jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Akash Singh | Yumeng Li
Source: AIAA SCITECH 2023 Forum (article, 2023-01-19)
Field: Materials Science — Materials Chemistry

2D Materials Guided Self-assembly of Polymer: Molecular Dynamics Simulation Study View Video Presentation: https://doi.org/10.2514/6.2023-0142.vid The interface between nanomaterials and polymer in nanocomposites is believed to impact the overall performance of hybrid material systems. Understanding such organic/inorganic interfaces can provide insights on tuning their performance and accelerate the development of new hybrid materials with optimal material properties. Polymer assembly behavior around nanofillers, for example two dimensional materials, determines the resulted solid structures around interface and interfacial properties of polymer nanocomposites. It is extremely challenging to investigate nanofiller-polymer interface through experimental testing due to very small spatiotemporal scales involved. In this study crystallisation of polyethylene on MoSe2 surface was investigated. Molecular dynamics (MD) simulations were conducted to evaluate the polyethylene assembly on MoSe2 surface and characterize the resulted polymer crystal structure. It is demonstrated that the developed model is able to simulate the crystallization process of n-pentacosane, a surrogate model for polyethylene, on the surface of MoSe2. Crystallization growth front starts from the polyethylene-MoSe2 interface and moves rapidly towards the bulk in the early stages of crystallisation process. The resulted crystal structure of polyethylene was found to have orthorhombic unit cells and its lattice parameters were also evaluated. Lattice parameters of crystallised polyethylene were found to be closely matching the experimental studies conducted earlier by other researchers . Materials science ; Polyethylene ; Crystallization ; Polymer ; Embossing ; Crystallization of polymers ; Nanocomposite ; Orthorhombic crystal system ; Nanomaterials ; Polymer nanocomposite ; Molecular dynamics ; Crystal (programming language) ; Composite material ; Crystal structure ; Chemical physics ; Nanotechnology ; Chemical engineering ; Crystallography ; Computer science ; Chemistry Graphene research and applications ; Polymer crystallization and properties ; Additive Manufacturing and 3D Printing Technologies
Authors: Dung‐Yi Wu | Todd C. Hufnagel
Source: Acta Materialia (article, 2023-11-28)
Field: Materials Science — Materials Chemistry

Efficient searching of processing parameter space to enable inverse microstructural design of materials Materials design can be accelerated by the use of physics-based forward models that predict the properties of new materials. In cases where the properties of the material depend on its microstructure, the models can be used as part of an optimization scheme to predict the microstructural features that are required to achieve the design objectives. Producing these microstructures, however, requires that we determine the processing parameters necessary to produce the target microstructure. Here we demonstrate the use of Bayesian optimization using simple analytic forward models to enable this inverse process design in the context of optimization of isothermal heat treatment of a commercial aluminum alloy to achieve (i) a target volume fraction of a specific intermetallic phase, (ii) a specified aluminum grain size distribution, and (iii) both objectives simultaneously. We discuss the use, and limitations, of Bayesian optimization with a scalar desirability function for solving multi-objective problems, and demonstrate Bayesian optimization with expected hypervolume improvement to determine the Pareto front describing the trade-off between multiple objectives. We compare Bayesian optimization with a genetic algorithm (NSGA-II) and show that while the two approaches produce similar Pareto fronts, the Bayesian optimization converges on the solution more quickly. We further illustrate the effect of parametric uncertainty in the forward models on the uncertainty of the Pareto front. Finally, we demonstrate experimentally that the optimized process parameters do indeed allow us to make samples with the desired microstructure. Bayesian optimization ; Multi-objective optimization ; Mathematical optimization ; Context (archaeology) ; Materials science ; Bayesian probability ; Parametric statistics ; Pareto principle ; Computer science ; Mathematics ; Artificial intelligence ; Statistics Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Aluminum Alloy Microstructure Properties
Authors: Zhiyong Hu | Dipak K. Dey
Source: Journal of Computational and Graphical Statistics (article, 2023-09-15)
Field: Chemistry — Analytical Chemistry

