jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andrej Turk | D. San Martı́n | Pedro E.J. Rivera-Díaz-del-Castillo | E.I. Galindo-Nava
Source: Scripta Materialia (article, 2018-04-25)
Field: Materials Science — Metals and Alloys

Correlation between vanadium carbide size and hydrogen trapping in ferritic steel Hydrogen trapping on vanadium carbides (VC) was studied in a low-carbon ferritic steel. Thermal desorption analysis was performed on two conditions with different carbide sizes but identical volume fractions. Smaller carbides with a higher effective surface area trapped significantly more hydrogen. A correlation between carbide size and hydrogen trap density was established, suggesting that trapping is surface-dominant and a scaling law for trap density was derived. The amount of trapped hydrogen was overall much lower than previously reported for VC-containing martensitic steels. It is therefore suggested that in the absence of a dislocated matrix VC traps relatively little hydrogen. Carbide ; Materials science ; Hydrogen ; Vanadium carbide ; Trapping ; Vanadium ; Thermal desorption ; Martensite ; Metallurgy ; Volume (thermodynamics) ; Desorption ; Analytical Chemistry (journal) ; Thermodynamics ; Microstructure ; Physical chemistry ; Adsorption ; Chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; Corrosion Behavior and Inhibition
Authors: José Ramón Durán Retamal | Dharmaraj Periyanagounder | Jr‐Jian Ke | Meng‐Lin Tsai | Jr‐Hau He
Source: Chemical Science (review, 2018-01-01)
Field: Materials Science — Materials Chemistry

Charge carrier injection and transport engineering in two-dimensional transition metal dichalcogenides Ever since two dimensional-transition (2D) metal dichalcogenides (TMDs) were discovered, their fascinating electronic properties have attracted a great deal of attention for harnessing them as critical components in novel electronic devices. 2D-TMDs endowed with an atomically thin structure, dangling bond-free nature, electrostatic integrity, and tunable wide band gaps enable low power consumption, low leakage, ambipolar transport, high mobility, superconductivity, robustness against short channel effects and tunneling in highly scaled devices. However, the progress of 2D-TMDs has been hampered by severe charge transport issues arising from undesired phenomena occurring at the surfaces and interfaces. Therefore, this review provides three distinct engineering strategies embodied with distinct innovative approaches to optimize both carrier injection and transport. First, contact engineering involves 2D-metal contacts and tunneling interlayers to overcome metal-induced interface states and the Fermi level pinning effect caused by low vacancy energy formation. Second, dielectric engineering covers high-<i>k</i> dielectrics, ionic liquids or 2D-insulators to screen scattering centers caused by carrier traps, imperfections and rough substrates, to finely tune the Fermi level across the band gap, and to provide dangling bond-free media. Third, material engineering focuses on charge transfer <i>via</i> substitutional, chemical and plasma doping to precisely modulate the carrier concentration and to passivate defects while preserving material integrity. Finally, we provide an outlook of the conceptual and technical achievements in 2D-TMDs to give a prospective view of the future development of highly scaled nanoelectronic devices. Transition metal ; Charge (physics) ; Materials science ; Charge carrier ; Chemical physics ; Nanotechnology ; Condensed matter physics ; Chemistry ; Physics ; Optoelectronics ; Quantum mechanics ; Organic chemistry 2D Materials and Applications ; Graphene research and applications ; Ga2O3 and related materials
Authors: Stefanie Herbst | Bartolomé Soberats | Pawaret Leowanawat | Matthias Stolte | Matthias Lehmann | Frank Würthner
Source: Nature Communications (article, 2018-07-02)
Field: Materials Science — Materials Chemistry

