jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dongwook Lee | Jiawei Zhou | Gang Chen | Yang Shao‐Horn
Source: Advanced Electronic Materials (article, 2018-11-22)
Field: Materials Science — Materials Chemistry

Enhanced Thermoelectric Properties for PEDOT:PSS/Undoped Ge Thin‐Film Bilayered Heterostructures Abstract Modulation doping is one of the strategies to improve thermoelectric power factors of nanocomposites and thin‐film bilayered heterostructures by effectively increasing electrical conductivity. Here, it is reported that thin‐film heterostructures of heavily doped p‐type organic conducting polymer, poly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) and undoped thin‐film Ge can enhance thermoelectric power factor by modulation doping. The maximum power factor and Seebeck coefficient of the bilayered heterostructures are 154 µW m −1 K −2 and 398 µV K −1 , respectively, corresponding to 47‐fold and 41‐fold increases compared to those of bulk PEDOT:PSS and 64‐fold increase compared to power factor of undoped Ge. The enhancements in power factor and Seebeck coefficient are quantitatively described by the hole transfer from PEDOT:PSS to Ge, which takes into account the band alignment at the interface detected by Kraut's method. Agreement between the simulation and experiment results also implies predictability of thermoelectric performances of nanoscale bilayered heterostructures in general, when band offset, Fermi level, and individual electronic properties are available. This work can be further extended to predict performance of other nanoscale combinations of thermoelectric and other electronic materials in general. Materials science ; Thermoelectric effect ; PEDOT:PSS ; Heterojunction ; Seebeck coefficient ; Doping ; Optoelectronics ; Thermoelectric materials ; Thin film ; Power factor ; Nanotechnology ; Thermal conductivity ; Composite material ; Voltage ; Electrical engineering ; Layer (electronics) Advanced Thermoelectric Materials and Devices ; Conducting polymers and applications ; Organic Electronics and Photovoltaics
Authors: Sabrina Presto | Antonio Barbucci | Maria Paola Carpanese | Feng Han | Rémi Costa | Massimo Viviani
Source: Crystals (article, 2018-03-13)
Field: Materials Science — Materials Chemistry

Application of La-Doped SrTiO3 in Advanced Metal-Supported Solid Oxide Fuel Cells Composite materials frequently allow the drawbacks of single components to be overcome thanks to a synergistic combination of material- and structure-specific features, leading to enhanced and also new properties. This is the case of a metallic-ceramic composite, a nickel-chromium-aluminum (NiCrAl) foam impregnated with La-doped Strontium Titanate (LST). This particular cermet has very interesting properties that can be used in different fields of application, namely: mechanical robustness provided by the metal foam; and chemical stability in harsh conditions of temperature and atmosphere by promotion of a thin protective layer of alumina (Al2O3); high electronic conductivity given by a percolating ceramic conducting phase, i.e., La-doped Strontium Titanate. In this paper, its application as a current collector in a metal-supported Solid Oxide Fuel Cells (SOFC) was studied. Firstly, the electronic properties of different compositions, stoichiometric and under stoichiometric, of LST were analyzed to choose the best one in terms of conductivity and phase purity. Then, LST chemical stability was studied in the presence of Al2O3 at different temperatures, gas compositions and aging times. Finally, stability and conductivity of LST-impregnated NiCrAl foam composite materials were measured, and LST was found to be fully compatible with the NiCrAl foam, as no reactions were detected in oxidizing and reducing atmosphere after up to 300 h operation at 750 °C and 900 °C between the Al2O3 layer and LST. Results showed that the composite is suitable as a current collector in innovative designs of metal-supported SOFC, like the Evolve cell, in which the metallic part is supposed not only to provide the structural stability to the cell, but also to play the role of current collector due to the impregnation of ceramic material. Materials science ; Ceramic ; Strontium titanate ; Oxidizing agent ; Composite number ; Oxide ; Titanate ; Cermet ; Conductivity ; Composite material ; Chemical engineering ; Metallurgy ; Nanotechnology ; Thin film Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions
Authors: Ana Luísa Cartaxo | Ana Rita Costa-Pinto | Albino Martins | Susana Faria | Virgínia M. F. Gonçalves | Maria Elizabeth Tiritan | Helena Ferreira | Nuno M. Neves
Source: Journal of Biomedical Materials Research Part A (article, 2018-11-28)
Field: Materials Science — Biomaterials