Generalized Variable Selection Algorithms for Gaussian Process Models by LASSO-Like Penalty AbstractWith the rapid development of modern technology, massive amounts of data with complex pattern are generated. Gaussian process models that can easily fit the nonlinearity in data become more and more popular nowadays. It is often the case that in some data only a few features are important or active. However, unlike classical linear models, it is challenging to identify active variables in Gaussian process models. One of the most commonly used methods for variable selection in Gaussian process models is automatic relevance determination, which is known to be open-ended. There is no rule of thumb to determine the threshold for dropping features, which makes the variable selection in Gaussian process models ambiguous. In this work, we propose two variable selection algorithms for Gaussian process models, which use the artificial nuisance columns as baseline for identifying the active features. Moreover, the proposed methods work for both regression and classification problems. The algorithms are demonstrated using comprehensive simulation experiments and an application to multi-subject electroencephalography data that studies alcoholic levels of experimental subjects. Supplementary materials for this article are available online.KEYWORDS: Automatic relevance determinationElectroencephalography dataGaussian processPrincipal component analysisVariable selection Supplementary MaterialsThe supplementary materials provide some proofs of results and additional simulation studies.Disclosure StatementThe authors confirm that there are no relevant financial or nonfinancial competing interests to report. Computer science ; Gaussian process ; Feature selection ; Variable (mathematics) ; Relevance (law) ; Lasso (programming language) ; Gaussian ; Machine learning ; Artificial intelligence ; Algorithm ; Process (computing) ; Data mining ; Selection (genetic algorithm) ; Mathematics Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Gaussian Processes and Bayesian Inference
Authors: Dang Van Hieu
Source: International Applied Mechanics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Nonlinear Random Vibrations of Micro-Beams Resting on Visco-Elastic Foundation Via the Couple Stress Theory Nonlinear system ; Random vibration ; Vibration ; Beam (structure) ; Timoshenko beam theory ; Bernoulli's principle ; Displacement (psychology) ; Foundation (evidence) ; Physics ; Stress (linguistics) ; Linearization ; Mechanics ; Classical mechanics ; Mathematical analysis ; Mathematics ; Acoustics ; Optics Nonlocal and gradient elasticity in micro/nano structures ; Numerical methods in engineering ; Composite Structure Analysis and Optimization
Authors: David Frich Hansen | Tommy Sonne Alstrøm | Mikkel N. Schmidt
Source: Chemometrics and Intelligent Laboratory Systems (article, 2023-08-30)
Field: Chemistry — Analytical Chemistry

Probabilistic signal estimation for vibrational spectroscopy with a flexible non-stationary Gaussian process baseline model Vibrational spectroscopy techniques enable accurate chemical detection and quantification, but the extraction of spectral peak parameters is frequently hampered by an underlying baseline. Because the signal and baseline are additive, it is difficult to distinguish between signal peaks and baseline effects when the baseline is not smooth. Using surface enhanced Raman spectroscopy (SERS) and near-infrared (NIR) spectroscopy as examples, we show how to estimate the signal and the baseline jointly while imposing a high-capacity non-stationary Gaussian process on the baseline. This allows us to both obtain accurate estimation and meaningful uncertainty estimates on interpretable peak parameters. We demonstrate this on artificially generated SERS maps, a challenging real-world SERS case, and a benchmark NIR dataset. Baseline (sea) ; Probabilistic logic ; Gaussian process ; SIGNAL (programming language) ; Gaussian ; Raman spectroscopy ; Process (computing) ; Computer science ; Statistical physics ; Algorithm ; Biological system ; Statistics ; Mathematics ; Chemistry ; Physics ; Artificial intelligence ; Computational chemistry ; Optics ; Geology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Bibek Dash | Suhail Haque | Abhijit Chatterjee
Source: arXiv (Cornell University) (preprint, 2023-07-21)
Field: Materials Science — Materials Chemistry

