jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hee Seong Kang | Jung Hoon Kang | Sol Lee | Kihyun Lee | Do Hyoung Koo | Yong‐Sung Kim | Young Joon Hong | Yong-Jin Kim | Kwanpyo Kim | Donghun Lee | Chul‐Ho Lee
Source: NPG Asia Materials (article, 2022-11-18)
Field: Materials Science — Materials Chemistry

Bowing-alleviated continuous bandgap engineering of wafer-scale WS2xSe2(1-x) monolayer alloys and their assembly into hetero-multilayers Abstract Bandgap engineering of compound semiconductors and the fabrication of bandgap-modulated heterostructures are important for enabling the development of modern optoelectronics. However, these engineering processes are challenging for two-dimensional (2D) semiconductors of transition metal dichalcogenides, particularly on a large scale. Herein, we report the wafer-scale homogeneous growth of composition-modulated WS 2 x Se 2(1- x ) alloys with a continuously tunable bandgap using metal–organic chemical vapor deposition. Well-optimized growth produces monolayer films with excellent homogeneity over the entire wafer. The substitutional atomic chalcogen (S, Se) concentration in WS 2 x Se 2(1- x ) alloys is precisely controlled by varying the flow rate of the metal–organic precursors, leading to a bandgap modulation from 1.67 to 2.05 eV, as determined from absorbance spectra. Notably, the optical bandgap of WS 2 x Se 2(1- x ) alloys exhibits a nearly linear relationship with the chalcogen composition, implying a low bowing effect. This bowing-alleviated bandgap modulation is attributed to the small lattice mismatch, strain relaxation, and thermodynamic miscibility in the WS 2 x Se 2(1- x ) alloys, as confirmed by density-functional theory calculations. Furthermore, the fabrication of hetero-multilayers by stacking differently alloyed films is demonstrated. The produced heterostructure film exhibits a broad spectral absorbance distinct from that of the individual layers. The findings of this study provide insights for the advancement of versatile design of functional 2D optoelectronics. Materials science ; Band gap ; Heterojunction ; Semiconductor ; Monolayer ; Optoelectronics ; Chemical vapor deposition ; Wafer ; Spinodal decomposition ; Direct and indirect band gaps ; Chalcogen ; Nanotechnology ; Crystallography ; Chemistry ; Phase (matter) ; Organic chemistry 2D Materials and Applications ; Perovskite Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Irene Casademont‐Reig | Tatiana Woller | Víctor García García | Julia Contreras‐García | William Tiznado | Miquel Torrent‐Sucarrat | Eduard Matito | Mercedes Alonso
Source: Chemistry - A European Journal (article, 2022-10-04)
Field: Chemistry — Organic Chemistry

Quest for the Most Aromatic Pathway in Charged Expanded Porphyrins Despite the central role of aromaticity in the chemistry of expanded porphyrins, the evaluation of aromaticity remains difficult for these extended macrocycles. The presence of multiple conjugation pathways and different planar and nonplanar π-conjugation topologies makes the quantification of global and local aromaticity even more challenging. In neutral expanded porphyrins, the predominance of the aromatic conjugation pathway passing through the imine-type nitrogens and circumventing the amino NH groups is established. However, for charged macrocycles, the question about the main conjugation circuit remains open. Accordingly, different conjugation pathways in a set of neutral, anionic, and cationic expanded porphyrins were investigated by means of several aromaticity indices rooted in the structural, magnetic, and electronic criteria. Overall, our results reveal the predominance of the conjugation pathway that passes through all nitrogen atoms to describe the aromaticity of deprotonated expanded porphyrins, while the outer pathway through the perimeter carbon atoms becomes the most aromatic in protonated macrocycles. In nonplanar and charged macrocycles, a discrepancy between electronic and magnetic descriptors is observed. Nevertheless, our work demonstrates AV<sub>min</sub> remains the best tool to determine the main conjugation pathway of expanded porphyrins. Aromaticity ; Protonation ; Chemistry ; Imine ; Deprotonation ; Cationic polymerization ; Antiaromaticity ; Computational chemistry ; Ion ; Molecule ; Organic chemistry Synthesis and Properties of Aromatic Compounds ; Porphyrin and Phthalocyanine Chemistry ; Fullerene Chemistry and Applications
Authors: Maoyin Li | Bensu Tunca | Bart Van Meerbeek | Jef Vleugels | Fei Zhang
Source: Journal of the European Ceramic Society (article, 2022-11-28)
Field: Materials Science — Ceramics and Composites

