jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Arthur France‐Lanord | Ryoji Asahi | B.P. LeBlanc | Joohwi Lee | E. Wimmer
Source: Scientific Reports (article, 2019-10-22)
Field: Materials Science — Polymers and Plastics

Highly efficient evaluation of diffusion networks in Li ionic conductors using a 3D-corrugation descriptor A highly efficient computational approach for the screening of Li ion conducting materials is presented and its performance is demonstrated for olivine-type oxides and thiophosphates. The approach is based on a topological analysis of the electrostatic (Coulomb) potential obtained from a single density functional theory calculation augmented by a Born-Mayer-type repulsive term between Li ions and the anions of the material. This 3D-corrugation descriptor enables the automatic determination of diffusion pathways in one, two, and three dimensions and reproduces migration barriers obtained from density functional theory calculations using nudged elastic band method within approximately 0.1 eV. Importantly, it correlates with Li ion conductivity. This approach thus offers an efficient tool for evaluating, ranking, and optimizing materials with high Li-ion conductivity. Density functional theory ; Ion ; Coulomb ; Conductivity ; Diffusion ; Ionic bonding ; Electrical conductor ; Ranking (information retrieval) ; Materials science ; Ionic conductivity ; Chemical physics ; Computational chemistry ; Computer science ; Chemistry ; Physics ; Physical chemistry ; Machine learning ; Thermodynamics ; Quantum mechanics Conducting polymers and applications ; Advanced Battery Materials and Technologies ; Machine Learning in Materials Science
Authors: Andreas Hermann | Dominik Kurzydłowski
Source: Crystals (article, 2019-09-04)
Field: Materials Science — Materials Chemistry

First-Principles Prediction of Structures and Properties in Crystals The term “first-principles calculations” is a synonym for the numerical determination of the electronic structure of atoms, molecules, clusters, or materials from ‘first principles’, i [...] Synonym (taxonomy) ; Molecule ; Term (time) ; Electronic structure ; Materials science ; Statistical physics ; Theoretical physics ; Computational chemistry ; Chemical physics ; Physics ; Chemistry ; Quantum mechanics ; Biology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Chemical Physics Studies
Authors: Marco Quaglio | Fabrizio Bezzo | Asterios Gavriilidis | Enhong Cao | Noor Al‐Rifai | Federico Galvanin
Source: AIChE Journal (article, 2019-06-27)
Field: Materials Science — Materials Chemistry

Identification of kinetic models of methanol oxidation on silver in the presence of uncertain catalyst behavior Abstract Catalytic oxidation of methanol to formaldehyde is an important industrial process due to the value of formaldehyde either as a final product or as a precursor of numerous chemicals. The study of kinetics in this system is hindered by sources of uncertainty that are inherently associated to the nature and state of the catalyst (e.g., uncertain reactivity level, deactivation phenomena), the measurement system and the structure of the kinetic model equations. In this work, a simplified kinetic model is identified from data collected from continuous flow microreactor systems where catalysts with assorted levels of reactivity are employed. Tailored model‐based data mining methods are proposed and applied for the effective estimation of the kinetic parameters and for identifying robust experimental conditions to be exploited for the kinetic characterization of catalysts with different reactivity, whose kinetic behavior is yet to be investigated. Catalysis ; Formaldehyde ; Reactivity (psychology) ; Methanol ; Kinetic energy ; Chemistry ; Microreactor ; Kinetics ; Work (physics) ; Thermodynamics ; Process (computing) ; Chemical engineering ; Organic chemistry ; Computer science ; Physics Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Thomas Quérette | Claire Bordes | Nathalie Sintès‐Zydowicz
Source: Colloids and Surfaces A Physicochemical and Engineering Aspects (article, 2019-12-23)
Field: Materials Science — Polymers and Plastics

Non-isocyanate polyurethane nanoprecipitation: Toward an optimized preparation of poly(hydroxy)urethane nanoparticles Pulmonary surfactant ; Dispersity ; Dynamic light scattering ; Materials science ; Particle size ; Isocyanate ; Polyurethane ; Polymer ; Chemical engineering ; Nanoparticle ; Solvent ; Micelle ; Particle-size distribution ; Aqueous solution ; Aqueous two-phase system ; Polymer chemistry ; Chemistry ; Nanotechnology ; Composite material ; Organic chemistry Polymer composites and self-healing ; Building materials and conservation ; Polymer Nanocomposites and Properties
Authors: Hesty Eka Mayasari | Noor Maryam Setyadewi
Source: Majalah Kulit Karet dan Plastik (article, 2019-07-28)
Field: Materials Science — Polymers and Plastics

