jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mohammad Alakel Abazid
Source: International Journal of Applied Mechanics (article, 2019-08-01)
Field: Materials Science — Materials Chemistry

The Nonlocal Strain Gradient Theory for Hygrothermo-Electromagnetic Effects on Buckling, Vibration and Wave Propagation in Piezoelectromagnetic Nanoplates A nonlocal strain gradient theory (NSGT) is utilized to investigate the thermal buckling, free vibration and wave propagation in smart piezoelectromagnetic nanoplates in hygrothermal environments embedded in an elastic substrate. The main advantage of the NSGT over other continuum theories is that it contains both nonlocal parameter and material length scale parameter. The elastic substrate is modeled as Pasternak foundation model. According to the NSGT and the sinusoidal two-variable shear deformation plate theory, the governing equations of motion are derived involving the material parameters and hygrothermo-electromagnetic effects. The present solutions are checked through comparisons with those presented in the literature. Numerical results show the impacts of the nonlocal and gradient parameters, side-to-thickness ratio, hygrothermo-electromagnetic loads and substrate stiffness on the thermal buckling, frequencies and wave propagation in the smart nanoplates. Buckling ; Vibration ; Wave propagation ; Materials science ; Mechanics ; Stiffness ; Classical mechanics ; Physics ; Composite material ; Acoustics ; Optics Nonlocal and gradient elasticity in micro/nano structures ; Thermoelastic and Magnetoelastic Phenomena ; Composite Structure Analysis and Optimization
Authors: Florian M. Wisser | Y. Mohr | Elsje Alessandra Quadrelli | J. Canivet
Source: ChemCatChem (article, 2019-12-11)
Field: Chemistry — Inorganic Chemistry

Porous Macroligands: Materials for Heterogeneous Molecular Catalysis Abstract The integration of catalytically active centers into a solid support without any loss of performance compared to the homogeneous analogue remains a major challenge. In this context, solid macroligands can be regarded as the key element to combine the advantages of homogeneous and heterogeneous catalysis. Porous materials such as periodic mesoporous organosilica, metal‐organic frameworks, porous organic polymers or covalent‐organic frameworks fulfill all requirements of a macroligand acting as a solid ligand of a molecular complex. They all can be tuned at the molecular scale to adapt the properties of the catalyst for a given application. This important feature also allows to directly compare different macroligands, regardless of their nature, and to choose the most appropriate for a target application. Mesoporous material ; Homogeneous ; Context (archaeology) ; Catalysis ; Metal-organic framework ; Heterogeneous catalysis ; Materials science ; Porosity ; Covalent bond ; Polymer ; Nanotechnology ; Ligand (biochemistry) ; Homogeneous catalysis ; Porous medium ; Chemistry ; Organic chemistry ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Polyoxometalates: Synthesis and Applications
Authors: Tuan Anh Nguyen | Quang Tung Nguyen | Trong Phuc Bach
Source: Journal of Chemistry (article, 2019-06-16)
Field: Materials Science — Polymers and Plastics

