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are controlled by the interfaces between minerals and their environment. The atomic-scale structure and chemical properties of mineral–solution interfaces are studied using in situ synchrotron X-ray techniques such as X-ray reflectivity, X-ray standing waves, and X-ray absorption spectroscopy as well as scanning probe microscopy. For example, studies of heavy metal or actinide adsorption onto mineral surfaces reveal molecular-scale details of adsorption, enabling more accurate predictions of how these contaminants travel through soils or disrupt natural dissolution–precipitation cycles. == Physics == Surface physics can be roughly defined as the study of physical interactions that occur at interfaces. It overlaps with surface chemistry. Some of the topics investigated in surface physics include friction, surface states, surface diffusion, surface reconstruction, surface phonons and plasmons, epitaxy, the emission and tunneling of electrons, spintronics, and the self-assembly of nanostructures on surfaces. Techniques to investigate processes at surfaces include surface X-ray scattering, scanning probe microscopy, surface-enhanced Raman spectroscopy and X-ray photoelectron spectroscopy. == Analysis techniques == The study and analysis of surfaces involves both physical and chemical analysis techniques. Several modern methods probe the topmost 1–10 nm of surfaces exposed to vacuum. These include angle-resolved photoemission spectroscopy (ARPES), X-ray photoelectron spectroscopy (XPS), Auger electron spectroscopy (AES), low-energy electron diffraction (LEED), electron energy loss spectroscopy (EELS), thermal desorption spectroscopy (TPD), ion scattering spectroscopy (ISS), secondary ion mass spectrometry, dual-polarization interferometry, and other surface analysis methods included in the list of materials analysis methods. Many of these techniques require vacuum as they rely on the detection of electrons or ions emitted from the surface under study. Moreover, in general ultra-high vacuum, in the range of 10−7 pascal pressure or better, it is necessary to reduce surface contamination by residual gas, by reducing the number of molecules reaching the sample over a given time period. At 0.1 mPa (10−6
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{
"page_id": 68513,
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"title": "Surface science"
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torr) partial pressure of a contaminant and standard temperature, it only takes on the order of 1 second to cover a surface with a one-to-one monolayer of contaminant to surface atoms, so much lower pressures are needed for measurements. This is found by an order of magnitude estimate for the (number) specific surface area of materials and the impingement rate formula from the kinetic theory of gases. Purely optical techniques can be used to study interfaces under a wide variety of conditions. Reflection-absorption infrared, dual polarisation interferometry, surface-enhanced Raman spectroscopy and sum frequency generation spectroscopy can be used to probe solid–vacuum as well as solid–gas, solid–liquid, and liquid–gas surfaces. Multi-parametric surface plasmon resonance works in solid–gas, solid–liquid, liquid–gas surfaces and can detect even sub-nanometer layers. It probes the interaction kinetics as well as dynamic structural changes such as liposome collapse or swelling of layers in different pH. Dual-polarization interferometry is used to quantify the order and disruption in birefringent thin films. This has been used, for example, to study the formation of lipid bilayers and their interaction with membrane proteins. Acoustic techniques, such as quartz crystal microbalance with dissipation monitoring, is used for time-resolved measurements of solid–vacuum, solid–gas and solid–liquid interfaces. The method allows for analysis of molecule–surface interactions as well as structural changes and viscoelastic properties of the adlayer. X-ray scattering and spectroscopy techniques are also used to characterize surfaces and interfaces. While some of these measurements can be performed using laboratory X-ray sources, many require the high intensity and energy tunability of synchrotron radiation. X-ray crystal truncation rods (CTR) and X-ray standing wave (XSW) measurements probe changes in surface and adsorbate structures with sub-Ångström resolution. Surface-extended X-ray absorption fine structure (SEXAFS) measurements reveal the coordination structure and chemical state of adsorbates. Grazing-incidence small angle X-ray scattering (GISAXS) yields
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{
"page_id": 68513,
"source": null,
"title": "Surface science"
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the size, shape, and orientation of nanoparticles on surfaces. The crystal structure and texture of thin films can be investigated using grazing-incidence X-ray diffraction (GIXD, GIXRD). X-ray photoelectron spectroscopy (XPS) is a standard tool for measuring the chemical states of surface species and for detecting the presence of surface contamination. Surface sensitivity is achieved by detecting photoelectrons with kinetic energies of about 10–1000 eV, which have corresponding inelastic mean free paths of only a few nanometers. This technique has been extended to operate at near-ambient pressures (ambient pressure XPS, AP-XPS) to probe more realistic gas–solid and liquid–solid interfaces. Performing XPS with hard X-rays at synchrotron light sources yields photoelectrons with kinetic energies of several keV (hard X-ray photoelectron spectroscopy, HAXPES), enabling access to chemical information from buried interfaces. Modern physical analysis methods include scanning-tunneling microscopy (STM) and a family of methods descended from it, including atomic force microscopy (AFM). These microscopies have considerably increased the ability of surface scientists to measure the physical structure of many surfaces. For example, they make it possible to follow reactions at the solid–gas interface in real space, if those proceed on a time scale accessible by the instrument. == See also == == References == == Further reading == Kolasinski, Kurt W. (2012-04-30). Surface Science: Foundations of Catalysis and Nanoscience (3 ed.). Wiley. ISBN 978-1119990352. Attard, Gary; Barnes, Colin (January 1998). Surfaces. Oxford Chemistry Primers. ISBN 978-0198556862. == External links == "Ram Rao Materials and Surface Science", a video from the Vega Science Trust Surface Chemistry Discoveries Surface Metrology Guide
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{
"page_id": 68513,
"source": null,
"title": "Surface science"
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Heterogeneous gold catalysis refers to the use of elemental gold as a heterogeneous catalyst. As in most heterogeneous catalysis, the metal is typically supported on metal oxide. Furthermore, as seen in other heterogeneous catalysts, activity increases with a decreasing diameter of supported gold clusters. Several industrially relevant processes are also observed such as H2 activation, Water-gas shift reaction, and hydrogenation. One or two gold-catalyzed reactions may have been commercialized. The high activity of supported gold clusters has been proposed to arise from a combination of structural changes, quantum-size effects and support effects that preferentially tune the electronic structure of gold such that optimal binding of adsorbates during the catalytic cycle is enabled. The selectivity and activity of gold nanoparticles can be finely tuned by varying the choice of support material, with e.g. titania (TiO2), hematite (α-Fe2O3), cobalt(II/III) oxide (Co3O4) and nickel(II) oxide (NiO) serving as the most effective support materials for facilitating the catalysis of CO combustion. Besides enabling an optimal dispersion of the nanoclusters, the support materials have been suggested to promote catalysis by altering the size, shape, strain and charge state of the cluster. A precise shape control of the deposited gold clusters has been shown to be important for optimizing the catalytic activity, with hemispherical, few atomic layers thick nanoparticles generally exhibiting the most desirable catalytic properties due to maximized number of high-energy edge and corner sites. == Proposed applications == In the past, heterogeneous gold catalysts have found preliminary commercial applications for the industrial production of vinyl chloride (precursor to polyvinyl chloride or PVC) and methyl methacrylate. Traditionally, PVC production uses mercury catalysts and leads to serious environmental concerns. China accounts for 50% of world's mercury emissions and 60% of China's mercury emission is caused by PVC production. Although gold catalysts are slightly expensive, overall production
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{
"page_id": 57084837,
"source": null,
"title": "Heterogeneous gold catalysis"
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cost is affected by only ~1%. Therefore, green gold catalysis is considered valuable. The price fluctuation in gold has later led to cease the operations based on their use in catalytic converters. Very recently, there has been a lot of developments in gold catalysis for the synthesis of organic molecules including the C-C bond forming homocoupling or cross-coupling reactions and it has been speculated that some of these catalysts could find applications in various fields. === CO oxidation === Gold can be a very active catalyst in oxidation of carbon monoxide (CO), i.e. the reaction of CO with molecular oxygen to produce carbon dioxide (CO2). Particles of 2 to 5 nm exhibit high catalytic activities. Supported gold clusters, thin films and nanoparticles are one to two orders of magnitude more active than atomically dispersed gold cations or unsupported metallic gold. Gold cations can be dispersed atomically on basic metal oxide supports such as MgO and La2O3. Monovalent and trivalent gold cations have been identified, the latter being more active but less stable than the former. The turnover frequency (TOF) of CO oxidation on these cationic gold catalysts is in the order of magnitude of 0.01 s−1, exhibiting the very high activation energy of 138 kJ/mol. Supported gold nanoclusters with a diameter < 2 nm are active to CO oxidation with turnover number (TOF) in the order of magnitude of 0.1 s−1. It has been observed that clusters with 8 to 100 atoms are catalytically active. The reason is that, on one hand, eight atoms are the minimum necessary to form a stable, discrete energy band structure, and on the other hand, d-band splitting decreases in clusters with more than 100 atoms, resembling the bulk electronic structure. The support has a substantial effect on the electronic structure of gold clusters. Metal
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{
"page_id": 57084837,
"source": null,
"title": "Heterogeneous gold catalysis"
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hydroxide supports such as Be(OH)2, Mg(OH)2, and La(OH)3, with gold clusters of < 1.5 nm in diameter constitute highly active catalysts for CO oxidation at 200 K (-73 °C). By means of techniques such as HR-TEM and EXAFS, it has been proven that the activity of these catalysts is due exclusively to clusters with 13 atoms arranged in an icosahedron structure. Furthermore, the metal loading should exceed 10 wt% for the catalysts to be active. Gold nanoparticles in the size range of 2 to 5 nm catalyze CO oxidation with a TOF of about 1 s−1 at temperatures below 273 K (0 °C). The catalytic activity of nanoparticles is brought about in the absence of moisture when the support is semiconductive or reducible, e.g. TiO2, MnO2, Fe2O3, ZnO, ZrO2, or CeO2. However, when the support is insulating or non-reducible, e.g. Al2O3 and SiO2, a moisture level > 5000 ppm is required for activity at room temperature. In the case of powder catalysts prepared by wet methods, the surface OH− groups on the support provide sufficient aid as co-catalysts, so that no additional moisture is necessary. At temperatures above 333 K (60 °C), no water is needed at all. The apparent activation energy of CO oxidation on supported gold powder catalysts prepared by wet methods is 2-3 kJ/mol above 333 K (60 °C) and 26-34 kJ/mol below 333 K. These energies are low, compared to the values displayed by other noble metal catalysts (80-120 kJ/mol). The change in activation energy at 333 K can be ascribed to a change in reaction mechanism. This explanation has been supported experimentally. At 400 K (127 °C), the reaction rate per surface Au atom is not dependent on particle diameter, but the reaction rate per perimeter Au atom is directly proportional to particle diameter. This
|
{
"page_id": 57084837,
"source": null,
"title": "Heterogeneous gold catalysis"
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suggests that the mechanism above 333 K takes place on the gold surfaces. By contrast, at 300 K (27 °C), the reaction rate per surface Au atom is inversely proportional to particle diameter, while the rate per perimeter interface does not depend on particle size. Hence, CO oxidation occurs on the perimeter sites at room temperature. Further information on the reaction mechanism has been revealed by studying the dependency of the reaction rate on the partial pressures of the reactive species. Both at 300 K and 400 K, there is a first order rate dependency on CO partial pressure up to 4 Torr (533 Pa), above which the reaction is zero order. With respect to O2, the reaction is zero order above 10 Torr (54.7 kPa) at both 300 and 400 K. The order with respect to O2 at lower partial pressures is 1 at 300 K and 0.5 at 400 K. The shift towards zero order indicates that the catalyst's active sites are saturated with the species in question. Hence, a Langmuir-Hinshelwood mechanism has been proposed, in which CO adsorbed on gold surfaces reacts with O adsorbed at the edge sites of the gold nanoparticles. The need to use oxide supports, and more specifically reducible supports, is due to their ability to activate dioxygen. Gold nanoparticles supported on inert materials such as carbon or polymers have been proven inactive in CO oxidation. The aforementioned dependency of some catalysts on water or moisture also relates to oxygen activation. The ability of certain reducible oxides, such as MnO2, Co3O4, and NiO to activate oxygen in dry conditions (< 0.1 ppm H2O) can be ascribed to the formation of oxygen defects during pretreatment. === Water gas shift === Water gas shift is the most widespread industrial process for the production of dihydrogen,
|
{
"page_id": 57084837,
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"title": "Heterogeneous gold catalysis"
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H2. It involves the reaction of carbon monoxide and water (syngas) to form hydrogen and carbon dioxide as a byproduct. In many catalytic reaction schemes, one of the elementary reactions is the oxidation of CO with an adsorbed oxygen species. Gold catalysts have been proposed as an alternative for water gas shift at low temperatures, viz. < 523 K (250 °C). This technology is essential to the development of solid oxide fuel cells. Hematite has been found to be an appropriate catalyst support for this purpose. Furthermore, a bimetallic Au-Ru/Fe2O3 catalyst has been proven highly active and stable for low-temperature water gas shift. Titania and ceria have also been used as supports for effective catalysts. Unfortunately, Au/CeO2 is prone to deactivation caused by surface-bound carbonate or formate species. Although gold catalysts are active at room temperature to CO oxidation, the high amounts of water involved in water gas shift require higher temperatures. At such temperatures, gold is fully reduced to its metallic form. However, the activity of e.g. Au/CeO2 has been enhanced by CN− treatment, whereby metallic gold is leached, leaving behind highly active cations. According to DFT calculations, the presence of such Au cations on the catalyst is allowed by empty, localized nonbonding f states in CeO2. On the other hand, STEM studies of Au/CeO2 have revealed nanoparticles of 3 nm in diameter. Water gas shift has been proposed to occur at the interface of Au nanoparticles and the reduced CeO2 support. === Epoxidations === Although the epoxidation of ethylene is routinely achieved in the industry with selectivities as high as 90% on Ag catalysts, most catalysts provided < 10% selectivity for propylene epoxidation. Using a gold catalyst supported on titanium silicate-1 (TS-1) molecular sieve, yields of 350 g/h per gram of gold were obtained at 473 K (200
|
{
"page_id": 57084837,
"source": null,
"title": "Heterogeneous gold catalysis"
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°C). The reaction took place in the gas phase. Furthermore, using mesoporous titanosilicate supports (Ti-MCM-41 and Ti-MCM-48), gold catalysts provided > 90% selectivity at ~ 7% propylene conversion, 40% H2 efficiency, and 433 K (160 °C). The active species in these catalysts were identified to be hemispherical gold nano-crystals of less than 2 nm in diameter in intimate contact with the support. Alkene epoxidation has been demonstrated in absence of H2 reductant in the liquid phase. For example, using 1% Au/graphite, ~80% selectivities of cis-cyclooctene to cyclooctene oxide (analogous to cyclohexene oxide) were obtained at 7-8% conversion, 353 K (80 °C), and 3 MPa O2 in absence of hydrogen or solvent. Other liquid-phase selective oxidations have been achieved with saturated hydrocarbons. For instance, cyclohexane has been converted to cyclohexanone and cyclohexanol with a combined selectivity of ~100% on gold catalysts. Product selectivities can be tuned in liquid phase reactions by the presence or absence of solvent and by the nature of the latter, viz. water, polar, or nonpolar. With gold catalysts, the catalyst's support has less influence on reactions in the liquid phase than on reactions in the gas phase. === Selective hydrogenations === Typical hydrogenation catalysts are based on metals from the 8, 9, and 10 groups, such as Ni, Ru, Pd, and Pt. By comparison, gold has a poor catalytic activity for hydrogenation. This low activity is caused by the difficulty of dihydrogen activation on gold. While hydrogen dissociates on Pd and Pt without an energy barrier, dissociation on Au(111) has an energy barrier of ~1.3 eV, according to DFT calculations. These calculations agree with experimental studies, in which hydrogen dissociation was not observed on gold (111) or (110) terraces, nor on (331) steps. No dissociation was observed on these surfaces either at room temperature or at 473
|
{
"page_id": 57084837,
"source": null,
"title": "Heterogeneous gold catalysis"
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K (200 °C). However, the rate of hydrogen activation increases for Au nanoparticles. Notwithstanding its poor activity, nano-sized gold immobilized in various supports has been found to provide a good selectivity in hydrogenation reactions. One of the early studies (1966) of hydrogenation on supported, highly dispersed gold was performed with 1-butene and cyclohexene in the gas phase at 383 K (110 °C). The reaction rate was found to be first order with respect to alkene pressure and second order with respect to chemisorbed hydrogen. In later works, it was shown that gold-catalyzed hydrogenation can be highly sensitive to Au loading (hence to particle size) and to the nature of the support. For example, 1-pentene hydrogenation occurred optimally on 0.04 wt% Au/SiO2, but not at all on Au/γ-Al2O3. By contrast, the hydrogenation of 1,3-butadiene to 1-butene was shown to be relatively insensitive to Au particle size in a study with a series of Au/Al2O3 catalysts prepared by different methods. With all the tested catalysts, conversion was ~100% and selectivity, < 60%. Concerning reaction mechanisms, in a study of propylene hydrogenation on Au/SiO2, reaction rates were determined using D2 and H2. Because the reaction with deuterium was substantially slower, it was suggested that the rate-determining step in alkene hydrogenation was the cleavage of the H-H bond. Lastly, ethylene hydrogenation was studied on Au/MgO at atmospheric pressure and 353 K (80 °C) with EXAFS, XANES and IR spectroscopy, suggesting that the active species might be Au+3 and the reaction intermediate, an ethylgold species. Gold catalysts are especially selective in the hydrogenation of α,β-insaturated aldehydes, i.e. aldehydes containing a C=C double bond on the carbon adjacent to the carbonyl. Gold catalysts are able to hydrogenate only the carbonyl group, so that the aldehyde is transformed to the corresponding alcohol, while leaving the C=C double