Influence of PDLA nanoparticles size on drug release and interaction with cells Polymeric nanoparticles (NPs) are strong candidates for the development of systemic and targeted drug delivery applications. Their size is a determinant property since it defines the NP-cell interactions, drug loading capacity, and release kinetics. Herein, poly(d,l-lactic acid) (PDLA) NPs were produced by the nanoprecipitation method, in which the influence of type and concentration of surfactant as well as PDLA concentration were assessed. The adjustment of these parameters allowed the successful production of NPs with defined medium sizes, ranging from 80 to 460 nm. The surface charge of the different NPs populations was consistently negative. Prednisolone was effectively entrapped and released from NPs with statistically different medium sizes (i.e., 80 or 120 nm). Release profiles indicate that these systems were able to deliver appropriate amounts of drug with potential applicability in the treatment of inflammatory conditions. Both NPs populations were cytocompatible with human endothelial and fibroblastic cells, in the range of concentrations tested (0.187-0.784 mg/mL). However, confocal microscopy revealed that within the range of sizes tested in our experiments, NPs presenting a medium size of 120 nm were able to be internalized in endothelial cells. In summary, this study demonstrates the optimization of the processing conditions to obtain PDLA NPs with narrow size ranges, and with promising performance for the treatment of inflammatory diseases. © 2018 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 107A: 482-493, 2019. Materials science ; Nanoparticle ; Drug delivery ; Drug ; Pulmonary surfactant ; Biophysics ; Confocal microscopy ; Nanotechnology ; Kinetics ; Chemical engineering ; Pharmacology ; Biology Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Hydrogels: synthesis, properties, applications
Authors: Manuel Gómez | Shima Kadkhodazadeh | Massimo Lazzari
Source: Chemical Communications (article, 2018-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Surface enhanced Raman scattering (SERS) in the visible range on scalable aluminum-coated platforms We demonstrate the potential of combining plasmonic Bragg grating structures, with the scattering effect of a rough Al layer to tune SERS enhancement in the visible region. Novel Al-based substrates with good analytical reproducibility and enhancement factors in the range of 107-109 are produced by a scalable procedure which meets the development of producing sustainable SERS platforms. Raman scattering ; Aluminium ; Materials science ; Range (aeronautics) ; Raman spectroscopy ; Surface (topology) ; Scattering ; Nanotechnology ; Optoelectronics ; Optics ; Composite material ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Surface Roughness and Optical Measurements ; Random lasers and scattering media
Authors: Georgios Koronis | Arlindo Silva
Source: Journal of Composites Science (article, 2018-02-11)
Field: Materials Science — Polymers and Plastics

Green Composites Reinforced with Plant-Based Fabrics: Cost and Eco-Impact Assessment This study considers a green composite under a twofold assessment; evaluating its process-based cost and environmental footprint profile. The initial objective was to project the manufacturing cost and allow for an additional material comparison of alternative scenarios in the resin transfer molding processes. The additional aim is to have an intermediate environmental assessment to assist in selecting materials and adjust manufacturing parameters which would minimize the energy spent and the CO2 emissions. As it has been noted in numerous applications, the incorporation of natural fiber fabrics, as opposed to glass fabrics, bring together weight savings and consequently cost savings. However, the economic analysis suggests that a glass reinforced composite is marginally cheaper at the production volume of 300 parts (1.9% lower cost) in contrast to a possible green solution (ramie). Considering jute instead of ramie as a reinforcement, the cost gets immediately lower, and further decreases with proposed improvements to the manufacturing process. Additional reduction of up to 10% in the production cost can be achieved by process upgrade. As indicated by the Eco-Audit analysis, 36% less energy and 44% CO2 per kilo will be generated, respectively when swapping from glass to ramie fabrics in the production of the automotive hood. Ramie ; Environmental impact assessment ; Automotive industry ; Production (economics) ; Natural fiber ; Composite number ; Production cost ; Materials science ; Environmental science ; Process engineering ; Composite material ; Fiber ; Engineering ; Mechanical engineering Natural Fiber Reinforced Composites ; biodegradable polymer synthesis and properties ; Additive Manufacturing and 3D Printing Technologies
Authors: Terézia Kovaĺová | T. Kovaĺ | Eva Benešová | Patricie Vodičková | Vojtěch Spiwok | Petra Lipovová | Jan Dohnálek
Source: Glycobiology (article, 2018-08-22)
Field: Materials Science — Materials Chemistry