Tough and damage-tolerant monolithic zirconia ceramics with transformation-induced plasticity by grain-boundary segregation Materials science ; Ceramic ; Plasticity ; Grain boundary ; Cubic zirconia ; Composite material ; Transformation (genetics) ; Microstructure Advanced ceramic materials synthesis ; Advanced materials and composites ; Nuclear materials and radiation effects
Authors: Shiro Sakai | Ryotaro Arita | Tomi Ohtsuki
Source: Physical Review Research (article, 2022-09-26)
Field: Materials Science — Materials Chemistry

Quantum phase transition between hyperuniform density distributions We study an electron distribution under a quasiperiodic potential in light of hyperuniformity, aiming to establish a classification and analysis method for aperiodic but orderly density distributions realized in, e.g., quasicrystals. Using the Aubry-Andr\'e-Harper model, we first reveal that the electron-charge distribution changes its character as the increased quasiperiodic potential alters the eigenstates from extended to localized ones. While these changes of the charge distribution are characterized by neither multifractality nor translational-symmetry breaking, they are characterized by hyperuniformity class and its order metric. We find a nontrivial relationship between the density of states at the Fermi level, a charge-distribution histogram, and the hyperuniformity class. The change to a different hyperuniformity class occurs as a first-order phase transition except for an electron-hole symmetric point, where the transition is of the third order. Moreover, we generalize the hyperuniformity order metric to a function, to capture more detailed features of the density distribution, in some analogy with a generalization of the fractal dimension to a multifractal one. Quasiperiodic function ; Quasicrystal ; Phase transition ; Multifractal system ; Charge density ; Physics ; Fractal ; Statistical physics ; Electron density ; Quantum mechanics ; Condensed matter physics ; Electron ; Mathematics ; Mathematical analysis Quasicrystal Structures and Properties ; Theoretical and Computational Physics
Authors: Abdullah Arafat | Md. Sherajul Islam | Naim Ferdous | A. S. M. Jannatul Islam | Md. Mosarof Hossain Sarkar | Catherine Stampfl | Jeongwon Park
Source: Scientific Reports (article, 2022-09-27)
Field: Materials Science — Materials Chemistry

Atomistic reaction mechanism of CVD grown MoS2 through MoO3 and H2S precursors Chemical vapor deposition (CVD) through sulfidation of MoO<sub>3</sub> is one of the most important synthesis techniques to obtain large-scale and high-quality two-dimensional (2D) MoS<sub>2</sub>. Recently, H<sub>2</sub>S precursor is being used in the CVD technique to synthesize 2D MoS<sub>2</sub>. Although several studies have been carried out to examine the mechanism of MoS<sub>2</sub> growth in the presence of sulfur and MoO<sub>3</sub> precursors, the growth of MoS<sub>2</sub> in the presence of H<sub>2</sub>S precursor has largely remained unknown. In this study, we present a Reactive molecular dynamics (RMD) simulation to investigate the reaction mechanism of MoS<sub>2</sub> from MoO<sub>3</sub> and H<sub>2</sub>S precursors. The intermediate molecules formation, the reason behind those formations, and the surface compositions of MoO<sub>x</sub>S<sub>y</sub>H<sub>z</sub> during the initial steps of CVD have all been quantified. Surprisingly, a sudden separation of sulfur atoms from the surface was observed in the H<sub>2</sub>S precursor system due to the substantial oxygen evolution after 1660 K. The sulfur detachments and oxygen evolution from the surface were found to have a linear relationship. In addition, the intermediate molecules and surface bonds of MoS<sub>2</sub> synthesized by MoO<sub>3</sub> and H<sub>2</sub>S precursors were compared to those of a system using S<sub>2</sub> and MoO<sub>3</sub> precursors. The most stable subsidiary formation from the H<sub>2</sub>S precursor was found to be H<sub>2</sub>O, whereas in case of S<sub>2</sub> precursor it was SO. These results provide a valuable insight in the formation of large-scale and high-quality 2D MoS<sub>2</sub> by the CVD technique. Sulfur ; Sulfidation ; Chemical vapor deposition ; Molecule ; Chemistry ; Oxygen ; Deposition (geology) ; Reaction mechanism ; Chemical engineering ; Crystallography ; Catalysis ; Organic chemistry 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Mei‐Huei Lin | Jakob B. Wolf | Eric T. Sletten | Dario Cambié | José Danglad‐Flores | Peter H. Seeberger
Source: ChemBioChem (article, 2022-11-16)
Field: Chemistry — Organic Chemistry