|
{
"page_id": 57084837,
"source": null,
"title": "Heterogeneous gold catalysis"
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bond untouched. In the hydrogenation of crotonaldehyde to crotyl alcohol, 80% selectivity was attained at 5-10% conversion and 523 K (250 °C) on Au/ZrO2 and Au/ZnO. The selectivity increased along with Au particle size in the range of ~2 to ~5 nm. Other instances of this reaction include acrolein, citral, benzal acetone, and pent-3-en-2-one. The activity and selectivity of gold catalysts for this reaction has been linked to the morphology of the nanoparticles, which in turn is influenced by the support. For example, round particles tend to form on TiO2, while ZnO promotes particles with clear facets, as observed by TEM. Because the round morphology provides a higher relative amount of low-coordinated metal surface sites, the higher activity observed with Au/TiO2 compared to Au/ZnO is explained. Finally, a bimetallic Au-In/ZnO catalyst has been observed to improve the selectivity towards the hydrogenation of the carbonyl in acrolein. It was observed in HRTEM images that indium thin films decorate some of the facets of the gold nanoparticle. The promoting effect on selectivity might result from the fact that only the Au sites that promote side-reactions are decorated by In. A strategy that in many reactions has succeeded at improving gold's catalytic activity without impairing its selectivity is to synthesize bimetallic Pd-Au or Pt-Au catalysts. For the hydrogenation of 1,3-butadiene to butenes, model surfaces of Au(111), Pd-Au(111), Pd-Au(110), and Pd(111) were studied with LEED, AES, and LEIS. A selectivity of ~100% was achieved on Pd70Au30(111) and it was suggested that Au might promote the desorption of the product during the reaction. A second instance is the hydrogenation of p-chloronitrobenzene to p-chloroaniline, in which selectivity suffers with typical hydrogenation catalysts due to the parallel hydrodechlorination to aniline. However, Pd-Au/Al2O3 (Au/Pd ≥20) has been proven thrice as active as the pure Au catalyst, while being
|
{
"page_id": 57084837,
"source": null,
"title": "Heterogeneous gold catalysis"
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~100% selective to p-chloroaniline. In a mechanistic study of hydrogenation of nitrobenzenes with Pt-Au/TiO2, the dissociation of H2 was identified as rate-controlling, hence the incorporation of Pt, an efficient hydrogenation metal, highly improved catalytic activity. Dihydrogen dissociated on Pt and the nitroaromatic compound was activated on the Au-TiO2 interface. Finally, hydrogenation was enabled by the spillover of activated H surface species from Pt to the Au surface. == Theoretical background == Bulk metallic gold is known to be inert, exhibiting a surface reactivity at room temperature only towards a few substances such as formic acid and sulphur-containing compounds, e.g. H2S and thiols. Within heterogeneous catalysis, reactants adsorb onto the surface of the catalyst thus forming activated intermediates. However, if the adsorption is weak such as in the case of bulk gold, a sufficient perturbation of the reactant electronic structure does not occur and catalysis is hindered (Sabatier's principle). When gold is deposited as nanosized clusters of less than 5 nm onto metal oxide supports, a markedly increased interaction with adsorbates is observed, thereby resulting in surprising catalytic activities. Evidently, nano-scaling and dispersing gold on metal oxide substrates makes gold less noble by tuning its electronic structure, but the precise mechanisms underlying this phenomenon are as of yet uncertain and hence widely studied. It is generally known that decreasing the size of metallic particles in some dimension to the nanometer scale will yield clusters with a significantly more discrete electronic band structure in comparison with the bulk material. This is an example of a quantum-size effect and has been previously correlated with an increased reactivity enabling nanoparticles to bind gas phase molecules more strongly. In the case of TiO2-supported gold nanoparticles, Valden et al. observed the opening of a band gap of approximately 0.2-0.6 eV in the gold electronic structure as
|
{
"page_id": 57084837,
"source": null,
"title": "Heterogeneous gold catalysis"
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the thickness of the deposited particles was decreased below three atomic layers. The two-layer thick supported gold clusters were also shown to be exceptionally active for CO combustion, based on which it was concluded that quantum-size effects inducing a metal-insulator transition play a key role in enhancing the catalytic properties of gold. However, decreasing the size further to a single atomic layer and a diameter of less than 3 nm was reported to again decrease the activity. This has later been explained by a destabilization of clusters composed of very few atoms, resulting in too strong bonding of adsorbates and thus poisoning of the catalyst. The properties of the metal d-band are central for describing the origin of catalytic activity based on electronic effects. According to the d-band model of heterogeneous catalysis, substrate-adsorbate bonds are formed as the discrete energy levels of the adsorbate molecule interacts with the metal d-band, thus forming bonding and antibonding orbitals. The strength of the formed bond depends on the position of the d-band center such that a d-band closer to the Fermi level ( E F {\displaystyle E_{\mathrm {F} }} ) will result in stronger interaction. The d-band center of bulk gold is located far below E F {\displaystyle E_{\mathrm {F} }} , which qualitatively explains the observed weak binding of adsorbates as both the bonding and antibonding orbitals formed upon adsorption will be occupied, resulting in no net bonding. However, as the size of gold clusters is decreased below 5 nm, it has been shown that the d-band center of gold shifts to energies closer to the Fermi level, such that the as formed antibonding orbital will be pushed to an energy above E F {\displaystyle E_{\mathrm {F} }} , hence reducing its filling. In addition to a shift in the d-band center
|
{
"page_id": 57084837,
"source": null,
"title": "Heterogeneous gold catalysis"
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of gold clusters, the size-dependency of the d-band width as well as the 5 d 3 / 2 - d 5 / 2 {\displaystyle 5d_{3/2}{\text{-}}d_{5/2}} spin-orbit splitting has been studied from the viewpoint of catalytic activity. As the size of the gold clusters is decreased below 150 atoms (diameter ca. 2.5 nm), rapid drops in both values occur. This can be attributed to d-band narrowing due to the decreased number of hybridizing valence states of small clusters as well as to the increased ratio of high-energy edge atoms with low coordination to the total number of Au atoms. The effect of the decreased 5 d 3 / 2 - d 5 / 2 {\displaystyle 5d_{3/2}{\text{-}}d_{5/2}} spin-orbit splitting as well as the narrower distribution of d-band states on the catalytic properties of gold clusters cannot be understood via simple qualitative arguments as in the case of the d-band center model. Nevertheless, the observed trends provide further evidence that a significant perturbation of the Au electronic structure occurs upon nanoscaling, which is likely to play a key role in the enhancement of the catalytic properties of gold nanoparticles. A central structural argument explaining the high activity of metal oxide supported gold clusters is based on the concept of periphery sites formed at the junction between the gold cluster and the substrate. In the case of CO oxidation, it has been hypothesized that CO adsorbs onto the edges and corners of the gold clusters, while the activation of oxygen occurs at the peripheral sites. The high activity of edge and corner sites towards adsorption can be understood by considering the high coordinative unsaturation of these atoms in comparison with terrace atoms. The low degree of coordination increases the surface energy of corner and edge sites, hence making them more active towards binding adsorbates.
|
{
"page_id": 57084837,
"source": null,
"title": "Heterogeneous gold catalysis"
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This is further coupled with the local shift of the d-band center of the unsaturated Au atoms towards energies closer to the Fermi level, which in accordance with the d-band model results in increased substrate-adsorbate interaction and lowering of the adsorption-dissociation energy barriers. Lopez et al. calculated the adsorption energy of CO and O2 on the Au(111) terrace on which the Au-atoms have a coordination number of 9 as well as on an Au10 cluster where the most reactive sites have a coordination of 4. They observed that the bond strengths are in general increased by as much as 1 eV, indicating a significant activation towards CO oxidation if one assumes that the activation barriers of surface reactions scale linearly with the adsorption energies (Brønsted-Evans-Polanyi principle). The observation that hemispherical two-layer gold clusters with a diameter of a few nanometers are most active for CO oxidation is well in line with the assumption that edge and corner atoms serve as the active sites, since for clusters of this shape and size the ratio of edge atoms to the total number of atoms is indeed maximized. The preferential activation of O2 at the perimeter sites is an example of a support effect that promotes the catalytic activity of gold nanoparticles. Besides enabling a proper dispersion of the deposited particles and hence a high surface-to-volume ratio, the metal oxide support also directly perturbs the electronic structure of the deposited gold clusters via various mechanisms, including strain-inducing and charge transfer. For gold deposited on magnesia (MgO), a charge transfer from singly charged oxygen vacancies (F-centers) at the MgO surface to the Au cluster has been observed. This charge transfer induces a local perturbation in the electronic structure of the gold clusters at the perimeter sites, enabling the formation of resonance states as the
|
{
"page_id": 57084837,
"source": null,
"title": "Heterogeneous gold catalysis"
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antibonding 2 π ∗ {\displaystyle 2\pi ^{*}} orbital of oxygen interacts with the metal d-band. As the antibonding orbital is occupied, the O-O bond is significantly weakened and stretched, i.e. activated. In gas-phase model studies, the formation of activated super-oxo species O2− is found to correlate with the size-dependent electronic properties of the clusters. The activation of O2 at the perimeter sites is also observed for defect-free surfaces and neutral gold clusters, but to a significantly smaller extent. The activity enhancing effect of charge transfer from the substrate to gold has also been reported by Chen and Goodman in the case of a gold bilayer supported on ultrathin TiO2 on Mo(112). In addition to charge transfer between the substrate and the gold nanoparticles, the support material has been observed to increase the catalytic activity of gold by inducing strain as a consequence of lattice mismatch. The induced strains especially affect the Au atoms close to the substrate-cluster interface, resulting in a shift of the local d-band center towards energies closer to the Fermi level. This corroborates the periphery hypothesis and the creation of catalytically active bifunctional sites at the cluster-support interface. Furthermore, the support-cluster interaction directly influences the size and shape of the deposited gold nanoparticles. In the case of weak interaction, less active 3D clusters are formed, whereas if the interaction is stronger more active 2D few-layer structures are formed. This illustrates the ability to fine-tune the catalytic activity of gold clusters via varying the support material as well as the underlying metal upon which the substrate has been grown. Finally, it has been observed that the catalytic activity of supported gold clusters towards CO oxidation is further enhanced by the presence of water. Invoking the periphery hypothesis, water promotes the activation of O2 by co-adsorption onto the perimeter
|
{
"page_id": 57084837,
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sites where it reacts with O2 to form adsorbed hydroxyl (OH*) and hydroperoxo (OOH*) species. The reaction of these intermediates with adsorbed CO is very rapid, and results in the efficient formation of CO2 with concomitant recovery of the water molecule. == See also == == References ==
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{
"page_id": 57084837,
"source": null,
"title": "Heterogeneous gold catalysis"
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Chemisorption is a kind of adsorption which involves a chemical reaction between the surface and the adsorbate. New chemical bonds are generated at the adsorbent surface. Examples include macroscopic phenomena that can be very obvious, like corrosion, and subtler effects associated with heterogeneous catalysis, where the catalyst and reactants are in different phases. The strong interaction between the adsorbate and the substrate surface creates new types of electronic bonds. In contrast with chemisorption is physisorption, which leaves the chemical species of the adsorbate and surface intact. It is conventionally accepted that the energetic threshold separating the binding energy of "physisorption" from that of "chemisorption" is about 0.5 eV per adsorbed species. Due to specificity, the nature of chemisorption can greatly differ, depending on the chemical identity and the surface structural properties. The bond between the adsorbate and adsorbent in chemisorption is either ionic or covalent. == Uses == An important example of chemisorption is in heterogeneous catalysis which involves molecules reacting with each other via the formation of chemisorbed intermediates. After the chemisorbed species combine (by forming bonds with each other) the product desorbs from the surface. == Self-assembled monolayers == Self-assembled monolayers (SAMs) are formed by chemisorbing reactive reagents with metal surfaces. A famous example involves thiols (RS-H) adsorbing onto the surface of gold. This process forms strong Au-SR bonds and releases H2. The densely packed SR groups protect the surface. == Gas-surface chemisorption == === Adsorption kinetics === As an instance of adsorption, chemisorption follows the adsorption process. The first stage is for the adsorbate particle to come into contact with the surface. The particle needs to be trapped onto the surface by not possessing enough energy to leave the gas-surface potential well. If it elastically collides with the surface, then it would return to the bulk gas.
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{
"page_id": 68518,
"source": null,
"title": "Chemisorption"
}
|
If it loses enough momentum through an inelastic collision, then it "sticks" onto the surface, forming a precursor state bonded to the surface by weak forces, similar to physisorption. The particle diffuses on the surface until it finds a deep chemisorption potential well. Then it reacts with the surface or simply desorbs after enough energy and time. The reaction with the surface is dependent on the chemical species involved. Applying the Gibbs energy equation for reactions: Δ G = Δ H − T Δ S {\displaystyle \Delta G=\Delta H-T\Delta S} General thermodynamics states that for spontaneous reactions at constant temperature and pressure, the change in free energy should be negative. Since a free particle is restrained to a surface, and unless the surface atom is highly mobile, entropy is lowered. This means that the enthalpy term must be negative, implying an exothermic reaction. Physisorption is given as a Lennard-Jones potential and chemisorption is given as a Morse potential. There exists a point of crossover between the physisorption and chemisorption, meaning a point of transfer. It can occur above or below the zero-energy line (with a difference in the Morse potential, a), representing an activation energy requirement or lack of. Most simple gases on clean metal surfaces lack the activation energy requirement. === Modeling === For experimental setups of chemisorption, the amount of adsorption of a particular system is quantified by a sticking probability value. However, chemisorption is very difficult to theorize. A multidimensional potential energy surface (PES) derived from effective medium theory is used to describe the effect of the surface on absorption, but only certain parts of it are used depending on what is to be studied. A simple example of a PES, which takes the total of the energy as a function of location: E ( { R
|
{
"page_id": 68518,
"source": null,
"title": "Chemisorption"
}
|
i } ) = E e l ( { R i } ) + V ion-ion ( { R i } ) {\displaystyle E(\{R_{i}\})=E_{el}(\{R_{i}\})+V_{\text{ion-ion}}(\{R_{i}\})} where E e l {\displaystyle E_{el}} is the energy eigenvalue of the Schrödinger equation for the electronic degrees of freedom and V i o n − i o n {\displaystyle V_{ion-ion}} is the ion interactions. This expression is without translational energy, rotational energy, vibrational excitations, and other such considerations. There exist several models to describe surface reactions: the Langmuir–Hinshelwood mechanism in which both reacting species are adsorbed, and the Eley–Rideal mechanism in which one is adsorbed and the other reacts with it. Real systems have many irregularities, making theoretical calculations more difficult: Solid surfaces are not necessarily at equilibrium. They may be perturbed and irregular, defects and such. Distribution of adsorption energies and odd adsorption sites. Bonds formed between the adsorbates. Compared to physisorption where adsorbates are simply sitting on the surface, the adsorbates can change the surface, along with its structure. The structure can go through relaxation, where the first few layers change interplanar distances without changing the surface structure, or reconstruction where the surface structure is changed. A direct transition from physisorption to chemisorption has been observed by attaching a CO molecule to the tip of an atomic force microscope and measuring its interaction with a single iron atom. For example, oxygen can form very strong bonds (~4 eV) with metals, such as Cu(110). This comes with the breaking apart of surface bonds in forming surface-adsorbate bonds. A large restructuring occurs by missing row. === Dissociative chemisorption === A particular brand of gas-surface chemisorption is the dissociation of diatomic gas molecules, such as hydrogen, oxygen, and nitrogen. One model used to describe the process is precursor-mediation. The absorbed molecule is adsorbed onto a surface
|
{
"page_id": 68518,
"source": null,
"title": "Chemisorption"
}
|
into a precursor state. The molecule then diffuses across the surface to the chemisorption sites. They break the molecular bond in favor of new bonds to the surface. The energy to overcome the activation potential of dissociation usually comes from translational energy and vibrational energy. An example is the hydrogen and copper system, one that has been studied many times over. It has a large activation energy of 0.35 – 0.85 eV. The vibrational excitation of the hydrogen molecule promotes dissociation on low index surfaces of copper. == See also == Adsorption Physisorption == References == == Bibliography == Tompkins, F.C. (1978). Chemisorption of gases on metals. Academic Press. ISBN 0126946507. Schlapbach, L.; Züttel, A. (15 November 2001). "Hydrogen-storage materials for mobile applications" (PDF). Nature. 414 (6861): 353–8. Bibcode:2001Natur.414..353S. doi:10.1038/35104634. PMID 11713542. S2CID 3025203.
