jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiong Deng | Chao Chen | Deyang Chen | Xiangbin Cai | Chao Xu | Xiaozhe Yin | Guo Tian | Zhen Fan | Zhipeng Hou | Minghui Qin | Xubing Lu | Guofu Zhou | Lang Chen | Ning Wang | Ye Zhu | Xingsen Gao | Jun‐Ming Liu
Source: SSRN Electronic Journal (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Strain Engineering of Epitaxial Oxide Heterostructures Beyond Substrate Limitations Strain engineering ; Epitaxy ; Substrate (aquarium) ; Heterojunction ; Materials science ; Oxide ; Strain (injury) ; Optoelectronics ; Engineering physics ; Nanotechnology ; Engineering ; Medicine ; Biology ; Metallurgy ; Silicon ; Internal medicine Ferroelectric and Piezoelectric Materials ; Electronic and Structural Properties of Oxides ; Multiferroics and related materials
Authors: Li Liu | Xin Qiao | Xindong Shi | Qunming Liu | Yinggang Shi
Source: Instrumentation Mesure Métrologie (article, 2019-08-30)
Field: Chemistry — Analytical Chemistry

A Novel Apple Size and Surface Quality Detection and Grading System This paper attempts to design an automatic grading system that can accurately and efficiently determine the size and surface quality of apples. For this purpose, apple images were processed by the LabVIEW toolkit (including IMAQ Vision), and the particle area analysis was performed to measure the size and evaluate the surface quality of apples, creating an apple size and surface quality detection and grading system. The proposed system can detect the size and surface defects of each apple, grade the surface quality, and transfer the apple to the storage point for apples in that grade. With clear principles, simple processes and good portability, the system can be applied independently or together with other detection methods to evaluate the internal quality of apples, such as sugar content, maturity and pesticide residue. The research findings make it possible to make a comprehensive evaluation of apple quality. Grading (engineering) ; Software portability ; Computer science ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: V. Hanuš | Sarayoo Kangaparambil | Seyedreza Larimian | Xinhua Xie | M. S. Schöffler | A. Staudte | G. G. Paulus | Andrius Baltuška | Markus Kitzler
Source: EPJ Web of Conferences (article, 2019-01-01)
Field: Chemistry — Spectroscopy

The molecular attoclock: sub-cycle control of electronic dynamics during H<sub>2</sub> double ionization We introduce and employ the molecular attoclock method. This allows us to simultaneously trace the nuclear and electron dynamics during H 2 fragmentation, and to CEP-control the two-electron emission dynamics on sub-cycle time scales. Fragmentation (computing) ; Ionization ; Molecular dynamics ; Electron ; TRACE (psycholinguistics) ; Dynamics (music) ; Atomic physics ; Physics ; Electron ionization ; Chemical physics ; Nuclear physics ; Ion ; Computer science ; Quantum mechanics Mass Spectrometry Techniques and Applications ; Advanced Chemical Physics Studies ; Atomic and Molecular Physics
Authors: A. Hamadou
Source: International Journal of Optics and Photonic Engineering (article, 2019-12-31)
Field: Chemistry — Spectroscopy

Dynamical Characteristics of External Cavity Quantum Cascade Lasers In this paper, we calculate the dynamical characteristics such as delay time, rise time, and time to steady state establishment, of a mid infrared quantum cascade laser coupled to external cavity. The approach is based on the three-level rate equations including the dependence of the loss on external cavity parameters. We find in particular that the threshold current of external cavity is strongly influenced by the external reflectivity and external cavity length. In addition, the equations that allow for the determination of the dynamical characteristics are derived within the premises of our model in the general case. The effects of the external cavity parameters on dynamical characteristics are also explored. Cascade ; Physics ; Laser ; Quantum ; Quantum cascade laser ; Rate equation ; Mechanics ; Optics ; Classical mechanics ; Quantum mechanics ; Chemistry Spectroscopy and Laser Applications ; Laser Design and Applications ; Semiconductor Lasers and Optical Devices
Authors: Jeff Gelb | D. J. Vine | Trevor Lancon | Sylvia Lewis | S. H. Lau | Wenbing Yun
Source: Microscopy and Microanalysis (article, 2019-08-01)
Field: Materials Science — Surfaces, Coatings and Films