Self-assembly of multi-stranded perylene dye J-aggregates in columnar liquid-crystalline phases Many discoid dyes self-assemble into columnar liquid-crystalline (LC) phases with packing arrangements that are undesired for photonic applications due to H-type exciton coupling. Here, we report a series of crystalline and LC perylene bisimides (PBIs) self-assembling into single or multi-stranded (two, three, and four strands) aggregates with predominant J-type exciton coupling. These differences in the supramolecular packing and optical properties are achieved by molecular design variations of tetra-bay phenoxy-dendronized PBIs with two N-H groups at the imide positions. The self-assembly is driven by hydrogen bonding, slipped π-π stacking, nanosegregation, and steric requirements of the peripheral building blocks. We could determine the impact of the packing motifs on the spectroscopic properties and demonstrate different J- and H-type coupling contributions between the chromophores. Our findings on structure-property relationships and strong J-couplings in bulk LC materials open a new avenue in the molecular engineering of PBI J-aggregates with prospective applications in photonics. Perylene ; Stacking ; Supramolecular chemistry ; Chromophore ; Exciton ; Self-assembly ; Materials science ; Steric effects ; J-aggregate ; Hydrogen bond ; Liquid crystalline ; Columnar phase ; Chemical physics ; Molecule ; Crystallography ; Nanotechnology ; Liquid crystal ; Chemistry ; Photochemistry ; Optoelectronics ; Stereochemistry ; Crystal structure ; Organic chemistry Luminescence and Fluorescent Materials ; Supramolecular Self-Assembly in Materials ; Liquid Crystal Research Advancements
Authors: Sayoni Ray | Zhao Li | Chao‐Hsiung Hsu | Lian‐Pin Hwang | Ying‐Chih Lin | Pi‐Tai Chou | Yung‐Ya Lin
Source: Theranostics (review, 2018-01-01)
Field: Materials Science — Polymers and Plastics

Dendrimer- and copolymer-based nanoparticles for magnetic resonance cancer theranostics Cancer theranostics is one of the most important approaches for detecting and treating patients at an early stage. To develop such a technique, accurate detection, specific targeting, and controlled delivery are the key components. Various kinds of nanoparticles have been proposed and demonstrated as potential nanovehicles for cancer theranostics. Among them, polymer-like dendrimers and copolymer-based core-shell nanoparticles could potentially be the best possible choices. At present, magnetic resonance imaging (MRI) is widely used for clinical purposes and is generally considered the most convenient and noninvasive imaging modality. Superparamagnetic iron oxide (SPIO) and gadolinium (Gd)-based dendrimers are the major nanostructures that are currently being investigated as nanovehicles for cancer theranostics using MRI. These structures are capable of specific targeting of tumors as well as controlled drug or gene delivery to tumor sites using pH, temperature, or alternating magnetic field (AMF)-controlled mechanisms. Recently, Gd-based pseudo-porous polymer-dendrimer supramolecular nanoparticles have shown 4-fold higher T<sub>1</sub> relaxivity along with highly efficient AMF-guided drug release properties. Core-shell copolymer-based nanovehicles are an equally attractive alternative for designing contrast agents and for delivering anti-cancer drugs. Various copolymer materials could be used as core and shell components to provide biostability, modifiable surface properties, and even adjustable imaging contrast enhancement. Recent advances and challenges in MRI cancer theranostics using dendrimer- and copolymer-based nanovehicles have been summarized in this review article, along with new unpublished research results from our laboratories. Dendrimer ; Nanotechnology ; Drug delivery ; Materials science ; Magnetic resonance imaging ; Nanoparticle ; Copolymer ; Gadolinium ; Cancer ; Polymer ; Medicine ; Radiology ; Polymer chemistry Dendrimers and Hyperbranched Polymers ; Nanoparticle-Based Drug Delivery ; Advanced biosensing and bioanalysis techniques
Authors: He Li | Feihua Liu | Huidong Tian | Chuang Wang | Zihao Guo | Peng Liu | Zongren Peng | Qing Wang
Source: Composites Science and Technology (article, 2018-09-04)
Field: Materials Science — Materials Chemistry

Synergetic enhancement of mechanical and electrical strength in epoxy/silica nanocomposites via chemically-bonded interface Materials science ; Epoxy ; Nanocomposite ; Composite material ; Glass transition ; Ultimate tensile strength ; Polymer ; Polymer nanocomposite ; Dielectric ; Nanoparticle ; Dielectric strength ; Filler (materials) ; Nanotechnology High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Polymer Nanocomposites and Properties
Authors: Volker L. Deringer | A. Miguel | Richard Jana | Anja Aarva | Stephen R. Elliott | Tomi Laurila | Gábor Cśanyi | Lars Pastewka
Source: Chemistry of Materials (article, 2018-09-10)
Field: Materials Science — Materials Chemistry