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{
"page_id": 68518,
"source": null,
"title": "Chemisorption"
}
|
cis-3-Hexenal, also known as (Z)-3-hexenal and leaf aldehyde, is an organic compound with the formula CH3CH2CH=CHCH2CHO. It is classified as an unsaturated aldehyde. It is a colorless liquid and an aroma compound with an intense odor of freshly cut grass and leaves. == Occurrence == It is one of the major volatile compounds in ripe tomatoes, although it tends to isomerize into the conjugated trans-2-hexenal. It is produced in small amounts by most plants and it acts as an attractant to many predatory insects. It is also a pheromone in many insect species. == See also == cis-3-Hexen-1-ol has a similar but weaker odor and is used in flavors and perfumes. 1-Hexanol, another volatile organic compound, also considered responsible for the freshly mowed grass odor == External links == Hexenal == References ==
|
{
"page_id": 1641386,
"source": null,
"title": "Cis-3-Hexenal"
}
|
The molecular formula C10H6O2 (molar mass: 158.15 g/mol, exact mass: 158.0368 u) may refer to: Naphthoquinone 1,2-Naphthoquinone 1,4-Naphthoquinone
|
{
"page_id": 23661478,
"source": null,
"title": "C10H6O2"
}
|
Jüri Kärner (25 April 1940 – 25 September 2010) was an Estonian biologist. 1988-1993 he was the rector of Tartu University. Awards: 1997: Order of the National Coat of Arms, III class. == References ==
|
{
"page_id": 68225964,
"source": null,
"title": "Jüri Kärner"
}
|
Dexbrompheniramine is an antihistamine with anticholinergic properties used to treat allergic conditions such as hay fever or urticaria. It is the pharmacologically active dextrorotatory isomer of brompheniramine. It was formerly marketed in combination with pseudoephedrine under the name Drixoral in the US and Canada. It is an alkylamine antihistamine. Dexbrompheniramine is a first generation antihistamine that reduces the effects of the neurotransmitter histamine in the body; sneezing, itching, watery eyes, and runny nose. == Interactions == MAO inhibitors within 14 days. MAO inhibitors include isocarboxazid, linezolid, phenelzine, rasagiline, selegiline, and tranylcypromine. Drinking alcohol can increase side effects of dexbrompheniramine. == References == == External links == Media related to Dexbrompheniramine at Wikimedia Commons
|
{
"page_id": 5114795,
"source": null,
"title": "Dexbrompheniramine"
}
|
AlvaDesc is a commercial software application for the calculation and analysis of molecular descriptors, fingerprints, and structural patterns. Developed by Alvascience, alvaDesc is used in cheminformatics and quantitative structure–activity relationship (QSAR) modeling to numerically describe molecular structures, aiding in chemical property prediction and machine learning applications. == Overview == Molecular descriptors and fingerprints serve as mathematical representations of chemical compounds, enabling computational models to predict properties such as bioactivity, toxicity, and solubility. == Features == AlvaDesc supports the calculation of molecular descriptors and fingerprints across multiple categories: 0D–3D molecular descriptors: including constitutional indices, topological indices, connectivity indices, geometrical descriptors, pharmacophore descriptors, charge descriptors, and more. Molecular properties: including LogP (octanol-water partition coefficient), molar refractivity, polar surface area (PSA), solvent-accessible surface area (SASA), and hydrogen bond donors/acceptors. Molecular fingerprints: including MACCS keys, Extended Connectivity Fingerprints (ECFP), Path-based fingerprints. Structural pattern detection: alvaDesc allows the identification of structural patterns in molecules using SMARTS-based matching, enabling the analysis of functional groups, substructures, and chemical motifs. 3D coordinate calculation: alvaDesc can generate 3D molecular structures from 1D/2D representations. The 3D coordinates calculation is performed using a Distance Geometry (DG) method, followed by a Force Field (FF) optimization. Descriptor analysis tools: including Principal Component Analysis (PCA), t-SNE analysis, correlation analysis, variable reduction. Support for disconnected structures: Handles salts, mixtures, ionic liquids, and metal complexes. Cross-platform compatibility: Available for Windows, macOS, and Linux, supporting both graphical user interface (GUI) and command-line interface (CLI). KNIME and Python integration: alvaDesc can be integrated into cheminformatics workflows using the KNIME analytics platform and Python scripting. == See also == Molecular descriptor Topological index Quantitative structure–activity relationship (QSAR) == References ==
|
{
"page_id": 78908332,
"source": null,
"title": "AlvaDesc"
}
|
The strong CP problem is a question in particle physics, which brings up the following quandary: why does quantum chromodynamics (QCD) seem to preserve CP-symmetry? In particle physics, CP stands for the combination of C-symmetry (charge conjugation symmetry) and P-symmetry (parity symmetry). According to the current mathematical formulation of quantum chromodynamics, a violation of CP-symmetry in strong interactions could occur. However, no violation of the CP-symmetry has ever been seen in any experiment involving only the strong interaction. As there is no known reason in QCD for it to necessarily be conserved, this is a "fine tuning" problem known as the strong CP problem. The strong CP problem is sometimes regarded as an unsolved problem in physics, and has been referred to as "the most underrated puzzle in all of physics." There are several proposed solutions to solve the strong CP problem. The most well-known is Peccei–Quinn theory, involving new pseudoscalar particles called axions. == Theory == CP-symmetry states that physics should be unchanged if particles were swapped with their antiparticles and then left-handed and right-handed particles were also interchanged. This corresponds to performing a charge conjugation transformation and then a parity transformation. The symmetry is known to be broken in the Standard Model through weak interactions, but it is also expected to be broken through strong interactions which govern quantum chromodynamics (QCD), something that has not yet been observed. To illustrate how the CP violation can come about in QCD, consider a Yang–Mills theory with a single massive quark. The most general mass term possible for the quark is a complex mass written as m e i θ ′ γ 5 {\displaystyle me^{i\theta '\gamma _{5}}} for some arbitrary phase θ ′ {\displaystyle \theta '} . In that case the Lagrangian describing the theory consists of four terms: L =
|
{
"page_id": 986029,
"source": null,
"title": "Strong CP problem"
}
|
− 1 4 F μ ν F μ ν + θ g 2 32 π 2 F μ ν F ~ μ ν + ψ ¯ ( i γ μ D μ − m e i θ ′ γ 5 ) ψ . {\displaystyle {\mathcal {L}}=-{\frac {1}{4}}F_{\mu \nu }F^{\mu \nu }+\theta {\frac {g^{2}}{32\pi ^{2}}}F_{\mu \nu }{\tilde {F}}^{\mu \nu }+{\bar {\psi }}(i\gamma ^{\mu }D_{\mu }-me^{i\theta '\gamma _{5}})\psi .} The first and third terms are the CP-symmetric kinetic terms of the gauge and quark fields. The fourth term is the quark mass term which is CP violating for non-zero phases θ ′ ≠ 0 {\displaystyle \theta '\neq 0} while the second term is the so-called θ-term or “vacuum angle”, which also violates CP-symmetry. Quark fields can always be redefined by performing a chiral transformation by some angle α {\displaystyle \alpha } as ψ ′ = e i α γ 5 / 2 ψ , ψ ¯ ′ = ψ ¯ e i α γ 5 / 2 , {\displaystyle \psi '=e^{i\alpha \gamma _{5}/2}\psi ,\ \ \ \ \ \ {\bar {\psi }}'={\bar {\psi }}e^{i\alpha \gamma _{5}/2},} which changes the complex mass phase by θ ′ → θ ′ − α {\displaystyle \theta '\rightarrow \theta '-\alpha } while leaving the kinetic terms unchanged. The transformation also changes the θ-term as θ → θ + α {\displaystyle \theta \rightarrow \theta +\alpha } due to a change in the path integral measure, an effect closely connected to the chiral anomaly. The theory would be CP invariant if one could eliminate both sources of CP violation through such a field redefinition. But this cannot be done unless θ = − θ ′ {\displaystyle \theta =-\theta '} . This is because even under such field redefinitions, the combination θ ′ + θ → ( θ ′ −
|
{
"page_id": 986029,
"source": null,
"title": "Strong CP problem"
}
|
α ) + ( θ + α ) = θ ′ + θ {\displaystyle \theta '+\theta \rightarrow (\theta '-\alpha )+(\theta +\alpha )=\theta '+\theta } remains unchanged. For example, the CP violation due to the mass term can be eliminated by picking α = θ ′ {\displaystyle \alpha =\theta '} , but then all the CP violation goes to the θ-term which is now proportional to θ ¯ {\displaystyle {\bar {\theta }}} . If instead the θ-term is eliminated through a chiral transformation, then there will be a CP violating complex mass with a phase θ ¯ {\displaystyle {\bar {\theta }}} . Practically, it is usually useful to put all the CP violation into the θ-term and thus only deal with real masses. In the Standard Model where one deals with six quarks whose masses are described by the Yukawa matrices Y u {\displaystyle Y_{u}} and Y d {\displaystyle Y_{d}} , the physical CP violating angle is θ ¯ = θ − arg det ( Y u Y d ) {\displaystyle {\bar {\theta }}=\theta -\arg \det(Y_{u}Y_{d})} . Since the θ-term has no contributions to perturbation theory, all effects from strong CP violation is entirely non-perturbative. Notably, it gives rise to a neutron electric dipole moment d N = ( 5.2 × 10 − 16 e ⋅ cm ) θ ¯ . {\displaystyle d_{N}=(5.2\times 10^{-16}{\text{e}}\cdot {\text{cm}}){\bar {\theta }}.} Current experimental upper bounds on the dipole moment give an upper bound of d N < 10 − 26 e ⋅ {\displaystyle d_{N}<10^{-26}{\text{e}}\cdot } cm, which requires θ ¯ < 10 − 10 {\displaystyle {\bar {\theta }}<10^{-10}} . The angle θ ¯ {\displaystyle {\bar {\theta }}} can take any value between zero and 2 π {\displaystyle 2\pi } , so it taking on such a particularly small value is a fine-tuning problem called
|
{
"page_id": 986029,
"source": null,
"title": "Strong CP problem"
}
|
the strong CP problem. == Proposed solutions == The strong CP problem is solved automatically if one of the quarks is massless. In that case one can perform a set of chiral transformations on all the massive quark fields to get rid of their complex mass phases and then perform another chiral transformation on the massless quark field to eliminate the residual θ-term without also introducing a complex mass term for that field. This then gets rid of all CP violating terms in the theory. The problem with this solution is that all quarks are known to be massive from experimental matching with lattice calculations. Even if one of the quarks was essentially massless to solve the problem, this would in itself just be another fine-tuning problem since there is nothing requiring a quark mass to take on such a small value. The most popular solution to the problem is through the Peccei–Quinn mechanism. This introduces a new global anomalous symmetry which is then spontaneously broken at low energies, giving rise to a pseudo-Goldstone boson called an axion. The axion ground state dynamically forces the theory to be CP-symmetric by setting θ ¯ = 0 {\displaystyle {\bar {\theta }}=0} . Axions are also considered viable candidates for dark matter and axion-like particles are also predicted by string theory. Other less popular proposed solutions exist such as Nelson–Barr models. These set θ ¯ = 0 {\displaystyle {\bar {\theta }}=0} at some high energy scale where CP-symmetry is exact but the symmetry is then spontaneously broken. The Nelson–Barr mechanism is a way of explaining why θ ¯ {\displaystyle {\bar {\theta }}} remains small at low energies while the CP breaking phase in the CKM matrix is large. == See also == Axion CP violation == References ==
|
{
"page_id": 986029,
"source": null,
"title": "Strong CP problem"
}
|
5-Formiminotetrahydrofolate is an intermediate in the catabolism of histidine. It is produced by glutamate formimidoyltransferase and then converted into 5,10-methenyltetrahydrofolate by formiminotransferase cyclodeaminase. == References ==
|
{
"page_id": 11471795,
"source": null,
"title": "5-Formiminotetrahydrofolate"
}
|
Glycomimetic is a term used to refer to molecules that have structures similar to carbohydrates, but with some variation. This will normally result in modified biological properties. == Introduction == Often, modification of the structure will take place around the glycosidic linkage. Replacement of one or other of the glycosidic oxygen atoms by carbon, sulfur, nitrogen etc. will alter the properties of the glycosidic bond. The molecules produced in this way would be called carbasugars or C-glycosides, thiosugars or thioglycosides, or iminosugars or glycosylamines. When nitrogen is introduced, the glycomimetic may become positively charged at physiological pH, meaning that it may act as an enzyme inhibitor, either by Coulombic interaction with carboxylate amino acid side-chains in the enzyme active site, or by mimicking positive-charge build-up at the transition state of the reaction, or both. Iminosugars (sometimes referred to erroneously as azasugars) are classic examples of molecules with this behaviour. Glycosylamines typically have a lower stability, being easily hydrolysed, which means that to exploit an exocyclic nitrogen substituent at C-1, further modification is necessary. An example of this would be the additional substitution of the ring-oxygen for carbon as is seen in valienamine. Altering the structure of a carbohydrate will normally result in several changes to the properties of the molecule. As well as changing the stability of the glycosidic bond, the ring-conformation may be affected. Also the conformation of the glycosidic bond may be affected. As well as obvious changes in the immediate vicinity of the substitution, e.g. that replacement of an acetal oxygen by methylene (CH2) would result in loss of a hydrogen-bond participatory atom, such a substitution is expected to have more subtle effects resulting from a change in the dipole of the molecule, such as slight changes in hydrogen bonding or pKa values of the unchanged hydroxyl
|
{
"page_id": 32967604,
"source": null,
"title": "Glycomimetic"
}
|
groups. Substitution by CF2 rather than methylene has been explored in efforts to address this and come up with better mimetics while still retaining the hydrolytic stability gained by the modification. == Examples of commercially relevant glycomimetic molecules == === Tamiflu === Tamiflu is a carbocyclic mimic of the cell-surface carbohydrate sialic acid. Tamiflu is an enzyme inhibitor that blocks the action of influenza virus neuraminidases (sialidases). === Acarbose === Acarbose is a pseudotetrasaccharide mimicking maltotetraose (a substructure of starch). One of the glucose units has been replaced by valienamine - a carbasugar, linked to the next carbohydrate by an amine bridge. Another of the glucose units appears as a 6-deoxy variant. Acarbose is an enzyme inhibitor that is used as a drug against type 2 diabetes. === Miglustat === Miglustat is an iminosugar in which the ring oxygen is replaced by nitrogen. Miglustat a drug used to treat some rare lysosomal storage disorder diseases. == References ==
|
{
"page_id": 32967604,
"source": null,
"title": "Glycomimetic"
}
|
Repulsive guidance molecules (RGMs) are members of a three gene family (in vertebrates) composed of RGMa, RGMb, and RGMc (also called hemojuvelin). RGMa has been implicated to play an important role in the developing brain and in the scar tissue that forms after a brain injury. For example, RGMa helps guide retinal ganglion cell (RGC) axons to the tectum in the midbrain. It has also been demonstrated that after induced spinal cord injury RGMa accumulates in the scar tissue around the lesion. Further research has shown that RGMa is an inhibitor of axonal outgrowth. Taken together, these findings highlight the importance of RGMa in axonal guidance and outgrowth. == Family members == == References ==
|
{
"page_id": 18156469,
"source": null,
"title": "Repulsive guidance molecule"
}
|
Inverse vulcanization is a process that produces polysulfide polymers, which also contain some organic linkers. In contrast, sulfur vulcanization produces material that is predominantly organic but has a small percentage of polysulfide crosslinks. == Synthesis == Like Thiokols and sulfur-vulcanization, inverse vulcanization uses the tendency of sulfur catenate. The polymers produced by inverse vulcanization consist of long sulfur linear chains interspersed with organic linkers. Traditional sulfur vulcanization produces a cross-linked material with short sulfur bridges, down to one or two sulfur atoms. The polymerization process begins with the heating of elemental sulfur above its melting point (115.21 °C), to favor the ring-opening polymerization process (ROP) of the S8 monomer, occurring at 159 °C. As a result, the liquid sulfur is constituted by linear polysulfide chains with diradical ends, which can be easily bridged together with small dienes, such as 1,3-Diisopropylbenzene(DIB), 1,4-diphenylbutadiyne, limonene, divinylbenzene (DVB), dicyclopentadiene, styrene, 4-vinylpyridine, cycloalkene and ethylidene norbornene, or longer organic molecules as polybenzoxazines, squalene and triglyceride. Chemically, the diene carbon-carbon double bond (C=C) of the substitutional group disappears, forming the carbon-sulfur single bond (C-S) which binds together the sulfur linear chains. The advantage of such a polymerization is the absence of a solvent; Sulphur acts as comonomer and solvent. This makes the process highly scalable at the industrial level, and kilogram-scale synthesis of the poly(S-r-DIB) has already been accomplished. == Products, characterization and properties == Vibrational spectroscopy was performed to investigate the chemical structure of the copolymers, and the presence of the C-S bonds was detected through Infrared or Raman spectroscopies. The high amount of S-S bonds makes the copolymer highly IR-inactive in the near and mid-infrared spectrum. As a consequence, sulfur-rich materials made via inverse vulcanization are characterized by a high refractive index (n~1.8), whose value depends again upon the composition and crosslinking species. As
|
{
"page_id": 59050931,
"source": null,
"title": "Inverse vulcanization"
}
|
shown by thermogravimetric analysis (TGA), the copolymer thermal stability increases with the amount of added crosslinker; however, all the tested compositions degrade above 222 °C. Copolymer behavior included that, the glass-transition temperature depends upon the composition and crosslinking species. For given comonomers, the behavior of the copolymers as a function of the temperature depends on the chemical composition; for example, the poly (sulfur-random-divinylbenzene) behaves as a plastomer for a diene content between 15 and 25%wt, and as a viscous resin with the 30–35%wt of DVB. On the other hand, the poly (sulfur-random-1,3-diisopropenylbenzene) acts as thermoplastic at 15–25%wt of DIB, while it becomes a thermoplastic-thermosetting polymer for a diene concentration of 30-35%wt. The potential to break and reform the chemical bonds along the polysulfide chains (S-S) allows the repair of the copolymer by simply heating above 100 °C. This increases the ability to reform and recycle the high molecular weight copolymer. == Potential applications == The sulfur-rich copolymers made via inverse vulcanization could in principle find diverse applications due to their simple synthesis process and thermoplasticity. === Lithium-sulfur batteries === This new way of sulfur processing has been exploited for the cathode preparation of long-cycling lithium-sulfur batteries. Such electrochemical systems are characterized by a greater energy density than commercial Li-ion batteries, but they are not stable for long service life. Simmonds et al. first demonstrated improved capacity retention for over 500 cycles with an inverse vulcanization copolymer, suppressing the typical capacity fading of sulfur-polymer composites. The poly (sulfur-random-1,3-diisopropenylbenzene), briefly defined as poly (S-r-DIB), showed a higher composition homogeneity compared with other cathodic materials, together with greater sulfur retention and an enhanced adjustment of the polysulfides' volume variations. These advantages made it possible to assemble a stable and durable Li-S cell. Subsequently, other copolymers were synthesized via inverse vulcanization and tested inside
|
{
"page_id": 59050931,
"source": null,
"title": "Inverse vulcanization"
}
|
these electrochemical devices, again providing high stability over their cycles. In order to overcome the disadvantages related to the materials' low electrical conductivity (1015–1016 Ω·cm), researchers have started to add special carbon-based particles to increase electron transport inside the copolymer. Furthermore, such carbonaceous additives improve the polysulfides' retention at the cathode through the polysulfides-capturing effect, increasing the battery performances. Examples of employed nanostructures are long carbon nanotubes, graphene, and carbon onions. === Capturing Mercury === The new materials could be used to remove toxic metals from soil or water. Pure sulfur cannot be employed to manufacture a functional filter because of its low mechanical properties; therefore, inverse vulcanization was investigated to produce porous materials, in particular for the mercury capturing process. The liquid metal binds together with the sulfur-rich copolymer, remaining mostly inside the filter. === Infrared transmission === Sulfur-rich copolymers, made via inverse vulcanization, have advantages over traditional IR optical materials due to the simple manufacturing process, low cost reagents, and high refractive index. As mentioned before, the latter depends upon the S-S bonds concentration, leading to the ability to tune the optical properties of the material by modifying the chemical formulation. The ability to change the material's refractive index to fulfill the specific application requirements makes these copolymers applicable in military, civil or medical fields. === Others === The inverse vulcanization process can also be employed for the synthesis of activated carbon with narrow pore-size distributions. The sulfur-rich copolymer acts as a template where the carbons are produced. The final material is doped with sulfur and exhibits a micro-porous network and high gas selectivity. Therefore, inverse vulcanization could also be used for gas separation applications. == See also == Sulfur Free-radical polymerization Lithium-sulfur batteries == References == == External links == "New “inverse vulcanization” process produces polymeric sulfur
|
{
"page_id": 59050931,
"source": null,
"title": "Inverse vulcanization"
}
|
that can function as high performance electrodes for Li-S batteries". 15 April 2013.