Effect of natural zeolite as substrate filler on the properties of NBR/EPDM blend The NBR/EPDM blend has a great ozone, oil, and heat resistance. Carbon black is the most commonly used filler material for rubber goods. But, it is less environmentally friendly and less healthy friendly. In this study, we will study the use of natural zeolite as a substitute for the filler. This blend was carried out by a two-roll mill and tested by rheometer and tensile strength tester. From the research that has been done, it is known that vulcanizate with carbon black is better than all of the vulcanizate using zeolite as filler substitute. Vulcanizate with CB provide the fastest curing time, the fastest reaction speed and also the best mechanical properties. Vulcanization with the 10 phr zeolite gives the optimum curing time and the best mechanical properties compared to the substitution of other fillers. This shows that 10 phr zeolite (16.7%) can be a suitable filling material for NBR/EPDM blend. Vulcanization ; Materials science ; Filler (materials) ; Composite material ; Curing (chemistry) ; Natural rubber ; Carbon black ; Ultimate tensile strength ; Zeolite ; Environmentally friendly ; EPDM rubber ; Catalysis ; Chemistry Polymer Nanocomposites and Properties ; Fiber-reinforced polymer composites ; Tribology and Wear Analysis
Authors: Piby Paul | Manikandan Muthu | Yojitha Chilukuri | Steve Haga | Se Chul Chun | Jae‐Wook Oh
Source: Processes (article, 2019-11-21)
Field: Chemistry — Spectroscopy

In Silico Tools and Phosphoproteomic Software Exclusives Proteomics and phosphoproteomics have been emerging as new dimensions of omics. Phosphorylation has a profound impact on the biological functions and applications of proteins. It influences everything from intrinsic activity and extrinsic executions to cellular localization. This post-translational modification has been subjected to detailed study and has been an object of analytical curiosity with the advent of faster instrumentation. The major strength of phosphoproteomic research lies in the fact that it gives an overall picture of the workforce of the cell. Phosphoproteomics gives deeper insights into understanding the mechanism behind development and progression of a disease. This review for the first time consolidates the list of existing bioinformatics tools developed for phosphoproteomics. The gap between development of bioinformatics tools and their implementation in clinical research is highlighted. The challenge facing progress is ideally believed to be the interdisciplinary arena this field of research is associated with. For meaningful solutions and deliverables, these tools need to be implemented in clinical studies for obtaining answers to pharmacodynamic questions, saving time, costs and energy. This review hopes to invoke some thought in this direction. Phosphoproteomics ; Computer science ; Deliverable ; Proteomics ; Mechanism (biology) ; Computational biology ; Data science ; Bioinformatics ; Biology ; Engineering ; Protein phosphorylation ; Phosphorylation ; Systems engineering Advanced Proteomics Techniques and Applications ; Genetics, Bioinformatics, and Biomedical Research ; Bioinformatics and Genomic Networks
Authors: Faramarz Djavanroodi | Zahid Hussain | Osama M. Irfan | Fahad A. Al‐Mufadi
Source: Metals (article, 2019-01-26)
Field: Materials Science — Materials Chemistry

