jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Miriam Navlani‐García | Kohsuke Mori | Yasutaka Kuwahara | Hiromi Yamashita
Source: NPG Asia Materials (article, 2018-04-01)
Field: Materials Science — Materials Chemistry

Recent strategies targeting efficient hydrogen production from chemical hydrogen storage materials over carbon-supported catalysts There is an evident urgent need to find a renewable and clean energy vector to ensure the worldwide energy supply while minimizing environmental impacts, and hydrogen stands out as a promising alternative energy carrier. The social concern around its safe storage is constantly fostering the search for alternative options to conventional storage methods and, in this context, chemical hydrogen storage materials have produced abundant investigations with particular attention to the design of heterogeneous catalysts that can boost the generation of molecular hydrogen. Among the chemical hydrogen storage materials, formic acid and ammonia–borane hold tremendous promise, and some of the recent strategies considered for the preparation of high-performance carbon-supported catalysts are summarized in this review. The outstanding features of carbon materials and their versatility combined with the tunability of the metal active phase properties (e.g., morphology, composition, and electronic features) provide numerous options for the design of promising catalysts. Precise control over the size and composition of metal nanoparticles is critical to the safe production of hydrogen from chemical storage systems. Kohsuke Mori and Hiromi Yamashita from Osaka University in Japan and colleagues review recent progress in producing hydrogen gas for fuel cell technology from the energy-rich molecules formic acid and ammonia borane. By immobilizing nanosized metal catalysts onto carbon-based supports with shapes including ultrasmall spheres, nanotubes, and graphene oxide sheets, researchers can tune hydrogen generation rates to record-high levels while still ensuring easy recovery and reuse. Catalytic reactions with formic acid can be improved by using noble metal-based palladium catalysts. While ruthenium nanocatalysts are favored for ammonia borane reactions, less expensive metal nickel and cobalt nanoparticles are gaining attention. This review recapitulates some of the most representative studies recently reported on carbon-supported catalysts for the hydrogen production from formic acid and ammonia borane by considering both active phase features and support properties. Several synthetic strategies are herein summarized to highlight the versatility of carbon materials in affording highly-performing catalysts for the hydrogen production from hydrogen carrier molecules. Hydrogen storage ; Ammonia borane ; Materials science ; Hydrogen production ; Catalysis ; Hydrogen ; Nanotechnology ; Carbon fibers ; Graphene ; Hydrogen fuel ; Energy storage ; Chemical engineering ; Chemistry ; Organic chemistry Hydrogen Storage and Materials ; Carbon dioxide utilization in catalysis ; Ammonia Synthesis and Nitrogen Reduction
Authors: Swati Sharma
Source: Materials (review, 2018-09-28)
Field: Materials Science — Materials Chemistry

Glassy Carbon: A Promising Material for Micro- and Nanomanufacturing When certain polymers are heat-treated beyond their degradation temperature in the absence of oxygen, they pass through a semi-solid phase, followed by the loss of heteroatoms and the formation of a solid carbon material composed of a three-dimensional graphenic network, known as glassy (or glass-like) carbon. The thermochemical decomposition of polymers, or generally of any organic material, is defined as pyrolysis. Glassy carbon is used in various large-scale industrial applications and has proven its versatility in miniaturized devices. In this article, micro and nano-scale glassy carbon devices manufactured by (i) pyrolysis of specialized pre-patterned polymers and (ii) direct machining or etching of glassy carbon, with their respective applications, are reviewed. The prospects of the use of glassy carbon in the next-generation devices based on the material's history and development, distinct features compared to other elemental carbon forms, and some large-scale processes that paved the way to the state-of-the-art, are evaluated. Selected support techniques such as the methods used for surface modification, and major characterization tools are briefly discussed. Barring historical aspects, this review mainly covers the advances in glassy carbon device research from the last five years (2013⁻2018). The goal is to provide a common platform to carbon material scientists, micro/nanomanufacturing experts, and microsystem engineers to stimulate glassy carbon device research. Nanomanufacturing ; Materials science ; Glassy carbon ; Carbon fibers ; Nanotechnology ; Composite material ; Chemistry ; Composite number Carbon Nanotubes in Composites ; Diamond and Carbon-based Materials Research ; Nanowire Synthesis and Applications
Authors: Liam Huber | Raheleh Hadian | Blazej Grabowski | Jörg Neugebauer
Source: npj Computational Materials (article, 2018-11-19)
Field: Materials Science — Materials Chemistry