|
{
"page_id": 59050931,
"source": null,
"title": "Inverse vulcanization"
}
|
Amidosulfites are chemical compounds containing the group =NS(O)O-. Substituents can attach two bonds to the nitrogen and one to the oxygen. These have the form RR'NS(O)OR" compounds can be a zwitterion with a positive charge on the nitrogen, and a negative charge on the oxygen, which then has no group attached. These can be called inner salts. This allows three groups to bond to the nitrogen: RR'R"N+S(O)O−. The simplest amidosulfite is amidosulfurous acid H2NS(O)OH. It has ammonium salt H2NS(O)O−NH4+. These are purportedly produced when sulfur dioxide mixes with ammonia in ratios 1:1 or 1:2. Known compounds include N-(2-dimethylammonio-ethyl)amidosulfite, N-(2-diethylammonio-ethyl)amidosulfite, N-[2-(1-Piperidinium-1-yl)-ethyl]amidosulfite, N-[2-(4-Morpholinium-4-yl)-ethyl]amidosulfite, sodium N-ethylamidosulfite (C2H5NHS(O)ONa), Ethyl N-ethylamidosulfite (C2H5NHS(O)OC2H5), diethyl-phosphanyl N-methylamidosulfite, diphenyl-phosphanyl N-methylamidosulfite, N,N-dimethylamidosulfurous acid, N,N-diethylamidosulfurous acid, N,N-bis(2-hydroxyethyl)diethylamidosulfurous acid, sodium N,N-dimethylamidosulfite, sodium N,N-diethylamidosulfite, lithium N,N-diethylamidosulfite, lithium N-hexafluoroisopropylideneamidosulfite (with double bond to nitrogen), sodium 1-piperidinesulfinate Na(CH2)5NHS(O)O. Organometallic substituents can produce for example trimethylsilyl N,N-diethylamidosulfite, trimethyltin N,N-dimethylamidosulfite, or dimethylthallium N,N-dimethylamidosulfite. == References ==
|
{
"page_id": 56429493,
"source": null,
"title": "Amidosulfite"
}
|
The Chemical Structure Association Trust (CSA Trust) is an internationally recognized, registered charity which promotes education, research and development in the field of storage, processing and retrieval of information about chemical structures, reactions and compounds. Since 2003 it has incorporated the activities of the former Chemical Structure Association. The Trust produces a Newsletter three times a year, and organizes conferences and training in cooperation with other organizations. It also provides a forum for advice and discussion on chemoinformatics. A Board of Trustees manage the Trust. The trust jointly organizes several conferences (including the International Conference on Chemical Structures and the Sheffield Conference on Chemoinformatics, each held every three years). It has been designated a Scholarly Society, and has been highlighted in various publications. == Awards and grants == The CSA Trust Mike Lynch Award, named in honour of Professor Mike Lynch, is given on a triennial basis. It consists of five thousand US dollars ($5,000) and an appropriate memento. The purpose of the Award is to recognize and encourage outstanding accomplishments in education, research and development activities that are related to the systems and methods used to store, process and retrieve information about chemical structures, reactions and properties. The 2011 award was made at the 9th International Conference on Chemical Structures (Noorwijkerhout, The Netherlands, 5–9 June 2011). The Trust makes annual Jacques-Émile Dubois Grants, which provide funding for the career development of young researchers (age 35 or younger) who have demonstrated excellence in the areas listed above. The value of each Grant may vary, with no Grant exceeding one thousand US dollars ($1,000). Grants are awarded for specific purposes, and each recipient of a Grant is required to submit a brief written report detailing how the Grant funds were allocated. Additionally, Bursaries may be made available at the discretion of
|
{
"page_id": 12782519,
"source": null,
"title": "CSA Trust"
}
|
the Trust. Bursaries, which are also for specific purposes, are age-neutral and requests follow the same procedure as that for the Grant applications and will be evaluated against the same criteria. The awarding of Bursaries in any given year will be determined by the availability of funds. == See also == Cheminformatics == References == == External links == CSA Trust Website
|
{
"page_id": 12782519,
"source": null,
"title": "CSA Trust"
}
|
The molecular formula C10H9N (molar mass: 143.19 g/mol, exact mass: 143.0735 u) may refer to: Benzazepine Lepidine, or 4-methylquinoline 1-Naphthylamine 2-Naphthylamine Quinaldine
|
{
"page_id": 23661497,
"source": null,
"title": "C10H9N"
}
|
Sodium hydrogenoxalate or sodium hydrogen oxalate is a chemical compound with the chemical formula NaHC2O4. It is an ionic compound. It is a sodium salt of oxalic acid H2C2O4. It is an acidic salt, because it consists of sodium cations Na+ and hydrogen oxalate anions HC2O−4 or HO−C(=O)−CO−2, in which only one acidic hydrogen atom in oxalic acid is replaced by sodium atom. The hydrogen oxalate anion can be described as the result of removing one hydrogen ion H+ from oxalic acid, or adding one to the oxalate anion C2O2−4. == Properties == === Hydrates === The compound is commonly encountered as the anhydrous form or as the monohydrate NaHC2O4·H2O. Both are colorless crystalline solids at ambient temperature. The monohydrate can be obtained by evaporating a solution of the compound at room temperature. The crystal structure of NaHC2O4·H2O is triclinic normal (pinacoidal, space group P1). The lattice parameters are a = 650.3 pm, b = 667.3 pm, c = 569.8 pm, α = 85.04°, β = 110.00°, γ = 105.02°, and Z = 2. The hydrogen oxalate ions are linked end to end in infinite chains by hydrogen bonds (257.1 pm). The chains are cross linked to form layers by both O−H···O bonds from the water molecules (280.8 pm, 282.6 pm) and by ionic bonds Na+···O. These layers are in turn held together by Na+···O bonds. The oxalate group is non-planar with an angle of twist about the C−C bond of 12.9°. === Reactions === Upon being heated, sodium hydrogenoxalate converts to oxalic acid and sodium oxalate, the latter of which decomposes into sodium carbonate and carbon monoxide. 2 NaHC2O4 → Na2C2O4 + H2C2O4 Na2C2O4 → Na2CO3 + CO == Toxicity == The health hazards posed by this compound are largely due to its acidity and to the toxic effects
|
{
"page_id": 58461116,
"source": null,
"title": "Sodium hydrogenoxalate"
}
|
of oxalic acid and other oxalate or hydrogenoxalate salts, which can follow ingestion or absorption through the skin. The toxic effects include necrosis of tissues due to sequestration of calcium ions Ca2+, and the formation of poorly soluble calcium oxalate stones in the kidneys that can obstruct the kidney tubules. == References ==
|
{
"page_id": 58461116,
"source": null,
"title": "Sodium hydrogenoxalate"
}
|
SeaSeep is a combination of 2D seismic data (a group of seismic lines acquired individually, as opposed to multiple closely space lines1), high resolution multibeam sonar which is an evolutionary advanced form of side-scan sonar, navigated piston coring (one of the more common sea floor sampling methods2), heat flow sampling (which serve a critical purpose in oil exploration and production3) and possibly gravity and magnetic data (refer to Dick Gibson's Primer on Gravity and Magnetics4). The term SeaSeep originally belonged to Black Gold Energy LLC5 and refers to a dataset that combines all of the available data into one integrated package that can be used in hydrocarbon exploration. With the acquisition of Black Gold Energy LLC by Niko Resources Ltd.6 in December 2009 the term now belongs to Niko Resources The concept of a SeaSeep dataset is the modern day offshore derivative of how many oil fields were found in the late 19th and early 20th century; by finding a large anticline structure with an associated oil seep. In the United States, many of the first commercial fields in California were found using this method including the Newhall Field discovered in 1876 and the Kern River Field discovered in 18997. Seeps have also been used to find offshore fields including the Cantarell Field in Mexico in 1976; the largest oil field in Mexico and one of the largest in the world. The field is named after a fisherman, Rudesindo Cantarell, who complained to PEMEX about his fishing nets being stained by oil seeps in the Bay of Campeche. The biological and geochemical manifestations of seepage leads to distinct bathymetrical features including positive relief mounds, pinnacles, mud volcanoes and negative relief pockmarks. These features can be detected by multibeam sonar and then sampled by navigated piston coring. Spec and proprietary multibeam
|
{
"page_id": 15600576,
"source": null,
"title": "SeaSeep"
}
|
seep mapping and core geochemistry by Texas A&M University's Geochemical & Environmental Research Group8 and later TDI Brooks9 demonstrated thermogenic charge in deepwater Angola and deepwater Nigeria leading to an aggressive exploration program by a number of oil companies and subsequent discoveries. The emphasis on, and marketplace acceptance of, multibeam mapping combined with navigated coring as an improvement over grid-based approaches to geochemical exploration is attributed to AOA Geophysics Inc10. == References == 1. Schlumberger Oilfield Glossary ([1]) 2. Piston Coring ([2]) 3. TDI Brooks ([3]) 4. Primer on Gravity and Magnetics ([4]) 5. Black Gold Energy ([5]) 6. Niko Resources Ltd. ([6]) 7. Natural Oil and Gas Seeps in California: ([7]) 8. Geochemical & Environmental Research Group ([8]) 9. TDI Brooks ([9]) 10. AOA Geophysics ([10])
|
{
"page_id": 15600576,
"source": null,
"title": "SeaSeep"
}
|
Soil gases (soil atmosphere) are the gases found in the air space between soil components. The spaces between the solid soil particles, if they do not contain water, are filled with air. The primary soil gases are nitrogen, carbon dioxide and oxygen. Oxygen is critical because it allows for respiration of both plant roots and soil organisms. Other natural soil gases include nitric oxide, nitrous oxide, methane, and ammonia. Some environmental contaminants below ground produce gas which diffuses through the soil such as from landfill wastes, mining activities, and contamination by petroleum hydrocarbons which produce volatile organic compounds. Gases fill soil pores in the soil structure as water drains or is removed from a soil pore by evaporation or root absorption. The network of pores within the soil aerates, or ventilates, the soil. This aeration network becomes blocked when water enters soil pores. Not only are both soil air and soil water very dynamic parts of soil, but both are often inversely related. == Composition == The composition of gases present in the soil's pores, referred to commonly as the soil atmosphere or atmosphere of the soil, is similar to that of the Earth's atmosphere. Unlike the atmosphere, moreover, soil gas composition is less stagnant due to the various chemical and biological processes taking place in the soil. The resulting changes in composition from these processes can be defined by their variation time (i.e. daily vs. seasonal). Despite this spatial- and temporal-dependent fluctuation, soil gases typically boast greater concentrations of carbon dioxide and water vapor in comparison to the atmosphere. Furthermore, concentration of other gases, such as methane and nitrous oxide, are relatively minor yet significant in determining greenhouse gas flux and anthropogenic impact on soils. == Processes == Gas molecules in soil are in continuous thermal motion according to
|
{
"page_id": 22940609,
"source": null,
"title": "Soil gas"
}
|
the kinetic theory of gases, and there is also collision between molecules – a random walk process. In soil, a concentration gradient causes net movement of molecules from high concentration to low concentration, which gives the movement of gas by diffusion. Numerically, it is explained by the Fick's law of diffusion. Soil gas migration, specifically that of hydrocarbon species with one to five carbons, can also be caused by microseepage. The soil atmosphere's variable composition and constant motion can be attributed to chemical processes such as diffusion, decomposition, and, in some regions of the world, thawing, among other processes. Diffusion of soil air with the atmosphere causes the preferential replacement of soil gases with atmospheric air. More significantly, moreover, variation in soil gas composition due to seasonal, or even daily, temperature and/or moisture change can influence the rate of soil respiration. According to the USDA, soil respiration refers to the quantity of carbon dioxide released from soil. This excess carbon dioxide is created by the decomposition of organic material by microbial organisms, in the presence of oxygen. Given the importance of both soil gases to soil life, significant fluctuation of carbon dioxide and oxygen can result in changes in rate of decay, while changes in microbial abundance can inversely influence soil gas composition. In regions of the world where freezing of soils or drought is common, soil thawing and rewetting due to seasonal or meteorological changes influences soil gas flux. Both processes hydrate the soil and increase nutrient availability leading to an increase in microbial activity. This results in greater soil respiration and influences the composition of soil gases. == Studies and Research == Soil gases have been used for multiple scientific studies to explore topics such as microseepage, earthquakes, and gaseous exchange between the soil and the atmosphere. Microseepage
|
{
"page_id": 22940609,
"source": null,
"title": "Soil gas"
}
|
refers to the limited release of hydrocarbons on the soil surface and can be used to look for petroleum deposits based on the assumption that hydrocarbons vertically migrate to the soil surface in small quantities. Migration of soil gases, specifically radon, can also be examined as earthquake precursors. Furthermore, for processes such as soil thawing and rewetting, for example, large sudden changes in soil respiration can cause increased flux of soil gases such as carbon dioxide and methane, which are greenhouse gases. These fluxes and interactions between soil gases and atmospheric air can further be analyzed by distance from the soil surface. == References ==
|
{
"page_id": 22940609,
"source": null,
"title": "Soil gas"
}
|
The School of Life Sciences at the University of Dundee conducts research into the molecular and cellular mechanisms underlying human health and disease. == History == Life Sciences research at the university began within the Department of Physiology. Following a campaign by Robert P. Cook who was a lecturer in Physiological Chemistry, the Department of Biochemistry was formed in 1965.... In 1970, the University of Dundee appointed Peter Garland CBE as its first Chair of Biochemistry based within the Department. Peter's arrival coincided with the opening of the Medical Sciences Institute (MSI) and Biological Sciences Institute (BSI) that provided up to date facilities for research to take place. The Department of Biochemistry moved from a converted stable block into the MSI alongside the Department of Anatomy and Physiology. In 1971, Peter recruited Sir Philip Cohen to Dundee, where he has remained ever since and who has played an instrumental role in the expansion of life sciences research in Dundee. The growth of the department was supported by Principal Adam Neville who redirected funds from other areas of the university. Peter had the ethos of employing the best is required to achieve the best, a strategy that exists to this day. Alongside his appointment of Philip, Peter also recruited David Lilley, Grahame Hardie and Chris Higgins. Further key appointments continued, this time led by Graham Warren; Peter's successor. In 1989, the Cancer Research Campaign supported the establishment of laboratories for David Glover, Birgitt Lane and David Lane. In 1990, the Medical Research Council set up the Protein Phosphorylation Unit which still exists today as the MRC Protein Phosphorylation and Ubiquitylation Unit (MRC PPU). === Expansion of research facilities === In 1994, the Wellcome Trust awarded £10 million to build the Wellcome Trust Biocentre which opened in 1997 and allowed further expansion
|
{
"page_id": 59182018,
"source": null,
"title": "School of Life Sciences (University of Dundee)"
}
|
in the research with several key appointments. As part of the fundraising efforts for this building, Sir Philip Cohen received a donation from Sir Sean Connery that came from part of his salary for his role in the 1991 film Robin Hood Prince of Thieves. A room in the building was named in his honour. The building originally housed the Divisions of Gene expression, Molecular Cell Biology and Molecular Parasitology that were affiliated with the Department of Biochemistry, while the Division of Cell and Development Biology was affiliated with the Department of Anatomy and Physiology. In 2007, the Sir James Black Centre (initially named the Centre for Interdisciplinary Research) was opened. It was named after the Nobel Prize winning Scottish pharmacologist and former University of Dundee Chancellor, Sir James W. Black. In 2008, Cohen established the Scottish Institute of Life Sciences (SCILLS) and in 2012, the MRC PPU expanded its remit to include SCILLS under the Directorship of Dario Alessi. Finally, in 2014 under the leadership of Sir Mike Ferguson, the Discovery Centre for Translational and Interdisciplinary Research was opened by Paul Nurse Undergraduate teaching is currently based in the Carnelley Building on City Campus. The building is named after Thomas Carnelley, the first Professor of Chemistry in Dundee. It is also home to the D'Arcy Thompson Zoology Museum. Plant Sciences research used to be based in the BSI building but following its closure, the division of Plant Sciences moved to the James Hutton Institute, Invergowrie as part of a partnership agreement with the institute. The BSI building was demolished in 2017 and the site is now a University allotment and garden === Changing name === The College of Life Sciences was formed on 1 August 2006 with Pete Downes as Vice Principal and Head of College. Sir Philip Cohen
|
{
"page_id": 59182018,
"source": null,
"title": "School of Life Sciences (University of Dundee)"
}
|
was named Director of Research and Dean of Life Sciences (Research). The college comprised the following research divisions: Biological Chemistry and Molecular Biology; Cell Biology and Immunology; Gene Regulation and Expression; Cell and Developmental Biology; Molecular Physiology; Environmental and Applied Biology; MRC Protein Phosphorylation and CRUK Nucleic Acid Research Group. Teaching was led by Life Sciences (Learning and Teaching). In 2015, following University restructuring the college became known as the School of Life Sciences and in April 2016, Julian Blow became Dean of the School of Life Sciences. == Research == The school contains a number of different divisions, centres and units which address different research themes: Biological Chemistry and Drug Discovery Cell and Developmental Biology Cell Signalling and Immunology Computational Biology Drug Discovery Unit Gene Regulation and Expression Geomicrobiology Group Medical Research Council Protein Phosphorylation and Ubiquitylation Unit Molecular Microbiology Nucleic Acid Structure Research Group Plant Sciences (based in Invergowrie at the James Hutton Institute) Wellcome Centre for Anti-Infectives Research == Teaching == Undergraduate teaching focusses on two degree streams, Biological and Biomedical Sciences. The D'Arcy Thompson Unit (DTU); named after Sir D'Arcy Wentworth Thompson, first Professor of Biology at Dundee; encompasses all School of Life Sciences staff based at the Carnelley Building on city campus. This building is home to biological and biomedical sciences undergraduate teaching. The DTU work closely with academic staff in the School of Life Sciences research divisions and in the School of Medicine. === Rankings === In recent University Guides, Biological Sciences and Biosciences at the university has been rated in the top 10 for those subjects: 3rd (Complete University Guide, 2020) 4th (Times/Sunday Times Good University Guide, 2019) 6th (Guardian University Guide, 2019) === Internationalisation === The school has forged partnerships with Xiamen University, China, National University Singapore and Central South University, China.
