jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yidi Shen | Jon Fuller | Qi An
Source: Journal of Applied Physics (article, 2021-04-12)
Field: Materials Science — Materials Chemistry

Mitigating the formation of amorphous shear band in boron carbide Boron carbide is super-strong and has many important engineering applications such as body armor and cutting tools. However, the extended applications of boron carbide have been limited by its low fracture toughness arising from anomalous brittle failure when subjected to hypervelocity impact or under high pressure. This abnormal brittle failure is directly related to the formation of a tiny amorphous shear band of 2–3 nm in width and several hundred nm in length. In this Perspective, we discuss mitigating the amorphous shear bands in boron carbide from various strategies including microalloying, grain boundary engineering, stoichiometry control, and the addition of a second phase. Combined with recent theoretical and experimental studies, we discuss strategies that can be applied in synthesizing and producing boron carbide-based materials with improved ductility by suppressing the formation of the amorphous shear band. Boron carbide ; Materials science ; Brittleness ; Shear band ; Amorphous solid ; Carbide ; Boron ; Shear (geology) ; Ductility (Earth science) ; Metallurgy ; Fracture toughness ; Wide-bandgap semiconductor ; Composite material ; Optoelectronics ; Chemistry ; Crystallography Boron and Carbon Nanomaterials Research ; Advanced materials and composites ; Diamond and Carbon-based Materials Research
Authors: Benwen Chen | Jingbo Wu | Weili Li | Caihong Zhang | Qiang Xue | Yaojia Chi | Qiye Wen | Biaobing Jin | Jian Chen | Peiheng Wu
Source: Research Square (preprint, 2021-03-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Programmable terahertz metamaterials with non-volatile memory Terahertz radiation ; Metamaterial ; Terahertz metamaterials ; Materials science ; Computer science ; Optoelectronics ; Physics ; Optics ; Far-infrared laser Metamaterials and Metasurfaces Applications ; Energy Harvesting in Wireless Networks ; Antenna Design and Analysis
Authors: Yongho Kim | Gilnam Ryu | Yongho Choi
Source: Mathematical Problems in Engineering (article, 2021-12-06)
Field: Materials Science — Materials Chemistry

Fast and Accurate Numerical Solution of Allen–Cahn Equation Simulation speed depends on code structures. Hence, it is crucial how to build a fast algorithm. We solve the Allen–Cahn equation by an explicit finite difference method, so it requires grid calculations implemented by many for-loops in the simulation code. In terms of programming, many for-loops make the simulation speed slow. We propose a model architecture containing a pad and a convolution operation on the Allen–Cahn equation for fast computation while maintaining accuracy. Also, the GPU operation is used to boost up the speed more. In this way, the simulation of other differential equations can be improved. In this paper, various numerical simulations are conducted to confirm that the Allen–Cahn equation follows motion by mean curvature and phase separation in two-dimensional and three-dimensional spaces. Finally, we demonstrate that our algorithm is much faster than an unoptimized code and the CPU operation. Allen–Cahn equation ; Computer science ; Code (set theory) ; Curvature ; Computation ; Convolution (computer science) ; Grid ; Differential equation ; Partial differential equation ; Parallel computing ; Computational science ; Speedup ; Finite difference ; Motion (physics) ; Applied mathematics ; Algorithm ; Mathematical optimization ; Mathematics ; Mathematical analysis ; Geometry ; Artificial intelligence Solidification and crystal growth phenomena ; Advanced Mathematical Modeling in Engineering ; Block Copolymer Self-Assembly
Authors: Noor Ul Huda Altaf | Muhammad Yasin Naz | Shazia Shukrullah | Haq Nawaz Bhatti | Muhammad Irfan | Mabkhoot Alsaiari | Saifur Rahman | Usama Muhammad Niazi | Adam Głowacz | Klaudia Proniewska | Lukasz Wzorek
Source: Materials (article, 2021-10-06)
Field: Materials Science — Materials Chemistry

