jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Feng‐Chang Gu
Source: IEEE Access (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Identification of Partial Discharge Defects in Gas-Insulated Switchgears by Using a Deep Learning Method In this study, a novel method based on deep learning was developed for partial discharge (PD) pattern recognition. Traditional PD recognition methods are crucial for extracting features from PD patterns. The method of extracting crucial features is the key to PD pattern identification. The fractal theory is commonly used to determine the features of discharge patterns. The feature distribution of different defect types can be determined according to the fractal dimensions and lacunarity. However, finding fractal features is a complicated process. In this study, a PD image was entered as an input into a deep learning system to reduce the complexity of finding features. First, four defect type of gas-insulated switchgear (GIS) experimental models are established. Then, an LDP-5 inductive sensor (L-sensor) was used to measure the ground line signals caused by PD phenomenon. Second, these electrical signals were transformed into the most representative 3D ($n$ -$q$ -$\varphi$ ) PD patterns. Finally, a convolutional neural network was employed for PD image pattern recognition. A total of 160 sets of PD patterns were measured using a 15-kV GIS. The results obtained with the proposed method were compared with those obtained with the fractal method. The results revealed that the proposed method is easy to use and can easily distinguish various defect types. The proposed approach can determine the GIS insulation status and provide information to construction units for maintenance. Switchgear ; Lacunarity ; Partial discharge ; Fractal ; Pattern recognition (psychology) ; Artificial intelligence ; Fractal dimension ; Computer science ; Convolutional neural network ; Feature extraction ; Feature (linguistics) ; Identification (biology) ; Artificial neural network ; Deep learning ; Voltage ; Mathematics ; Engineering ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Elevator Systems and Control
Authors: Eunjung Ju | Myung Geol Choi
Source: IEEE Access (article, 2020-01-01)
Field: Materials Science — Polymers and Plastics

Estimating Cloth Simulation Parameters From a Static Drape Using Neural Networks We present a neural network learning approach for estimating a set of cloth simulation parameters from a static drape of a given fabric. We use a variant of Cusick's drape, which is used in the fashion textile industry to classify fabric according to mechanical properties. In order to produce a large enough set of reliable training data, we first randomly sample simulation parameters using a Gaussian mixture model that is fitted with 400 sets of primary simulation parameters derived from real fabrics. Then, we simulate our modified Cusick's drape for each sample parameter set. To learn the training data, we propose a two-stream fully connected neural network model. We prove the suitability of our neural network model through comparisons of the learning errors and accuracy with other similar neural network and linear regression models. Additionally, to demonstrate the practicality of our method, we reproduce the drape shapes of real fabrics using the simulation parameters estimated from the trained neural networks. Artificial neural network ; Computer science ; Set (abstract data type) ; Sample (material) ; Artificial intelligence ; Gaussian ; Training set ; Machine learning ; Pattern recognition (psychology) ; Algorithm Textile materials and evaluations ; 3D Shape Modeling and Analysis ; Computer Graphics and Visualization Techniques
Authors: Baohua Zhang | Ning Guo | Jichao Huang | Baoxing Gu | Jun Zhou
Source: Journal of Sensors (article, 2020-11-18)
Field: Chemistry — Analytical Chemistry

Computer Vision Estimation of the Volume and Weight of Apples by Using 3D Reconstruction and Noncontact Measuring Methods A computer vision system for the estimation of apple volume and weight by using 3D reconstruction and noncontact measuring methods was investigated. The 3D surface of the apples could be reconstructed by using a single multispectral camera and near-infrared linear-array structured light. Both the traditional image feature and height information were extracted from the height maps. Two different type height features (Type I and II) were extracted, and both of them were fused with a projection area to form combination features (Combination Feature I and II). Partial least squares analysis and least squares-support vector machine were implemented for calibration models with projection area and combination features as inputs. Grid-Search Technique and Leave-One-Out Cross-Validation were also investigated to find out the optimal parameter values of the RBF kernel. The optimal LS-SVM models with Combination Feature II outperformed PLS models. The coefficient and root mean square error of prediction for the best prediction by LS-SVM were 0.9032 and 10.1155 for volume, whereas 0.8602 and 9.9556 for weight, respectively. The overall results indicated that height information can improve the prediction performance, and the proposed system could be applied as an alternative to the traditional methods for noncontract measurement of the volume and weight of apple fruits. Projection (relational algebra) ; Support vector machine ; Mean squared error ; Volume (thermodynamics) ; Feature (linguistics) ; Artificial intelligence ; Kernel (algebra) ; Mathematics ; Multispectral image ; Pattern recognition (psychology) ; Least-squares function approximation ; Partial least squares regression ; Calibration ; Computer vision ; Computer science ; Statistics ; Algorithm Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Smart Agriculture and AI
Authors: Marco Podda | Davide Bacciu | Alessio Micheli
Source: arXiv (Cornell University) (preprint, 2020-02-28)
Field: Materials Science — Materials Chemistry