|
{
"page_id": 59182018,
"source": null,
"title": "School of Life Sciences (University of Dundee)"
}
|
== Reputation == In 2003, an international survey of life scientists by the journal The Scientist named the School the third-best place to work outside the US. In 2011, the College of Life Sciences won the award for the 'Greatest delivery of impact' from the Biotechnology and Biological Sciences Research Council (BBSRC). The school contributes to many of the accolades awarded to the university. These include: Top University in the UK for the quality of our Biological Sciences research (Research Excellence Framework, 2014) The only UK University ranked in the world top 50 of the 2017 Nature Innovation Index Ranked number 1 in the world in influencing pharmaceuticals (Clarivate Analytics, 2017) === Commercialisation === In 1998, Sir Philip Cohen and Peter Downes founded the Division of Signal Transduction Therapy (DSTT). The DSTT is a collaboration between the commercial pharmaceutical industry and an academic research institute. Over the last 20 years, the collaboration has attracted £60million of investment and helped accelerate the development of numerous drugs. In 2006, Mike Ferguson and Alan Fairlamb established the Drug Discovery Unit (DDU) to address the unmet need for developing drugs against Neglected Tropical Diseases such as Leishmaniasis, Chagas' Disease and African Sleeping Sickness. Working in partnership with industry the Unit has developed pre-clinical drug candidates for malaria and visceral leishmaniasis. These findings were published in the journal Nature. The candidate for malaria is now in first in man clinical trials. Scientists from the school have been responsible for the formation of a number of spin-out companies (and the attraction of spin-in and start-up companies). Examples include Cyclacel, Exscientia, Glencoe Software, Platinum Informatics and Ubiquigent. === Public Engagement === The School holds a Gold Engage Watermark from the National Co-ordinating Centre for Public Engagement (NCCPE). The first faculty in the UK to do so ===
|
{
"page_id": 59182018,
"source": null,
"title": "School of Life Sciences (University of Dundee)"
}
|
Athena SWAN === The school was awarded in October 2018 a Silver Athena SWAN award under the extended charter. == Notable alumni == Through its many incarnations over the years, many notable scientists have conducted research in Dundee. Notable faculty alumni include (dates in brackets refer to their time as faculty researchers in Dundee): Geoffrey Dutton FRSE (1957 - 1975) Chris Higgins (1981 - 1989) Graham Warren (1985 - 1988) Steve Homans (1988 - 1994) David Glover (1989 - 1999) Sir David Lane (1989 - 2009) Birgitte Lane FRSE FMedSci (1989 - 2009) Cheryll Tickle (1998 - 2007) Sir Pete Downes OBE FMedSci FRSE (1989 - 2009) Tracy Palmer FRS FRSE (2007 - 2018) == References ==
|
{
"page_id": 59182018,
"source": null,
"title": "School of Life Sciences (University of Dundee)"
}
|
In theoretical physics, fine-tuning is the process in which parameters of a model must be adjusted very precisely in order to fit with certain observations. Theories requiring fine-tuning are regarded as problematic in the absence of a known mechanism to explain why the parameters happen to have precisely the observed values that they return. The heuristic rule that parameters in a fundamental physical theory should not be too fine-tuned is called naturalness. == Background == The idea that naturalness will explain fine tuning was brought into question by Nima Arkani-Hamed, a theoretical physicist, in his talk "Why is there a Macroscopic Universe?", a lecture from the mini-series "Multiverse & Fine Tuning" from the "Philosophy of Cosmology" project, a University of Oxford and Cambridge Collaboration 2013. In it he describes how naturalness has usually provided a solution to problems in physics; and that it had usually done so earlier than expected. However, in addressing the problem of the cosmological constant, naturalness has failed to provide an explanation though it would have been expected to have done so a long time ago. The necessity of fine-tuning leads to various problems that do not show that the theories are incorrect, in the sense of falsifying observations, but nevertheless suggest that a piece of the story is missing. For example, the cosmological constant problem (why is the cosmological constant so small?); the hierarchy problem; and the strong CP problem, among others. == Example == An example of a fine-tuning problem considered by the scientific community to have a plausible "natural" solution is the cosmological flatness problem, which is solved if inflationary theory is correct: inflation forces the universe to become very flat, answering the question of why the universe is today observed to be flat to such a high degree. == Measurement == Although
|
{
"page_id": 986051,
"source": null,
"title": "Fine-tuning (physics)"
}
|
fine-tuning was traditionally measured by ad hoc fine-tuning measures, such as the Barbieri-Giudice-Ellis measure, over the past decade many scientists recognized that fine-tuning arguments were a specific application of Bayesian statistics. == See also == Anthropic principle Fine-tuned universe Hierarchy problem Strong CP problem == References == == External links == Quotations related to Fine-tuning (physics) at Wikiquote
|
{
"page_id": 986051,
"source": null,
"title": "Fine-tuning (physics)"
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|
Counterimmunoelectrophoresis is a laboratory technique used to evaluate the binding of an antibody to its antigen, it is similar to immunodiffusion, but with the addition of an applied electrical field across the diffusion medium, usually an agar or polyacrylamide gel. The effect is rapid migration of the antibody and antigen out of their respective wells towards one another to form a line of precipitation, or a precipitin line, indicating binding. == See also == Electrophoresis Immunoelectrophoresis == References == Sherris, John C.; Ryan, Kenneth J.; Ray, C. L. (2004). Sherris medical microbiology: an introduction to infectious diseases. New York: McGraw-Hill. Chp 15 Principles of Laboratory Diagnosis. ISBN 978-0-8385-8529-0. == External links == Counterimmunoelectrophoresis at the U.S. National Library of Medicine Medical Subject Headings (MeSH) https://web.archive.org/web/20070613005107/http://www.lib.mcg.edu/edu/esimmuno/ch4/electro.htm
|
{
"page_id": 8850372,
"source": null,
"title": "Counterimmunoelectrophoresis"
}
|
The molecular formula C22H27N3O (molar mass: 349.4713 g/mol) may refer to: AL-LAD, or 6-allyl-6-nor-LSD CYP-LAD CUMYL-PINACA, also known as SGT-24
|
{
"page_id": 61017029,
"source": null,
"title": "C22H27N3O"
}
|
The molecular formula C23H27N3O2 (molar mass: 377.48 g/mol, exact mass: 377.2103 u) may refer to: CUMYL-THPINACA (SGT-42) Morazone
|
{
"page_id": 61017030,
"source": null,
"title": "C23H27N3O2"
}
|
The Radiological Research Accelerator Facility (RARAF), located on the Columbia University Nevis Laboratories campus in Irvington, New York is a National Institute of Biomedical Imaging and Bioengineering biotechnology resource center (P41) specializing in microbeam technology. The facility is currently built around a 5MV Singletron, a particle accelerator similar to a Van de Graaff. The RARAF microbeam can produce with high accuracy and precision: 70-120 keV/μm alpha particles 8-25 keV/μm protons 0.6 μm diameter focused beam spot 10,000 cells/hour throughput == History == RARAF was conceived by Victor P. Bond and Harald H. Rossi in the late 1960s . Their aim was to provide a source of monoenergetic neutrons designed and operated specifically for studies in radiation biology, dosimetry, and microdosimetry. The facility was built around the 4 MV Van de Graaff particle accelerator that originally served as the injector for the Cosmotron, a 2 GeV accelerator operated at Brookhaven National Laboratory (BNL) in the 1950s and 1960s. RARAF operated at BNL from 1967 until 1980, when it was dismantled to make room for the ISABELLE project, a very large accelerator which was never completed. A new site for RARAF was found at the Nevis Laboratories of Columbia University where its cyclotron was being disassembled. The U.S. Department of Energy provided funds to move RARAF to Nevis Laboratories and reassemble it in a new multi-level facility constructed within the cyclotron building. The new RARAF has been routinely operating for research since mid-1984. RARAF was one of the first three microbeam facilities to be built, and it is the only original microbeam facility still in operation. In 2006 the Van de Graaff was replaced by a 5 MV Singletron from High Voltage Engineering Europa (HVEE) in the Netherlands. == Microbeam Development == As an NIBIB biotechnology resource center, RARAF is dedicated to
|
{
"page_id": 27397063,
"source": null,
"title": "RARAF"
}
|
developing and improving microbeam technologies. Developments focus on adding and improving imaging techniques to the existing microbeam. Neutron and x-ray microbeams are also in development. Some examples of microbeam developments are listed below. === Microbeam lens === In order to focus charged particles in the RARAF microbeam, an electrostatic lens consisting of six quadrupole arranged in two triplets with each successive quadrupole rotated by 90° around its axis, is used. Each quadrupole triplet consists of 4 ceramic rods on which gold electrodes were plated. This design ensures alignment of the three quadrupoles in the triplet and allows a small pole-gap and better focusing properties. === Subcellular targeting === Due to the nature of the RARAF microbeam, sub-cellular targets such as the cell nucleus or the cell cytoplasm have been possible for years. With a sub-micrometre diameter beam routinely available, additional targets within cellular systems are accessible. For instance, preliminary radiation experiments that target mitochondria have been conducted on small airway epithelial cells. === Point and shoot microbeam === During microbeam irradiation, cells to be irradiated are moved to the beam position using a high-speed high-resolution three-axis piezo-electric stage. In order to further reduce targeting time, and making use of the fact that a focused microbeam, unlike a collimated one, is not restricted to a single location on the accelerator exit window, we have implemented a magnetic-coil-based fast deflector, placed between the two quadrupole triplets, that allows deflecting the beam to any position in the field of view of the microscope used to observe the cells during irradiation. Moving the beam to the cell position magnetically can be performed much faster than moving the stage. The deflector used in this system can move the beam to as many as 1000 separate locations per second—more than 5 times the speed of movement
|
{
"page_id": 27397063,
"source": null,
"title": "RARAF"
}
|
of the stage—dramatically reducing the irradiation time. === X-ray microbeam === The RARAF microbeam is adding an x-ray microbeam using characteristic Kα x rays from Ti. The x rays will be generated using an electrostatic lens system to focus protons onto a thick Ti target. The x rays generated are demagnified using a zone plate. By using the already focused proton microbeam to generate characteristic x rays, it is possible to obtain a nearly monochromatic x-ray beam (very low bremsstrahlung yield) and a reasonably small x-ray source (~20 μm diameter), reducing the requirements on the zone plate. There are considerable benefits in using soft x-ray microbeams for both mechanistic and risk estimation end-points. The higher spatial resolution achievable with modern state-of-the-art x-ray optics elements combined with the localized damage produced by the absorption of low energy photons (~1 keV) represents a unique tool to investigate the radio-sensitivity of sub-cellular and eventually sub-nuclear targets. Also, since low-energy x rays undergo very little scattering, by using x rays with an energy of ~5 keV it will be possible to irradiate with micrometre precision individual cells and/or parts of cells up to a few hundred micrometres deep inside a tissue sample in order to investigate the relevance of effects such as the bystander effect in 3-D structured cell systems. == Microbeam experiments == RARAF is also a user facility for biologists interested in performing microbeam studies. The prominent theme of research undertaken using the RARAF microbeam is damage signal transduction, both within cells and between cells, which is of interest due in part to the discovery of the radiation-induced bystander effect. Early inter-cellular signal transduction studies were done with cells plated in 2D monolayers. More recently due to the significance of the extracellular environment and technological developments, studies involving 3D tissue systems, including
|
{
"page_id": 27397063,
"source": null,
"title": "RARAF"
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|
living organisms, have become more common. == Applications of Microfluidics == RARAF is developing various microfluidic devices which add to the irradiation capabilities of the facility. The precision control and manipulation of fluids and biological materials afforded by microfluidics are ideal to interface with the microbeam. Additional microfluidic systems beyond those listed here are currently under development. === Flow and Shoot === The Flow and Shoot microbeam system allows for controlled transport of cells through a microfluidic channel which intersects with the point and shoot microbeam. A high speed camera allows for dynamic targeting of the flowing cells with flow rates of 1–10 mm/s, allowing for total throughput upwards of 100,000 cells per hour. === Optofluidic Cell Manipulation === An optoelectronic tweezer platform has been interfaced with the RARAF microbeam. This allows precision manipulation of cell position before, during, and after irradiation. === Caenorhabditis elegans immobilization === RARAF has implemented a microfluidic platform for the immobilization of Caenorhabditis elegans during microbeam irradiation. The device avoids the use of anesthetics that might interfere with normal physiological processes by capturing the C. elegans worms in tapered microfluidic channels. It is possible to target specific regions of interest within C. elegans using this technology. == Other Technologies == Broad beam irradiations are also possible. Particles with linear energy transfer (LET) between 10 and 200 keV/μm are available utilizing beams of protons, deuterons, helium-3, and helium-4 ions. Additionally, energetic and thermal neutrons and x rays can be used in broad beam irradiations. == Training Scientists == RARAF has trained scientists at all levels: high school students, undergraduates, graduate students, post docs, and senior scientists. The lab estimates that about 45 scientists have received training in microbeam physics and or biology in the past 5 years. RARAF is an active participant in the Columbia University
|
{
"page_id": 27397063,
"source": null,
"title": "RARAF"
}
|
Research Experience for Undergraduates program. In addition, RARAF has become a de facto training center for developers of new microbeams. A virtual microbeam training course, complete with videos and handouts, is also available online. == References ==
|
{
"page_id": 27397063,
"source": null,
"title": "RARAF"
}
|
The molecular formula C23H46N6O13 (molar mass: 614.64 g/mol, exact mass: 614.3123 u) may refer to: Neomycin
|
{
"page_id": 23661513,
"source": null,
"title": "C23H46N6O13"
}
|
Policy gradient methods are a class of reinforcement learning algorithms. Policy gradient methods are a sub-class of policy optimization methods. Unlike value-based methods which learn a value function to derive a policy, policy optimization methods directly learn a policy function π {\displaystyle \pi } that selects actions without consulting a value function. For policy gradient to apply, the policy function π θ {\displaystyle \pi _{\theta }} is parameterized by a differentiable parameter θ {\displaystyle \theta } . == Overview == In policy-based RL, the actor is a parameterized policy function π θ {\displaystyle \pi _{\theta }} , where θ {\displaystyle \theta } are the parameters of the actor. The actor takes as argument the state of the environment s {\displaystyle s} and produces a probability distribution π θ ( ⋅ ∣ s ) {\displaystyle \pi _{\theta }(\cdot \mid s)} . If the action space is discrete, then ∑ a π θ ( a ∣ s ) = 1 {\displaystyle \sum _{a}\pi _{\theta }(a\mid s)=1} . If the action space is continuous, then ∫ a π θ ( a ∣ s ) d a = 1 {\displaystyle \int _{a}\pi _{\theta }(a\mid s)\mathrm {d} a=1} . The goal of policy optimization is to find some θ {\displaystyle \theta } that maximizes the expected episodic reward J ( θ ) {\displaystyle J(\theta )} : J ( θ ) = E π θ [ ∑ t ∈ 0 : T γ t R t | S 0 = s 0 ] {\displaystyle J(\theta )=\mathbb {E} _{\pi _{\theta }}\left[\sum _{t\in 0:T}\gamma ^{t}R_{t}{\Big |}S_{0}=s_{0}\right]} where γ {\displaystyle \gamma } is the discount factor, R t {\displaystyle R_{t}} is the reward at step t {\displaystyle t} , s 0 {\displaystyle s_{0}} is the starting state, and T {\displaystyle T} is the time-horizon (which can be infinite). The
|
{
"page_id": 53349322,
"source": null,
"title": "Policy gradient method"
}
|
policy gradient is defined as ∇ θ J ( θ ) {\displaystyle \nabla _{\theta }J(\theta )} . Different policy gradient methods stochastically estimate the policy gradient in different ways. The goal of any policy gradient method is to iteratively maximize J ( θ ) {\displaystyle J(\theta )} by gradient ascent. Since the key part of any policy gradient method is the stochastic estimation of the policy gradient, they are also studied under the title of "Monte Carlo gradient estimation". == REINFORCE == === Policy gradient === The REINFORCE algorithm was the first policy gradient method. It is based on the identity for the policy gradient ∇ θ J ( θ ) = E π θ [ ∑ t ∈ 0 : T ∇ θ ln π θ ( A t ∣ S t ) ∑ t ∈ 0 : T ( γ t R t ) | S 0 = s 0 ] {\displaystyle \nabla _{\theta }J(\theta )=\mathbb {E} _{\pi _{\theta }}\left[\sum _{t\in 0:T}\nabla _{\theta }\ln \pi _{\theta }(A_{t}\mid S_{t})\;\sum _{t\in 0:T}(\gamma ^{t}R_{t}){\Big |}S_{0}=s_{0}\right]} which can be improved via the "causality trick" ∇ θ J ( θ ) = E π θ [ ∑ t ∈ 0 : T ∇ θ ln π θ ( A t ∣ S t ) ∑ τ ∈ t : T ( γ τ R τ ) | S 0 = s 0 ] {\displaystyle \nabla _{\theta }J(\theta )=\mathbb {E} _{\pi _{\theta }}\left[\sum _{t\in 0:T}\nabla _{\theta }\ln \pi _{\theta }(A_{t}\mid S_{t})\sum _{\tau \in t:T}(\gamma ^{\tau }R_{\tau }){\Big |}S_{0}=s_{0}\right]} Thus, we have an unbiased estimator of the policy gradient: ∇ θ J ( θ ) ≈ 1 N ∑ n = 1 N [ ∑ t ∈ 0 : T ∇ θ ln π θ ( A t , n ∣ S