Enabling Technologies in Carbohydrate Chemistry: Automated Glycan Assembly, Flow Chemistry and Data Science The synthesis of defined oligosaccharides is a complex task. Several enabling technologies have been introduced in the last two decades to facilitate synthetic access to these valuable biomolecules. In this concept, we describe the technological solutions that have advanced glycochemistry using automated glycan assembly, flow chemistry and data science as examples. We highlight how the synergies between these different technologies can further advance the field, with progress toward the realization of a self-driving lab for glycan synthesis. Glycan ; Chemistry ; Flow chemistry ; Nanotechnology ; Synthetic biology ; Glycomics ; Field (mathematics) ; Realization (probability) ; Computer science ; Biochemical engineering ; Data science ; Computational biology ; Engineering ; Continuous flow ; Biochemistry ; Materials science ; Biology ; Glycoprotein Carbohydrate Chemistry and Synthesis ; Glycosylation and Glycoproteins Research ; Chemical Synthesis and Analysis
Authors: David Choque-Quispe | Yudith Choque-Quispe | Carlos A. Ligarda-Samanez | Diego E. Peralta-Guevara | Aydeé M. Solano-Reynoso | Betsy S. Ramos-Pacheco | Fredy Taipe-Pardo | Edgar L. Martínez-Huamán | John Peter Aguirre Landa | Henrry W. Agreda Cerna | Julio C. Loayza-Céspedes | Miluska M. Zamalloa-Puma | Genaro Julio Álvarez-López | Alan Zamalloa-Puma | Elibet Moscoso-Moscoso | Yadyra Quispe-Quispe
Source: Nanomaterials (article, 2022-09-29)
Field: Materials Science — Biomaterials

Effect of the Addition of Corn Husk Cellulose Nanocrystals in the Development of a Novel Edible Film The cellulose from agroindustrial waste can be treated and converted into nanocrystals or nanofibers. It could be used to produce biodegradable and edible films, contributing to the circular economy and being environmentally friendly. This research aimed to develop an edible film elaborated with activated cellulose nanocrystals, native potato starch, and glycerin. The activated cellulose nanocrystals were obtained by basic/acid digestion and esterification with citric acid from corn husks. The starch was extracted from the native potato cultivated at 3500 m of altitude. Four film formulations were elaborated with potato starch (2.6 to 4.4%), cellulose nanocrystals (0.0 to 0.12%), and glycerin (3.0 to 4.2%), by thermoforming at 60 °C. It was observed that the cellulose nanocrystals reported an average size of 676.0 nm. The films mainly present hydroxyl, carbonyl, and carboxyl groups that stabilize the polymeric matrix. It was observed that the addition of cellulose nanocrystals in the films significantly increased (p-value < 0.05) water activity (0.409 to 0.447), whiteness index (96.92 to 97.27), and organic carbon content. In opposition to gelatinization temperature (156.7 to 150.1 °C), transparency (6.69 to 6.17), resistance to traction (22.29 to 14.33 N/mm), and solubility in acidic, basic, ethanol, and water media decreased. However, no significant differences were observed in the thermal decomposition of the films evaluated through TGA analysis. The addition of cellulose nanocrystals in the films gives it good mechanical and thermal resistance qualities, with low solubility, making it a potential food-coating material. Cellulose ; Materials science ; Husk ; Nanocrystal ; Starch ; Chemical engineering ; Crystallinity ; Solubility ; Absorption of water ; Citric acid ; Nuclear chemistry ; Organic chemistry ; Chemistry ; Composite material ; Nanotechnology ; Botany Advanced Cellulose Research Studies ; Nanocomposite Films for Food Packaging ; Polysaccharides and Plant Cell Walls
Authors: Baopeng Lu | Shuaibing Li | Yi Cui | Xiaowei Zhao | Daqi Zhang | Yongqiang Kang | Haiying Dong
Source: Energies (article, 2022-12-28)
Field: Materials Science — Materials Chemistry