Active site complementation and hexameric arrangement in the GH family 29; a structure–function study of α-l-fucosidase isoenzyme 1 from Paenibacillus thiaminolyticus α-l-Fucosidase isoenzyme 1 from bacterium Paenibacillus thiaminolyticus is a member of the glycoside hydrolase family GH29 capable of cleaving l-fucose from nonreducing termini of oligosaccharides and glycoconjugates. Here we present the first crystal structure of this protein revealing a novel quaternary state within this family. The protein is in a unique hexameric assembly revealing the first observed case of active site complementation by a residue from an adjacent monomer in this family. Mutation of the complementing tryptophan residue caused changes in the catalytic properties including a shift of the pH optimum, a change of affinity to an artificial chromogenic substrate and a decreased reaction rate for a natural substrate. The wild-type enzyme was active on most of the tested naturally occurring oligosaccharides and capable of transglycosylation on a variety of acceptor molecules, including saccharides, alcohols or chromogenic substrates. Mutation of the complementing residue changed neither substrate specificity nor the preference for the type of transglycosylation acceptor molecule; however, the yields of the reactions were lower in both cases. Maltose molecules bound to the enzyme in the crystal structure identified surface carbohydrate-binding sites, possibly participating in binding of larger oligosaccharides. Chemistry ; Active site ; Protein quaternary structure ; Fucosidase ; Stereochemistry ; Complementation ; Glycoside hydrolase ; Enzyme ; Biochemistry ; Glycoconjugate ; Hydrolase ; Fucosyltransferase ; Residue (chemistry) ; Substrate analog ; Chromogenic ; Binding site ; Fucose ; Galactose ; Mutant ; Protein subunit Enzyme Structure and Function ; Carbohydrate Chemistry and Synthesis ; Enzyme Production and Characterization
Authors: Marcello La Rosa | Sergey A. Denisov | Gediminas Jonušauskas | Nathan D. McClenaghan | Alberto Credi
Source: Angewandte Chemie (article, 2018-01-31)
Field: Materials Science — Materials Chemistry

Designed Long‐Lived Emission from CdSe Quantum Dots through Reversible Electronic Energy Transfer with a Surface‐Bound Chromophore Abstract The size‐tunable emission of luminescent quantum dots (QDs) makes them highly interesting for applications that range from bioimaging to optoelectronics. For the same applications, engineering their luminescence lifetime, in particular, making it longer, would be as important; however, no rational approach to reach this goal is available to date. We describe a strategy to prolong the emission lifetime of QDs through electronic energy shuttling to the triplet excited state of a surface‐bound molecular chromophore. To implement this idea, we made CdSe QDs of different sizes and carried out self‐assembly with a pyrene derivative. We observed that the conjugates exhibit delayed luminescence, with emission decays that are prolonged by more than 3 orders of magnitude (lifetimes up to 330 μs) compared to the parent CdSe QDs. The mechanism invokes unprecedented reversible quantum dot to organic chromophore electronic energy transfer. Quantum dot ; Chromophore ; Luminescence ; Excited state ; Photochemistry ; Pyrene ; Chemical physics ; Materials science ; Energy transfer ; Nanotechnology ; Chemistry ; Optoelectronics ; Atomic physics ; Physics Quantum Dots Synthesis And Properties ; Luminescence and Fluorescent Materials ; Advanced biosensing and bioanalysis techniques
Authors: О. P. Yasnii | Oleh Pastukh | Yu. І. Pyndus | Nadiia Lutsyk | Iryna Didych
Source: Materials Science (article, 2018-11-01)
Field: Materials Science — Materials Chemistry