|
{
"page_id": 53349322,
"source": null,
"title": "Policy gradient method"
}
|
t , n ) ∑ τ ∈ t : T ( γ τ R τ , n ) ] {\displaystyle \nabla _{\theta }J(\theta )\approx {\frac {1}{N}}\sum _{n=1}^{N}\left[\sum _{t\in 0:T}\nabla _{\theta }\ln \pi _{\theta }(A_{t,n}\mid S_{t,n})\sum _{\tau \in t:T}(\gamma ^{\tau }R_{\tau ,n})\right]} where the index n {\displaystyle n} ranges over N {\displaystyle N} rollout trajectories using the policy π θ {\displaystyle \pi _{\theta }} . The score function ∇ θ ln π θ ( A t ∣ S t ) {\displaystyle \nabla _{\theta }\ln \pi _{\theta }(A_{t}\mid S_{t})} can be interpreted as the direction in the parameter space that increases the probability of taking action A t {\displaystyle A_{t}} in state S t {\displaystyle S_{t}} . The policy gradient, then, is a weighted average of all possible directions to increase the probability of taking any action in any state, but weighted by reward signals, so that if taking a certain action in a certain state is associated with high reward, then that direction would be highly reinforced, and vice versa. === Algorithm === The REINFORCE algorithm is a loop: Rollout N {\displaystyle N} trajectories in the environment, using π θ t {\displaystyle \pi _{\theta _{t}}} as the policy function. Compute the policy gradient estimation: g i ← 1 N ∑ n = 1 N [ ∑ t ∈ 0 : T ∇ θ t ln π θ ( A t , n ∣ S t , n ) ∑ τ ∈ t : T ( γ τ R τ , n ) ] {\displaystyle g_{i}\leftarrow {\frac {1}{N}}\sum _{n=1}^{N}\left[\sum _{t\in 0:T}\nabla _{\theta _{t}}\ln \pi _{\theta }(A_{t,n}\mid S_{t,n})\sum _{\tau \in t:T}(\gamma ^{\tau }R_{\tau ,n})\right]} Update the policy by gradient ascent: θ i + 1 ← θ i + α i g i {\displaystyle \theta _{i+1}\leftarrow \theta _{i}+\alpha _{i}g_{i}} Here, α i
|
{
"page_id": 53349322,
"source": null,
"title": "Policy gradient method"
}
|
{\displaystyle \alpha _{i}} is the learning rate at update step i {\displaystyle i} . == Variance reduction == REINFORCE is an on-policy algorithm, meaning that the trajectories used for the update must be sampled from the current policy π θ {\displaystyle \pi _{\theta }} . This can lead to high variance in the updates, as the returns R ( τ ) {\displaystyle R(\tau )} can vary significantly between trajectories. Many variants of REINFORCE has been introduced, under the title of variance reduction. === REINFORCE with baseline === A common way for reducing variance is the REINFORCE with baseline algorithm, based on the following identity: ∇ θ J ( θ ) = E π θ [ ∑ t ∈ 0 : T ∇ θ ln π θ ( A t | S t ) ( ∑ τ ∈ t : T ( γ τ R τ ) − b ( S t ) ) | S 0 = s 0 ] {\displaystyle \nabla _{\theta }J(\theta )=\mathbb {E} _{\pi _{\theta }}\left[\sum _{t\in 0:T}\nabla _{\theta }\ln \pi _{\theta }(A_{t}|S_{t})\left(\sum _{\tau \in t:T}(\gamma ^{\tau }R_{\tau })-b(S_{t})\right){\Big |}S_{0}=s_{0}\right]} for any function b : States → R {\displaystyle b:{\text{States}}\to \mathbb {R} } . This can be proven by applying the previous lemma. The algorithm uses the modified gradient estimator g i ← 1 N ∑ n = 1 N [ ∑ t ∈ 0 : T ∇ θ t ln π θ ( A t , n | S t , n ) ( ∑ τ ∈ t : T ( γ τ R τ , n ) − b i ( S t , n ) ) ] {\displaystyle g_{i}\leftarrow {\frac {1}{N}}\sum _{n=1}^{N}\left[\sum _{t\in 0:T}\nabla _{\theta _{t}}\ln \pi _{\theta }(A_{t,n}|S_{t,n})\left(\sum _{\tau \in t:T}(\gamma ^{\tau }R_{\tau ,n})-b_{i}(S_{t,n})\right)\right]} and the original REINFORCE algorithm is the
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special case where b i ≡ 0 {\displaystyle b_{i}\equiv 0} . === Actor-critic methods === If b i {\textstyle b_{i}} is chosen well, such that b i ( S t ) ≈ ∑ τ ∈ t : T ( γ τ R τ ) = γ τ V π θ i ( S t ) {\textstyle b_{i}(S_{t})\approx \sum _{\tau \in t:T}(\gamma ^{\tau }R_{\tau })=\gamma ^{\tau }V^{\pi _{\theta _{i}}}(S_{t})} , this could significantly decrease variance in the gradient estimation. That is, the baseline should be as close to the value function V π θ i ( S t ) {\displaystyle V^{\pi _{\theta _{i}}}(S_{t})} as possible, approaching the ideal of: ∇ θ J ( θ ) = E π θ [ ∑ t ∈ 0 : T ∇ θ ln π θ ( A t | S t ) ( ∑ τ ∈ t : T ( γ τ R τ ) − γ t V π θ ( S t ) ) | S 0 = s 0 ] {\displaystyle \nabla _{\theta }J(\theta )=\mathbb {E} _{\pi _{\theta }}\left[\sum _{t\in 0:T}\nabla _{\theta }\ln \pi _{\theta }(A_{t}|S_{t})\left(\sum _{\tau \in t:T}(\gamma ^{\tau }R_{\tau })-\gamma ^{t}V^{\pi _{\theta }}(S_{t})\right){\Big |}S_{0}=s_{0}\right]} Note that, as the policy π θ t {\displaystyle \pi _{\theta _{t}}} updates, the value function V π θ i ( S t ) {\displaystyle V^{\pi _{\theta _{i}}}(S_{t})} updates as well, so the baseline should also be updated. One common approach is to train a separate function that estimates the value function, and use that as the baseline. This is one of the actor-critic methods, where the policy function is the actor and the value function is the critic. The Q-function Q π {\displaystyle Q^{\pi }} can also be used as the critic, since ∇ θ J ( θ ) = E π θ [
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∑ 0 ≤ t ≤ T γ t ∇ θ ln π θ ( A t | S t ) ⋅ Q π θ ( S t , A t ) | S 0 = s 0 ] {\displaystyle \nabla _{\theta }J(\theta )=E_{\pi _{\theta }}\left[\sum _{0\leq t\leq T}\gamma ^{t}\nabla _{\theta }\ln \pi _{\theta }(A_{t}|S_{t})\cdot Q^{\pi _{\theta }}(S_{t},A_{t}){\Big |}S_{0}=s_{0}\right]} by a similar argument using the tower law. Subtracting the value function as a baseline, we find that the advantage function A π ( S , A ) = Q π ( S , A ) − V π ( S ) {\displaystyle A^{\pi }(S,A)=Q^{\pi }(S,A)-V^{\pi }(S)} can be used as the critic as well: ∇ θ J ( θ ) = E π θ [ ∑ 0 ≤ t ≤ T γ t ∇ θ ln π θ ( A t | S t ) ⋅ A π θ ( S t , A t ) | S 0 = s 0 ] {\displaystyle \nabla _{\theta }J(\theta )=E_{\pi _{\theta }}\left[\sum _{0\leq t\leq T}\gamma ^{t}\nabla _{\theta }\ln \pi _{\theta }(A_{t}|S_{t})\cdot A^{\pi _{\theta }}(S_{t},A_{t}){\Big |}S_{0}=s_{0}\right]} In summary, there are many unbiased estimators for ∇ θ J θ {\textstyle \nabla _{\theta }J_{\theta }} , all in the form of: ∇ θ J ( θ ) = E π θ [ ∑ 0 ≤ t ≤ T ∇ θ ln π θ ( A t | S t ) ⋅ Ψ t | S 0 = s 0 ] {\displaystyle \nabla _{\theta }J(\theta )=E_{\pi _{\theta }}\left[\sum _{0\leq t\leq T}\nabla _{\theta }\ln \pi _{\theta }(A_{t}|S_{t})\cdot \Psi _{t}{\Big |}S_{0}=s_{0}\right]} where Ψ t {\textstyle \Psi _{t}} is any linear sum of the following terms: ∑ 0 ≤ τ ≤ T ( γ τ R τ ) {\textstyle \sum _{0\leq \tau \leq T}(\gamma ^{\tau }R_{\tau })}
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: never used. γ t ∑ t ≤ τ ≤ T ( γ τ − t R τ ) {\textstyle \gamma ^{t}\sum _{t\leq \tau \leq T}(\gamma ^{\tau -t}R_{\tau })} : used by the REINFORCE algorithm. γ t ∑ t ≤ τ ≤ T ( γ τ − t R τ ) − b ( S t ) {\textstyle \gamma ^{t}\sum _{t\leq \tau \leq T}(\gamma ^{\tau -t}R_{\tau })-b(S_{t})} : used by the REINFORCE with baseline algorithm. γ t ( R t + γ V π θ ( S t + 1 ) − V π θ ( S t ) ) {\textstyle \gamma ^{t}\left(R_{t}+\gamma V^{\pi _{\theta }}(S_{t+1})-V^{\pi _{\theta }}(S_{t})\right)} : 1-step TD learning. γ t Q π θ ( S t , A t ) {\textstyle \gamma ^{t}Q^{\pi _{\theta }}(S_{t},A_{t})} . γ t A π θ ( S t , A t ) {\textstyle \gamma ^{t}A^{\pi _{\theta }}(S_{t},A_{t})} . Some more possible Ψ t {\textstyle \Psi _{t}} are as follows, with very similar proofs. γ t ( R t + γ R t + 1 + γ 2 V π θ ( S t + 2 ) − V π θ ( S t ) ) {\textstyle \gamma ^{t}\left(R_{t}+\gamma R_{t+1}+\gamma ^{2}V^{\pi _{\theta }}(S_{t+2})-V^{\pi _{\theta }}(S_{t})\right)} : 2-step TD learning. γ t ( ∑ k = 0 n − 1 γ k R t + k + γ n V π θ ( S t + n ) − V π θ ( S t ) ) {\textstyle \gamma ^{t}\left(\sum _{k=0}^{n-1}\gamma ^{k}R_{t+k}+\gamma ^{n}V^{\pi _{\theta }}(S_{t+n})-V^{\pi _{\theta }}(S_{t})\right)} : n-step TD learning. γ t ∑ n = 1 ∞ λ n − 1 1 − λ ⋅ ( ∑ k = 0 n − 1 γ k R t + k + γ n V π θ ( S t + n )
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− V π θ ( S t ) ) {\textstyle \gamma ^{t}\sum _{n=1}^{\infty }{\frac {\lambda ^{n-1}}{1-\lambda }}\cdot \left(\sum _{k=0}^{n-1}\gamma ^{k}R_{t+k}+\gamma ^{n}V^{\pi _{\theta }}(S_{t+n})-V^{\pi _{\theta }}(S_{t})\right)} : TD(λ) learning, also known as GAE (generalized advantage estimate). This is obtained by an exponentially decaying sum of the n-step TD learning ones. == Natural policy gradient == The natural policy gradient method is a variant of the policy gradient method, proposed by Sham Kakade in 2001. Unlike standard policy gradient methods, which depend on the choice of parameters θ {\displaystyle \theta } (making updates coordinate-dependent), the natural policy gradient aims to provide a coordinate-free update, which is geometrically "natural". === Motivation === Standard policy gradient updates θ i + 1 = θ i + α ∇ θ J ( θ i ) {\displaystyle \theta _{i+1}=\theta _{i}+\alpha \nabla _{\theta }J(\theta _{i})} solve a constrained optimization problem: { max θ i + 1 J ( θ i ) + ( θ i + 1 − θ i ) T ∇ θ J ( θ i ) ‖ θ i + 1 − θ i ‖ ≤ α ⋅ ‖ ∇ θ J ( θ i ) ‖ {\displaystyle {\begin{cases}\max _{\theta _{i+1}}J(\theta _{i})+(\theta _{i+1}-\theta _{i})^{T}\nabla _{\theta }J(\theta _{i})\\\|\theta _{i+1}-\theta _{i}\|\leq \alpha \cdot \|\nabla _{\theta }J(\theta _{i})\|\end{cases}}} While the objective (linearized improvement) is geometrically meaningful, the Euclidean constraint ‖ θ i + 1 − θ i ‖ {\displaystyle \|\theta _{i+1}-\theta _{i}\|} introduces coordinate dependence. To address this, the natural policy gradient replaces the Euclidean constraint with a Kullback–Leibler divergence (KL) constraint: { max θ i + 1 J ( θ i ) + ( θ i + 1 − θ i ) T ∇ θ J ( θ i ) D ¯ K L ( π θ i + 1 ‖ π θ i ) ≤ ϵ
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{\displaystyle {\begin{cases}\max _{\theta _{i+1}}J(\theta _{i})+(\theta _{i+1}-\theta _{i})^{T}\nabla _{\theta }J(\theta _{i})\\{\bar {D}}_{KL}(\pi _{\theta _{i+1}}\|\pi _{\theta _{i}})\leq \epsilon \end{cases}}} where the KL divergence between two policies is averaged over the state distribution under policy π θ i {\displaystyle \pi _{\theta _{i}}} . That is, D ¯ K L ( π θ i + 1 ‖ π θ i ) := E s ∼ π θ i [ D K L ( π θ i + 1 ( ⋅ | s ) ‖ π θ i ( ⋅ | s ) ) ] {\displaystyle {\bar {D}}_{KL}(\pi _{\theta _{i+1}}\|\pi _{\theta _{i}}):=\mathbb {E} _{s\sim \pi _{\theta _{i}}}[D_{KL}(\pi _{\theta _{i+1}}(\cdot |s)\|\pi _{\theta _{i}}(\cdot |s))]} This ensures updates are invariant to invertible affine parameter transformations. === Fisher information approximation === For small ϵ {\displaystyle \epsilon } , the KL divergence is approximated by the Fisher information metric: D ¯ K L ( π θ i + 1 ‖ π θ i ) ≈ 1 2 ( θ i + 1 − θ i ) T F ( θ i ) ( θ i + 1 − θ i ) {\displaystyle {\bar {D}}_{KL}(\pi _{\theta _{i+1}}\|\pi _{\theta _{i}})\approx {\frac {1}{2}}(\theta _{i+1}-\theta _{i})^{T}F(\theta _{i})(\theta _{i+1}-\theta _{i})} where F ( θ ) {\displaystyle F(\theta )} is the Fisher information matrix of the policy, defined as: F ( θ ) = E s , a ∼ π θ [ ∇ θ ln π θ ( a | s ) ( ∇ θ ln π θ ( a | s ) ) T ] {\displaystyle F(\theta )=\mathbb {E} _{s,a\sim \pi _{\theta }}\left[\nabla _{\theta }\ln \pi _{\theta }(a|s)\left(\nabla _{\theta }\ln \pi _{\theta }(a|s)\right)^{T}\right]} This transforms the problem into a problem in quadratic programming, yielding the natural policy gradient update: θ i + 1 = θ i + α F ( θ i
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) − 1 ∇ θ J ( θ i ) {\displaystyle \theta _{i+1}=\theta _{i}+\alpha F(\theta _{i})^{-1}\nabla _{\theta }J(\theta _{i})} The step size α {\displaystyle \alpha } is typically adjusted to maintain the KL constraint, with α ≈ 2 ϵ ( ∇ θ J ( θ i ) ) T F ( θ i ) − 1 ∇ θ J ( θ i ) {\textstyle \alpha \approx {\sqrt {\frac {2\epsilon }{(\nabla _{\theta }J(\theta _{i}))^{T}F(\theta _{i})^{-1}\nabla _{\theta }J(\theta _{i})}}}} . Inverting F ( θ ) {\displaystyle F(\theta )} is computationally intensive, especially for high-dimensional parameters (e.g., neural networks). Practical implementations often use approximations. == Trust Region Policy Optimization (TRPO) == Trust Region Policy Optimization (TRPO) is a policy gradient method that extends the natural policy gradient approach by enforcing a trust region constraint on policy updates. Developed by Schulman et al. in 2015, TRPO improves upon the natural policy gradient method. The natural gradient descent is theoretically optimal, if the objective is truly a quadratic function, but this is only an approximation. TRPO's line search and KL constraint attempts to restrict the solution to within a "trust region" in which this approximation does not break down. This makes TRPO more robust in practice. === Formulation === Like natural policy gradient, TRPO iteratively updates the policy parameters θ {\displaystyle \theta } by solving a constrained optimization problem specified coordinate-free: { max θ L ( θ , θ i ) D ¯ K L ( π θ ‖ π θ i ) ≤ ϵ {\displaystyle {\begin{cases}\max _{\theta }L(\theta ,\theta _{i})\\{\bar {D}}_{KL}(\pi _{\theta }\|\pi _{\theta _{i}})\leq \epsilon \end{cases}}} where L ( θ , θ i ) = E s , a ∼ π θ i [ π θ ( a | s ) π θ i ( a | s ) A π θ i
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( s , a ) ] {\displaystyle L(\theta ,\theta _{i})=\mathbb {E} _{s,a\sim \pi _{\theta _{i}}}\left[{\frac {\pi _{\theta }(a|s)}{\pi _{\theta _{i}}(a|s)}}A^{\pi _{\theta _{i}}}(s,a)\right]} is the surrogate advantage, measuring the performance of π θ {\displaystyle \pi _{\theta }} relative to the old policy π θ i {\displaystyle \pi _{\theta _{i}}} . ϵ {\displaystyle \epsilon } is the trust region radius. Note that in general, other surrogate advantages are possible: L ( θ , θ i ) = E s , a ∼ π θ i [ π θ ( a | s ) π θ i ( a | s ) Ψ π θ i ( s , a ) ] {\displaystyle L(\theta ,\theta _{i})=\mathbb {E} _{s,a\sim \pi _{\theta _{i}}}\left[{\frac {\pi _{\theta }(a|s)}{\pi _{\theta _{i}}(a|s)}}\Psi ^{\pi _{\theta _{i}}}(s,a)\right]} where Ψ {\displaystyle \Psi } is any linear sum of the previously mentioned type. Indeed, OpenAI recommended using the Generalized Advantage Estimate, instead of the plain advantage A π θ {\displaystyle A^{\pi _{\theta }}} . The surrogate advantage L ( θ , θ t ) {\displaystyle L(\theta ,\theta _{t})} is designed to align with the policy gradient ∇ θ J ( θ ) {\displaystyle \nabla _{\theta }J(\theta )} . Specifically, when θ = θ t {\displaystyle \theta =\theta _{t}} , ∇ θ L ( θ , θ t ) {\displaystyle \nabla _{\theta }L(\theta ,\theta _{t})} equals the policy gradient derived from the advantage function: ∇ θ J ( θ ) = E ( s , a ) ∼ π θ [ ∇ θ ln π θ ( a | s ) ⋅ A π θ ( s , a ) ] = ∇ θ L ( θ , θ t ) {\displaystyle \nabla _{\theta }J(\theta )=\mathbb {E} _{(s,a)\sim \pi _{\theta }}\left[\nabla _{\theta }\ln \pi _{\theta }(a|s)\cdot A^{\pi _{\theta }}(s,a)\right]=\nabla _{\theta }L(\theta ,\theta _{t})}
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However, when θ ≠ θ i {\displaystyle \theta \neq \theta _{i}} , this is not necessarily true. Thus it is a "surrogate" of the real objective. As with natural policy gradient, for small policy updates, TRPO approximates the surrogate advantage and KL divergence using Taylor expansions around θ t {\displaystyle \theta _{t}} : L ( θ , θ i ) ≈ g T ( θ − θ i ) , D ¯ KL ( π θ ‖ π θ i ) ≈ 1 2 ( θ − θ i ) T H ( θ − θ i ) , {\displaystyle {\begin{aligned}L(\theta ,\theta _{i})&\approx g^{T}(\theta -\theta _{i}),\\{\bar {D}}_{\text{KL}}(\pi _{\theta }\|\pi _{\theta _{i}})&\approx {\frac {1}{2}}(\theta -\theta _{i})^{T}H(\theta -\theta _{i}),\end{aligned}}} where: g = ∇ θ L ( θ , θ i ) | θ = θ i {\displaystyle g=\nabla _{\theta }L(\theta ,\theta _{i}){\big |}_{\theta =\theta _{i}}} is the policy gradient. F = ∇ θ 2 D ¯ KL ( π θ ‖ π θ i ) | θ = θ i {\displaystyle F=\nabla _{\theta }^{2}{\bar {D}}_{\text{KL}}(\pi _{\theta }\|\pi _{\theta _{i}}){\big |}_{\theta =\theta _{i}}} is the Fisher information matrix. This reduces the problem to a quadratic optimization, yielding the natural policy gradient update: θ i + 1 = θ i + 2 ϵ g T F − 1 g F − 1 g . {\displaystyle \theta _{i+1}=\theta _{i}+{\sqrt {\frac {2\epsilon }{g^{T}F^{-1}g}}}F^{-1}g.} So far, this is essentially the same as natural gradient method. However, TRPO improves upon it by two modifications: Use conjugate gradient method to solve for x {\displaystyle x} in F x = g {\displaystyle Fx=g} iteratively without explicit matrix inversion. Use backtracking line search to ensure the trust-region constraint is satisfied. Specifically, it backtracks the step size to ensure the KL constraint and policy improvement. That is, it tests each of the
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following test-solutions θ i + 1 = θ i + 2 ϵ x T F x x , θ i + α 2 ϵ x T F x x , θ i + α 2 2 ϵ x T F x x , … {\displaystyle \theta _{i+1}=\theta _{i}+{\sqrt {\frac {2\epsilon }{x^{T}Fx}}}x,\;\theta _{i}+\alpha {\sqrt {\frac {2\epsilon }{x^{T}Fx}}}x,\;\theta _{i}+\alpha ^{2}{\sqrt {\frac {2\epsilon }{x^{T}Fx}}}x,\;\dots } until it finds one that both satisfies the KL constraint D ¯ K L ( π θ i + 1 ‖ π θ i ) ≤ ϵ {\displaystyle {\bar {D}}_{KL}(\pi _{\theta _{i+1}}\|\pi _{\theta _{i}})\leq \epsilon } and results in a higher L ( θ i + 1 , θ i ) ≥ L ( θ i , θ i ) {\displaystyle L(\theta _{i+1},\theta _{i})\geq L(\theta _{i},\theta _{i})} . Here, α ∈ ( 0 , 1 ) {\displaystyle \alpha \in (0,1)} is the backtracking coefficient. == Proximal Policy Optimization (PPO) == A further improvement is proximal policy optimization (PPO), which avoids even computing F ( θ ) {\displaystyle F(\theta )} and F ( θ ) − 1 {\displaystyle F(\theta )^{-1}} via a first-order approximation using clipped probability ratios. Specifically, instead of maximizing the surrogate advantage max θ L ( θ , θ t ) = E s , a ∼ π θ t [ π θ ( a | s ) π θ t ( a | s ) A π θ t ( s , a ) ] {\displaystyle \max _{\theta }L(\theta ,\theta _{t})=\mathbb {E} _{s,a\sim \pi _{\theta _{t}}}\left[{\frac {\pi _{\theta }(a|s)}{\pi _{\theta _{t}}(a|s)}}A^{\pi _{\theta _{t}}}(s,a)\right]} under a KL divergence constraint, it directly inserts the constraint into the surrogate advantage: max θ E s , a ∼ π θ t [ { min ( π θ ( a | s ) π θ t ( a | s