Insulation Degradation Mechanism and Diagnosis Methods of Offshore Wind Power Cables: An Overview The marine environment in which offshore wind turbines are located is very complex and subjected to a variety of random loads that vary with time and space. As an important component of offshore wind power, the cable also bears the impact of the environment in which most of the turbines are located. Under the long-term action of mechanical stresses such as tension, torsion, and vibration, the cable insulation will crack due to stress fatigue leading to partial discharge, which seriously affects its electrical performance. The study of the mechanism of the change of electrical properties of cable insulation due to mechanical behavior is of great theoretical guidance to improve the reliable operation of cables. This paper first introduces the basic characteristics and operating conditions of torsion-resistant cables and submarine cables. Then the mechanical behavior of the cables is summarized, and the deterioration mechanism and deterioration effect of wind power cable insulation under the influence of multiple factors such as heat, oxygen, and mechanical stress are sorted out. Then, the basic principles of wind power cable operation condition monitoring methods and their characteristics are described. Finally, the relevant methods for the detection of hidden defects inside the insulation are summarized. Offshore wind power ; Marine engineering ; Wind power ; Submarine pipeline ; Power cable ; Mechanism (biology) ; Engineering ; Submarine ; Structural engineering ; Torsion (gastropod) ; Vibration ; Electrical engineering ; Geotechnical engineering ; Materials science ; Acoustics High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Icing and De-icing Technologies
Authors: Hui Sun | Zichao Que | Huinan Wei | Ao Zhou | Xuan Peng | Wei Cui | Xi Wang
Source: Nanotechnology Reviews (article, 2022-01-01)
Field: Materials Science — Biomaterials

Tunning matrix rheology and mechanical performance of ultra-high performance concrete using cellulose nanofibers Abstract With the growing demand for sustainability and rapid development of nanotechnology, nanocellulose materials extracted from natural plants have attracted great attention. The incorporation of nanocellulose materials leads to a change in viscosity and yield stress on ultra-high performance concrete (UHPC). Rheological parameters affect the mechanical strength and steel fiber distribution of UHPC significantly. Therefore, it is essential to adjust the matrix rheology within an appropriate range through nanocellulose materials. This study aims to propose a novel method to optimize steel fiber distribution and mechanical properties of UHPC by adjusting the matrix rheology with cellulose nanofibers (CNFs) suspensions. The relationship among CNF concentration, steel fiber distribution, and the mechanical properties of UHPC was established. Test results showed that the failure mode of UHPC containing CNFs changed from single cracking to multiple cracking, accompanied by 11–23% enhancement in tensile strength. With the increase of CNF concentrations, the probability density distribution functions of steel fiber orientation showed the trend toward the distribution with a larger inclination. The addition of CNF suspensions effectively reduced the number of steel fibers settling to the bottom of the specimens. Scanning electron microscopy analyses demonstrated that the nanoscale reinforcement by CNFs was conducive to improving the mechanical properties of UHPC. Materials science ; Nanocellulose ; Rheology ; Nanofiber ; Composite material ; Ultimate tensile strength ; Fiber ; Cellulose ; Scanning electron microscope ; Chemical engineering Advanced Cellulose Research Studies ; Innovations in Concrete and Construction Materials ; Natural Fiber Reinforced Composites
Authors: Hengrui Zhang | Wei Chen | Akshay Iyer | Daniel W. Apley
Source: Scientific Reports (article, 2022-11-17)
Field: Materials Science — Materials Chemistry

Uncertainty-aware mixed-variable machine learning for materials design Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure, and synthesis methods. Bayesian optimization (BO) employs uncertainty-aware machine learning models to select promising designs to evaluate, hence reducing the cost. However, BO with mixed numerical and categorical variables, which is of particular interest in materials design, has not been well studied. In this work, we survey frequentist and Bayesian approaches to uncertainty quantification of machine learning with mixed variables. We then conduct a systematic comparative study of their performances in BO using a popular representative model from each group, the random forest-based Lolo model (frequentist) and the latent variable Gaussian process model (Bayesian). We examine the efficacy of the two models in the optimization of mathematical functions, as well as properties of structural and functional materials, where we observe performance differences as related to problem dimensionality and complexity. By investigating the machine learning models' predictive and uncertainty estimation capabilities, we provide interpretations of the observed performance differences. Our results provide practical guidance on choosing between frequentist and Bayesian uncertainty-aware machine learning models for mixed-variable BO in materials design. Frequentist inference ; Machine learning ; Computer science ; Artificial intelligence ; Categorical variable ; Bayesian optimization ; Curse of dimensionality ; Bayesian probability ; Gaussian process ; Surrogate model ; Bayesian inference ; Gaussian Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electron and X-Ray Spectroscopy Techniques
Authors: Yixiao Chen | Linfeng Zhang | Han Wang | E Weinan
Source: Computer Physics Communications (article, 2022-09-09)
Field: Materials Science — Materials Chemistry