A Deep Generative Model for Fragment-Based Molecule Generation Molecule generation is a challenging open problem in cheminformatics. Currently, deep generative approaches addressing the challenge belong to two broad categories, differing in how molecules are represented. One approach encodes molecular graphs as strings of text, and learns their corresponding character-based language model. Another, more expressive, approach operates directly on the molecular graph. In this work, we address two limitations of the former: generation of invalid and duplicate molecules. To improve validity rates, we develop a language model for small molecular substructures called fragments, loosely inspired by the well-known paradigm of Fragment-Based Drug Design. In other words, we generate molecules fragment by fragment, instead of atom by atom. To improve uniqueness rates, we present a frequency-based masking strategy that helps generate molecules with infrequent fragments. We show experimentally that our model largely outperforms other language model-based competitors, reaching state-of-the-art performances typical of graph-based approaches. Moreover, generated molecules display molecular properties similar to those in the training sample, even in absence of explicit task-specific supervision. Fragment (logic) ; Cheminformatics ; Computer science ; Generative model ; Generative grammar ; Artificial intelligence ; Language model ; Graph ; Theoretical computer science ; Natural language processing ; Algorithm ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Miaomiao Jin | Penghui Cao | Michael P. Short
Source: Acta Materialia (article, 2020-01-14)
Field: Materials Science — Materials Chemistry

Achieving exceptional radiation tolerance with crystalline-amorphous nanocrystalline structures Materials science ; Nanocrystalline material ; Amorphous solid ; Radiation damage ; Irradiation ; Molecular dynamics ; Radiation ; Radiation resistance ; Chemical physics ; Composite material ; Nanotechnology ; Crystallography ; Optics ; Nuclear physics Nuclear materials and radiation effects ; Ion-surface interactions and analysis ; Metallic Glasses and Amorphous Alloys
Authors: Andreas Drexler | W. Siegl | Werner Ecker | Michael Tkadletz | Gerhard Klösch | Holger Schnideritsch | Gregor Mori | J. Svoboda | F.D. Fischer
Source: Corrosion Science (article, 2020-09-23)
Field: Materials Science — Metals and Alloys

Cycled hydrogen permeation through Armco iron – A joint experimental and modeling approach Trapping ; Hydrogen ; Permeation ; Materials science ; Hydrogen embrittlement ; Diffusion ; Embrittlement ; Grain boundary ; Precipitation ; Metallurgy ; Corrosion ; Thermodynamics ; Chemical physics ; Chemistry ; Microstructure ; Physics ; Membrane Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Microstructure and Mechanical Properties of Steels
Authors: Toyin Omojola | Ian P. Silverwood | Alexander J. O’Malley
Source: Catalysis Science & Technology (article, 2020-01-01)
Field: Chemistry — Inorganic Chemistry

Molecular behaviour of methanol and dimethyl ether in H-ZSM-5 catalysts as a function of Si/Al ratio: a quasielastic neutron scattering study Qualitative and quantitative differences are found in methanol and dimethyl ether mobility in H-ZSM-5 catalysts of varying Si/Al ratios (Brønsted acid site concentrations) using quasielastic neutron scattering. Dimethyl ether ; Methanol ; Catalysis ; Quasielastic neutron scattering ; ZSM-5 ; Chemistry ; Ether ; Neutron scattering ; Physical chemistry ; Nuclear chemistry ; Neutron ; Organic chemistry ; Inelastic neutron scattering ; Zeolite ; Nuclear physics ; Physics Zeolite Catalysis and Synthesis ; Advanced NMR Techniques and Applications ; X-ray Diffraction in Crystallography
Authors: Joanna Szafraniec-Szczęsny | Małgorzata Janik-Hazuka | Joanna Odrobińska | Szczepan Zapotoczny
Source: Polymers (review, 2020-09-02)
Field: Materials Science — Surfaces, Coatings and Films