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) , 1 + ϵ ) A π θ t ( s , a ) if A π θ t ( s , a ) > 0 max ( π θ ( a | s ) π θ t ( a | s ) , 1 − ϵ ) A π θ t ( s , a ) if A π θ t ( s , a ) < 0 ] {\displaystyle \max _{\theta }\mathbb {E} _{s,a\sim \pi _{\theta _{t}}}\left[{\begin{cases}\min \left({\frac {\pi _{\theta }(a|s)}{\pi _{\theta _{t}}(a|s)}},1+\epsilon \right)A^{\pi _{\theta _{t}}}(s,a)&{\text{ if }}A^{\pi _{\theta _{t}}}(s,a)>0\\\max \left({\frac {\pi _{\theta }(a|s)}{\pi _{\theta _{t}}(a|s)}},1-\epsilon \right)A^{\pi _{\theta _{t}}}(s,a)&{\text{ if }}A^{\pi _{\theta _{t}}}(s,a)<0\end{cases}}\right]} and PPO maximizes the surrogate advantage by stochastic gradient descent, as usual. In words, gradient-ascending the new surrogate advantage function means that, at some state s , a {\displaystyle s,a} , if the advantage is positive: A π θ t ( s , a ) > 0 {\displaystyle A^{\pi _{\theta _{t}}}(s,a)>0} , then the gradient should direct θ {\displaystyle \theta } towards the direction that increases the probability of performing action a {\displaystyle a} under the state s {\displaystyle s} . However, as soon as θ {\displaystyle \theta } has changed so much that π θ ( a | s ) ≥ ( 1 + ϵ ) π θ t ( a | s ) {\displaystyle \pi _{\theta }(a|s)\geq (1+\epsilon )\pi _{\theta _{t}}(a|s)} , then the gradient should stop pointing it in that direction. And similarly if A π θ t ( s , a ) < 0 {\displaystyle A^{\pi _{\theta _{t}}}(s,a)<0} . Thus, PPO avoids pushing the parameter update too hard, and avoids changing the policy too much. To be more precise, to update θ t {\displaystyle \theta _{t}} to θ t + 1 {\displaystyle \theta _{t+1}} requires multiple update steps on the
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same batch of data. It would initialize θ = θ t {\displaystyle \theta =\theta _{t}} , then repeatedly apply gradient descent (such as the Adam optimizer) to update θ {\displaystyle \theta } until the surrogate advantage has stabilized. It would then assign θ t + 1 {\displaystyle \theta _{t+1}} to θ {\displaystyle \theta } , and do it again. During this inner-loop, the first update to θ {\displaystyle \theta } would not hit the 1 − ϵ , 1 + ϵ {\displaystyle 1-\epsilon ,1+\epsilon } bounds, but as θ {\displaystyle \theta } is updated further and further away from θ t {\displaystyle \theta _{t}} , it eventually starts hitting the bounds. For each such bound hit, the corresponding gradient becomes zero, and thus PPO avoid updating θ {\displaystyle \theta } too far away from θ t {\displaystyle \theta _{t}} . This is important, because the surrogate loss assumes that the state-action pair s , a {\displaystyle s,a} is sampled from what the agent would see if the agent runs the policy π θ t {\displaystyle \pi _{\theta _{t}}} , but policy gradient should be on-policy. So, as θ {\displaystyle \theta } changes, the surrogate loss becomes more and more off-policy. This is why keeping θ {\displaystyle \theta } proximal to θ t {\displaystyle \theta _{t}} is necessary. If there is a reference policy π ref {\displaystyle \pi _{\text{ref}}} that the trained policy should not diverge too far from, then additional KL divergence penalty can be added: − β E s , a ∼ π θ t [ log ( π θ ( a | s ) π ref ( a | s ) ) ] {\displaystyle -\beta \mathbb {E} _{s,a\sim \pi _{\theta _{t}}}\left[\log \left({\frac {\pi _{\theta }(a|s)}{\pi _{\text{ref}}(a|s)}}\right)\right]} where β {\displaystyle \beta } adjusts the strength of the penalty.
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This has been used in training reasoning language models with reinforcement learning from human feedback. The KL divergence penalty term can be estimated with lower variance using the equivalent form (see f-divergence for details): − β E s , a ∼ π θ t [ log ( π θ ( a | s ) π ref ( a | s ) ) + π ref ( a | s ) π θ ( a | s ) − 1 ] {\displaystyle -\beta \mathbb {E} _{s,a\sim \pi _{\theta _{t}}}\left[\log \left({\frac {\pi _{\theta }(a|s)}{\pi _{\text{ref}}(a|s)}}\right)+{\frac {\pi _{\text{ref}}(a|s)}{\pi _{\theta }(a|s)}}-1\right]} === Group Relative Policy Optimization (GRPO) === The Group Relative Policy Optimization (GRPO) is a minor variant of PPO that omits the value function estimator V {\displaystyle V} . Instead, for each state s {\displaystyle s} , it samples multiple actions a 1 , … , a G {\displaystyle a_{1},\dots ,a_{G}} from the policy π θ t {\displaystyle \pi _{\theta _{t}}} , then calculate the group-relative advantage A π θ t ( s , a j ) = r ( s , a j ) − μ σ {\displaystyle A^{\pi _{\theta _{t}}}(s,a_{j})={\frac {r(s,a_{j})-\mu }{\sigma }}} where μ , σ {\displaystyle \mu ,\sigma } are the mean and standard deviation of r ( s , a 1 ) , … , r ( s , a G ) {\displaystyle r(s,a_{1}),\dots ,r(s,a_{G})} . That is, it is the standard score of the rewards. Then, it maximizes the PPO objective, averaged over all actions: max θ 1 G ∑ i = 1 G E ( s , a 1 , … , a G ) ∼ π θ t [ { min ( π θ ( a i | s ) π θ t ( a i | s ) , 1 + ϵ )
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A π θ t ( s , a i ) if A π θ t ( s , a i ) > 0 max ( π θ ( a i | s ) π θ t ( a i | s ) , 1 − ϵ ) A π θ t ( s , a i ) if A π θ t ( s , a i ) < 0 ] {\displaystyle \max _{\theta }{\frac {1}{G}}\sum _{i=1}^{G}\mathbb {E} _{(s,a_{1},\dots ,a_{G})\sim \pi _{\theta _{t}}}\left[{\begin{cases}\min \left({\frac {\pi _{\theta }(a_{i}|s)}{\pi _{\theta _{t}}(a_{i}|s)}},1+\epsilon \right)A^{\pi _{\theta _{t}}}(s,a_{i})&{\text{ if }}A^{\pi _{\theta _{t}}}(s,a_{i})>0\\\max \left({\frac {\pi _{\theta }(a_{i}|s)}{\pi _{\theta _{t}}(a_{i}|s)}},1-\epsilon \right)A^{\pi _{\theta _{t}}}(s,a_{i})&{\text{ if }}A^{\pi _{\theta _{t}}}(s,a_{i})<0\end{cases}}\right]} Intuitively, each policy update step in GRPO makes the policy more likely to respond to each state with an action that performed relatively better than other actions tried at that state, and less likely to respond with one that performed relatively worse. As before, the KL penalty term can be applied to encourage the trained policy to stay close to a reference policy. GRPO was first proposed in the context of training reasoning language models by researchers at DeepSeek. == See also == Reinforcement learning Deep reinforcement learning Actor-critic method == References == Sutton, Richard S.; Barto, Andrew G. (2018). Reinforcement learning: an introduction. Adaptive computation and machine learning series (2 ed.). Cambridge, Massachusetts: The MIT Press. ISBN 978-0-262-03924-6. Bertsekas, Dimitri P. (2019). Reinforcement learning and optimal control (2 ed.). Belmont, Massachusetts: Athena Scientific. ISBN 978-1-886529-39-7. Grossi, Csaba (2010). Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning (1 ed.). Cham: Springer International Publishing. ISBN 978-3-031-00423-0. Mohamed, Shakir; Rosca, Mihaela; Figurnov, Michael; Mnih, Andriy (2020). "Monte Carlo Gradient Estimation in Machine Learning". Journal of Machine Learning Research. 21 (132): 1–62. arXiv:1906.10652. ISSN 1533-7928. == External links == Weng,
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Lilian (2018-04-08). "Policy Gradient Algorithms". lilianweng.github.io. Retrieved 2025-01-25. "Vanilla Policy Gradient — Spinning Up documentation". spinningup.openai.com. Retrieved 2025-01-25.
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Intramolecular aglycon delivery is a synthetic strategy for the construction of glycans. This approach is generally used for the formation of difficult glycosidic linkages. == Introduction == Glycosylation reactions are very important reactions in carbohydrate chemistry, leading to the synthesis of oligosaccharides, preferably in a stereoselective manner. The stereoselectivity of these reactions has been shown to be affected by both the nature and the configuration of the protecting group at C-2 on the glycosyl donor ring. While 1,2-trans-glycosides (e.g. α-mannosides and β-glucosides) can be synthesised easily in the presence of a participating group (such as OAc, or NHAc) at the C-2 position in the glycosyl donor ring, 1,2-cis-glycosides are more difficult to prepare. 1,2-cis-glycosides with the α configuration (e.g. glucosides or galactosides) can often be prepared using a non-participating protecting group (such as Bn, or All) on the C-2 hydroxy group. However, 1,2-cis-glycosides with the β configuration are the most difficult to achieve, and present the greatest challenge in glycosylation reactions. One of the most recent approaches to prepare 1,2-cis-β-glycosides in a stereospecific manner is termed ‘Intramolecular Aglycon Delivery’, and various methods have been developed based on this approach. In this approach, the glycosyl acceptor is tethered onto the C-2-O-protecting group (X) in the first step. Upon activation of the glycosyl donor group (Y) (usually SR, OAc, or Br group) in the next step, the tethered aglycon traps the developing oxocarbenium ion at C-1, and is transferred from the same face as OH-2, forming the glycosidic bond stereospecifically. The yield of this reaction drops as the bulkiness of the alcohol increases. == Intramolecular Aglycon Delivery (IAD) methods == === Carbon tethering === ==== Acid-catalysed tethering to enol ethers ==== In this method, the glycosyl donor is protected at the C-2 position by an OAc group. The C-2-OAc protecting group is
|
{
"page_id": 22416330,
"source": null,
"title": "Intramolecular aglycon delivery"
}
|
transformed into an enol ether by the Tebbe reagent (Cp2Ti=CH2), and then the glycosyl acceptor is tethered to the enol ether under acid-catalysed conditions to generate a mixed acetal. In a subsequent step, the β-mannoside is formed upon activation of the anomeric leaving group (Y), followed by work up. ==== Iodonium tethering to enol ethers ==== This method is similar to the previous method in that the glycosyl donor is protected at C-2 by an OAc group, which is converted into an enol ether by the Tebbe reagent. However, in this approach, N-iodosuccinimide (NIS) is used to tether the glycosyl acceptor to the enol ether, and in a second step, activation of the anomeric leaving group leads to intramolecular delivery of the aglycon to C-1 and formation of the 1,2-cis-glycoside product. ==== Iodonium tethering to prop-1-enyl ethers ==== The glycosyl donor is protected at C-2 by OAll group. The allyl group is then isomerized to a prop-1-enyl ether using a rhodium hydride generated from Wilkinson's catalyst ((PPh3)3RhCl) and butyllithium (BuLi). The resulting enol ether is then treated with NIS and the glycosyl acceptor to generate a mixed acetal. The 1,2-cis (e.g. β-mannosyl) product is formed in a final step through activation of the anomeric leaving group, delivery of the aglycon from the mixed acetal and finally hydrolytic work-up to remove the remains of the propenyl ether from O-2. ==== Oxidative tethering to para-methoxybenzyl (PMB) ethers ==== In this method, the glycosyl donor is protected at C-2 by a para-methoxybenzyl (PMB) group. The glycosyl acceptor is then tethered at the benzylic position of the PMB protecting group in the presence of 2,3-Dichloro-5,6-dicyano-1,4-benzoquinone (DDQ). The anomeric leaving group (Y) is then activated, and the developing oxocarbenium ion is captured by the tethered aglycon alcohol (OR) to give 1,2-cis β-glycoside product. ==== Solid-supported
|
{
"page_id": 22416330,
"source": null,
"title": "Intramolecular aglycon delivery"
}
|
oxidative tethering to para-alkoxybenzyl ethers ==== This is a modification of the method of oxidative tethering to a para-methoxybenzyl ether. The difference here is that the para-alkoxybenzyl group is attached to a solid support; the β-mannoside product is released into the solution phase in the last step, while the by-products remain attached to the solid phase. This makes the purification of the β-glycoside easier; it is formed as the almost exclusive product. === Silicon tethering === The initial step in this method involves the formation of a silyl ether at the C-2 hydroxy group of the glycosyl donor upon addition of dimethyldichlorosilane in the presence of a strong base such as butyllithium (BuLi); then the glycosyl acceptor is added to form a mixed silaketal. Activation of the anomeric leaving group in the presence of a hindered base then leads to the β-glycoside. A modified silicon-tethering method involves mixing of the glycosyl donor with the glycosyl acceptor and dimethyldichlorosilane in the presence of imidazole to give the mixed silaketal in one pot. Activation of the tethered intermediate then leads to the β-glycoside product. == See also == Chemical glycosylation Glycosyl halide == References ==
|
{
"page_id": 22416330,
"source": null,
"title": "Intramolecular aglycon delivery"
}
|
Combustion analysis is a method used in both organic chemistry and analytical chemistry to determine the elemental composition (more precisely empirical formula) of a pure organic compound by combusting the sample under conditions where the resulting combustion products can be quantitatively analyzed. Once the number of moles of each combustion product has been determined the empirical formula or a partial empirical formula of the original compound can be calculated. Applications for combustion analysis involve only the elements of carbon (C), hydrogen (H), nitrogen (N), and sulfur (S) as combustion of materials containing them convert these elements to their oxidized form (CO2, H2O, NO or NO2, and SO2) under high temperature high oxygen conditions. Notable interests for these elements involve measuring total nitrogen in food or feed to determine protein percentage, measuring sulfur in petroleum products, or measuring total organic carbon (TOC) in water. == History == The method was invented by Joseph Louis Gay-Lussac. Justus von Liebig studied the method while working with Gay-Lussac between 1822 and 1824 and improved the method in the following years to a level that it could be used as standard procedure for organic analysis. == Combustion train == A combustion train is an analytical tool for the determination of elemental composition of a chemical compound. With knowledge of elemental composition a chemical formula can be derived. The combustion train allows the determination of carbon and hydrogen in a succession of steps: combustion of the sample at high temperatures with Copper(II) oxide as the oxidizing agent, collection of the resulting gas in a hygroscopic agent (magnesium perchlorate or calcium chloride) to trap generated water, collection of the remainder gas in a strong base (for instance potassium hydroxide) to trap generated carbon dioxide. Analytical determination of the amounts of water and carbon dioxide produced from a
|
{
"page_id": 3738572,
"source": null,
"title": "Combustion analysis"
}
|
known amount of sample gives the empirical formula. For every hydrogen atom in the compound 1/2 equivalent of water is produced, and for every carbon atom in the compound 1 equivalent of carbon dioxide is produced. Nowadays, modern instruments are sufficiently automated to be able to do these analyses routinely. Samples required are also extremely small — 0.5 mg of sample can be sufficient to give satisfactory CHN analysis. == CHN analyzer == A CHN analyzer (also known as a carbon hydrogen and nitrogen analyzer) is a scientific instrument which is used to measure carbon, hydrogen and nitrogen elemental concentrations in a given sample with accuracy and precision. Sample sizes are most often just a few milligrams, but may differ depending on system. For some sample matrices larger mass is preferred due to sample heterogeneity. These analysers are capable of handling a wide variety of sample types, including solids, liquids, volatile and viscous samples, in the fields of pharmaceuticals, polymers, chemicals, environment, food and energy. This instrument calculates the percentages of elemental concentrations based on the Dumas method, using flash combustion of the sample to cause an instantaneous oxidization into simple compounds which are then detected with thermal conductivity detection or infrared spectroscopy. Separation of interference is done by chemical reagents. == Modern methods == The water vapor, carbon dioxide and other products can be separated via gas chromatography and analysed via a thermal conductivity detector. == See also == Elementar, a large manufacturer of combustion analyzers LECO Corporation, a large manufacturer of combustion analyzers Kjeldahl method, an alternative analysis of CHN content == References == == External links == ECO Core Analytical Services: Elemental Analysis Geostandards and Geological Research Luvak Laboratories
|
{
"page_id": 3738572,
"source": null,
"title": "Combustion analysis"
}
|
Western equine encephalitis virus is the causative agent of the relatively uncommon viral disease Western equine encephalitis (WEE). An alphavirus of the family Togaviridae, the WEE virus is an arbovirus (arthropod-borne virus) transmitted by mosquitoes of the genera Culex and Culiseta. WEE is a recombinant virus between two other alphaviruses, an ancestral Sindbis virus-like virus, and an ancestral Eastern equine encephalitis virus-like virus. There have been under 700 confirmed cases in the U.S. since 1964. This virus contains an envelope that is made up of glycoproteins and nucleic acids. The virus is transmitted to people and horses by bites from infected mosquitoes (Culex tarsalis and Aedes taeniorhynchus) and birds during wet, summer months. According to the CDC, geographic occurrence for this virus is worldwide, and tends to be more prevalent in places in and around swampy areas where human populations tend to be limited. In North America, WEE is seen primarily in U. S. states and Canadian provinces west of the Mississippi River. The disease is also seen in countries of South America. WEE is commonly a subclinical infection; symptomatic infections are uncommon. However, the disease can cause serious sequelae in infants and children. Unlike Eastern equine encephalitis, the overall mortality of WEE is low (approximately 4%) and is associated mostly with infection in the elderly. Approximately 15–20% of horses that acquire the virus will die or be put down. There is no human vaccine for WEE and there are no licensed therapeutic drugs in the U.S. for this infection. The virus affects the brain and spinal cord of the infected host. == History == WEE was discovered in 1930 when a number of horses in the San Joaquin Valley of California, USA died of a mysterious encephalitis. Karl Friedrich Meyer investigated but was not able to isolate the pathogen