Mechanical Properties and Flame Retardancy of Epoxy Resin/Nanoclay/Multiwalled Carbon Nanotube Nanocomposites This paper reports the improvement of the mechanical properties and flame retardant properties of epoxy Epikote 240/nanoclay I.30E/multiwalled carbon nanotube nanocomposite prepared by mechanical stirring method combined with ultrasonic vibration, nanoclay I.30E content (1; 2; 3 wt.%) and content of MWNT (0.01; 0.02; 0.03 wt.%). When burned, MWCNT reduces degradation speed of epoxy Epikote 240 resin and increases the char yield, and nanoclay acts as an energy storage medium to hinder the heat transfer in epoxy resin. The limiting oxygen index value and UL94 test indicated improvement of flame retardancy of the nanocomposites. The results exhibit the potentiality of these based epoxy Epikote 240 resin/nanoclay I.30E/MWCNTs nanocomposites for multifaceted advanced applications. These fillers can produce environmental friendly products with high thermal and mechanical properties. Epoxy ; Limiting oxygen index ; Nanocomposite ; Fire retardant ; Composite material ; Char ; Carbon nanotube ; Thermal stability ; Materials science ; Dynamic mechanical analysis ; Polymer ; Chemistry ; Combustion ; Organic chemistry Flame retardant materials and properties ; Polymer Nanocomposites and Properties ; Polymer composites and self-healing
Authors: Namrata Deka | Rajesh Patidar | S. Kasthuri | N. Venkatramaiah | Gitish K. Dutta
Source: Materials Chemistry Frontiers (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Triazine based polyimide framework derived N-doped porous carbons: a study of their capacitive behaviour in aqueous acidic electrolyte Nitrogen-doped porous carbons have been synthesized from triazine based polyimide frameworks using ZnCl<sub>2</sub> as an activating agent for electrochemical energy storage. Polyimide ; Electrolyte ; Materials science ; Aqueous solution ; Porosity ; Triazine ; Capacitive sensing ; Doping ; Chemical engineering ; Polymer chemistry ; Inorganic chemistry ; Organic chemistry ; Nanotechnology ; Chemistry ; Composite material ; Physical chemistry ; Electrode ; Optoelectronics ; Computer science ; Layer (electronics) Covalent Organic Framework Applications ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Rui Wang | Congzhen Xie | Shoukang Luo | Bin Gou | Huasong Xu | Leilei Zeng
Source: RSC Advances (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

The influence mechanism of nanoparticles on the dielectric properties of epoxy resin In order to study the influence mechanism of nanoparticles on the dielectric properties of epoxy resin materials for composite insulators under different nanoparticle filling amounts, the free volume, dielectric relaxation, breakdown strength and trap distribution of the samples were tested by positron annihilation lifetime spectroscopy, breakdown strength, broadband dielectric spectroscopy (BDS) and thermally stimulated current (TSC). The results show that the limiting effect of nanoparticles rapidly reduced the number of traps in the amorphous zone of materials at a low filling amount. As a result, the free path of carriers was increased and the concentration of free volume was decreased, which can limit the injection and transportation of carriers, resulting in the increase of material breakdown strength. At a high filling amount, a large number of interfacial deep traps were introduced into the nanoparticles, and the carrier free volume concentration and size were reduced. The traps inside the material were mainly interfacial deep traps. Under the action of an external electric field, a hetero polar charge was formed on the other end to cause electric field distortion, thus the breakdown field strength of the material was weakened. Epoxy ; Dielectric ; Nanoparticle ; Mechanism (biology) ; Materials science ; Chemical engineering ; Composite material ; Nanotechnology ; Optoelectronics ; Physics High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Electromagnetic wave absorption materials
Authors: Samrendra Singh | Karteek K. Bejagam | Yaxin An | Sanket A. Deshmukh
Source: The Journal of Physical Chemistry A (article, 2019-05-31)
Field: Materials Science — Materials Chemistry

Machine-Learning Based Stacked Ensemble Model for Accurate Analysis of Molecular Dynamics Simulations Accurate, faster, and on-the-fly analysis of the molecular dynamics (MD) simulations trajectory becomes very critical during the discovery of new materials or while developing force-field parameters due to automated nature of these processes. Here to overcome the drawbacks of algorithm based analysis approaches, we have developed and utilized an approach that integrates machine-learning (ML) based stacked ensemble model (SEM) with MD simulations, for the first time. As a proof-of-concept, two SEMs were developed to analyze two dynamical properties of a water droplet, its contact angle, and hydrogen bonds. The two SEMs consisted of two layered networks of random forest, artificial neural network, support vector regression, Kernel ridge regression, and k-nearest neighbors ML models. The root-mean-square error values, uncertainty quantification, and sensitivity analysis of both the SEMs suggested that the final result was more accurate as compared to that of the individual ML models. This new computational framework is very general, robust, and has a huge potential in analyzing large size MD simulation trajectories as it can capture critical information very accurately. Computer science ; Molecular dynamics ; Artificial neural network ; Support vector machine ; Random forest ; Kernel (algebra) ; Mean squared error ; Force field (fiction) ; Algorithm ; Artificial intelligence ; Ensemble learning ; Field (mathematics) ; Sensitivity (control systems) ; Ensemble forecasting ; Machine learning ; Mathematics ; Statistics ; Engineering ; Physics Machine Learning in Materials Science ; Software Engineering Research ; Protein Structure and Dynamics
Authors: Siddhartha Laghuvarapu | Yashaswi Pathak | U. Deva Priyakumar
Source: Journal of Computational Chemistry (article, 2019-12-17)
Field: Materials Science — Materials Chemistry