Polymer Capsules with Hydrophobic Liquid Cores as Functional Nanocarriers Recent developments in the fabrication of core-shell polymer nanocapsules, as well as their current and future applications, are reported here. Special attention is paid to the newly introduced surfactant-free fabrication method of aqueous dispersions of nanocapsules with hydrophobic liquid cores stabilized by amphiphilic copolymers. Various approaches to the efficient stabilization of such vehicles, tailoring their cores and shells for the fabrication of multifunctional, navigable nanocarriers and/or nanoreactors useful in various fields, are discussed. The emphasis is placed on biomedical applications of polymer nanocapsules, including the delivery of poorly soluble active compounds and contrast agents, as well as their use as theranostic platforms. Other methods of fabrication of polymer-based nanocapsules are briefly presented and compared in the context of their biomedical applications. Nanocapsules ; Nanocarriers ; Nanotechnology ; Materials science ; Fabrication ; Polymer ; Nanoreactor ; Amphiphile ; Context (archaeology) ; Copolymer ; Drug delivery ; Nanoparticle ; Composite material Polymer Surface Interaction Studies ; Electrohydrodynamics and Fluid Dynamics ; Nanoparticle-Based Drug Delivery
Authors: Yuchen Liu | Dechang Jia | Yu Zhou | Yanchun Zhou | Juanli Zhao | Qian Li | Bin Liu
Source: Journal of Materiomics (article, 2020-06-11)
Field: Materials Science — Ceramics and Composites

Discovery of ABO4 scheelites with the extra low thermal conductivity through high-throughput calculations Searching for new materials with extra low thermal conductivities is significant in numerous fields like thermal barrier coatings and thermoelectric devices. Traditional multiple-component design has successfully reduced the thermal conductivity, but it also dramatically increases the complexity of manufactural technologies and the risk of material failures. In this work, a specific category known as ABO4 scheelites that with both simple crystal structure and the structural signature of the low lattice thermal conductivity is explored. High-throughput calculations are employed to screen for the materials with the targeted performance by multi-dimensional mechanical/thermal property criteria and a database of 46 stable scheelites is constructed. Seven scheelites with both ultra-low thermal conductivities (<1.2 W/(m∙K)) and quasi-ductility are predicted to be novel thermal insulation materials. Low thermal conductivities prefer the scheelites with large valence disparity between “A” and “B” cations and/or small ionic radius ratio. The adopted strategy starting from the structural fingerprint and the data-driven material selection is expected to be a reference of future structural and functional ceramics design. Throughput ; Materials science ; Thermal conductivity ; Conductivity ; Nanotechnology ; Engineering physics ; Computer science ; Composite material ; Physics ; Telecommunications ; Physical chemistry ; Chemistry Advanced ceramic materials synthesis ; Nuclear materials and radiation effects ; Thermal and Kinetic Analysis
Authors: Stefan J. Eder | Philipp G. Grützmacher | Manel Rodríguez Ripoll | Daniele Dini | Carsten Gachot
Source: Materials (article, 2020-12-25)
Field: Materials Science — Materials Chemistry

Effect of Temperature on the Deformation Behavior of Copper Nickel Alloys under Sliding The microstructural evolution in the near-surface regions of a dry sliding interface has considerable influence on its tribological behavior and is driven mainly by mechanical energy and heat. In this work, we use large-scale molecular dynamics simulations to study the effect of temperature on the deformation response of FCC CuNi alloys of several compositions under various normal pressures. The microstructural evolution below the surface, marked by mechanisms spanning grain refinement, grain coarsening, twinning, and shear layer formation, is discussed in depth. The observed results are complemented by a rigorous analysis of the dislocation activity near the sliding interface. Moreover, we define key quantities corresponding to deformation mechanisms and analyze the time-independent differences between 300 K and 600 K for all simulated compositions and normal pressures. Raising the Ni content or reducing the temperature increases the energy barrier to activate dislocation activity or promote plasticity overall, thus increasing the threshold stress required for the transition to the next deformation regime. Repeated distillation of our quantitative analysis and successive elimination of spatial and time dimensions from the data allows us to produce a 3D map of the dominating deformation mechanism regimes for CuNi alloys as a function of composition, normal pressure, and homologous temperature. Materials science ; Dislocation ; Crystal twinning ; Deformation (meteorology) ; Plasticity ; Deformation mechanism ; Copper ; Grain size ; Work (physics) ; Tribology ; Metallurgy ; Stacking-fault energy ; Composite material ; Stress (linguistics) ; Thermodynamics ; Microstructure Microstructure and mechanical properties ; Metal and Thin Film Mechanics ; Aluminum Alloys Composites Properties
Authors: Rebecca Lindsey | Laurence E. Fried | Nir Goldman | Sorin Bastea
Source: The Journal of Chemical Physics (article, 2020-10-07)
Field: Materials Science — Materials Chemistry