Reduced collinearity, low-dimensional cluster expansion model for adsorption of halides (Cl, Br) on Cu(100) surface using principal component analysis The cluster expansion model (CEM) provides a powerful computational framework for rapid estimation of configurational properties in disordered systems. However, the traditional CEM construction procedure is still plagued by two fundamental problems: (i) even when only a handful of site cluster types are included in the model, these clusters can be correlated and therefore they cannot independently predict the material property, and (ii) typically few tens-hundreds of datapoints are required for training the model. To address the first problem of collinearity, we apply the principal component analysis method for constructing a CEM. Such an approach is shown to result in a low-dimensional CEM that can be trained using a small DFT dataset. We use the ab initio thermodynamic modeling of Cl and Br adsorption on Cu(100) surface as an example to demonstrate these concepts. A key result is that a CEM containing 10 effective cluster interactions build with only 8 DFT energies (note, number of training configurations &gt; number of principal components) is found to be accurate and the thermodynamic behavior obtained is consistent with experiments. This paves the way for construction of high-fidelity CEMs with sparse/limited DFT data. Collinearity ; Principal component analysis ; Cluster (spacecraft) ; Cluster expansion ; Component (thermodynamics) ; Computer science ; Halide ; Ab initio ; Decomposition ; Adsorption ; Chemistry ; Biological system ; Materials science ; Statistical physics ; Mathematics ; Physics ; Physical chemistry ; Thermodynamics ; Artificial intelligence ; Geometry Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; X-ray Diffraction in Crystallography
Authors: Chuang Tian | Runhan Xiao | Yanping Sui | Yuhan Feng | Haomin Wang | Sunwen Zhao | Jiawen Liu | Xiuli Gao | Hao Sun | Songang Peng | Zhi Jin | Xinyu Liu | Shuang Wang | Pai Li | Guanghui Yu
Source: Science China Materials (article, 2023-11-10)
Field: Materials Science — Materials Chemistry

Opposite doping distribution in TMD monolayer regulated by VLS and VSS growth mechanism Dopant ; Materials science ; Doping ; Monolayer ; Chemical vapor deposition ; Microelectronics ; Nanotechnology ; Chemical engineering ; Chemical physics ; Optoelectronics ; Chemistry 2D Materials and Applications ; Perovskite Materials and Applications ; Transition Metal Oxide Nanomaterials
Authors: Muhammad Babar Shamim | Marian Hörsting | Stephan Wulfinghoff
Source: Actuators (article, 2023-01-10)
Field: Materials Science — Materials Chemistry

Variational Reduced-Order Modeling of Thermomechanical Shape Memory Alloy Based Cooperative Bistable Microactuators This article presents the formulation and application of a reduced-order thermomechanical finite strain shape memory alloy (SMA)-based microactuator model for switching devices under thermal loading by Joule heating. The formulation is cast in the generalized standard material framework with an extension for thermomechanics. The proper orthogonal decomposition (POD) is utilized for capturing a reduced basis from a precomputed finite element method (FEM) full-scale model. The modal coefficients are computed by optimization of the underlying incremental thermomechanical potential, and the weak form for the mechanical and thermal problem is formulated in reduced-order format. The reduced-order model (ROM) is compared with the FEM model, and the exemplary mean absolute percentage errors for the displacement and temperature are 0.973% and 0.089%, respectively, with a speedup factor of 9.56 for a single SMA-based actuator. The ROM presented is tested for single and cooperative beam-like actuators. Furthermore, cross-coupling effects and the bistability phenomenon of the microactuators are investigated. Microactuator ; Shape-memory alloy ; Finite element method ; Bistability ; Actuator ; Materials science ; Joule heating ; Model order reduction ; SMA* ; Smart material ; Displacement (psychology) ; Thermal ; Mechanical engineering ; Speedup ; Structural engineering ; Computer science ; Composite material ; Physics ; Engineering ; Thermodynamics ; Algorithm ; Electrical engineering Shape Memory Alloy Transformations ; Composite Structure Analysis and Optimization ; Topology Optimization in Engineering
Authors: Nurlaila Hayati | Asep Kadarohman | Wahyu Sopandi | Muhamad Abdulkadir Martoprawiro | Amelinda Pratiwi
Source: Pertanika journal of social science & humanities (article, 2023-12-13)
Field: Chemistry — Physical and Theoretical Chemistry