Statistically Optimized Production of Saccharides Stabilized Silver Nanoparticles Using Liquid–Plasma Reduction Approach for Antibacterial Treatment of Water Various conventional approaches have been reported for the synthesis of nanomaterials without optimizing the role of synthesis parameters. The unoptimized studies not only raise the process cost but also complicate the physicochemical characteristics of the nanostructures. The liquid-plasma reduction with optimized synthesis parameters is an environmentally friendly and low-cost technique for the synthesis of a range of nanomaterials. This work is focused on the statistically optimized production of silver nanoparticles (AgNPs) by using a liquid-plasma reduction process sustained with an argon plasma jet. A simplex centroid design (SCD) was made in Minitab statistical package to optimize the combined effect of stabilizers on the structural growth and UV absorbance of AgNPs. Different combinations of glucose, fructose, sucrose and lactose stabilizers were tested at five different levels (-2, -1, 0, 1, 2) in SCD. The effect of individual and mixed stabilizers on AgNPs growth parameters was assumed significant when p-value in SCD is less than 0.05. A surface plasmon resonance band was fixed at 302 nm after SCD optimization of UV results. A bond stretching at 1633 cm<sup>-1</sup> in FTIR spectra was assigned to C=O, which slightly shifts towards a larger wavelength in the presence of saccharides in the solution. The presence of FCC structured AgNPs with an average size of 15 nm was confirmed from XRD and EDX spectra under optimized conditions. The antibacterial activity of these nanoparticles was checked against Staphylococcus aureus and Escherichia coli strains by adopting the shake flask method. The antibacterial study revealed the slightly better performance of AgNPs against Staph. aureus strain than <i>Escherichia coli</i>. Reduction (mathematics) ; Nanoparticle ; Plasma ; Silver nanoparticle ; Materials science ; Chemical engineering ; Chemistry ; Nuclear chemistry ; Nanotechnology ; Mathematics ; Engineering Nanoparticles: synthesis and applications ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Vasiliy S. Krasnikov | Alexander E. Mayer | Victor V. Pogorelko | Marat Gazizov
Source: Applied Sciences (article, 2021-05-26)
Field: Materials Science — Materials Chemistry

Influence of θ′ Phase Cutting on Precipitate Hardening of Al–Cu Alloy during Prolonged Plastic Deformation: Molecular Dynamics and Continuum Modeling We investigate the prolonged plastic deformation of aluminum containing θ′ phase with a multistage approach combining molecular dynamics (MD), continuum modeling (CM) and discrete dislocation dynamics (DDD). The time of performed MD calculations is sufficient for about a hundred dislocation–precipitate interactions. With this number of interactions, the inclusion of θ′ is not only cut, but also scattered into individual copper atoms in an aluminum matrix. Damage to the crystal structure of inclusion and activation of the cross-slip of dislocation segments cause a decrease in acting stresses in the MD system. The rate of this effect depends on θ′ diameter and occurs faster for small inclusions. The effect of decreasing the resistance of precipitate is further introduced into the dislocation–precipitate interaction CM by reducing the precipitate effective diameter with an increase in the number of interactions. A model of dislocation–precipitate interaction accounting for the softening of inclusions is further implemented into DDD. Dependences of flow stress in aluminum with θ′ phases on volume fraction and typical diameter of precipitates are obtained. Manifestation of inclusion softening is possible in such an alloy, which leads to the flow stress decrease during deformation. The range of volume fractions and typical diameters of θ′ phases corresponding to the possible decrease in flow stress is distinguished. Materials science ; Volume fraction ; Softening ; Alloy ; Flow stress ; Hardening (computing) ; Dislocation ; Molecular dynamics ; Plasticity ; Aluminium ; Metallurgy ; Crystallography ; Composite material ; Chemistry ; Computational chemistry Microstructure and mechanical properties ; Aluminum Alloy Microstructure Properties ; High Temperature Alloys and Creep
Authors: Karthik Raja Anandan
Source: Indian Journal of Science and Technology (article, 2021-01-23)
Field: Chemistry — Analytical Chemistry