Computational Surface Chemistry of Tetrahedral Amorphous Carbon by Combining Machine Learning and Density Functional Theory Tetrahedral amorphous carbon (ta-C) is widely used for coatings because of its superior mechanical properties and has been suggested as an electrode material for detecting biomolecules. Despite extensive research, however, the complex atomic-scale structures and chemical reactivity of ta-C surfaces are incompletely understood. Here, we combine machine learning, density functional tight binding, and density functional theory simulations to shed new light on this long-standing problem. We make atomistic models of ta-C surfaces, characterize them by local structural fingerprints, and provide a library of structures at different system sizes. We then move beyond the pure element and exemplify how chemical reactivity (hydrogenation and oxidation) can be modeled at the surfaces. Our work opens up new perspectives for modeling the surfaces and interfaces of amorphous solids, which will advance studies of ta-C and other functional materials. Density functional theory ; Tetrahedron ; Amorphous carbon ; Amorphous solid ; Carbon fibers ; Reactivity (psychology) ; Materials science ; Chemical physics ; Nanotechnology ; Biomolecule ; Computational chemistry ; Chemistry ; Crystallography ; Composite material Machine Learning in Materials Science ; Diamond and Carbon-based Materials Research ; Surface Chemistry and Catalysis
Authors: Tristan A. Sharp | Spencer L. Thomas | Ekin D. Cubuk | Samuel S. Schoenholz | David J. Srolovitz | Andrea J. Liu
Source: Proceedings of the National Academy of Sciences (article, 2018-10-09)
Field: Materials Science — Materials Chemistry

Machine learning determination of atomic dynamics at grain boundaries In polycrystalline materials, grain boundaries are sites of enhanced atomic motion, but the complexity of the atomic structures within a grain boundary network makes it difficult to link the structure and atomic dynamics. Here, we use a machine learning technique to establish a connection between local structure and dynamics of these materials. Following previous work on bulk glassy materials, we define a purely structural quantity (softness) that captures the propensity of an atom to rearrange. This approach correctly identifies crystalline regions, stacking faults, and twin boundaries as having low likelihood of atomic rearrangements while finding a large variability within high-energy grain boundaries. As has been found in glasses, the probability that atoms of a given softness will rearrange is nearly Arrhenius. This indicates a well-defined energy barrier as well as a well-defined prefactor for the Arrhenius form for atoms of a given softness. The decrease in the prefactor for low-softness atoms indicates that variations in entropy exhibit a dominant influence on the atomic dynamics in grain boundaries. Grain boundary ; Dynamics (music) ; Materials science ; Computer science ; Statistical physics ; Metallurgy ; Physics ; Psychology ; Microstructure ; Pedagogy Machine Learning in Materials Science ; Microstructure and mechanical properties ; Advanced Materials Characterization Techniques
Authors: Michael Schneider | Adam Belsom | Juri Rappsilber
Source: Trends in Biochemical Sciences (review, 2018-02-01)
Field: Chemistry — Spectroscopy

Protein Tertiary Structure by Crosslinking/Mass Spectrometry Observing the structures of proteins within the cell and tracking structural changes under different cellular conditions are the ultimate challenges for structural biology. This, however, requires an experimental technique that can generate sufficient data for structure determination and is applicable in the native environment of proteins. Crosslinking/mass spectrometry (CLMS) and protein structure determination have recently advanced to meet these requirements and crosslinking-driven de novo structure determination in native environments is now possible. In this opinion article, we highlight recent successes in the field of CLMS with protein structure modeling and challenges it still holds. Mass spectrometry ; Protein tertiary structure ; Chemistry ; Computational biology ; Biochemistry ; Chromatography ; Biology Mass Spectrometry Techniques and Applications ; Enzyme Structure and Function ; Advanced Proteomics Techniques and Applications
Authors: Yogendra Kumar | Alfa Sharma | Parasharam M. Shirage
Source: Journal of Alloys and Compounds (article, 2018-11-15)
Field: Materials Science — Materials Chemistry