The Effect of the RADEC Model on Conceptual Understanding of Polycyclic Aromatic Hydrocarbons (PAHs) Topic This study was conducted to analyze the conceptual understanding of students and their struggle on Polycyclic Aromatic Hydrocarbons (PAHs) compounds topic through the implementation of the RADEC Model. Respondents consisted of 37 students from Chemistry Education Department and 33 students from the Chemistry Department at a university in Bandung, Indonesia. This study is an experimental study with a pre-experimental type. Data on conceptual understanding were obtained through pre-tests and post-tests, while data on students’ difficulties were obtained through survey. The data were analyzed using SPSS 24. It was found that there was an increase in the conceptual understanding of the students of Chemistry Education and Chemistry Department in the medium category with n-gain values of 0.33 and 0.38, respectively. The results of inferential analysis through the Wilcoxon Test with Asymp. Sig. (2-tailed) were &lt;0.05 for the two groups of students indicating differences in the pre-test and post-test results with a higher average post-test score. Friedel-Crafts acylation is a concept that is difficult to understand by students from the Chemistry Education Department and the Chemistry Department with the same percentage of 33%. Chemistry Department students also have difficulty understanding the electrophilic substitution concept with a percentage of 33%. As a practical implication, this study successfully showed the effective implementation of the RADEC model, serving as an innovative and beneficial learning approach. The model offered substantial support to students in grasping complex topic such as PAHs compounds. Chemistry ; Mathematics education ; Chemistry education ; Test (biology) ; Conceptual model ; Psychology ; Computer science ; Social psychology Various Chemistry Research Topics ; Innovative Teaching Methods ; Science Education and Pedagogy
Authors: Thabo Mhlongo | Thomas Dipogiso Sedumedi
Source: Indonesian Journal of Science and Mathematics Education (article, 2023-07-21)
Field: Chemistry — Physical and Theoretical Chemistry

Problems with Periodic Table Theory-Praxis in Chemistry Topics Teaching The two main chemical concepts that are the subject of this investigation are chemical bonds and related reactions. This research examines how the periodic table concept can help teachers improve science teaching. To help students understand the contents of the periodic table and the helpful information its settings provide students. This study looks at how teachers use the periodic table in class. The study focused on schools as a single case and adopted a qualitative methodological approach. Two physics teachers participated. It was found that according to teachers, the periodic table may impact how they use or ignore its use when teaching students about its content or as a framework for understanding material and chemical reactions. Therefore, the concept of the periodic table can help teachers improve the quality of teaching. There needs to be more in-depth research on the apparent relationship between teacher knowledge representation in chemistry and teachers' approaches to teaching using the periodic table. Periodic table ; Mathematics education ; Table (database) ; Praxis ; Class (philosophy) ; Representation (politics) ; Chemistry education ; Computer science ; Subject (documents) ; Teaching method ; Quality (philosophy) ; Chemistry ; Psychology ; Physics ; Epistemology ; Organic chemistry ; Artificial intelligence History and advancements in chemistry ; Various Chemistry Research Topics ; Chemistry Education and Research
Authors: Hualei Sun | Liang Qiu | Yifeng Han | Yunwei Zhang | Weiliang Wang | Chaoxin Huang | Naitian Liu | Mengwu Huo | Lisi Li | Hui Liu | Zengjia Liu | Peng Cheng | Hongxia Zhang | Hongliang Wang | Lijie Hao | Man‐Rong Li | Dao‐Xin Yao | Yusheng Hou | Pengcheng Dai | Meng Wang
Source: Communications Physics (article, 2023-03-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Exchange field enhanced upper critical field of the superconductivity in compressed antiferromagnetic EuTe2 Abstract Understanding the interplay between superconductivity and magnetism has been a longstanding challenge in condensed matter physics. Here we report high pressure studies on the C -type antiferromagnetic semiconductor EuTe 2 up to 36.0 GPa. A structural transition from the I4/mcm to the C2/m space group is identified at ~16 GPa. Superconductivity is observed above ~5 GPa in both structures. In the low-pressure phase, magnetoresistance measurements reveal strong couplings between the local moments of Eu 2+ and the conduction electrons of Te 5 p orbits. The upper critical field of superconductivity is well above the Pauli limit. While EuTe 2 becomes nonmagnetic in the high-pressure phase and the upper critical field drops below the Pauli limit. Our results demonstrate that the high upper critical field of EuTe 2 in the low-pressure phase is due to the exchange field compensation effect of Eu 2+ and the superconductivity in both structures may arise in the framework of the Bardeen-Cooper-Schrieffer theory. Superconductivity ; Condensed matter physics ; Antiferromagnetism ; Critical field ; Pauli exclusion principle ; Magnetoresistance ; Magnetism ; Physics ; Field (mathematics) ; Magnetic field ; Quantum mechanics Iron-based superconductors research ; Rare-earth and actinide compounds ; Magnetic and transport properties of perovskites and related materials
Authors: Qingyi Feng | Hongxiang Deng | Biyi Wang | Bo Li | Xia Xiang | Li Li | Bo Zhong | Sean Li | Xiaotao Zu
Source: Journal of the American Ceramic Society (article, 2023-07-12)
Field: Materials Science — Ceramics and Composites