BAND NN: A Deep Learning Framework for Energy Prediction and Geometry Optimization of Organic Small Molecules Recent advances in artificial intelligence along with the development of large data sets of energies calculated using quantum mechanical (QM)/density functional theory (DFT) methods have enabled prediction of accurate molecular energies at reasonably low computational cost. However, machine learning models that have been reported so far require the atomic positions obtained from geometry optimizations using high-level QM/DFT methods as input in order to predict the energies and do not allow for geometry optimization. In this study, a transferable and molecule size-independent machine learning model bonds (B), angles (A), nonbonded (N) interactions, and dihedrals (D) neural network (BAND NN) based on a chemically intuitive representation inspired by molecular mechanics force fields is presented. The model predicts the atomization energies of equilibrium and nonequilibrium structures as sum of energy contributions from bonds (B), angles (A), nonbonds (N), and dihedrals (D) at remarkable accuracy. The robustness of the proposed model is further validated by calculations that span over the conformational, configurational, and reaction space. The transferability of this model on systems larger than the ones in the data set is demonstrated by performing calculations on selected large molecules. Importantly, employing the BAND NN model, it is possible to perform geometry optimizations starting from nonequilibrium structures along with predicting their energies. © 2019 Wiley Periodicals, Inc. Energy minimization ; Molecular geometry ; Density functional theory ; Non-equilibrium thermodynamics ; Artificial neural network ; Robustness (evolution) ; Quantum ; Molecule ; Bond length ; Statistical physics ; Computer science ; Geometry ; Physics ; Computational chemistry ; Chemistry ; Artificial intelligence ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Kenya Tanaka | Kengo Hachiya | Wenjin Zhang | Kazunari Matsuda | Yuhei Miyauchi
Source: ACS Nano (article, 2019-10-04)
Field: Materials Science — Materials Chemistry

Machine-Learning Analysis to Predict the Exciton Valley Polarization Landscape of 2D Semiconductors We demonstrate the applicability of employing machine-learning-based analysis to predict the low-temperature exciton valley polarization landscape of monolayer tungsten diselenide (1L-WSe<sub>2</sub>) using position-dependent information extracted from its photoluminescence (PL) spectra at room temperature. We performed low- and room-temperature polarization-resolved PL mapping and used the obtained experimental data to create regression models for the prediction using the Random Forest machine-learning algorithm. The local information extracted from the room-temperature PL spectra and the low-temperature exciton valley polarization was used as the input and output data for the machine-learning process, respectively. The spatial distribution of the exciton valley polarization in a 1L-WSe<sub>2</sub> sample that was not used for the learning of the decision trees was successfully predicted. Furthermore, we numerically obtained the degree of importance for each input variable and demonstrated that this parameter provides helpful information for examining the physics that shape the spatially heterogeneous valley polarization landscape of 1L-WSe<sub>2</sub>. Polarization (electrochemistry) ; Exciton ; Random forest ; Materials science ; Tungsten diselenide ; Spectral line ; Semiconductor ; Monolayer ; Optoelectronics ; Machine learning ; Artificial intelligence ; Computer science ; Condensed matter physics ; Physics ; Nanotechnology ; Chemistry ; Physical chemistry ; Quantum mechanics 2D Materials and Applications ; Perovskite Materials and Applications ; Quantum Dots Synthesis And Properties
Authors: Gabriel R. Schleder | A. C. M. Padilha | Alexandre Reily Rocha | Gustavo M. Dalpian | A. Fazzio
Source: Journal of Chemical Information and Modeling (review, 2019-10-25)
Field: Materials Science — Materials Chemistry

