jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: J. Malheiro | Jean‐Yves Maillard | Fernanda Borges | Manuel Simões
Source: Molecules (article, 2019-10-30)
Field: Chemistry — Organic Chemistry

Biocide Potentiation Using Cinnamic Phytochemicals and Derivatives Surface disinfection is of utmost importance in the prevention of bacterial infections. This study aims to assess the ability of ten phytochemicals and related derivatives as potentiators of two commonly used biocides-cetyltrimethylammonium bromide (CTAB) and lactic acid (LA). LA in combination with cinnamic, hydrocinnamic, α-methylcinnamic, and α-fluorocinnamic acids had a factional inhibitory concentration index (FICI) ≤ 1 for <i>Escherichia</i> <i>coli</i> and <i>Staphylococcus</i> <i>aureus</i>. Several phytochemicals/derivatives in combination with biocides improved the biocidal efficacy against early sessile bacteria. The most effective combination was LA with allyl cinnamate (2.98 ± 0.76 log CFU.cm<sup>-2</sup> reduction) against <i>E. coli</i>. The combination with CTAB was successful for most phytochemicals/derivatives with a maximum bactericidal efficacy against sessile <i>E. coli</i> when combined with allyl cinnamate (2.20 ± 0.07 log CFU.cm<sup>-2</sup> reduction) and for <i>S. aureus</i> when combined with α-methylcinnamic acid (1.68 ± 0.30 log CFU.cm<sup>-2</sup> reduction). This study highlights the potential of phytochemicals and their derivatives to be used in biocide formulations. Biocide ; Chemistry ; Escherichia coli ; Cinnamic acid ; Lactic acid ; Staphylococcus aureus ; Bromide ; Antimicrobial ; Bacteria ; Food science ; Microbiology ; Organic chemistry ; Biochemistry ; Biology Antimicrobial agents and applications ; Essential Oils and Antimicrobial Activity ; Bacterial biofilms and quorum sensing
Authors: Kazem Nazari | Prina Mehta | Muhammad Sohail Arshad | Shahabuddin Ahmed | Eleftherios G. Andriotis | Neenu Singh | Omar Qutachi | Ming‐Wei Chang | Dimitrios G. Fatouros | Zeeshan Ahmad
Source: Pharmaceutics (article, 2019-12-18)
Field: Materials Science — Biomaterials

Quality by Design Micro-Engineering Optimisation of NSAID-Loaded Electrospun Fibrous Patches The purpose of this study was to apply the Quality by Design (QbD) approach to the electrospinning of fibres loaded with the nonsteroidal anti-inflammatory drugs (NSAIDs) indomethacin (INDO) and diclofenac sodium (DICLO). A Quality Target Product Profile (QTPP) was made, and risk assessments (preliminary hazard analysis) were conducted to identify the impact of material attributes and process parameters on the critical quality attributes (CQAs) of the fibres. A full factorial design of experiments (DoE) of 20 runs was built, which was used to carry out experiments. The following factors were assessed: Drugs, voltage, flow rate, and the distance between the processing needle and collector. Release studies exhibited INDO fibres had greater total release of active drug compared to DICLO fibres. Voltage and distance were found to be the most significant factors of the experiment. Multivariate statistical analytical software helped to build six feasible design spaces and two flexible, universal design spaces for both drugs, at distances of 5 cm and 12.5 cm, along with a flexible control strategy. The current findings and their analysis confirm that QbD is a viable and invaluable tool to enhance product and process understanding of electrospinning for the assurance of high-quality fibres. Quality by Design ; Critical quality attributes ; Electrospinning ; Factorial experiment ; Design of experiments ; Diclofenac Sodium ; Computer science ; Quality assurance ; Quality (philosophy) ; Diclofenac ; Process (computing) ; Fractional factorial design ; Process engineering ; Biomedical engineering ; New product development ; Materials science ; Mathematics ; Engineering ; Chemistry ; Composite material ; Chromatography ; Statistics ; Pharmacology ; Operations management ; Medicine ; Machine learning Electrospun Nanofibers in Biomedical Applications ; biodegradable polymer synthesis and properties ; Nerve injury and regeneration
Authors: Daniel Parrott | Wasundara Fernando | André F. Martins
Source: Inorganics (article, 2019-02-09)
Field: Materials Science — Materials Chemistry