Gamma‐ray irradiation effect on microstructure and physical performances of porous silica Abstract In this paper, the gamma irradiation effect on the microstructure and physical performances of porous silica, including mechanical, thermal, and optical performances, are systematically investigated by using molecular dynamics and density‐functional theory‐based methods. The study of bond angle distribution, pair distribution function, coordination number distribution, and average ring size distribution show that, after gamma‐ray irradiation, the microstructure of porous silica is obviously modified. The tight packing of SiO 2 tetrahedrons in the porous silica network is broken by gamma‐ray irradiation. Defects of three‐coordinated Si and non‐bridging oxygen are induced by gamma‐ray irradiation. Moreover, we find that the defects concentrations rapidly grow as gamma‐ray dose increases. The mechanical, thermal, and optical performances of porous silica are all seriously degenerated by gamma‐ray irradiation. Our results show that, for mechanical performance, Young's modulus, Bulk modulus, and Shear modulus first decrease and then keep stable as gamma‐ray dose increases, but the change of Poisson's ratio is slight. For thermal performance, the thermal conductivity decreases exponentially as gamma‐ray dose increases. For optical performance, light absorption coefficients increase exponentially and light transmittance drops as gamma‐ray dose increases in the working range (photon energy range around 3.5 eV) of inertial confinement fusion. Present work is expected to be valuable for studying the degradation mechanism of silicate materials under gamma radiation and developing gamma‐ray irradiation protection technology. Materials science ; Irradiation ; Microstructure ; Porosity ; Composite material ; Physics ; Nuclear physics Glass properties and applications ; Nuclear materials and radiation effects ; Advanced ceramic materials synthesis
Authors: Ruicong Xu | Songbai Cheng
Source: Applied Sciences (article, 2023-05-23)
Field: Materials Science — Materials Chemistry

Characteristics and Mechanisms of Debris Bed Formation Behavior in Severe Accidents of Sodium-Cooled Fast Reactors: Experimental and Modeling Studies A Sodium-cooled Fast Reactor (SFR) is one of the optimized candidates in Generation IV nuclear reactor systems, but safety is an essential issue for SFR development and application. Most knowledge was accumulated through SFR safety investigations, especially for Core Disruptive Accidents (CDAs). During the CDA of SFRs, the molten materials in the core region are likely to discharge into subcooled sodium and form a debris bed on the lower region of the reactor vessel. Noticing that elaboration on the characteristics and mechanisms of Debris Bed Formation (DBF) behavior should be essential for the subsequent analysis of debris bed coolability and accident progression through various experimental and modeling studies, much knowledge was obtained during the past decades. Motivated to promote future investigations on CDAs of SFRs, the previous experiments and modeling studies on DBF behavior are systematically reviewed and discussed in this paper. The experimental results showed that the flow-regime and accumulated-bed characteristics during DBF were influenced by varying parameters and realistic conditions. Through the modeling studies, several empirical models were proposed for predicting the flow regime and accumulated-bed characteristics in DBF. In addition, to promote further development of research, the future prospects concerning DBF behavior are also described. Sodium-cooled fast reactor ; Subcooling ; Nuclear engineering ; Debris ; Environmental science ; Engineering ; Mechanics ; Meteorology ; Heat transfer ; Physics Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear Engineering Thermal-Hydraulics