A machine learning approach to model solute grain boundary segregation Abstract Even minute amounts of one solute atom per one million bulk atoms may give rise to qualitative changes in the mechanical response and fracture resistance of modern structural materials. These changes are commonly related to enrichment by several orders of magnitude of the solutes at structural defects in the host lattice. The underlying concept—segregation—is thus fundamental in materials science. To include it in modern strategies of materials design, accurate and realistic computational modelling tools are necessary. However, the enormous number of defect configurations as well as sites solutes can occupy requires models which rely on severe approximations. In the present study we combine a high-throughput study containing more than 1 million data points with machine learning to derive a computationally highly efficient framework which opens the opportunity to model this important mechanism on a routine basis. Computer science ; Lattice (music) ; Grain boundary ; Throughput ; Atom (system on chip) ; Mechanism (biology) ; Statistical physics ; Algorithm ; Theoretical computer science ; Materials science ; Parallel computing ; Physics ; Microstructure ; Metallurgy Machine Learning in Materials Science ; Microstructure and mechanical properties ; Advanced Materials Characterization Techniques
Authors: Luigi Bonati | Michele Parrinello
Source: Physical Review Letters (article, 2018-12-26)
Field: Materials Science — Materials Chemistry

Silicon Liquid Structure and Crystal Nucleation from <i>Ab Initio</i> Deep Metadynamics Studying the crystallization process of silicon is a challenging task since empirical potentials are not able to reproduce well the properties of both a semiconducting solid and metallic liquid. On the other hand, nucleation is a rare event that occurs in much longer timescales than those achievable by ab initio molecular dynamics. To address this problem, we train a deep neural network potential based on a set of data generated by metadynamics simulations using a classical potential. We show how this is an effective way to collect all the relevant data for the process of interest. In order to efficiently drive the crystallization process, we introduce a new collective variable based on the Debye structure factor. We are able to encode the long-range order information in a local variable which is better suited to describe the nucleation dynamics. The reference energies are then calculated using the strongly constrained and appropriately normed (SCAN) exchange-correlation functional, which is able to get a better description of the bonding complexity of the Si phase diagram. Finally, we recover the free energy surface with a density functional theory accuracy, and we compute the thermodynamics properties near the melting point, obtaining a good agreement with experimental data. In addition, we study the early stages of the crystallization process, unveiling features of the nucleation mechanism. Metadynamics ; Nucleation ; Crystallization ; Ab initio ; Materials science ; Statistical physics ; Molecular dynamics ; Chemical physics ; Thermodynamics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Theoretical and Computational Physics ; Advanced Electron Microscopy Techniques and Applications
Authors: Augustus W. Lang | Yuanyuan Li | Michel De Keersmaecker | D. Eric Shen | Anna M. Österholm | Lars A. Berglund | John R. Reynolds
Source: ChemSusChem (article, 2018-02-01)
Field: Materials Science — Polymers and Plastics

Transparent Wood Smart Windows: Polymer Electrochromic Devices Based on Poly(3,4‐Ethylenedioxythiophene):Poly(Styrene Sulfonate) Electrodes Transparent wood composites, with their high strength and toughness, thermal insulation, and excellent transmissivity, offer a route to replace glass for diffusely transmitting windows. Here, conjugated-polymer-based electrochromic devices (ECDs) that switch on-demand are demonstrated using transparent wood coated with poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) as a transparent conducting electrode. These ECDs exhibit a vibrant magenta-to-clear color change that results from a remarkably colorless bleached state. Furthermore, they require low energy and power inputs of 3 mWh m<sup>-2</sup> at 2 W m<sup>-2</sup> to switch due to a high coloration efficiency (590 cm<sup>2</sup> C<sup>-1</sup> ) and low driving voltage (0.8 V). Each device component is processed with high-throughput methods, which highlights the opportunity to apply this approach to fabricate mechanically robust, energy-efficient smart windows on a large scale. Electrochromism ; PEDOT:PSS ; Materials science ; Electrochromic devices ; Electrode ; Sulfonate ; Styrene ; Polymer ; Poly(3,4-ethylenedioxythiophene) ; Conductive polymer ; Composite material ; Optoelectronics ; Chemistry ; Sodium ; Copolymer Transition Metal Oxide Nanomaterials ; Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Tangxin Xiao | Weiwei Zhong | Ling Zhou | Lixiang Xu | Xiaoqiang Sun | Robert B. P. Elmes | Xiao‐Yu Hu | Leyong Wang
Source: Chinese Chemical Letters (article, 2018-05-19)
Field: Chemistry — Organic Chemistry

