diff --git "a/frontend/src/components/DocumentProcessor.jsx" "b/frontend/src/components/DocumentProcessor.jsx"
--- "a/frontend/src/components/DocumentProcessor.jsx"
+++ "b/frontend/src/components/DocumentProcessor.jsx"
@@ -93,3407 +93,2265 @@ function DocumentProcessor() {
     // Add test preloaded highlights data - keyed by chunk index
     // Lennart version
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-    "text": "With the aim to establish a low-carbon and environmentally\r friendly society, next-generation lithium-ion batteries (LIBs)\r are urgently required to promote the large-scale application of\r electric vehicles.1,2 Since Ni element dominates the reversible\r capacity, Ni-rich layered oxides receive great expectations due\r to their exceptional advantages in energy density and\r production cost, especially for ultrahigh-nickel Li-\r NixCoyMn1‑x‑yO2 (NCM, x ≥ 0.9).3−8 However, the conven-\r tional spherical NCM cathodes formed by agglomeration of\r primary particles always suffer from severe structural\r degradation with the generation of intergranular microcracks\r and detrimental phase transitions during cycling.9−12 Recently,\r the NCM fabrication with quasi single-crystal morphology\r (SNCM) has attracted enormous research attention both in\r industry and laboratory, which is widely considered as a\r promising modification strategy.13,14 The SNCM cathode\r consists of single dispersed particles of 2−5 μm with an\r enhanced crystalline structure and internal boundary-free\r configuration.15,16 Benefiting from its exceptional morphology,\r SNCM can significantly alleviate the accumulation of\r anisotropic stresses and intergranular microcrack formation,\r leading to a considerable enhancement in capacity retention"
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-    "text": "and cycled particle integrity.17,18 Meanwhile, its excellent\r mechanical strength is beneficial in further increasing the\r compaction density of cathodes to achieve higher specific\r energy density with relative low cost.19,20\r While the transformation of agglomerated nanoparticles into\r separate micron-sized single-crystals presents numerous\r advantages, it also unveils some inherent challenges.21 The\r lower specific surface area and prolonged Li+ diffusion distance\r in single-crystal particles can contribute to the sluggish Li+\r diffusion kinetics, resulting in poor capacity and inferior rate\r performance of SNCM.22−24 This will further trigger\r inhomogeneous distribution of Li+ concentration during\r cycling, causing the coexistence of multiple phases with severe\r lattice strain within single-crystals.25 As a result, lattice strain\r exacerbates planar gliding and intragranular cracks’ generation\r of microsized single-crystals, which has been considered as the\r root cause of surface degradation and structural damage.26,27\r Therefore, the poor Li+ diffusion kinetics is the origin of\r various degradation mechanisms in SNCM and urgently needs\r to be improved to advance its application potential."
+    "text": "Recurrent neural networks, long short-term memory [13] and gated recurrent [7] neural networks\r in particular, have been firmly established as state of the art approaches in sequence modeling and\r transduction problems such as language modeling and machine translation [ 35 , 2 , 5]. Numerous\r efforts have since continued to push the boundaries of recurrent language models and encoder-decoder\r architectures [38, 24, 15].\r Recurrent models typically factor computation along the symbol positions of the input and output\r sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden\r states ht, as a function of the previous hidden state ht−1 and the input for position t. This inherently\r sequential nature precludes parallelization within training examples, which becomes critical at longer\r sequence lengths, as memory constraints limit batching across examples. Recent work has achieved\r significant improvements in computational efficiency through factorization tricks [ 21 ] and conditional\r computation [ 32 ], while also improving model performance in case of the latter. The fundamental\r constraint of sequential computation, however, remains."
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-    "text": "Element doping has been widely confirmed as a promising\r approach to enhance the Li+ diffusion kinetics and structural\r stability of SNCM.28−34 However, most current research has\r predominantly focused on the dry-doping method, where\r dopants are added during the mixing stage between lithium\r and SNCM precursors.16,35 For this approach, certain\r substitution elements face challenges in incorporating into\r the cathode lattice due to their large atomic mass and limited\r solid solubility,31 resulting in an inhomogeneous distribution\r within the bulk and causing internal structural degradation in\r SNCM. Moreover, the dopants tend to react with the lithium\r source at the surface to generate a nonuniformly distributed\r impurity layer that obstructs Li+ diffusion channels. Overly\r increasing the sintering time and temperature to promote the\r diffusion of doping ions may lead to severe Li/Ni disorder-\r ing."