Energy Tunability in Laboratory 3D Nano-XRM Nano- ; Materials science ; Energy (signal processing) ; Nanotechnology ; Optoelectronics ; Composite material ; Physics Electron and X-Ray Spectroscopy Techniques ; Integrated Circuits and Semiconductor Failure Analysis ; Advancements in Photolithography Techniques
Authors: Satyendra Nath Mandal | Sanket Dan | Pritam Ghosh | Subhranil Mustafi | Kunal Roy | Kaushik Mukherjee | Dilip Kumar Hajra | S. Banik
Source: Science & Technology Journal (article, 2019-07-01)
Field: Chemistry — Analytical Chemistry

Pig Breeds Classification using Neuro-Statistic Model Image classification using fully connected neural network is not efficient due to huge number of parameters in each layer. In this paper, we propose a Neuro-Statistic model for classification of five different pig breeds from pig images. The model consists of four sub modules which work together as a layered structure. We captured multiple individual pig images of five different pig breeds from different organized farms to conduct this research, segmented the captured pig images using hue based segmentation algorithm and then calculated the statistical properties like entropy, standard deviation, variance, mean, median, mode and color properties like H.S.V from the content of the individual segmented images. We fed all the extracted properties into Neural Network for Pig Breed (NNPB) to perform pig breed prediction with the classification module and analyzed the best performance, regression error plot, Error histogram and training state of NNPB. The performance of NNPB network was accepted based on error analysis and finally, we used the trained model to predict the breed of 50 pig images and achieved the prediction accuracy of 90%. Pattern recognition (psychology) ; Statistic ; Artificial neural network ; Artificial intelligence ; Histogram ; Segmentation ; Computer science ; Standard deviation ; Breed ; Homogeneity (statistics) ; Statistics ; Mathematics ; Image (mathematics) ; Biology Spectroscopy and Chemometric Analyses ; Effects of Environmental Stressors on Livestock ; Meat and Animal Product Quality
Authors: Галина Кравченко | Елена Труфанова | Victoria Sheremet | Алина Шарап
Source: Construction and Architecture (article, 2019-09-28)
Field: Materials Science — Materials Chemistry

MODELING THE NONLINEAR WORK OF CONCRETE OF A MONOLITHIC FLOOR SLAB USING THE FINITE ELEMENT METHOD The article discusses the modeling of the nonlinear work of the material of the floor slab of a monolithic frame building. Two laws of the deformation of materials in the calculation by the finite element method are investigated. An analysis of the influence of the laws of deformation on the deflections of the floor slab, the main stresses and the destruction schemes of the floor slab by layers allowed us to give recommendations on the consideration of the nonlinear properties of materials when performing strength calculations. Slab ; Finite element method ; Nonlinear system ; Structural engineering ; Deformation (meteorology) ; Work (physics) ; Frame (networking) ; Materials science ; Geology ; Engineering ; Geotechnical engineering ; Mechanical engineering ; Composite material ; Physics Structural mechanics and materials ; Construction Management and Sustainability
Authors: Jacob Nilsson | Fredrik Sandin | Jerker Delsing
Source: arXiv (Cornell University) (preprint, 2019-03-26)
Field: Materials Science — Materials Chemistry