Artificial light-harvesting systems fabricated by supramolecular host–guest interactions Supramolecular chemistry ; Host (biology) ; Artificial light ; Nanotechnology ; Materials science ; Chemistry ; Biology ; Ecology ; Physics ; Molecule ; Organic chemistry Supramolecular Chemistry and Complexes ; Supramolecular Self-Assembly in Materials ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Nongnuch Artrith | Alexander Urban | Gerbrand Ceder
Source: The Journal of Chemical Physics (article, 2018-03-16)
Field: Materials Science — Materials Chemistry

Constructing first-principles phase diagrams of amorphous Li<i>x</i>Si using machine-learning-assisted sampling with an evolutionary algorithm The atomistic modeling of amorphous materials requires structure sizes and sampling statistics that are challenging to achieve with first-principles methods. Here, we propose a methodology to speed up the sampling of amorphous and disordered materials using a combination of a genetic algorithm and a specialized machine-learning potential based on artificial neural networks (ANNs). We show for the example of the amorphous LiSi alloy that around 1000 first-principles calculations are sufficient for the ANN-potential assisted sampling of low-energy atomic configurations in the entire amorphous Li<sub>x</sub>Si phase space. The obtained phase diagram is validated by comparison with the results from an extensive sampling of Li<sub>x</sub>Si configurations using molecular dynamics simulations and a general ANN potential trained to ∼45 000 first-principles calculations. This demonstrates the utility of the approach for the first-principles modeling of amorphous materials. Sampling (signal processing) ; Amorphous solid ; Computer science ; Phase diagram ; Molecular dynamics ; Artificial neural network ; Genetic algorithm ; Phase space ; Algorithm ; Phase (matter) ; Artificial intelligence ; Statistical physics ; Machine learning ; Materials science ; Physics ; Thermodynamics ; Chemistry ; Computational chemistry ; Crystallography Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Memory and Neural Computing
Authors: Zile Li | Qi Dai | Muhammad Qasim Mehmood | Guangwei Hu | Boris Luk 'yanchuk | Jin Tao | Chenglong Hao | Inki Kim | Heonyeong Jeong | Guoxing Zheng | Shaohua Yu | Andrea Alù | Junsuk Rho | Cheng‐Wei Qiu
Source: Light Science & Applications (article, 2018-09-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Full-space Cloud of Random Points with a Scrambling Metasurface With the rapid progress in computer science, including artificial intelligence, big data and cloud computing, full-space spot generation can be pivotal to many practical applications, such as facial recognition, motion detection, augmented reality, etc. These opportunities may be achieved by using diffractive optical elements (DOEs) or light detection and ranging (LIDAR). However, DOEs suffer from intrinsic limitations, such as demanding depth-controlled fabrication techniques, large thicknesses (more than the wavelength), Lambertian operation only in half space, etc. LIDAR nevertheless relies on complex and bulky scanning systems, which hinders the miniaturization of the spot generator. Here, inspired by a Lambertian scatterer, we report a Hermitian-conjugate metasurface scrambling the incident light to a cloud of random points in full space with compressed information density, functioning in both transmission and reflection spaces. Over 4044 random spots are experimentally observed in the entire space, covering angles at nearly 90°. Our scrambling metasurface is made of amorphous silicon with a uniform subwavelength height, a nearly continuous phase coverage, a lightweight, flexible design, and low-heat dissipation. Thus, it may be mass produced by and integrated into existing semiconductor foundry designs. Our work opens important directions for emerging 3D recognition sensors, such as motion sensing, facial recognition, and other applications. Terabyte ; Computer science ; Ranging ; Lidar ; Point cloud ; Miniaturization ; Scrambling ; Holography ; Optics ; Artificial intelligence ; Physics ; Materials science ; Telecommunications ; Nanotechnology Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Advanced Antenna and Metasurface Technologies
Authors: Mathieu Massicotte | Fabien Vialla | Peter Schmidt | Mark B. Lundeberg | Simone Latini | Sten Haastrup | Mark Danovich | Diana Davydovskaya | Kenji Watanabe | Takashi Taniguchi | Vladimir I. Fal’ko | Kristian S. Thygesen | Thomas Garm Pedersen | Frank H. L. Koppens
Source: RePEc: Research Papers in Economics (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Dissociation of two-dimensional excitons in monolayer WSe&lt;sub&gt;2&lt;/sub&gt; Abstract Two-dimensional (2D) semiconducting materials are promising building blocks for optoelectronic applications, many of which require efficient dissociation of excitons into free electrons and holes. However, the strongly bound excitons arising from the enhanced Coulomb interaction in these monolayers suppresses the creation of free carriers. Here, we identify the main exciton dissociation mechanism through time and spectrally resolved photocurrent measurements in a monolayer WSe2 p–n junction. We find that under static in-plane electric field, excitons dissociate at a rate corresponding to the one predicted for tunnel ionization of 2D Wannier–Mott excitons. This study is essential for understanding the photoresponse of 2D semiconductors and offers design rules for the realization of efficient photodetectors, valley dependent optoelectronics, and novel quantum coherent phases. Exciton ; Monolayer ; Dissociation (chemistry) ; Semiconductor ; Photocurrent ; Photodetector ; Trion ; Impact ionization ; Biexciton ; Electron ; Multiple exciton generation ; Coulomb ; Optoelectronics ; Ionization ; Heterojunction ; Materials science ; Physics ; Condensed matter physics ; Chemistry ; Quantum dot ; Nanotechnology ; Quantum mechanics 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Hamid Vatanparast | Farshid Shahabi | Alireza Bahramian | Aliyar Javadi | R. Miller
Source: Scientific Reports (article, 2018-05-02)
Field: Chemistry — Organic Chemistry