|
{
"page_id": 7539661,
"source": null,
"title": "Western equine encephalitis virus"
}
|
from necropsies of horses that had been dead for some time and needed samples from an animal in the earlier stages of disease. When the team heard of a horse that appeared to have encephalitis, its owner threatened to shoot the scientists. However Meyer was able to convince the farmer's wife that the horse was dying anyway, and to secretly signal him when the farmer was asleep in exchange for $20 (as this was during the Great Depression, this was a substantial amount of money). Meyer and his colleagues hid in the bushes until the signal, euthanized the horse and stole its head. They successfully isolated WEEV from the brain tissue. == Biological weapon == Western equine encephalitis virus was one of more than a dozen agents that the United States researched as potential biological weapons before the nation suspended its biological weapons program. == See also == Eastern equine encephalitis virus == References == == External links == United States Centers for Disease Control and Prevention (CDC) Fact Sheet
|
{
"page_id": 7539661,
"source": null,
"title": "Western equine encephalitis virus"
}
|
In astrophysics, the Eddington number, NEdd, is the number of protons in the observable universe. Eddington originally calculated it as about 1.57×1079; current estimates make it approximately 1080. The term is named for British astrophysicist Arthur Eddington, who in 1940 was the first to propose a value of NEdd and to explain why this number might be important for physical cosmology and the foundations of physics. == History == Eddington argued that the value of the fine-structure constant, α, could be obtained by pure deduction. He related α to the Eddington number, which was his estimate of the number of protons in the universe. This led him in 1929 to conjecture that α was exactly 1/136. He devised a "proof" that NEdd = 136 × 2256, or about 1.57×1079. Other physicists did not adopt this conjecture and did not accept his argument. It even led to a major journal publishing a joke article making fun of the idea. During a course of lectures that he delivered in 1938 as Tarner Lecturer at Trinity College, Cambridge, Eddington averred that: I believe there are 15747724136275002577605653961181555468044717914527116709366231425076185631031296 protons in the universe and the same number of electrons. This large number was soon named the "Eddington number". Shortly thereafter, improved measurements of α yielded values closer to 1/137, whereupon Eddington changed his "proof" to show that α had to be exactly 1/137. Current estimates of NEdd point to a value of about 1080. These estimates assume that all matter can be taken to be hydrogen and require assumed values for the number and size of galaxies and stars in the universe. == Recent theory == The modern CODATA recommended value of α−1 is 137.035999177(21). Consequently, no reliable source maintains any longer that α is the reciprocal of an integer, nor does anyone take seriously a
|
{
"page_id": 461770,
"source": null,
"title": "Eddington number"
}
|
mathematical relationship between α and NEdd. On possible roles for NEdd in contemporary cosmology, especially its connection with large number coincidences, see Barrow (2002) (easier) and Barrow & Tipler (1986, pp. 224–231) (harder). == See also == Eddington–Dirac number The Sand Reckoner Universe == References == == Bibliography == Barrow, John D. (2002). The Constants of Nature from Alpha to Omega: The Numbers That Encode the Deepest Secrets of the Universe. New York: Pantheon Books. ISBN 978-0-375-42221-8. Barrow, John D.; Tipler, Frank J. (1986). The anthropic cosmological principle. Oxford: Oxford University Press. ISBN 978-0-19-851949-2. Dingle, Herbert (1954). The Sources of Eddington's Philosophy. London: Cambridge University Press. OCLC 531389. Eddington, Arthur Stanley (1928). The Nature of the Physical World. London: Cambridge University Press. OCLC 645108. Eddington, Arthur Stanley (1935). New Pathways in Science. London: Cambridge University Press. OCLC 522390. Eddington, Arthur Stanley (1939). The Philosophy of Physical Science. London: Cambridge University Press. OCLC 669925. Eddington, Arthur Stanley (1946). Whittaker, E. T. (ed.). Fundamental Theory. Cambridge: Cambridge University Press. OCLC 2484474. Kilmister, C.W.; Tupper, B.O.J. (1962). Eddington's Statistical Theory. London: Oxford University Press. OCLC 1294788. Slater, Noel Bryan (1957). Development and Meaning in Eddington's Fundamental Theory. London: Cambridge University Press. OCLC 843162. Whittaker, E. T. (1951). Eddington's Principle in the Philosophy of Science. London: Cambridge University Press. OCLC 1453203. Whittaker, E. T. (1958). From Euclid to Eddington. New York: Dover. OCLC 8119156.
|
{
"page_id": 461770,
"source": null,
"title": "Eddington number"
}
|
In applied statistics, the Morris method for global sensitivity analysis is a so-called one-factor-at-a-time method, meaning that in each run only one input parameter is given a new value. It facilitates a global sensitivity analysis by making a number r {\displaystyle r} of local changes at different points x ( 1 → r ) {\displaystyle x(1\rightarrow r)} of the possible range of input values. == Method's details == === Elementary effects' distribution === The finite distribution of elementary effects associated with the i t h {\displaystyle i_{th}} input factor, is obtained by randomly sampling different x {\displaystyle x} from Ω {\displaystyle \Omega } , and is denoted by F i {\displaystyle F_{i}} . === Variations === In the original work of Morris the two sensitivity measures proposed were respectively the mean, μ {\displaystyle \mu } , and the standard deviation, σ {\displaystyle \sigma } , of F i {\displaystyle F_{i}} . However, choosing Morris has the drawback that, if the distribution F i {\displaystyle F_{i}} contains negative elements, which occurs when the model is non-monotonic, when computing the mean some effects may cancel each other out. Thus, the measure μ {\displaystyle \mu } on its own is not reliable for ranking factors in order of importance. It is necessary to consider at the same time the values of μ {\displaystyle \mu } and σ {\displaystyle \sigma } , as a factor with elementary effects of different signs (that cancel each other out) would have a low value of μ {\displaystyle \mu } but a considerable value of σ {\displaystyle \sigma } that avoids underestimating the factors. === === μ ∗ {\displaystyle \mu *} If the distribution F i {\displaystyle F_{i}} contains negative elements, which occurs when the model is non-monotonic, when computing the mean some effects may cancel each other
|
{
"page_id": 33557455,
"source": null,
"title": "Morris method"
}
|
out. When the goal is to rank factors in order of importance by making use of a single sensitivity measure, scientific advice is to use μ ∗ {\displaystyle \mu *} , which by making use of the absolute value, avoids the occurrence of effects of opposite signs. In Revised Morris method μ ∗ {\displaystyle \mu *} is used to detect input factors with an important overall influence on the output. σ {\displaystyle \sigma } is used to detect factors involved in interaction with other factors or whose effect is non-linear. == Method's steps == The method starts by sampling a set of start values within the defined ranges of possible values for all input variables and calculating the subsequent model outcome. The second step changes the values for one variable (all other inputs remaining at their start values) and calculates the resulting change in model outcome compared to the first run. Next, the values for another variable are changed (the previous variable is kept at its changed value and all other ones kept at their start values) and the resulting change in model outcome compared to the second run is calculated. This goes on until all input variables are changed. This procedure is repeated r {\displaystyle r} times (where r {\displaystyle r} is usually taken between 5 and 15), each time with a different set of start values, which leads to a number of r ( k + 1 ) {\displaystyle r(k+1)} runs, where k is the number of input variables. Such number is very efficient compared to more demanding methods for sensitivity analysis. A sensitivity analysis method widely used to screen factors in models of large dimensionality is the design proposed by Morris. The Morris method deals efficiently with models containing hundreds of input factors without relying on strict
|
{
"page_id": 33557455,
"source": null,
"title": "Morris method"
}
|
assumptions about the model, such as for instance additivity or monotonicity of the model input-output relationship. The Morris method is simple to understand and implement, and its results are easily interpreted. Furthermore, it is economic in the sense that it requires a number of model evaluations that is linear in the number of model factors. The method can be regarded as global as the final measure is obtained by averaging a number of local measures (the elementary effects), computed at different points of the input space. == See also == Monte Carlo method == References == == External links == Morris method paper Campolongo, F., S. Tarantola and A. Saltelli. (1999). "Tackling quantitatively large dimensionality problems". Computer Physics Communications. 1999 (1–2): 75–85. Bibcode:1999CoPhC.117...75C. doi:10.1016/S0010-4655(98)00165-9.{{cite journal}}: CS1 maint: multiple names: authors list (link)
|
{
"page_id": 33557455,
"source": null,
"title": "Morris method"
}
|
In planetary science, any material that has a relatively high equilibrium condensation temperature is called refractory. The opposite of refractory is volatile. The refractory group includes elements and compounds like metals and silicates (commonly termed rocks) which make up the bulk of the mass of the terrestrial planets and asteroids in the inner belt. A fraction of the mass of other asteroids, giant planets, their moons and trans-Neptunian objects is also made of refractory materials. == Classification == The elements can be divided into several categories: The condensation temperatures are the temperatures at which 50% of the element will be in the form of a solid (rock) under a pressure of 10−4 bar. However, slightly different groups and temperature ranges are used sometimes. Refractory material are also often divided into refractory lithophile elements and refractory siderophile elements. == References ==
|
{
"page_id": 19074000,
"source": null,
"title": "Refractory (planetary science)"
}
|
Glycopolymer is a synthetic polymer with pendant carbohydrates. Glycopolymers play an important role in many biological recognition events such as cell–cell adhesion, the development of new tissues and the infectious behavior of virus and bacteria. They have high potential in targeted drug delivery, tissue engineering and synthesis of bio-compatible materials. The first glycopolymer was synthesized in 1978 by free-radical polymerization. Subsequent efforts have been devoted to synthesizing glycopolymers with various structures and sizes, and the synthesis techniques have widened to controlled/living radical polymerisation, ring-opening polymerization, ring-opening metathesis polymerization and post-functionalization. == References ==
|
{
"page_id": 31394768,
"source": null,
"title": "Glycopolymer"
}
|
The Doebner reaction is the chemical reaction of an aniline with an aldehyde and pyruvic acid to form quinoline-4-carboxylic acids. The reaction serves as an alternative to the Pfitzinger reaction. == Reaction mechanism == The reaction mechanism is not exactly known; two proposals are presented here. One possibility is at first an aldol condensation, starting from the enol form of the pyruvic acid (1) and the aldehyde, forming an β,γ-unsaturated α-ketocarboxylic acid (2). This is followed by a Michael addition with aniline to form an aniline derivative (3). After a cyclization at the benzene ring and two proton shifts, the quinoline-4-carboxylic acid (4) is formed by water elimination: An alternative mechanism is based on the aniline and the aldehyde forming at first the Schiff base upon water elimination. The subsequent reaction with the enol form of pyruvic acid (1) leads to the formation of the above-mentioned aniline derivative (3) followed by the above-described reaction mechanism: == Side reactions == It is reported in the literature that the Doebner reaction fails in case of 2-chloro-5-aminopyridine. In this case the cyclization would take place at the amino group instead of the benzene ring and lead to a pyrrolidine derivative. == Alternative reactions == Alternative syntheses of quinoline derivatives are for example: Pfitzinger reaction Conrad-Limpach reaction Doebner-Miller reaction Combes quinoline synthesis == References ==
|
{
"page_id": 13503440,
"source": null,
"title": "Doebner reaction"
}
|
Quantum machine learning is the integration of quantum algorithms within machine learning programs. The most common use of the term refers to machine learning algorithms for the analysis of classical data executed on a quantum computer, i.e. quantum-enhanced machine learning. While machine learning algorithms are used to compute immense quantities of data, quantum machine learning utilizes qubits and quantum operations or specialized quantum systems to improve computational speed and data storage done by algorithms in a program. This includes hybrid methods that involve both classical and quantum processing, where computationally difficult subroutines are outsourced to a quantum device. These routines can be more complex in nature and executed faster on a quantum computer. Furthermore, quantum algorithms can be used to analyze quantum states instead of classical data. Beyond quantum computing, the term "quantum machine learning" is also associated with classical machine learning methods applied to data generated from quantum experiments (i.e. machine learning of quantum systems), such as learning the phase transitions of a quantum system or creating new quantum experiments. Quantum machine learning also extends to a branch of research that explores methodological and structural similarities between certain physical systems and learning systems, in particular neural networks. For example, some mathematical and numerical techniques from quantum physics are applicable to classical deep learning and vice versa. Furthermore, researchers investigate more abstract notions of learning theory with respect to quantum information, sometimes referred to as "quantum learning theory". == Machine learning with quantum computers == Quantum-enhanced machine learning refers to quantum algorithms that solve tasks in machine learning, thereby improving and often expediting classical machine learning techniques. Such algorithms typically require one to encode the given classical data set into a quantum computer to make it accessible for quantum information processing. Subsequently, quantum information processing routines are applied and the
|
{
"page_id": 44108758,
"source": null,
"title": "Quantum machine learning"
}
|
result of the quantum computation is read out by measuring the quantum system. For example, the outcome of the measurement of a qubit reveals the result of a binary classification task. While many proposals of quantum machine learning algorithms are still purely theoretical and require a full-scale universal quantum computer to be tested, others have been implemented on small-scale or special purpose quantum devices. === Quantum associative memories and quantum pattern recognition === Associative (or content-addressable memories) are able to recognize stored content on the basis of a similarity measure, rather than fixed addresses, like in random access memories. As such they must be able to retrieve both incomplete and corrupted patterns, the essential machine learning task of pattern recognition. Typical classical associative memories store p patterns in the O ( n 2 ) {\displaystyle O(n^{2})} interactions (synapses) of a real, symmetric energy matrix over a network of n artificial neurons. The encoding is such that the desired patterns are local minima of the energy functional and retrieval is done by minimizing the total energy, starting from an initial configuration. Unfortunately, classical associative memories are severely limited by the phenomenon of cross-talk. When too many patterns are stored, spurious memories appear which quickly proliferate, so that the energy landscape becomes disordered and no retrieval is anymore possible. The number of storable patterns is typically limited by a linear function of the number of neurons, p ≤ O ( n ) {\displaystyle p\leq O(n)} . Quantum associative memories (in their simplest realization) store patterns in a unitary matrix U acting on the Hilbert space of n qubits. Retrieval is realized by the unitary evolution of a fixed initial state to a quantum superposition of the desired patterns with probability distribution peaked on the most similar pattern to an input. By its
|
{
"page_id": 44108758,
"source": null,
"title": "Quantum machine learning"
}
|
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