Interoperability and machine-to-machine translation model with mappings\n to machine learning tasks Modern large-scale automation systems integrate thousands to hundreds of\nthousands of physical sensors and actuators. Demands for more flexible\nreconfiguration of production systems and optimization across different\ninformation models, standards and legacy systems challenge current system\ninteroperability concepts. Automatic semantic translation across information\nmodels and standards is an increasingly important problem that needs to be\naddressed to fulfill these demands in a cost-efficient manner under constraints\nof human capacity and resources in relation to timing requirements and system\ncomplexity. Here we define a translator-based operational interoperability\nmodel for interacting cyber-physical systems in mathematical terms, which\nincludes system identification and ontology-based translation as special cases.\nWe present alternative mathematical definitions of the translator learning task\nand mappings to similar machine learning tasks and solutions based on recent\ndevelopments in machine learning. Possibilities to learn translators between\nartefacts without a common physical context, for example in simulations of\ndigital twins and across layers of the automation pyramid are briefly\ndiscussed.\n Computer science ; Interoperability ; Automation ; Control reconfiguration ; Context (archaeology) ; Artificial intelligence ; Machine translation ; Cyber-physical system ; Task (project management) ; Ontology ; Software engineering ; Machine learning ; Systems engineering ; Embedded system ; Engineering ; World Wide Web Machine Learning in Materials Science ; Flexible and Reconfigurable Manufacturing Systems ; Digital Transformation in Industry
Authors: Stefan Langner | Florian Häse | José Darío Perea | Tobias Stubhan | Jens Hauch | Loı̈c M. Roch | Thomas Heumueller | Alán Aspuru‐Guzik | Christoph J. Brabec
Source: arXiv (Cornell University) (preprint, 2019-09-08)
Field: Materials Science — Materials Chemistry

Beyond Ternary OPV: High-Throughput Experimentation and Self-Driving\n Laboratories Optimize Multi-Component Systems Fundamental advances to increase the efficiency as well as stability of\norganic photovoltaics (OPVs) are achieved by designing ternary blends which\nrepresents a clear trend towards multi-component active layer blends. We report\nthe development of high-throughput and autonomous experimentation methods for\nthe effective optimization of multi-component polymer blends for OPVs. A method\nfor automated film formation enabling the fabrication of up to 6048 films per\nday is introduced. Equipping this automated experimentation platform with a\nBayesian optimization, a self-driving laboratory is constructed that\nautonomously evaluates measurements to design and execute the next experiments.\nTo demonstrate the potential of these methods, a four-dimensional parameter\nspace of quaternary OPV blends is mapped and optimized for photo-stability.\nWhile with conventional approaches roughly 100 mg of material would be\nnecessary, the robot based platform can screen 2,000 combinations with less\nthan 10 mg and machine learning enabled autonomous experimentation identifies\nthe stable compositions with less than 1 mg.\n Ternary operation ; Component (thermodynamics) ; Bayesian optimization ; Throughput ; Stability (learning theory) ; Computer science ; Materials science ; Process engineering ; Nanotechnology ; Artificial intelligence ; Engineering ; Machine learning ; Operating system ; Physics Machine Learning in Materials Science ; Modular Robots and Swarm Intelligence ; Electrowetting and Microfluidic Technologies
Authors: Árni Sturluson | Melanie T. Huynh | Alec Kaija | Caleb Laird | Sunghyun Yoon | Feier Hou | Zhenxing Feng | Christopher E. Wilmer | Yamil J. Colón | Y. Barry Chung | Daniel W. Siderius | Cory M. Simon
Source: ChemRxiv (preprint, 2019-03-27)
Field: Chemistry — Inorganic Chemistry

The Role of Molecular Modeling &amp; Simulation in the Discovery and Deployment of Metal-Organic Frameworks for Gas Storage and Separation Metal-organic frameworks (MOFs) are highly tunable, extended-network, crystalline, nanoporous materials with applications in gas storage, separations, and sensing. We review how molecular models and simulations of gas adsorption in MOFs have informed the discovery of performant MOFs for methane, hydrogen, and oxygen storage, xenon, carbon dioxide, and chemical warfare agent capture, and xylene enrichment. Particularly, we highlight how large, open databases of MOF crystal structures, post-processed to enable molecular simulations, are a platform for computational materials discovery. We discuss how to orient research efforts to routinize the computational discovery of MOFs for adsorption-based engineering applications. Nanoporous ; Metal-organic framework ; Adsorption ; Methane ; Nanotechnology ; Materials science ; Gas separation ; Hydrogen storage ; Computer science ; Hydrogen ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Jon Paul Janet | Sahasrajit Ramesh | Chenru Duan | Heather J. Kulik
Source: ChemRxiv (preprint, 2019-12-19)
Field: Materials Science — Materials Chemistry