The Role of Electrostatic Repulsion on Increasing Surface Activity of Anionic Surfactants in the Presence of Hydrophilic Silica Nanoparticles Hydrophilic silica nanoparticles alone are not surface active. They, however, develop a strong electrostatic interaction with ionic surfactants and consequently affect their surface behavior. We report the interfacial behavior of n-heptane/anionic-surfactant-solutions in the presence of hydrophilic silica nanoparticles. The surfactants are sodium dodecyl sulfate (SDS) and dodecyl benzene sulfonic acid (DBSA), and the diameters of the used particles are 9 and 30 nm. Using experimental tensiometry, we show that nanoparticles retain their non-surface-active nature in the presence of surfactants and the surface activity of surfactant directly increases with the concentration of nanoparticles. This fact was attributed to the electrostatic repulsive interaction between the negatively charged nanoparticles and the anionic surfactant molecules. The role of electrostatic repulsion on increasing surface activity of the surfactant has been discussed. Further investigations have been performed for screening the double layer charge of the nanoparticles in the presence of salt. Moreover, the hydrolysis of SDS molecules in the presence of silica nanoparticles and the interaction of nanoparticles with SDS inherent impurities have been studied. According to our experimental observations, silica nanoparticles alleviate the effects of dodecanol, formed by SDS hydrolysis, on the interfacial properties of SDS solution. Pulmonary surfactant ; Nanoparticle ; Sodium dodecyl sulfate ; Dodecanol ; Surface charge ; Chemical engineering ; Chemistry ; Molecule ; Electrostatics ; Hydrolysis ; Surface modification ; Inorganic chemistry ; Organic chemistry ; Physical chemistry Surfactants and Colloidal Systems ; Pickering emulsions and particle stabilization ; Enhanced Oil Recovery Techniques
Authors: Aditya Sood | Feng Xiong | Shunda Chen | Haotian Wang | Daniele Selli | Jinsong Zhang | Connor J. McClellan | Jie Sun | Davide Donadio | Yi Cui | Eric Pop | Kenneth E. Goodson
Source: Nature Communications (article, 2018-10-24)
Field: Materials Science — Materials Chemistry