<i>Ab Initio</i> Simulations and Materials Chemistry in the Age of Big Data In this perspective, we discuss computational advances in the last decades, both in algorithms as well as in technologies, that enabled the development, widespread use, and maturity of simulation methods for molecular and materials systems. Such advances led to the generation of large amounts of data, which required the creation of several computational databases. Within this scenario, with the democratization of data access, the field now encounters several opportunities for data-driven approaches toward chemical and materials problems. Specifically, machine learning methods for predictions of novel materials or properties are being increasingly used with great success. However, black box usage fails in many instances; several technical details require expert knowledge in order for the predictions to be useful, such as with descriptors and algorithm selection. These approaches represent a direction for further developments, notably allowing advances for both developed and emerging countries with modest computational infrastructures. Data science ; Computer science ; Field (mathematics) ; Big data ; Maturity (psychological) ; Perspective (graphical) ; Management science ; Artificial intelligence ; Data mining ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Amanda S. Barnard | George Opletal
Source: Nanoscale (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Predicting structure/property relationships in multi-dimensional nanoparticle data using t-distributed stochastic neighbour embedding and machine learning Combining researchers' domain expertise and advanced dimension reduction methods we demonstrate how visually comparing the distribution of nanoparticles mapped from multiple dimensions to a two dimensional plane can rapidly identify possible single-structure/property relationships and to a lesser extent multi-structure/property relationships. These relationships can be further investigated and confirmed with machine learning, using genetic programming to inform the choice of property-specific models and their hyper-parameters. In the case of our nanodiamond case study, we visually identify and confirm a strong relationship between the size and the probability of observation (stability) and a more complicated (and visually ambiguous) relationship between the ionisation potential and band gaps with a range of different structural, chemical and statistical surface features, making it more difficult to engineer in practice. Property (philosophy) ; Embedding ; Nanoparticle ; Computer science ; Materials science ; Biological system ; Artificial intelligence ; Nanotechnology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Nikita O. Azarapin | Aleksandr S. Aleksandrovsky | Victor V. Atuchin∥⊥ | Tatyana A. Gavrilova | А. С. Крылов | Мaxim S. Моlokeev | Shaibal Mukherjee | Aleksandr S. Oreshonkov | О. В. Андреев
Source: Journal of Alloys and Compounds (article, 2019-11-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Synthesis, structural and spectroscopic properties of orthorhombic compounds BaLnCuS3 (Ln = Pr, Sm) Orthorhombic crystal system ; Crystallography ; Raman spectroscopy ; Crystal structure ; Ternary operation ; Chemistry ; Rietveld refinement ; Transition metal ; Group (periodic table) ; Space group ; Infrared spectroscopy ; Metal ; X-ray crystallography ; Diffraction Crystal Structures and Properties ; Iron-based superconductors research ; Advanced Condensed Matter Physics
Authors: Daniel Caballero | Marta Bevilacqua | José Manuel Amigo
Source: Journal of Spectral Imaging (article, 2019-01-22)
Field: Chemistry — Analytical Chemistry

Application of hyperspectral imaging and chemometrics for classifying plastics with brominated flameretardants Most plastics need to incorporate flame retardants to meet fire safety standards requirements. The amount and the type of flame retardants can differ, so that in waste plastics a large variety of polymers and flame retardants can be found. The recycling of plastics containing flame retardants is increasing. However, only plastics of the same polymer type and the same additive content can be recycled together. Three models based on different chemometrics techniques applied to hyperspectral imaging in the near infrared range were developed [partial least square-discriminant analysis, decision tree (DT) and hierarchical model (HM)]. Optimal results were obtained for all classification techniques. HM shows the highest error at all levels due to the noisy spectra of the black plastics. However, DT classification gave outstanding results, considering that the sensitivity was higher than 0.9 in all cases. Thus, the application of DT with hyperspectral imaging could be used to sort plastic samples with respect to the type of polymer and the flame retardant used with a high degree of accuracy in an automated way. These findings are highly valuable for the plastic and waste management industries. Chemometrics ; Hyperspectral imaging ; Fire retardant ; Polymer ; Materials science ; Environmental science ; Process engineering ; Computer science ; Composite material ; Artificial intelligence ; Machine learning ; Engineering Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Remote-Sensing Image Classification
Authors: Ying Liu | Matthias Militzer | Michel Perez
Source: Materials (article, 2019-12-05)
Field: Materials Science — Materials Chemistry