DeePKS-kit: A package for developing machine learning-based chemically accurate energy and density functional models Python (programming language) ; Computer science ; Artificial neural network ; Software ; Computational science ; Artificial intelligence ; Machine learning ; Algorithm ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Jiaxin Dai | Dongmei Fu | Guangxuan Song | Lingwei Ma | Xin Guo | J.M.C. Mol | Ivan Cole | Dawei Zhang
Source: Corrosion Science (article, 2022-10-22)
Field: Materials Science — Materials Chemistry

Cross-category prediction of corrosion inhibitor performance based on molecular graph structures via a three-level message passing neural network model Corrosion ; Corrosion inhibitor ; Artificial neural network ; Computer science ; Graph ; Materials science ; Artificial intelligence ; Composite material ; Theoretical computer science Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Yifan Wang | Yaqiong Su | Emiel J. M. Hensen | Dionisios G. Vlachos
Source: Chemistry of Materials (article, 2022-02-02)
Field: Materials Science — Materials Chemistry

Insights into Supported Subnanometer Catalysts Exposed to CO <i>via</i> Machine-Learning-Enabled Multiscale Modeling Subnanometer catalysts offer high noble metal utilization and superior performance for several reactions. However, understanding their structures and properties on an atomic scale under working conditions is challenging due to the large configurational space. Here, we introduce an efficient multiscale framework to predict their stability exposed to an adsorbate. The framework integrates a comprehensive toolset including density functional theory (DFT) calculations, cluster expansion, machine learning, and structure optimization. The end-to-end machine-learning workflow guides DFT data generation and enables significant computational acceleration. We demonstrate the approach for CO-adsorbed Pdn (n = 1–55) clusters on CeO2(111). Simulation results reveal that CO can facilitate restructuring by stabilizing smaller planar structures and bilayer structures of specific intermediate sizes, consistent with experimental reports. Metal–support interactions, preferential CO adsorption, and metal nuclearity and structure control catalyst stability. The framework allows automatic discovery of stable catalyst structures and a systematic strategy to exploit properties in the subnanometer scale. Density functional theory ; Materials science ; Multiscale modeling ; Stability (learning theory) ; Catalysis ; Workflow ; Computer science ; Nanotechnology ; Cluster (spacecraft) ; Noble metal ; Computational science ; Chemistry ; Computational chemistry ; Metal ; Machine learning Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Yuanming Bai | Leslie Vogt-Maranto | Mark E. Tuckerman | William J. Glover
Source: Nature Communications (article, 2022-11-17)
Field: Materials Science — Materials Chemistry

Machine learning the Hohenberg-Kohn map for molecular excited states The Hohenberg-Kohn theorem of density-functional theory establishes the existence of a bijection between the ground-state electron density and the external potential of a many-body system. This guarantees a one-to-one map from the electron density to all observables of interest including electronic excited-state energies. Time-Dependent Density-Functional Theory (TDDFT) provides one framework to resolve this map; however, the approximations inherent in practical TDDFT calculations, together with their computational expense, motivate finding a cheaper, more direct map for electronic excitations. Here, we show that determining density and energy functionals via machine learning allows the equations of TDDFT to be bypassed. The framework we introduce is used to perform the first excited-state molecular dynamics simulations with a machine-learned functional on malonaldehyde and correctly capture the kinetics of its excited-state intramolecular proton transfer, allowing insight into how mechanical constraints can be used to control the proton transfer reaction in this molecule. This development opens the door to using machine-learned functionals for highly efficient excited-state dynamics simulations. Time-dependent density functional theory ; Excited state ; Density functional theory ; Observable ; Kohn–Sham equations ; Physics ; Intramolecular force ; Bijection ; Proton ; Molecular dynamics ; Statistical physics ; Atomic physics ; Computer science ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies