruskal-Wallis tests. The box extends from the 25th to 75th percentiles and the line in the middle is plotted as the median. The whiskers are drawn down to the 10th percentile and up to the 90th percentile. Points below and above the whiskers represent individual lesions. c. The correlations between model parameters. d. The correlations between model parameters and tumor baseline volume. The size of the dots represents lesion number (reported in panel b). The dashed lines with gray area are the linear",
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+  },
+  {
+    "type": "image",
+    "img_path": "images/Figure_4.jpg",
+    "caption": "Fig. 4 Patient relapse sequence association with patient survival. a. Patients were clustered into five",
+    "footnote": [],
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+  }
+]