Impact of different morphologies of CoFe2O4 nanoparticles for tuning of structural, optical and magnetic properties Spinel ; Materials science ; Raman spectroscopy ; Nanoparticle ; Ferrite (magnet) ; Octahedron ; X-ray photoelectron spectroscopy ; Crystal structure ; Cobalt ; Hydrothermal circulation ; Nanotechnology ; Crystallography ; Analytical Chemistry (journal) ; Nuclear magnetic resonance ; Chemical engineering ; Optics ; Chemistry ; Composite material ; Metallurgy Magnetic Properties and Synthesis of Ferrites ; Iron oxide chemistry and applications ; Copper-based nanomaterials and applications
Authors: Georges Chedid | Ali Yassin
Source: Nanomaterials (review, 2018-11-07)
Field: Materials Science — Materials Chemistry

Recent Trends in Covalent and Metal Organic Frameworks for Biomedical Applications Materials science has seen a great deal of advancement and development. The discovery of new types of materials sparked the study of their properties followed by applications ranging from separation, catalysis, optoelectronics, sensing, drug delivery and biomedicine, and many other uses in different fields of science. Metal organic frameworks (MOFs) and covalent organic frameworks (COFs) are a relatively new type of materials with high surface areas and permanent porosity that show great promise for such applications. The current study aims at presenting the recent work achieved in COFs and MOFs for biomedical applications, and to examine some challenges and future directions which the field may take. The paper herein surveys their synthesis, and their use as Drug Delivery Systems (DDS), in non-drug delivery therapeutics and for biosensing and diagnostics. Nanotechnology ; Drug delivery ; Biomedicine ; Metal-organic framework ; Materials science ; Chemistry ; Bioinformatics Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Luminescence and Fluorescent Materials
Authors: Oliver T. Unke | Markus Meuwly
Source: The Journal of Chemical Physics (article, 2018-03-15)
Field: Materials Science — Materials Chemistry

A reactive, scalable, and transferable model for molecular energies from a neural network approach based on local information Despite the ever-increasing computer power, accurate ab initio calculations for large systems (thousands to millions of atoms) remain infeasible. Instead, approximate empirical energy functions are used. Most current approaches are either transferable between different chemical systems, but not particularly accurate, or they are fine-tuned to a specific application. In this work, a data-driven method to construct a potential energy surface based on neural networks is presented. Since the total energy is decomposed into local atomic contributions, the evaluation is easily parallelizable and scales linearly with system size. With prediction errors below 0.5 kcal mol<sup>-1</sup> for both unknown molecules and configurations, the method is accurate across chemical and configurational space, which is demonstrated by applying it to datasets from nonreactive and reactive molecular dynamics simulations and a diverse database of equilibrium structures. The possibility to use small molecules as reference data to predict larger structures is also explored. Since the descriptor only uses local information, high-level ab initio methods, which are computationally too expensive for large molecules, become feasible for generating the necessary reference data used to train the neural network. Artificial neural network ; Scalability ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Alexander A. Guda | С. А. Гуда | Kirill A. Lomachenko | Mikhail A. Soldatov | И. А. Панкин | А. В. Солдатов | Luca Braglia | Aram L. Bugaev | Andrea Martini | Matteo Signorile | Elena Groppo | Alessandro Piovano | Elisa Borfecchia | Carlo Lamberti
Source: Catalysis Today (article, 2018-11-08)
Field: Materials Science — Materials Chemistry

Quantitative structural determination of active sites from in situ and operando XANES spectra: From standard ab initio simulations to chemometric and machine learning approaches XANES ; Catalysis ; Chemistry ; Ab initio ; Spectral line ; Physical chemistry ; Organic chemistry ; Physics Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Gabriele C. Sosso | Volker L. Deringer | Stephen R. Elliott | Gábor Cśanyi
Source: Molecular Simulation (article, 2018-03-13)
Field: Materials Science — Materials Chemistry