Smart MRI Agents for Detecting Extracellular Events In Vivo: Progress and Challenges Many elegant inorganic designs have been developed to aid medical imaging. We know better now how to improve imaging due to the enormous efforts made by scientists in probe design and other fundamental sciences, including inorganic chemistry, physiochemistry, analytical chemistry, and biomedical engineering. However, despite several years being invested in the development of diagnostic probes, only a few examples have shown applicability in MRI in vivo. In this short review, we aim to show the reader the latest advances in the application of inorganic agents in preclinical MRI. Nanotechnology ; Medical imaging ; Computer science ; In vivo ; Biochemical engineering ; Data science ; Engineering ; Materials science ; Artificial intelligence ; Biology ; Biotechnology Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Nanoplatforms for cancer theranostics
Authors: Ulises R. Meza | Bernardo S. Mendoza | W. Luis Mochán
Source: Physical review. B./Physical review. B (article, 2019-03-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Second-harmonic generation in nanostructured metamaterials We conduct a theoretical and numerical study on the second-harmonic (SH) optical response of a nanostructured metamaterial composed of a periodic array of inclusions. Both the inclusions and their surrounding matrix are made of centrosymmetrical materials, for which SH is strongly suppressed, but, by appropriately choosing the shape of the inclusions, we may produce a geometrically noncentrosymmetric system which does allow efficient SH generation. Variations in the geometrical configuration allow tuning the linear and quadratic spectra of the optical response of the system. We develop a theory that allows the calculation of the nonlinear polarization from the geometry of the system and its linear dielectric function at the fundamental and second-harmonic frequencies, and we implement an efficient scheme for its numerical computation, extending a formalism for the calculation of the macroscopic dielectric function using Haydock's recursion method. We apply our formalism to an array of holes within an Ag matrix, but it can be readily applied to any metamaterial made of arbitrary materials and for inclusions of any geometry within the long-wavelength regime. Metamaterial ; Materials science ; Second-harmonic generation ; Optoelectronics ; Optics ; Physics ; Laser Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Antenna Design and Analysis
Authors: Hani Benguesmia | Badis Bakri | Saad Khadar | Hamrit Fareh | Nassima M’ziou
Source: Diagnostyka (article, 2019-07-01)
Field: Materials Science — Materials Chemistry