+    "text": "In this work we propose the Transformer, a model architecture eschewing recurrence and instead\r relying entirely on an attention mechanism to draw global dependencies between input and output."
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+    "text": "The Transformer follows this overall architecture using stacked self-attention and point-wise, fully\r connected layers for both the encoder and decoder, shown in the left and right halves of Figure 1,\r respectively.\r 3.1 Encoder and Decoder Stacks\r Encoder: The encoder is composed of a stack of N = 6 identical layers. Each layer has two\r sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, position-\r wise fully connected feed-forward network. We employ a residual connection [ 11 ] around each of\r the two sub-layers, followed by layer normalization [1]. That is, the output of each sub-layer is\r LayerNorm(x + Sublayer(x)), where Sublayer(x) is the function implemented by the sub-layer\r itself. To facilitate these residual connections, all sub-layers in the model, as well as the embedding\r layers, produce outputs of dimension dmodel = 512.\r Decoder: The decoder is also composed of a stack of N = 6 identical layers. In addition to the two\r sub-layers in each encoder layer, the decoder inserts a third sub-layer, which performs multi-head\r attention over the output of the encoder stack. Similar to the encoder, we employ residual connections\r around each of the sub-layers, followed by layer normalization. We also modify the self-attention\r sub-layer in the decoder stack to prevent positions from attending to subsequent positions. This\r masking, combined with fact that the output embeddings are offset by one position, ensures that the\r predictions for position i can depend only on the known outputs at positions less than i."
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-    "text": "added during the coprecipitation reaction for precursor\r preparation, can result in a homogeneous modification effect\r from the inside out and effectively address aforementioned\r problems.9,37 Although the in situ doping possesses obvious\r merits for holistically enhancing the Li+ diffusion kinetics of\r SNCM, there is a high threshold for attaining the desired\r effect. The choice of dopant needs to take into account its\r solubility and thermodynamic diffusion properties. Meanwhile,\r the amount of dopant addition requires precise regulation to\r prevent affecting the supersaturation within the reaction\r system and thus producing low-quality precursors.\r In this work, we thoroughly investigated the in situ doping\r technology to achieve the optimal dopant element (Nb, Zr, W,\r etc), doping amount and reaction conditions, enabling precise\r control over the preparation of in situ doped single-crystal\r LiNi0.92Co0.03Mn0.05O2 (SNCM). It is confirmed that in situ\r doping offers distinct advantages in the successful incorpo-\r ration and uniform dispersion of dopants into the precursor\r lattice, resulting in modulated precursor primary particle\r morphology. "
+    "text": "An attention function can be described as mapping a query and a set of key-value pairs to an output,\r where the query, keys, values, and output are all vectors. The output is computed as a weighted sum"
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+    "text": "of the values, where the weight assigned to each value is computed by a compatibility function of the\r query with the corresponding key.\r 3.2.1 Scaled Dot-Product Attention\r We call our particular attention \"Scaled Dot-Product Attention\" (Figure 2). The input consists of\r queries and keys of dimension dk, and values of dimension dv . We compute the dot products of the\r query with all keys, divide each by √dk, and apply a softmax function to obtain the weights on the\r values.\r In practice, we compute the attention function on a set of queries simultaneously, packed together\r into a matrix Q. The keys and values are also packed together into matrices K and V . We compute\r the matrix of outputs as:\r Attention(Q, K, V ) = softmax( QKT\r √dk\r )V (1)\r The two most commonly used attention functions are additive attention [ 2], and dot-product (multi-\r plicative) attention. Dot-product attention is identical to our algorithm, except for the scaling factor\r of 1√dk\r . Additive attention computes the compatibility function using a feed-forward network with\r a single hidden layer. While the two are similar in theoretical complexity, dot-product attention is\r much faster and more space-efficient in practice, since it can be implemented using highly optimized\r matrix multiplication code.\r While for small values of dk the two mechanisms perform similarly, additive attention outperforms\r dot product attention without scaling for larger values of dk [3 ]. We suspect that for large values of\r dk, the dot products grow large in magnitude, pushing the softmax function into regions where it has\r extremely small gradients 4. To counteract this effect, we scale the dot products by 1√dk\r "