Accurate Multi-Objective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization The accelerated discovery of materials for real world applications requires the achievement of multiple design objectives. Satisfaction of such constraints requires exploration of multi-million compound libraries over which even density-functional theory (DFT) screening is intractable. Machine learning (ML, e.g., artificial neural network, ANN, or Gaussian process, GP) models for this task are limited by training data availability and predictive uncertainty quantification (UQ). We overcome such limitations by using efficient global optimization (EGO) with the multi-dimensional expected improvement (EI) criterion. EGO balances exploitation of a trained model with acquisition of new DFT data at the Pareto front, the region of chemical space that contains the optimal trade-off between multiple design criteria. We demonstrate this approach for the simultaneous optimization of redox potential and solubility in candidate M(II)/M(III) redox couples for redox flow batteries from a space of 2.8M transition metal complexes designed for stability in practical RFB applications. We employ latent-distance-based UQ with a multi-task ANN to enable model generalization that surpasses that of a GP. With this approach, ANN prediction and EI scoring of the full 2.8M complex space is achieved in minutes. Starting from ca. 100 representative points, EGO improves both properties by 3-4 standard deviations in only five generations. Analysis of lookahead errors confirms rapid ANN model improvement during the EGO process, achieving suitable accuracy for predictive design in the space of transition metal complexes. The ANN-driven EI approach achieves at least 500-fold acceleration over random search, identifying a Pareto-optimal design in around five weeks instead of fifty years. Computer science ; Artificial neural network ; Chemical space ; Pareto principle ; Artificial intelligence ; Mathematical optimization ; Machine learning ; Mathematics ; Chemistry Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Computational Drug Discovery Methods
Authors: Aculina Aricu | Raisa Nastas | Maria Cocu
Source: Chemistry Journal of Moldova (article, 2019-06-01)
Field: Materials Science — Materials Chemistry

The Institute of Chemistry at 60 Years Anniversary. Brief History, Achievements and Perspectives At present, the scientific potential of the Chemistry ; History of chemistry ; Engineering ethics ; Library science ; Engineering physics ; Engineering ; History of science ; Epistemology ; Philosophy ; Computer science Advanced Materials and Semiconductor Technologies
Authors: Sue J. Mecham | Crista L. Farrell | Joseph M. DeSimone
Source: UNC Libraries (article, 2019-08-29)
Field: Chemistry — Organic Chemistry

Organic Polymer Chemistry in the Context of Novel Processes This article was written to shed light on a series of what some have stated are not so obvious connections that link polymer synthesis in supercritical CO2 to cancer treatment and vaccines, nonflammable polymer electrolytes for lithium ion batteries, and 3D printing. In telling this story, we also attempt to show the value of versatility in applying one's primary area of expertise to address pertinent questions in science and in society. In this Outlook, we attempted to identify key factors to enable a versatile and nimble research effort to take shape in an effort to influence diverse fields and have a tangible impact in the private sector through the translation of discoveries into the marketplace. Context (archaeology) ; Polymer science ; Polymer ; Chemistry ; Environmental chemistry ; Chemical engineering ; Organic chemistry ; Engineering ; History ; Archaeology Inorganic and Organometallic Chemistry
Authors: Geoffrey Fox | Shantenu Jha
Source:  (preprint, 2019-09-01)
Field: Materials Science — Materials Chemistry

Learning Everywhere: A Taxonomy for the Integration of Machine Learning and Simulations We present a taxonomy of research on Machine Learning (ML) applied to enhance simulations together with a catalog of some activities. We cover eight patterns for the link of ML to the simulations or systems plus three algorithmic areas: particle dynamics, agent-based models and partial differential equations. The patterns are further divided into three action areas: Improving simulation with Configurations and Integration of Data, Learn Structure, Theory and Model for Simulation, and Learn to make Surrogates. Taxonomy (biology) ; Computer science ; Artificial intelligence ; Machine learning ; Cover (algebra) ; Action (physics) ; Engineering ; Ecology Machine Learning in Materials Science ; Topic Modeling ; Model Reduction and Neural Networks
Authors: L Yu Makhotkina | А. А. Халилова
Source:  (article, 2019-09-15)
Field: Materials Science — General Materials Science