Strain Behavior of Nickel Alloy 200 during Multiaxial Forging through Finite Element Modeling Multiaxial forging (MAF) is one of the appealing methods of severe plastic deformation (SPD) techniques to fabricate ultrafine-grained (UFG) materials. In this study; the influence of process parameters such as strain rate; friction; and initial temperature has been assessed through finite element simulation of Nickel 200 alloy. The Johnson–Cook equation was applied in simulating the MAF process. The homogeneous microstructure of a material processed by MAF is an important requirement to obtain uniform mechanical and other properties. The uniformity in properties was evaluated by the investigation of the hardness measurements; effective strain (ES), and inhomogeneous factor (IF) or coefficient of standard deviation. The results showed that the inhomogeneous factor decreases with an increase in strain rate and decrease in temperature. It was found that a more homogeneous structure is observed with an increasing number of MAF cycles and the strain rate strain. Furthermore; the average grain size reduced from 850 nm to 220 nm after three cycles of MAF. Finally; experimental work was performed to validate the results. Materials science ; Forging ; Microstructure ; Alloy ; Finite element method ; Strain rate ; Homogeneous ; Work (physics) ; Metallurgy ; Grain size ; Deformation (meteorology) ; Nickel ; Strain (injury) ; Composite material ; Standard deviation ; Structural engineering ; Thermodynamics ; Mathematics Microstructure and mechanical properties ; Metallurgy and Material Forming ; Advanced materials and composites
Authors: Elif GÜLTEKİN | Halil İbrahim Çelık | L. Canan Dülger | Halil İbrahim SÜNBÜL
Source: Tekstil ve Mühendis (article, 2019-12-31)
Field: Materials Science — Polymers and Plastics

Image Processing Applications on Yarn Characteristics and Fault Inspection New developments in machine vision and automation technologies provide more sensitive process control and quality inspection in each stage of the production line. Industry 4.0 and Image Processing techniques have been used in many areas in textile industry in last decade. Image processing techniques have been also used in textile industry on automatic detection of fiber, yarn and fabric characteristics with improved accuracy and quicker results. In this study, a machine vision system for automatic inspection of yarn bobbin and fabric abrage defect is presented. The prototype system is presented with its components. An image processing algorithm is developed and it is applied on different bobbin and fabric samples including abrage fault. The success of the given machine vision system is discussed herein. Yarn ; Fault (geology) ; Image processing ; Computer science ; Engineering drawing ; Computer vision ; Artificial intelligence ; Reliability engineering ; Image (mathematics) ; Engineering ; Mechanical engineering ; Geology ; Seismology Textile materials and evaluations ; Industrial Vision Systems and Defect Detection
Authors: Mateusz Skałoń | Ricardo Henrique Buzolin | J. Kazior | Christof Sommitsch | Marek Hebda
Source: Materials (article, 2019-06-03)
Field: Materials Science — Ceramics and Composites

Improving the Dimensional Stability and Mechanical Properties of AISI 316L + B Sinters by Si3N4 Addition The following paper describes a new and effective method to obtain high-density sinters with simultaneously decreased distortions, produced by one press and sinter operation. This effect was achieved through the induced disappearance of the eutectic liquid phase. The study was carried out on AISI 316L stainless steel powder that was mixed with elemental boron and silicon nitride. Boron was used as a sintering process activator. The scientific novelty of this publication consists of the use of a silicon nitride as a solid-state nitrogen carrier that was intended to change the borides' morphology by binding boron. Based on the thermodynamic calculations, 20 blends of various compositions were tested for physical properties, porosity, microstructure, and mechanical properties. Moreover, phase compositions for selected samples were analyzed. It was shown that the addition of silicon nitride as a nitrogen carrier decreases the boron-based eutectic phase volume and both increases the mechanical properties and decreases after-sintering distortions. An explanation of the observed phenomena was also proposed. Materials science ; Composite material ; Metallurgy Advanced ceramic materials synthesis ; Advanced materials and composites ; Advanced machining processes and optimization
Authors: Lisa M. Pietrek | Lukas S. Stelzl | Gerhard Hummer
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2019-08-09)
Field: Chemistry — Spectroscopy