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-    "text": "A schematic diagram is provided to better understand the\r mechanism of coprecipitation reaction and the underlying\r reason for the morphological variation (Figure 1g). During the\r preparation of precursors, the main transition metal ions (Ni2+,\r Mn2+, Co2+) in the system rapidly combine with hydroxide and\r precipitate to form massive crystal nuclei.42,43 Subsequently,\r the massive crystal nuclei promptly bind ions from the solution\r to form primary particles. In general, the {010} planes combine\r ions faster than other planes due to its higher surface\r energy,41,44 thus resulting in that the primary particles prefer\r to grow horizontally to obtain high exposure {001} planes.45\r Meanwhile, the {001} planes with lower surface energy can\r merely agglomerate fewer stacked layers in the longitudinal\r direction. However, when Nb-ions are continuously added into\r the reaction system, the {010} planes will interact with doping\r ions predominantly because Nb5+ are subjected to stronger\r electrostatic adsorption force due to their more positive\r charges than Ni2+, Co2+ and Mn2+. The substantial amounts of\r Nb-ions accumulated on {010} planes exhibit an inhibiting\r effect on its horizontal growth, resulting in the morphology of\r high exposure {010} planes. Furthermore, the surface energy of\r {001} planes relatively get raised, thus tending to agglomerate\r more longitudinal stacked layers. It is obvious that all these\r characterizations substantiate the controlled morphology\r modulation and homogeneous Nb doping of NCMOH-Nb,\r demonstrating the success of precursor engineering for in situ\r doping. Notably, considering that the cathodes incline to well\r inherit the morphological structure of precursors, both the\r high exposure {010} planes and more longitudinal stacked\r layers of NCMOH-Nb precursor are favorable to provide more\r Li+ channels and enhanced diffusion kinetics for SNCM-Nb-\r wet cathode.46,47"
+    "text": "4To illustrate why the dot products get large, assume that the components of q and k are independent random\r variables with mean 0 and variance 1. Then their dot product, q · k = Pdk\r i=1 qiki, has mean 0 and variance dk ."
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-    "text": "Effects of In Situ Doping on Structural Stability and\r Li+ Diffusion Kinetics. To comprehensively investigate the\r effects of dry and wet Nb doping on the structural evolution\r and phase transition reversibility during Li+ (de)intercalation,\r in situ XRD measurements were conducted with a potential\r range of 2.5−4.5 V at 0.2 C. "
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+    "text": "output values. These are concatenated and once again projected, resulting in the final values, as\r depicted in Figure 2.\r Multi-head attention allows the model to jointly attend to information from different representation\r subspaces at different positions. With a single attention head, averaging inhibits this.\r MultiHead(Q, K, V ) = Concat(head1, ..., headh)W O\r where headi = Attention(QW Q\r i , KW K\r i , V W V\r i )\r Where the projections are parameter matrices W Q\r i ∈ Rdmodel×dk , W K\r i ∈ Rdmodel×dk , W V\r i ∈ Rdmodel×dv\r and W O ∈ Rhdv ×dmodel .\r In this work we employ h = 8 parallel attention layers, or heads. For each of these we use\r dk = dv = dmodel/h = 64. Due to the reduced dimension of each head, the total computational cost\r is similar to that of single-head attention with full dimensionality."
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+    "text": "The Transformer uses multi-head attention in three different ways:\r • In \"encoder-decoder attention\" layers, the queries come from the previous decoder layer,\r and the memory keys and values come from the output of the encoder. This allows every\r position in the decoder to attend over all positions in the input sequence. This mimics the\r typical encoder-decoder attention mechanisms in sequence-to-sequence models such as\r [38, 2, 9].\r • The encoder contains self-attention layers. In a self-attention layer all of the keys, values\r and queries come from the same place, in this case, the output of the previous layer in the\r encoder. Each position in the encoder can attend to all positions in the previous layer of the\r encoder.\r • Similarly, self-attention layers in the decoder allow each position in the decoder to attend to\r all positions in the decoder up to and including that position. We need to prevent leftward\r information flow in the decoder to preserve the auto-regressive property. We implement this\r inside of scaled dot-product attention by masking out (setting to −∞) all values in the input\r of the softmax which correspond to illegal connections"
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-    "text": " Figure 3a exhibits a\r magnified view of the (003) peak and the corresponding lattice\r parameter variations during the initial cycle. When charging\r from 2.5 to 4.18 V, the (003) diffraction peaks of all cathodes\r are slightly shifted to a smaller 2θ angle. With further charging\r to 4.5 V, the (003) diffraction peaks shift significantly to a\r larger 2θ angle, illustrating that the cathodes encounter a\r sudden decay of c-axis parameters during the H2−H3 phase\r transition.53,54 Because it is difficult to fully recover, H2−H3 is\r generally considered to be an irreversible phase transition. Its\r shift angle is a sign of structural stability for the SNCM\r cathode.55,56 In comparison, it is revealed that SNCM-Nb-wet\r features the minimum H2−H3 phase transition angle of 1.11°,\r outperforming the 1.23° of SNCM-Nb-dry and 1.30° of\r SNCM-pure, which indicates the advantages of in situ doping\r for suppressing the dramatic contraction of the lattice\r parameters"
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-    "text": "In comparison, SNCM-Nb-wet experiences a\r smooth H2−H3 phase transition accompanied by a confined\r width and intensity change during the entire charging/\r discharging process (Figure 3d). Simultaneously, its structural\r transition is fully reversible without residual phases. This\r phenomenon is due to that the internal homogeneous Nb5+\r modification formed by in situ doping can effectively build\r stronger metal−oxygen bonds and stabilize the crystal\r structure,48,58 thus alleviating the Li+ migration hindrance\r and hysteresis caused by the long transport distance inside the\r single-crystal particles. Thus, SNCM-Nb-wet obtained a\r smoother phase transition and improved Li+ diffusion kinetics."