Experimental study of pollution and simulation on insulators using COMSOL® under AC voltage The flashover of pollution, observed on the insulators used in high voltage transmission, is one of the most important power transmission stakes. It is a very complex problem due to several factors including the modelling difficulties of complex shapes of insulators, different pollution densities at different regions, non-homogeneous pollution distribution on the insulator surface and unknown effect of humidity on the pollution. In the literature, some static and dynamic models have been developed by making some assumptions and omissions to predict the flashover voltages of polluted insulators. Support vector machine ; Arc flash ; Voltage ; Pollution ; Environmental science ; Computer science ; Materials science ; Nuclear engineering ; Engineering ; Electrical engineering ; Artificial intelligence ; Biology High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation
Authors: Dongyang Wang | Lijun Zhou | Zhi-Xin Yang | Wei Liao | Lujia Wang | Lei Guo
Source: IEEE Access (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Simulation for Transient Moisture Distribution and Effects on the Electric Field in Stable Condition: 110 kV Oil-Immersed Insulation Paper Bushing Oil-immersed insulation paper (OIP) bushing is one of the most important equipment in power transmission and distribution systems. Moisture is one of the most serious factors that affect the insulation status of OIP bushing. In this paper, the OIP bushing was assumed to be in a stable and simplicity condition, and then the transient moisture distribution of capacitor core and its effects on the electric field were studied. First, by dissecting a 110 kV OIP bushing, the specific sizes for modelling were obtained. Afterwards, a moisture diffusion model for OIP bushing was established based on COMSOL Multiphysics software, and it was also verified by moisture diffusion experiment. Then, the moisture distribution for different conditions were obtained and analyzed. Finally, a model for the electric field of OIP bushing was established, and then the effects of moisture on the electric field were studied. Bushing ; Moisture ; Multiphysics ; Materials science ; Transient (computer programming) ; Electric field ; Composite material ; Diffusion ; Finite element method ; Structural engineering ; Engineering ; Computer science ; Physics ; Thermodynamics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Thermal Analysis in Power Transmission
Authors: Klaus Szielasko | Bernd Wolter | Ralf Tschuncky | Sargon Youssef
Source: tm - Technisches Messen (article, 2019-10-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Micromagnetic materials characterization using machine learning Abstract Micromagnetic materials characterization is a nondestructive means of predicting mechanical properties and stress of steel and iron products. The method is based on the circumstance that both mechanical and magnetic behaviour relate to microstructure over similar interaction mechanisms, which leads to characteristic correlations between mechanical and magnetic properties of ferromagnetic materials. The prediction of mechanical properties or stress from micromagnetic parameters represents an inverse problem commonly addressed by regression and classification approaches. Challenges for the industrial application of micromagnetic methods lie in the development of robust sensors, definition of significant features, and implementation of powerful machine learning algorithms for a reliable quantitative target value prediction by processing of the micromagnetic features. This contribution briefly explains the background of micromagnetics, describes the typical challenges experienced in practice and provides insight into latest progress in the application of machine learning to micromagnetic data. Micromagnetics ; Characterization (materials science) ; Ferromagnetism ; Artificial intelligence ; Computer science ; Machine learning ; Materials science ; Stress (linguistics) ; Condensed matter physics ; Physics ; Nanotechnology ; Magnetization ; Magnetic field Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Magnetic Properties of Alloys
Authors: L. Burzawa | S. Liu | E. W. Carlson
Source: Physical Review Materials (article, 2019-03-29)
Field: Materials Science — Materials Chemistry

Classifying surface probe images in strongly correlated electronic systems via machine learning Scanning probe experiments such as scanning tunneling microscopy (STM) and atomic force microscopy (AFM) on strongly correlated electronic systems often reveal complex pattern formation on multiple length scales. By studying the universal scaling in these images, we have shown in several distinct correlated electronic systems that the pattern formation is driven by proximity to a disorder-driven critical point, revealing a unification of the pattern formation in these materials. As an alternative approach to this image classification problem of novel materials, here we report an investigation of the machine learning method to determine which underlying physical model is driving pattern formation in a system. Using a neural network architecture, we are able to achieve $97%$ accuracy on classifying configuration images from three models with Ising symmetry. This investigation also demonstrates that machine learning can capture the implicit universal behavior of a physical system. This broadens our understanding of what machine learning can do, and we expect more synergy between machine learning and condensed matter physics in the future. Scanning tunneling microscope ; Artificial neural network ; Surface (topology) ; Atomic force microscopy ; Scaling ; Ising model ; Pattern recognition (psychology) ; Deep learning Machine Learning in Materials Science ; Quantum many-body systems ; Electronic and Structural Properties of Oxides
Authors: Yoshitaka Umeno | Atsushi Kubo
Source: Computational Materials Science (article, 2019-06-14)
Field: Materials Science — Materials Chemistry

Prediction of electronic structure in atomistic model using artificial neural network Artificial neural network ; Discretization ; Electronic structure ; Scheme (mathematics) ; Computer science ; Statistical physics ; Sampling (signal processing) ; Sampling scheme ; Materials science ; Biological system ; Artificial intelligence ; Algorithm ; Mathematics ; Physics ; Chemistry ; Computational chemistry ; Mathematical analysis ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Semiconductor materials and interfaces
Authors: Brandon D. Snow | Dustin D. Doty | Oliver K. Johnson
Source: Frontiers in Materials (article, 2019-05-31)
Field: Materials Science — Materials Chemistry