Phase Field Modelling of Abnormal Grain Growth Heterogeneous grain structures may develop due to abnormal grain growth during processing of polycrystalline materials ranging from metals and alloys to ceramics. The phenomenon must be controlled in practical applications where typically homogeneous grain structures are desired. Recent advances in experimental and computational techniques have, thus, stimulated the need to revisit the underlying growth mechanisms. Here, phase field modelling is used to systematically evaluate conditions for initiation of abnormal grain growth. Grain boundaries are classified into two classes, i.e., high- and low-mobility boundaries. Three different approaches are considered for having high- and low-mobility boundaries: (i) critical threshold angle of grain boundary disorientation above which boundaries are highly mobile, (ii) two grain types A and B with the A-B boundaries being highly mobile, and (iii) three grain types, A, B and C with the A-B boundaries being fast. For these different scenarios, 2D simulations have been performed to quantify the effect of variations in the mobility ratio, threshold angle and fractions of grain types, respectively, on the potential onset of abnormal grain growth and the degree of heterogeneity in the resulting grain structures. The required mobility ratios to observe abnormal grain growth are quantified as a function of the fraction of high-mobility boundaries. The scenario with three grain types (A, B, C) has been identified as one that promotes strongly irregular abnormal grains including island grains, as observed experimentally. Grain boundary ; Grain growth ; Materials science ; Abnormal grain growth ; Crystallite ; Grain boundary strengthening ; Phase (matter) ; Grain size ; Homogeneous ; Chemical physics ; Condensed matter physics ; Crystallography ; Microstructure ; Metallurgy ; Statistical physics ; Chemistry ; Physics Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; Metallurgy and Material Forming
Authors: Kévin Jacq | Yves Perrette | Bernard Fanget | Pierre Sabatier | Didier Coquin | Ruth Martinez-Lamas | Maxime Debret | Fabien Arnaud
Source: The Science of The Total Environment (article, 2019-01-26)
Field: Chemistry — Analytical Chemistry

High-resolution prediction of organic matter concentration with hyperspectral imaging on a sediment core Hyperspectral imaging ; Partial least squares regression ; Organic matter ; Environmental science ; Sediment ; Total organic carbon ; Correlation coefficient ; Loss on ignition ; Soil science ; Remote sensing ; Geology ; Environmental chemistry ; Chemistry ; Geomorphology ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Isotope Analysis in Ecology ; Atmospheric and Environmental Gas Dynamics
Authors: Tailei Wang | Rensheng Xie | Shouzheng Zhu | Jianjun Gao | Mingbo Xin | Sensong An | Bowen Zheng | Hang Li | Yuankun Lin | Hualiang Zhang | Guohua Zhai | Jun Ding
Source: IEEE Access (article, 2019-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dual-Band High Efficiency Terahertz Meta-Devices Based on Reflective Geometric Metasurfaces The wavelength-dependent behavior of the metasurface is one of the severe disadvantages in the metasurface-based applications, which greatly limits its applications. In this work, a novel reflective building block (unit cell) has been proposed to work at two arbitrary terahertz frequency bands with independent phase control at each band. The geometric phase building block can reflect the incident circularly polarized wave and convert it to the wave with opposite helicity and high conversion efficiencies at both bands. As proof of concept demonstrations, a dual-band cylindrical meta-lens and two vortex beam generators operating at 0.45 and 0.7 THz are investigated to verify the performance of the proposed building block. The simulated results of the metalens agree very well with the theoretical calculations, which are consistent with the design goals. Moreover, the designed vortex metasurfaces reveal a convenient and low-cost way to realize the vortex beam carrying different/same orbital angular momentum modes at two bands. Terahertz radiation ; Optics ; Physics ; Metamaterial ; Multi-band device ; Angular momentum ; Block (permutation group theory) ; Helicity ; Frequency band ; Vortex ; Optical vortex ; Phase (matter) ; Optoelectronics ; Beam (structure) ; Computer science ; Telecommunications ; Bandwidth (computing) Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Orbital Angular Momentum in Optics