Understanding the thermal properties of amorphous solids using machine-learning-based interatomic potentials Understanding the thermal properties of disordered systems is of fundamental importance for condensed matter physics - and for practical applications as well. While quantities such as the thermal conductivity are usually well characterised experimentally, their microscopic origin is often largely unknown - hence the pressing need for molecular simulations. However, the time and length scales involved with thermal transport phenomena are typically well beyond the reach of ab initio calculations. On the other hand, many amorphous materials are characterised by a complex structure, which prevents the construction of classical interatomic potentials. One way to get past this deadlock is to harness machine-learning (ML) algorithms to build interatomic potentials: these can be nearly as computationally efficient as classical force fields while retaining much of the accuracy of first-principles calculations. Here, we discuss neural network potentials (NNPs) and Gaussian approximation potentials (GAPs), two popular ML frameworks. We review the work that has been devoted to investigate, via NNPs, the thermal properties of phase-change materials, systems widely used in non-volatile memories. In addition, we present recent results on the vibrational properties of amorphous carbon, studied via GAPs. In light of these results, we argue that ML-based potentials are among the best options available to further our understanding of the vibrational and thermal properties of complex amorphous solids. Amorphous solid ; Thermal ; Ab initio ; Statistical physics ; Work (physics) ; Interatomic potential ; Thermal conductivity ; Materials science ; Molecular dynamics ; Chemical physics ; Physics ; Chemistry ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Thermal properties of materials ; Phase-change materials and chalcogenides
Authors: David Balcells | Ainara Nova
Source: ACS Catalysis (article, 2018-03-16)
Field: Chemistry — Organic Chemistry

Designing Pd and Ni Catalysts for Cross-Coupling Reactions by Minimizing Off-Cycle Species This Perspective presents an overview on recent experimental and computational studies on the off-cycle reactions of palladium- and nickel-catalyzed cross-couplings. Several reactions entering or leaving the catalytic cycle have been characterized, including the activation of Pd(II) precatalysts by H-shift and the deactivation of Ni(II) precatalysts by comproportionation. A fundamental difference between the off-cycle chemistries of palladium and nickel is the larger diversity of species yielded by the latter, with a rich combination of different oxidation states, nuclearity, and ligand coordination modes. The molecular-level understanding of off-cycle reactions has been exploited in catalyst design, including the stereoelectronic fine-tuning of the ligands aimed at maximizing the activation of the precatalyst meanwhile preventing its deactivation. Despite several challenges, which concern both experiments (e.g., isolation and characterization of transient species) and computations (e.g., comprehensive mapping of the potential energy surface), this approach has already been applied with success in the optimization of popular catalytic platforms (e.g., NHC–Pd–allyl precatalysts) and shows promise for the development of highly active and robust catalysts based on nickel. Catalytic cycle ; Catalysis ; Nickel ; Palladium ; Chemistry ; Comproportionation ; Ligand (biochemistry) ; Combinatorial chemistry ; Organic chemistry ; Physical chemistry Catalytic Cross-Coupling Reactions ; Asymmetric Hydrogenation and Catalysis ; Catalytic C–H Functionalization Methods
Authors: Xiuquan Jia | Jiping Ma | Fei Xia | Yongming Xu | Jin Gao | Jie Xu
Source: Nature Communications (article, 2018-02-26)
Field: Chemistry — Organic Chemistry

Carboxylic acid-modified metal oxide catalyst for selectivity-tunable aerobic ammoxidation Controlling the reaction selectivity of a heterobifunctional molecule is a fundamental challenge in many catalytic processes. Recent efforts to design chemoselective catalysts have focused on modifying the surface of metal nanoparticle materials having tunable properties. However, precise control over the surface properties of base-metal oxide catalysts remains a challenge. Here, we show that green modification of the surface with carboxylates can be used to tune the ammoxidation selectivity toward the desired products during the reaction of hydroxyaldehyde on manganese oxide catalysts. These modifications improve the selectivity for hydroxynitrile from 0 to 92% under identical reaction conditions. The product distribution of dinitrile and hydroxynitrile can be continuously tuned by adjusting the amount of carboxylate modifier. This property was attributed to the selective decrease in the hydroxyl adsorption affinity of the manganese oxides by the adsorbed carboxylate groups. The selectivity enhancement is not affected by the tail structure of the carboxylic acid. Ammoxidation ; Catalysis ; Selectivity ; Oxide ; Carboxylic acid ; Metal ; Chemistry ; Organic chemistry ; Acrylonitrile ; Polymer Nanomaterials for catalytic reactions ; Catalytic Processes in Materials Science ; Oxidative Organic Chemistry Reactions