A Simple Approach to Atomic Structure Characterization for Machine Learning of Grain Boundary Structure-Property Models Grain boundaries (GBs) have a significant influence on the properties of crystalline materials. Machine learning approaches present an attractive route to develop atomic structure-property models for GBs because of the complexity of their structure. However, the application of such techniques requires an appropriate descriptor of the atomic structure. Unfortunately, common crystal structure identification techniques cannot be applied to characterize the structure of the vast majority of GB atoms (50-98% are classified as "other"). This suggests a critical need for atomic structure descriptors capable of identifying arbitrary atomic environments. In this work we present a simple procedure that facilitates the identification of arbitrary atomic structures present in GBs. We apply this approach to characterize the atomic structure of the 388 GBs from the Olmsted data set (Olmsted 2009). We show how this approach facilitates visualization of GB atomic structures in a way that reveals important structural information. We test the recently proposed hypothesis that Σ3 GBs contain facets of the GBs that form the corners of the corresponding GB plane fundamental zone. Finally, we briefly demonstrate how the structure descriptors resulting from our approach can be used as inputs to machine learning approaches for the development of atomic structure-property models for GBs. Characterization (materials science) ; Property (philosophy) ; Computer science ; Crystal structure ; Identification (biology) ; Set (abstract data type) ; Boundary (topology) ; Simple (philosophy) ; Grain boundary ; Visualization ; Artificial intelligence ; Algorithm ; Machine learning ; Materials science ; Crystallography ; Nanotechnology ; Mathematics ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Corrosion Behavior and Inhibition
Authors: Naima Melzi | Hamid Zentou | Maamar Laidi | Salah Hanini | Yamina Ammi | Latifa Khaouane
Source: Kemija u industriji (article, 2019-01-01)
Field: Chemistry — Organic Chemistry

Usporedna studija predviđanja koeficijenta molekularne difuzije za polarni i nepolarni binarni plin pomoću neuronskih mreža i višestrukih linearnih regresija In the current study, an artificial neural network (ANN) and multiple linear regressions (MLR) have been used to develop predictive models for the estimation of molecular diffusion coefficients of 1252 polar and non-polar binary gases at multiple pressures over a wide range of temperatures and substances. The quality and reliability of each method were estimated in terms of the correlation coefficient (R), mean squared errors (MSE), root mean squared error (RMSE), and in terms of external validation coefficients (Q2ext). The comparison between the artificial neural network (ANN) and the multiple linear regressions (MLR) revealed that the neural network models showed a good predicting ability with lower errors (the roots of the mean squared errors in the total database were 0.1400 for ANN1 and 0.1300 for ANN2), and (root mean squared errors in the total databases were 0.5172 for MLR1 and 0.5000 for MLR2). Mean squared error ; Mathematics ; Statistics ; Linear regression ; Artificial neural network ; Correlation coefficient ; Reliability (semiconductor) ; Computer science ; Artificial intelligence ; Thermodynamics ; Physics Chemical Thermodynamics and Molecular Structure ; Water Quality Monitoring and Analysis ; Electrochemical Analysis and Applications
Authors: Pınar Acar
Source: AIAA Journal (article, 2019-06-03)
Field: Materials Science — Metals and Alloys