+    "text": "Since our model contains no recurrence and no convolution, in order for the model to make use of the\r order of the sequence, we must inject some information about the relative or absolute position of the\r tokens in the sequence. To this end, we add \"positional encodings\" to the input embeddings at the\r bottoms of the encoder and decoder stacks. The positional encodings have the same dimension dmodel\r as the embeddings, so that the two can be summed. There are many choices of positional encodings,\r learned and fixed [9].\r In this work, we use sine and cosine functions of different frequencies:\r P E(pos,2i) = sin(pos/100002i/dmodel )\r P E(pos,2i+1) = cos(pos/100002i/dmodel )\r where pos is the position and i is the dimension. That is, each dimension of the positional encoding\r corresponds to a sinusoid. The wavelengths form a geometric progression from 2π to 10000 · 2π. We\r chose this function because we hypothesized it would allow the model to easily learn to attend by\r relative positions, since for any fixed offset k, P Epos+k can be represented as a linear function of\r P Epos.\r We also experimented with using learned positional embeddings [9] instead, and found that the two\r versions produced nearly identical results (see Table 3 row (E)). We chose the sinusoidal version\r because it may allow the model to extrapolate to sequence lengths longer than the ones encountered\r during training."
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-    "text": "Comparison of Electrochemical Performances of Dry/\r In Situ Doped Cathodes. After demonstrating that in situ\r doping is beneficial in providing excellent structural stability\r and lithium-ion diffusion kinetics, we performed electro-\r chemical measurements under different operating temperatures\r in order to verify the advantages it brings to lithium storage\r performance. As illustrated in Figure 4a, the initial discharge\r capacities of Nb-doped cathodes at 1 C (SNCM-Nb-dry: 184.1\r mAh g−1, SNCM-Nb-wet: 182.7 mAh g−1) appear slightly\r lower than that of pristine cathode (SNCM-pure: 187.6 mAh\r g−1) due to the doping of inert Nb-ions.62 Whereas, SNCM-\r Nb-wet displays the best cycling stability among these three\r single-crystal cathodes, with an excellent cycling retention of\r 76.1% after 400 cycles. In comparison, SNCM-pure and\r SNCM-Nb-dry display a consistent capacity decay, showing\r cycling retentions of 56.8% and 65.1%, respectively. "
+    "text": "Motivating our use of self-attention we\r consider three desiderata.\r One is the total computational complexity per layer. Another is the amount of computation that can\r be parallelized, as measured by the minimum number of sequential operations required.\r The third is the path length between long-range dependencies in the network. Learning long-range\r dependencies is a key challenge in many sequence transduction tasks. One key factor affecting the\r ability to learn such dependencies is the length of the paths forward and backward signals have to\r traverse in the network. The shorter these paths between any combination of positions in the input\r and output sequences, the easier it is to learn long-range dependencies [12]. Hence we also compare\r the maximum path length between any two input and output positions in networks composed of the\r different layer types.\r As noted in Table 1, a self-attention layer connects all positions with a constant number of sequentially\r executed operations, whereas a recurrent layer requires O(n) sequential operations. In terms of\r computational complexity, self-attention layers are faster than recurrent layers when the sequence"
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-    "text": "Benefiting from the\r improved Li+ diffusion kinetics inside the micron-sized\r single-crystal particles by in situ doping, SNCM-Nb-wet still\r can exhibit enhanced cycling stability at high rate of 5 C with\r 76.5% after 200 cycles, significantly superior to 64.6% of\r SNCM-Nb-dry and 44.2% of SNCM-pure (Figure 4c).\r Furthermore, increasing the operating temperature of Ni-\r rich SNCM is commonly regarded as an effective method to\r improve the discharge specific capacities, but it generally\r causes thermal safety issues during the practical application.69\r Consequently, the prepared samples are further evaluated at\r increased temperatures up to 45 °C to demonstrate the\r advantages of in situ doping. Figure 4d displays the cycling\r performances of pristine and Nb-doped cathodes at 1 C and 45\r °C. SNCM-Nb-wet exhibits an outstanding capacity stability of\r 75.5% after 200 cycles, obviously better than 63.6% of SNCM-\r Nb-dry and 51.2% of SNCM-pure. "