Development of Hydrophobic Textile Materials with Organosilicon Impregnation for the Work Clothes Production In recent years, special attention has been paid to studying the properties of hydrophobic surfaces of textile materials. The authors provide an overview of the applied textile-processing chemicals and of the methods for imparting hydrophobic properties to textile materials. According to the analysis, it was found that water-and splash-proofing treatment occupies an important place in the finishing processes of textile materials, contributing to a change in their characteristics. The purpose of the research is the development of hydrophobic textile materials for the work clothes production. The aqueous solution based on organosilicon compounds was used for textiles hydrophobization with retention of their performance and hygienic characteristics. The research results of the effect of aqueous solutions based on silane (A-1100 and A-187 trademarks) on the hydrophobic characteristics of textile materials is presented in the article. Organosilicon ; Textile ; Clothing ; Materials science ; Work (physics) ; Production (economics) ; Composite material ; Polymer chemistry ; Engineering ; Mechanical engineering Material Properties and Applications ; Polymer Science and Applications ; Material Science and Thermodynamics
Authors: Mamta Bishnoi
Source: The Pharma Innovation (article, 2019-01-01)
Field: Chemistry — Spectroscopy

Pharmacodynamics: Drug mechanisms of action Pharmacodynamics elucidates the intricate mechanisms underlying the actions of drugs within biological systems, providing critical insights into their therapeutic effects and potential adverse reactions. This research paper delves into the diverse mechanisms through which drugs exert their effects, encompassing interactions with molecular targets, signaling pathways, and physiological processes. By exploring the dynamic interplay between drugs and their targets, this study aims to enhance our understanding of pharmacological interventions and facilitate the development of safer and more efficacious therapeutics. Through comprehensive analysis and synthesis of existing literature, this paper aims to shed light on the multifaceted nature of drug mechanisms of action, offering valuable perspectives for future research and clinical practice. Pharmacodynamics ; Pharmacology ; Drug ; Action (physics) ; Drug action ; Medicine ; Pharmacokinetics Analytical Chemistry and Chromatography ; Analytical Methods in Pharmaceuticals
Authors: Josiane Soares Costa
Source: theses.fr (ABES) (preprint, 2019-06-12)
Field: Materials Science — Materials Chemistry

Étude de la réduction de l’oxygène sur des films minces de ZnO élaborés par pulvérisation cathodique Corrosion is a local electrochemical process involving an oxidation reaction that is balanced by a reduction reaction. The oxygen reduction reaction (ORR) is one of the major cathodic reactions in corrosion. The oxygen can be reduced directly into OH- ions or indirectly with hydrogen peroxide as intermediate species. In this Ph.D. thesis, ORR has been investigated on ZnO that is one the most common degraded forms of zinc, a metal of major industrial interest. Model electrodes (textured and non-textured ZnO films) have been deposited on copper substrates using the RF-sputtering technique. The ORR has been investigated in alkaline electrolyte using a rotating ring-disk electrode. During the measurement of the polarization curves, potential-dependent rupturing of the textured ZnO films was observed. Electrochemical impedance spectroscopy was utilized to detect more finely at which potential the rupture of the film was initiated. Non-textured ZnO thin films exhibited a significantly improved mechanical stability and higher ORR activity compared to textured films. A reaction mechanism was proposed for ORR. The prevalence of the direct reduction was quantitatively established but the parallel indirect reduction was active as well. It was possible to show that indirect reduction pathway did not stop at the production of hydrogen peroxide. The latter was further reduced to OH-anions and/or decomposed into oxygen molecules. Characterization (materials science) ; Reduction (mathematics) ; Cavity magnetron ; Oxygen reduction ; Materials science ; Metallurgy ; Nanotechnology ; Sputtering ; Chemistry ; Thin film ; Mathematics ; Physical chemistry ; Geometry ZnO doping and properties