Active learning for robust, high-complexity reactive atomistic simulations Machine learned reactive force fields based on polynomial expansions have been shown to be highly effective for describing simulations involving reactive materials. Nevertheless, the highly flexible nature of these models can give rise to a large number of candidate parameters for complicated systems. In these cases, reliable parameterization requires a well-formed training set, which can be difficult to achieve through standard iterative fitting methods. Here, we present an active learning approach based on cluster analysis and inspired by Shannon information theory to enable semi-automated generation of informative training sets and robust machine learned force fields. The use of this tool is demonstrated for development of a model based on linear combinations of Chebyshev polynomials explicitly describing up to four-body interactions, for a chemically and structurally diverse system of C/O under extreme conditions. We show that this flexible training database management approach enables development of models exhibiting excellent agreement with Kohn-Sham density functional theory in terms of structure, dynamics, and speciation. Materials science ; Computer science ; Statistical physics ; Nanotechnology ; Physics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electronic and Structural Properties of Oxides
Authors: Sheela Kumari | Sethuraman Muthuramalingam | Ashish Kumar Dhara | Udai P. Singh | Ramasamy Mayilmurugan | Kaushik Ghosh
Source: Dalton Transactions (article, 2020-01-01)
Field: Chemistry — Inorganic Chemistry

Cu(<scp>i</scp>) complexes obtained<i>via</i>spontaneous reduction of Cu(<scp>ii</scp>) complexes supported by designed bidentate ligands: bioinspired Cu(<scp>i</scp>) based catalysts for aromatic hydroxylation Copper(i) complexes [Cu(L1-7)2](ClO4) (1-7) of bidentate ligands (L1-L7) have been synthesized via spontaneous reduction and characterized as catalysts for aromatic C-H activation using H2O2 as the oxidant. The single crystal X-ray structure of 1 exhibited a distorted tetrahedral geometry. All the copper(i) complexes catalyzed direct hydroxylation of benzene to form phenol with good selectivity up to 98%. The determined kinetic isotope effect (KIE) values, 1.69-1.71, support the involvement of a radical type mechanism. The isotope-labeling experiments using H218O2 showed 92% incorporation of 18O into phenol and confirm that H2O2 is the key oxygen supplier. Overall, the catalytic efficiencies of the complexes are strongly influenced by the electronic and steric factor of the ligand, which is fine-tuned by the ligand architecture. The benzene hydroxylation reaction possibly proceeded via a radical mechanism, which was confirmed by the addition of radical scavengers (TEMPO) to the catalytic reaction that showed a reduction in phenol formation. Hydroxylation ; Chemistry ; Selectivity ; Catalysis ; Denticity ; Phenol ; Benzene ; Stereochemistry ; Medicinal chemistry ; Crystal structure ; Crystallography ; Organic chemistry Metal-Catalyzed Oxygenation Mechanisms ; Porphyrin and Phthalocyanine Chemistry ; CO2 Reduction Techniques and Catalysts
Authors: Amin Liu | Kai Wu | Suping Chen | Chengheng Wu | Dongwen Gao | Lu Chen | Dan Wei | Hongrong Luo | Jing Sun | Hongsong Fan
Source: Biomacromolecules (article, 2020-07-27)
Field: Materials Science — Biomaterials