Prediction of the Diagrams of Fatigue Fracture of D16T Aluminum Alloy by the Methods of Machine Learning Artificial neural network ; Fracture (geology) ; Alloy ; Materials science ; Solid mechanics ; Aluminium ; Random forest ; Support vector machine ; Structural material ; Diagram ; Stress (linguistics) ; Artificial intelligence ; Structural engineering ; Machine learning ; Metallurgy ; Computer science ; Composite material ; Mathematics ; Engineering ; Statistics Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Fatigue and fracture mechanics
Authors: Shreyas Honrao | Bryan E. Anthonio | Rohit Ramanathan | Joshua J. Gabriel | Richard G. Hennig
Source: Computational Materials Science (article, 2018-11-28)
Field: Materials Science — Materials Chemistry

Machine learning of ab-initio energy landscapes for crystal structure predictions We present a machine learning approach to calculate unrelaxed and relaxed formation energies of compounds relative to the ground state crystal structure of the pure components in the context of structure predictions in binary systems. Typical methods for structure predictions such as genetic algorithms often rely on density-functional theory codes to perform such calculations at a relatively high computational cost. In this work, we explore two commonly used kernel-based learning algorithms, kernel ridge regression and support vector regression . The efficiency of machine learning approaches relies on suitable data representations that encode the relevant physical information about the crystal structures. We select partial radial distribution functions to represent this structural information. We apply the machine learning approaches to the binary Li-Ge system and show that these methods provide small root-mean square prediction errors of about 20 meV/atom across the composition and structure space. Furthermore, we demonstrate that the model can be trained to predict the formation energies of the relaxed structures with the same accuracy when given unrelaxed structures as input. The high accuracy for the prediction of the relaxed energies of unrelaxed structures suggests that the machine-learning method can eliminate unlikely candidate structures from a genetic algorithm search, thus reducing the computational cost required for the explorations of energy landscapes and improving the performance of genetic algorithms for structure predictions. Computer science ; Crystal structure prediction ; Artificial intelligence ; Machine learning ; Context (archaeology) ; Algorithm ; Kernel (algebra) ; Support vector machine ; Ab initio ; Statistical physics ; Crystal structure ; Mathematics ; Physics ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Nicholas Walker | Ka-Ming Tam | Brian Novak | Mark Jarrell
Source: Physical review. E (article, 2018-11-07)
Field: Materials Science — Materials Chemistry

Identifying structural changes with unsupervised machine learning methods Unsupervised machine learning methods are used to identify structural changes using the melting point transition in classical molecular dynamics simulations as an example application of the approach. Dimensionality reduction and clustering methods are applied to instantaneous radial distributions of atomic configurations from classical molecular dynamics simulations of metallic systems over a large temperature range. Principal component analysis is used to dramatically reduce the dimensionality of the feature space across the samples using an orthogonal linear transformation that preserves the statistical variance of the data under the condition that the new feature space is linearly independent. From there, $k$-means clustering is used to partition the samples into solid and liquid phases through a criterion motivated by the geometry of the reduced feature space of the samples, allowing for an estimation of the melting point transition. This pattern criterion is conceptually similar to how humans interpret the data but with far greater throughput, as the shapes of the radial distributions are different for each phase and easily distinguishable by humans. The transition temperature estimates derived from this machine learning approach produce comparable results to other methods on similarly small system sizes. These results show that machine learning approaches can be applied to structural changes in physical systems. Curse of dimensionality ; Principal component analysis ; Cluster analysis ; Dimensionality reduction ; Feature (linguistics) ; Unsupervised learning ; Computer science ; Artificial intelligence ; Transformation (genetics) ; Feature vector ; Pattern recognition (psychology) ; Statistical physics ; Biological system ; Mathematics ; Algorithm ; Physics ; Chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Theoretical and Computational Physics
Authors: Linfeng Zhang | Han Wang | Weinan E
Source: The Journal of Chemical Physics (article, 2018-10-17)
Field: Materials Science — Materials Chemistry