An electrochemical thermal transistor The ability to actively regulate heat flow at the nanoscale could be a game changer for applications in thermal management and energy harvesting. Such a breakthrough could also enable the control of heat flow using thermal circuits, in a manner analogous to electronic circuits. Here we demonstrate switchable thermal transistors with an order of magnitude thermal on/off ratio, based on reversible electrochemical lithium intercalation in MoS<sub>2</sub> thin films. We use spatially-resolved time-domain thermoreflectance to map the lithium ion distribution during device operation, and atomic force microscopy to show that the lithiated state correlates with increased thickness and surface roughness. First principles calculations reveal that the thermal conductance modulation is due to phonon scattering by lithium rattler modes, c-axis strain, and stacking disorder. This study lays the foundation for electrochemically-driven nanoscale thermal regulators, and establishes thermal metrology as a useful probe of spatio-temporal intercalant dynamics in nanomaterials. Thermal ; Nanoscopic scale ; Transistor ; Lithium (medication) ; Phonon ; Stacking ; Thermal conduction ; Thermal conductivity ; Phonon scattering Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies
Authors: Felix C. Mocanu | Konstantinos Konstantinou | Tae Hoon Lee | Noam Bernstein | Volker L. Deringer | Gábor Cśanyi | Stephen R. Elliott
Source: The Journal of Physical Chemistry B (article, 2018-09-01)
Field: Materials Science — Materials Chemistry

Modeling the Phase-Change Memory Material, Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub>, with a Machine-Learned Interatomic Potential The phase-change material, Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub>, is the canonical material ingredient for next-generation storage-class memory devices used in novel computing architectures, but fundamental questions remain regarding its atomic structure and physicochemical properties. Here, we introduce a machine-learning (ML)-based interatomic potential that enables large-scale atomistic simulations of liquid, amorphous, and crystalline Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub> with an unprecedented combination of speed and density functional theory (DFT) level of accuracy. Two applications exemplify the usefulness of such an ML-driven approach: we generate a 7200-atom structural model, hitherto inaccessible with DFT simulations, that affords new insight into the medium-range structural order and we create an ensemble of uncorrelated, smaller structures, for studies of their chemical bonding with statistical significance. Our work opens the way for new atomistic insights into the fascinating and chemically complex class of phase-change materials that are used in real nonvolatile memory devices. Phase-change memory ; Uncorrelated ; Amorphous solid ; Interatomic potential ; Atomic units ; Atom (system on chip) ; Computer science ; Phase (matter) ; Materials science ; Density functional theory ; Molecular dynamics ; Class (philosophy) ; Statistical physics ; Nanotechnology ; Chemistry ; Artificial intelligence ; Computational chemistry ; Physics ; Crystallography ; Parallel computing Phase-change materials and chalcogenides ; Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films
Authors: Christopher D. Taylor | Pin Lu | James E. Saal | G. S. Frankel | John R. Scully
Source: npj Materials Degradation (article, 2018-01-19)
Field: Materials Science — Materials Chemistry

Integrated computational materials engineering of corrosion resistant alloys Abstract Structure, composition and surface properties dictate corrosion resistance in any given environment. The degrees of freedom in alloy design are too numerous in emerging materials such as high entropy alloys and bulk metallic glasses for the use of high-throughput methods or trial and error. We review three domains of knowledge that can be applied towards the goal of corrosion resistant alloy (CRA) design: (a) the aggregation of knowledge gained through experience in developing CRAs empirically, (b) data-driven approaches that use descriptive metrics for alloy composition optimization, and (c) first-principles models of elementary processes that regulate corrosion informed by theory and inspired by phenomenological models in the literature. A path forward for integrated computational materials engineering (ICME) of CRAs that unites these three knowledge domains is introduced. Corrosion ; Alloy ; Materials science ; Computer science ; Mechanical engineering ; Metallurgy ; Engineering Machine Learning in Materials Science ; High Entropy Alloys Studies ; High-Temperature Coating Behaviors