Tunable Fast Relaxation in Imine-Based Nanofibrillar Hydrogels Stimulates Cell Response through TRPV4 Activation As a key mechanical signal of natural extracellular matrix (ECM), stress relaxation plays an essential role in cell fate decision. However, the biomimetic matrix with fast stress relaxation and its cellular response mechanism have received little attention. Meanwhile, the nanofibrillar architecture which is conductive to mechanical transduction has invariably been ignored in the previous viscoelastic matrix design. Herein, by introducing a dynamic covalent imine bond into a physically cross-linked collagen hydrogel, we prepared bionic fast-relaxing nanofibrillar hydrogels with relaxation time less than 10 s. Through a single control of imine bond content, we realized fine-tuning of the relaxation rate while maintaining a constant initial modulus and fiber density. Using MC3T3-E1 cells as a model, we then proved that the nanofibrillar matrix with fast relaxation mechanics can effectively promote cell spreading and differentiation. In particular, TRPV4 as a molecular sensor of matrix viscoelasticity was demonstrated to regulate cell fate on the nanofibrillar hydrogels by mediating calcium influx. It is expected that the material design principle combining both nanofibrillar structure and tunable fast-relaxation can provide a more broadly adaptable materials platform for simulating natural ECM mechanical cues, and the investigation of the TRPV4 ion channel mediated cellular response will facilitate discovery of more fundamental mechanisms in tissue growth and development. Self-healing hydrogels ; Stress relaxation ; Extracellular matrix ; Viscoelasticity ; Nanotechnology ; Materials science ; Matrix (chemical analysis) ; Imine ; Relaxation (psychology) ; Dynamic mechanical analysis ; Chemistry ; Biophysics ; Polymer ; Polymer chemistry ; Composite material ; Organic chemistry Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Silk-based biomaterials and applications
Authors: Piero Mazzolini | Andreas Falkenstein | Zbigniew Galazka | Manfred Martin | Oliver Bierwagen
Source: Applied Physics Letters (article, 2020-11-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Offcut-related step-flow and growth rate enhancement during (100) <b> <i>β</i> </b>-Ga2O3 homoepitaxy by metal-exchange catalyzed molecular beam epitaxy (MEXCAT-MBE) Homoepitaxial β-Ga2O3 layers grown via molecular beam epitaxy (MBE) have exhibited prohibitively low growth rates on (100) oriented substrates in the past. In this work, we investigate the possibility to employ indium-assisted metal exchange catalyzed (MEXCAT) MBE to overcome this limit. We demonstrate that the fine tuning of the MEXCAT growth parameters and the choice of a proper substrate offcut allow for the deposition of thin films with high structural quality via the step-flow growth mechanism at relatively high growth rates for β-Ga2O3 homoepitaxy (i.e., around 1.5 nm/min, ≈ 45% incorporation of the incoming Ga flux), making MBE growth in this orientation feasible. Moreover, through the employment of the four investigated different (100) substrate offcuts along the [001¯] direction (i.e., 0°, 2°, 4°, and 6°), we give experimental evidence on the fundamental role of the (2¯01) step edges as nucleation sites in the growth of (100)-oriented Ga2O3 films by MBE. Molecular beam epitaxy ; Indium ; Nucleation ; Substrate (aquarium) ; Epitaxy ; Materials science ; Thin film ; Catalysis ; Deposition (geology) ; Crystallography ; Growth rate ; Optoelectronics ; Analytical Chemistry (journal) ; Chemistry ; Nanotechnology ; Layer (electronics) Ga2O3 and related materials ; ZnO doping and properties ; Advanced Photocatalysis Techniques
Authors: Ani Ozcelik | Raquel Pereira-Cameselle | Nataša Poklar Ulrih | Ana G. Petrovic | José Lorenzo Alonso‐Gómez
Source: Sensors (review, 2020-02-12)
Field: Chemistry — Spectroscopy

Chiroptical Sensing: A Conceptual Introduction Chiroptical responses have been an essential tool over the last decades for chemical structural elucidation due to their exceptional sensitivity to geometry and intermolecular interactions. In recent times, there has been an increasing interest in the search for more efficient sensing by the rational design of tailored chiroptical systems. In this review article, advances made in chiroptical systems towards their implementation in sensing applications are summarized. Strategies to generate chiroptical responses are illustrated. Theoretical approaches to assist in the design of these systems are discussed. The development of efficient chiroptical reporters in different states of matter, essential for the implementation in sensing devises, is reviewed. In the last part, remarkable examples of chiroptical sensing applications are highlighted. Nanotechnology ; Design elements and principles ; Systems engineering ; Computer science ; Biochemical engineering ; Engineering ; Materials science Molecular spectroscopy and chirality ; Photoreceptor and optogenetics research ; Molecular Sensors and Ion Detection