Hierarchical Ensembles of Intrinsically Disordered Proteins at Atomic Resolution in Molecular Dynamics Simulations Abstract Intrinsically disordered proteins (IDPs) constitute a large fraction of the human proteome and are critical in the regulation of cellular processes. A detailed understanding of the conformational dynamics of IDPs could help to elucidate their roles in health and disease. However the inherent flexibility of IDPs makes structural studies and their interpretation challenging. Molecular dynamics (MD) simulations could address this challenge in principle, but inaccuracies in the simulation models and the need for long simulations have stymied progress. To overcome these limitations, we adopt an hierarchical approach that builds on the “flexible meccano” model of Bernadó et al. (J. Am. Chem. Soc. 2005, 127, 17968-17969). First, we exhaustively sample small IDP fragments in all-atom simulations to capture local structure. Then, we assemble the fragments into full-length IDPs to explore the stereochemically possible global structures of IDPs. The resulting ensembles of three-dimensional structures of full-length IDPs are highly diverse, much more so than in standard MD simulation. For the paradigmatic IDP α -synuclein, our ensemble captures both local structure, as probed by nuclear magnetic resonance (NMR) spectroscopy, and its overall dimension, as obtained from small-angle X-ray scattering (SAXS) in solution. By generating representative and meaningful starting ensembles, we can begin to exploit the massive parallelism afforded by current and future high-performance computing resources for atomic-resolution characterization of IDPs. Intrinsically disordered proteins ; Molecular dynamics ; Small-angle X-ray scattering ; Statistical physics ; Physics ; Computer science ; Nanotechnology ; Biological system ; Chemical physics ; Chemistry ; Materials science ; Scattering ; Biology ; Computational chemistry ; Nuclear magnetic resonance Advanced NMR Techniques and Applications ; Protein Structure and Dynamics ; Enzyme Structure and Function
Authors: Yuhki Tsukada | Shion Takeno | Masayuki Karasuyama | Hitoshi Fukuoka | M. Shiga | Toshiyuki Koyama
Source: Scientific Reports (article, 2019-10-31)
Field: Materials Science — Materials Chemistry

Estimation of material parameters based on precipitate shape: efficient identification of low-error region with Gaussian process modeling In this study, an efficient method for estimating material parameters based on the experimental data of precipitate shape is proposed. First, a computational model that predicts the energetically favorable shape of precipitate when a d-dimensional material parameter (x) is given is developed. Second, the discrepancy (y) between the precipitate shape obtained through the experiment and that predicted using the computational model is calculated. Third, the Gaussian process (GP) is used to model the relation between x and y. Finally, for identifying the "low-error region (LER)" in the material parameter space where y is less than a threshold, we introduce an adaptive sampling strategy, wherein the estimated GP model suggests the subsequent candidate x to be sampled/calculated. To evaluate the effectiveness of the proposed method, we apply it to the estimation of interface energy and lattice mismatch between MgZn<sub>2</sub> ([Formula: see text]) and α-Mg phases in an Mg-based alloy. The result shows that the number of computational calculations of the precipitate shape required for the LER estimation is significantly decreased by using the proposed method. Algorithm ; Computer science ; Kriging ; Gaussian process ; Gaussian ; Estimation theory ; Materials science ; Artificial intelligence ; Thermodynamics ; Machine learning ; Physics ; Chemistry ; Computational chemistry X-ray Diffraction in Crystallography ; Aluminum Alloy Microstructure Properties ; Titanium Alloys Microstructure and Properties
Authors: Chae Ryon Kang
Source: Carolina Digital Repository (University of North Carolina at Chapel Hill) (article, 2019-08-12)
Field: Chemistry — Analytical Chemistry

New statistical learning methods for chemical toxicity data analysis In the first part of the dissertation, we introduce the change-line classification and regression method to study latent subgroups. The proposed method finds a line which optimally divides a feature space into two heterogeneous subgroups, each of which yields a response having a different probability distribution or having a different regression model. The procedure is useful for classifying biochemicals on the basis of toxicity, where the feature space consists of chemical descriptors and the response is toxicity activity. In this setting, the goal is to identify subgroups of chemicals with different toxicity profiles. The split-line algorithm is utilized to reduce computational complexity. A two step estimation procedure, using either least squares or maximum likelihood for implementation, is described. Two sets of simulation studies and a data analysis applying our method to rat acute toxicity data are presented to demonstrate utility of the proposed method. Second, the asymptotic properties in the change-line regression model are studied, including consistency and the rates of convergence of M-estimators in the change-line regression model through empirical process techniques. We proved that the estimators of the regression parameters achieve a square-root n-consistency while the estimators of the change-line parameters achieve n-consistency. Last, we introduce the Interactive Decision Committee method for classification when high-dimensional feature variables are grouped into feature categories. The proposed method uses the interactive relationships among feature categories to build base classifiers which are combined using decision committees. The proposed procedure is useful for classifying biochemicals on the basis of toxicity activity, where the feature space consists of chemical descriptors belonging to at least one feature category, and the responses are binary indicators of toxicity activity. The support vector machine, the random forests, and the tree-based AdaBoost algorithms are utilized as classifier inducers. To combine base classifiers, the voting method with forward selection given the number of base classifiers by 5-fold CV and a stacked generalization with two different learning algorithms were utilized. We applied the proposed method to two chemical toxicity data sets. For these data sets, the proposed method improved the classification performance with respect to the average prediction accuracy compared to a single classifier. Statistical learning ; Statistical analysis ; Computer science ; Artificial intelligence ; Statistics ; Mathematics Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Advanced Statistical Methods and Models
Authors: Bahamin Bazooyar | Fariborz Shaahmadi | Abolfazl Jomekian | Hamidreza Gohari Darabkhani
Source: Journal of Petroleum Science and Engineering (article, 2019-11-08)
Field: Chemistry — Analytical Chemistry