Adaptive coupling of a deep neural network potential to a classical force field An adaptive modeling method (AMM) that couples a deep neural network potential and a classical force field is introduced to address the accuracy-efficiency dilemma faced by the molecular simulation community. The AMM simulated system is decomposed into three types of regions. The first type captures the important phenomena in the system and requires high accuracy, for which we use the Deep Potential Molecular Dynamics (DeePMD) model in this work. The DeePMD model is trained to accurately reproduce the statistical properties of the <i>ab initio</i> molecular dynamics. The second type does not require high accuracy, and a classical force field is used to describe it in an efficient way. The third type is used for a smooth transition between the first and the second types of regions. By using a force interpolation scheme and imposing a thermodynamics force in the transition region, we make the DeePMD region embedded in the AMM simulated system as if it were embedded in a system that is fully described by the accurate potential. A representative example of the liquid water system is used to show the feasibility and promise of this method. Force field (fiction) ; Artificial neural network ; Type (biology) ; Field (mathematics) ; Interpolation (computer graphics) ; Molecular dynamics ; Coupling (piping) ; Potential field Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Model Reduction and Neural Networks
Authors: Corey Petty | Rene F. K. Spada | Francisco B. C. Machado | Bill Poirier
Source: The Journal of Chemical Physics (article, 2018-07-13)
Field: Materials Science — Materials Chemistry

Accurate rovibrational energies of ozone isotopologues up to <i>J</i> = 10 utilizing artificial neural networks In recent years, ozone and its isotopologues have been a topic of interest in many fields of research, due to its importance in atmospheric chemistry and its anomalous isotopic enrichment-or the so-called "mass-independent fractionation." In the field of potential energy surface (PES) creation, debate over the existence of a potential barrier just under the dissociation threshold (referred to as a "potential reef") has plagued research for some years. Recently, Dawes and co-workers [Dawes, Lolur, Li, Jiang, and Guo (DLLJG) J. Chem. Phys. 139, 201103 (2013)] created a highly accurate global PES, for which the reef is found to be replaced with a (monotonic) "plateau." Subsequent dynamical calculations on this "DLLJG" PES have shown improved agreement with experiment, particularly the vibrational spectrum. However, it is well known that reaction dynamics is also highly influenced by the rovibrational states, especially in cases like ozone that assume a Lindemann-type mechanism. Accordingly, we present the first significant step toward a complete characterization of the rovibrational spectrum for various isotopologues of ozone, computed using the DLLJG PES together with the ScalIT suite of parallel codes. Additionally, artificial neural networks are used in an innovative fashion-not to construct the PES function per se but rather to greatly speed up its evaluation. Isotopologue ; Rotational–vibrational spectroscopy ; Artificial neural network ; Statistical physics ; Physics ; Artificial intelligence ; Computer science ; Atomic physics ; Quantum mechanics ; Molecule ; Excited state Machine Learning in Materials Science ; Nuclear Physics and Applications ; Spectroscopy and Laser Applications
Authors: Ayesha Zafar | Lisa I. Pilkington | Natalie A. Haverkate | Michelle van Rensburg | Euphemia Leung | Sisira Kumara | William A. Denny | David Barker | Ali Alsuraifi | Clare Hoskins | Jóhannes Reynisson
Source: Molecules (article, 2018-01-11)
Field: Chemistry — Organic Chemistry

Investigation into Improving the Aqueous Solubility of the Thieno[2,3-b]pyridine Anti-Proliferative Agents It is now established that the thieno[2,3-<i>b</i>]pyridines are a potent class of antiproliferatives. One of the main issues encountered for their clinical application is their low water solubility. In order to improve this, two strategies were pursued. First, a morpholine moiety was tethered to the molecular scaffold by substituting the sulphur atom with nitrogen, resulting in a 1<i>H</i>-pyrrolo[2,3-<i>b</i>]pyridine core structure. The water solubility was increased by three orders of magnitude, from 1.2 µg/mL (<b>1</b>-thieno[2,3-<i>b</i>]pyridine) to 1.3 mg/mL (<b>3</b>-pyrrolo[2,3-<i>b</i>]pyridine), however, it was only marginally active against cancer cells. The second strategy involved loading a very potent thieno[2,3-<i>b</i>]pyridine derivative (<b>2</b>) into a cholesteryl-poly(allylamine) polymer matrix for water solubilisation. Suppression of human pancreatic adenocarcinoma (BxPC-3) viability was observed to an IC<sub>50</sub> value of 0.5 μg/mL (1.30 μM) in conjunction with the polymer, which is a five-fold (×5) increase in potency as compared to the free drug alone, demonstrating the utility of this formulation approach. Pyridine ; Chemistry ; Moiety ; Solubility ; Morpholine ; Stereochemistry ; Combinatorial chemistry ; Organic chemistry Chemical Reaction Mechanisms ; Cancer therapeutics and mechanisms ; Synthesis and biological activity