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-    "text": "Simulation of Modified Mechanism in In Situ Doped\r Cathodes. According to the aforementioned discussions, in\r situ doped cathodes show a smoother phase transition process,\r which may lead to a more uniform internal Li+ concentration\r distribution and superior Li+ diffusion kinetics. To visualize\r this modification effect, COMSOL software is utilized to\r simulate the internal Li+ concentration distribution and the\r surface Li+ concentration for in situ- and dry-doped cathodes."
+    "text": "length n is smaller than the representation dimensionality d, which is most often the case with\r sentence representation"
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-    "text": "ubsequently, along\r with the detachment of Li+, SNCM-Nb-dry (Figure 5a) has an\r aggravated Li+ concentration gradient distribution at all voltage\r states. This corresponds to the severe two-phase coexistence\r observed in the in situ XRD pattern, which will generate\r inhomogeneous intragranular stresses, eventually leading to\r structural degradation. In contrast, SNCM-Nb-wet (Figure 5b)\r displays a relatively uniform and moderate Li+ concentration\r variation during the whole charging/discharge process, which\r is beneficial for the single-crystal cathode to maintain the\r structural stability throughout the long-term cycling. "
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-    "text": "To explore the modification mechanism of in\r situ doping on structure stability, the morphology and\r microstructure evolution of SNCM-Nb-dry and SNCM-Nb-\r wet were detected by SEM and HRTEM after 100 cycles at 5\r C. As seen in Figure 6a, the internal cross-sectional structure of\r SNCM-Nb-dry is damaged after cycling and contains a\r multitude of cracks and holes."
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-    "text": "As for SNCM-Nb-\r wet, it can be discovered that after cycles the cross-sectional\r morphology remains dense and integrated (Figure 6c). The\r HRTEM image of SNCM-Nb-wet (Figure 6d) also confirms\r that the cathode maintains the intact R3̅ m layered structure at\r the particle surface"
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-    "text": "In Situ Doping Promotion for Other Elements. More\r importantly, we introduce dopants Zr4+ and W6+ in order to\r further demonstrate the advantages of in situ doping on the\r cathode properties. The dry and in situ doped cathodes are\r obtained by the same coprecipitation and two-stage calcination\r process. According to Figure 7a and Figure S24, the Zr-doped\r materials exhibit similar single-crystal particle morphology."
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-    "text": "As\r seen in Figure 7g, SNCM-Zr-wet presents the best cycling\r retention with 90.8% after 100 cycles, obviously better than"
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-    "text": "80.5% of SNCM-Zr-dry and 69.9% of SNCM-pure. "
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-    "text": "ased\r on the successful attempt of various doping ions, it is evident\r that in situ doping achieves a uniform and consistent\r modification effect within single-crystal particles and demon-\r strates a certain applicability for different doping elements\r (Nb, Zr, W, etc)."
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-    "text": "In\r comparison, the in situ doped cathodes benefited from the\r uniform distribution of doping ions and can substantially\r accelerate the Li+ diffusion kinetics and migration rate from the\r inside out. Furthermore, the fluent Li+ transportation results in\r a uniform phase transition process and optimized Li+\r concentration distribution, which can ensure the reversibility\r of the H2/H3 transition and alleviate the inner stress without\r the formation of intragranular cracks. Hence, the in situ doped\r cathode maintains an integrated structure and excellent\r electrochemical performances after long-term cycling."
-  }
-}]};
+}]
+    };
 
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