Machine Learning Approach for Identification of Microstructure–Process Linkages The present work addresses a machine learning approach to study the linkage between deformation processing and microstructural texture evolution. The texture evolution is represented with a one-point probability descriptor, orientation distribution function (ODF) by employing a rate-independent single-crystal plasticity model. The ODF relates to the volume densities of different orientations in a microstructure, and thus it quantifies the texture during processing. First, a database of microstructural textures and deformation processes is built to generate the training dataset. The Latin hypercube sampling method is implemented to create uniformly distributed ODF values that represent the initial texture. Next, the crystal plasticity simulations using the initial ODF samples are performed for different deformation processes such as tension, plane strain compression, shear, shear, and shear; and the final texture information is stored in the database. Transductive learning is employed by using the training data to predict the label (optimum process) that can produce a given unlabeled texture. Two example process design applications are discussed for a galfenol alloy. In the first problem, the transductive learning approach is verified by using the previously optimized ODF solutions for a vibration tuning problem. In the second example, the process design is performed for a multiphysics optimization problem. Artificial intelligence ; Computer science ; Materials science ; Texture (cosmology) ; Machine learning ; Algorithm Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science ; Metallurgy and Material Forming
Authors: Guo‐Yu Yang | Kechen Wu
Source: Molecular Systems Design & Engineering (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Two-dimensional nonlinear optical materials predicted by network visualization Machine learning and network visualization were applied to predict two-dimensional nonlinear optical materials by selecting key elements and connections. Visualization ; Key (lock) ; Nonlinear system ; Nonlinear optical ; Computer science ; Artificial intelligence ; Physics 2D Materials and Applications ; Photonic Crystals and Applications ; Photonic and Optical Devices
Authors: Rui Shi | Boyang Sun | Xinhui Liu | Weishang Yao | Jia Xiaowen | Tan Hui‐min | Luo Wen | Xi Haijun | Yu Chen
Source: Polymer Testing (article, 2019-06-07)
Field: Materials Science — Materials Chemistry

A mesoscopic damage model of solid propellants under thermo-mechanical coupling loads Propellant ; Materials science ; Composite material ; Coupling (piping) ; Stress (linguistics) ; Composite number ; Mesoscopic physics ; Particle (ecology) ; Work (physics) ; Fractal dimension ; Structural engineering ; Fractal ; Mechanical engineering ; Aerospace engineering High-Velocity Impact and Material Behavior ; Energetic Materials and Combustion ; Structural Response to Dynamic Loads
Authors: Ada Azevedo Barbosa | Ramon Vinícius Santos de Aquino | Ana Flávia B. Oliveira | Renato F. Dantas | Josivan Pedro da Silva | Marta Maria Menezes Bezerra Duarte | Otidene Rossiter Sá da Rocha
Source: Desalination and Water Treatment (article, 2019-04-29)
Field: Chemistry — Inorganic Chemistry

Development of a new photocatalytic reactor built from recyclable material for the treatment of textile industry effluents Photocatalysis ; Effluent ; Textile ; Textile industry ; Waste management ; Business ; Process engineering ; Manufacturing engineering ; Pulp and paper industry ; Engineering ; Materials science ; Chemistry ; Composite material ; Catalysis ; Political science Pigment Synthesis and Properties ; TiO2 Photocatalysis and Solar Cells ; Water and Wastewater Treatment
Authors: Kuo-Feng Lin | Chin-Chuan Hsieh | Shui-Chuan Hsin | Wen–Feng Hsieh
Source: Applied Optics (article, 2019-11-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Achieving high numerical aperture near-infrared imaging based on an ultrathin cylinder dielectric metalens An artificial subwavelength dielectric metalens (ML), the realization of being ultrathin and light-weight, provides a potential candidate with replacing a traditional bulky curved lens with a high image quality. A ML with 1.5 mm in diameter having numerical aperture (NA) $\sim{0.60}$∼0.60 at the near-infrared wavelength of $\lambda = 0.94 \,\,{\unicode{x00B5}{\rm m}}$λ=0.94µm was designed by the finite-difference time-domain (FDTD) method with speeding up optimization of the MLs' scheme by the deep neural network (DNN) model. Additionally, an ultrathin high NA ML was achieved by cost effective semiconductor manufacturing technology. The fabricated ML can focus an incident light down to a spot as small as $ \sim{5.2}\,\,{\rm \unicode{x00B5}{\rm m}} $∼5.2µm with high optical efficiency of $\sim{88.4}\% $∼88.4% (focusing efficiency achieved, 23.7%). We also provided an efficient MLs' semiconductor manufacturing technology for the development of an optical device in near-infrared image technology. Optics ; Finite-difference time-domain method ; Numerical aperture ; Materials science ; Dielectric ; Image quality ; Aperture (computer memory) ; Optoelectronics ; Infrared ; Wavelength ; Physics ; Image (mathematics) ; Computer science Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Optical Coatings and Gratings