Modelling of wax deposition by perturbed hard sphere chain equation of state Wax ; Deposition (geology) ; Equation of state ; Chain (unit) ; Thermodynamics ; Statistical physics ; Materials science ; Physics ; Geology ; Composite material Petroleum Processing and Analysis ; Phase Equilibria and Thermodynamics ; Heat transfer and supercritical fluids
Authors: Luis M. Moreno-Ramírez | Antonio Delgado-Matarín | Jia Yan Law | V. Franco | A. Conde | Anit K. Giri
Source: Metals (article, 2019-10-25)
Field: Materials Science — Materials Chemistry

Influence of Thermal and Magnetic History on Direct ΔTad Measurements of Ni49+xMn36−xIn15 Heusler Alloys In the present work, using Heusler Ni49+xMn36-xIn15 (with x = 0 and 0.5) alloys, it is shown that the choice of the appropriate measurement protocol (erasing the prior state of the sample in between experiments) in ∆Tad first shot characterization is crucial for obtaining reliable results. Unlike indirect measurements, for which incorrect protocols produce overestimates of the characteristics of the material, erroneous direct measurements underestimate ∆Tad in the region close to its first order phase transition. The error in ∆Tad is found to be dependent on the temperature step used, being up to ~40% underestimation, including a slight shift in its peak temperature. Materials science ; Condensed matter physics ; Work (physics) ; Thermal ; Characterization (materials science) ; Thermodynamics ; Physics ; Nanotechnology Shape Memory Alloy Transformations ; Heusler alloys: electronic and magnetic properties ; Magnetic Properties and Applications
Authors: Zhao Guo | Jianxin Zhou | Yajun Yin | Xu Shen | Xiaoyuan Ji
Source: Metals (article, 2019-01-09)
Field: Materials Science — Materials Chemistry

Numerical Simulation of Three-Dimensional Mesoscopic Grain Evolution: Model Development, Validation, and Application to Nickel-Based Superalloys The mesoscopic grain model is a multiscale model which takes into account both the dendrite growth mechanism and the vast numerical computation of the actual castings. Due to the pursuit of efficient computation, the mesoscopic grain calculation accuracy is lower than that of dendrite growth model. Improving the accuracy of mesoscopic grain model is a problem to be solved urgently. In this study, referring to the calculation method of solid fraction in microscopic dendrite growth model, a cellular automata model of 3D mesoscopic grain evolution for solid fraction calculated quantitatively at the scale of cell is developed. The developed model and algorithm validation for grain growth simulation is made by comparing the numerical results with the benchmark experimental data and the analytical predictions. The results show that the 3D grain envelopes simulated by the developed model and algorithm are coincident with the shape predicted by the analytical model to a certain extent. Then, the developed model is applied to the numerical simulation of solidification process of nickel-based superalloys, including equiaxed and columnar dendritic grain growth. Our results show good agreement with the related literature. Mesoscopic physics ; Superalloy ; Equiaxed crystals ; Dendrite (mathematics) ; Computation ; Materials science ; Computer simulation ; Grain growth ; Grain size ; Statistical physics ; Benchmark (surveying) ; Mechanics ; Metallurgy ; Computer science ; Algorithm ; Mathematics ; Geology ; Physics ; Geometry ; Alloy ; Condensed matter physics Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; Metallurgy and Material Forming