Convolutional Neural Network approach for the prediction of Soil texture properties Background/Objectives: The main objective is to achieve improved performance of soil properties prediction for hyperspectral data. In this work, convolutional neural network is trained to understand the pattern of hyperspectral data by spatial interpolation. Methods/Statistical analysis: The proposed methodology is used to predict six soil properties- Organic Carbon content (OC), Cation Exchange Capacity (CEC), Nitrogen Content (N), pH level in water, Clay particle and Sand Particle. Soil texture which defines the relative content of soil particles is determined by the percentage of clay, sand and silt in the soil. The input to the Convolutional Neural Network (CNN) is the Hyperspectral data in the form of multiple arrays. The statistical evaluation of model performance is evaluated using root-mean-square error and r square. Findings: In this research, deep learning approach is used to capture the pattern hidden in the soil. Deep learning is a kind of neural network which can model complex relationship for representing non-linearity for a scalable data. The main challenge is predicting a soil type, as it involves complex structural characteristics and soil features. Novelty/Improvements: The performance of soil texture prediction is improved by automatic feature learning capability in the proposed CNN model. The average rmse value obtained in proposed method for all the six soil texture properties is 5.68%. Keywords: Soil texture; convolutional neural network; hyperspectral data; deep learning Soil texture ; Hyperspectral imaging ; Convolutional neural network ; Silt ; Mean squared error ; Computer science ; Artificial intelligence ; Texture (cosmology) ; Pattern recognition (psychology) ; Feature (linguistics) ; Artificial neural network ; Soil science ; Soil water ; Environmental science ; Mathematics ; Statistics ; Geology ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Thuy-Quynh Duong | Assil Bouzid | Carlo Massobrio | Guido Ori | Mauro Boero | Évelyne Martin
Source: RSC Advances (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

First-principles thermal transport in amorphous Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub> at the nanoscale Achieving a precise understanding of nanoscale thermal transport in phase change materials (PCMs), such as Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub> (GST), is the key of thermal management in nanoelectronics, photonic and neuromorphic applications using non-volatile memories. By resorting to a first-principles approach to calculate the thermal conductivity of amorphous GST, we found that size effects and heat transport <i>via</i> propagative modes persist well beyond extended range order distances typical of disordered network-forming materials. Values obtained are in quantitative agreement with the experimental data, by revealing a strong size dependence of the thermal conductivity down to the 1.7-10 nm range, fully covering the scale of current PCMs-based devices. In particular, a reduction of thermal conductivity as large as 75% occurs for dimensions lying below 2 nm. These results provide a quantitative description of the thermal properties of amorphous GST at the nanoscale and are expected to underpin the development of PCM-based device applications. Nanoscopic scale ; Amorphous solid ; Materials science ; Thermal ; Amorphous semiconductors ; Crystallography ; Nanotechnology ; Chemical physics ; Physical chemistry ; Chemistry ; Physics ; Thermodynamics Phase-change materials and chalcogenides ; Nonlinear Optical Materials Studies ; Chalcogenide Semiconductor Thin Films
Authors: Juliana Pereira Lopes Gonçalves | Christine Bollwein | Anna Melissa Schlitter | Benedikt Martin | Bruno Märkl | Kirsten Utpatel | Wilko Weichert | Kristina Schwamborn
Source: Metabolites (article, 2021-10-30)
Field: Chemistry — Spectroscopy

The Impact of Histological Annotations for Accurate Tissue Classification Using Mass Spectrometry Imaging Knowing the precise location of analytes in the tissue has the potential to provide information about the organs' function and predict its behavior. It is especially powerful when used in diagnosis and prognosis prediction of pathologies, such as cancer. Spatial proteomics, in particular mass spectrometry imaging, together with machine learning approaches, has been proven to be a very helpful tool in answering some histopathology conundrums. To gain accurate information about the tissue, there is a need to build robust classification models. We have investigated the impact of histological annotation on the classification accuracy of different tumor tissues. Intrinsic tissue heterogeneity directly impacts the efficacy of the annotations, having a more pronounced effect on more heterogeneous tissues, as pancreatic ductal adenocarcinoma, where the impact is over 20% in accuracy. On the other hand, in more homogeneous samples, such as kidney tumors, histological annotations have a slenderer impact on the classification accuracy. Computer science ; Homogeneous ; Mass spectrometry imaging ; Pancreatic ductal adenocarcinoma ; MALDI imaging ; Artificial intelligence ; Pancreatic cancer ; Mass spectrometry ; Pathology ; Cancer ; Pattern recognition (psychology) ; Computational biology ; Medicine ; Biology ; Chemistry ; Matrix-assisted laser desorption/ionization ; Mathematics ; Internal medicine Advanced Proteomics Techniques and Applications ; Molecular Biology Techniques and Applications ; Mass Spectrometry Techniques and Applications
Authors: Alba Nin‐Hill | Nicolas Pierre Friedrich Mueller | Carla Molteni | Carme Rovira | Mercedes Alfonso‐Prieto
Source: International Journal of Molecular Sciences (review, 2021-11-08)
Field: Materials Science — Materials Chemistry

Photopharmacology of Ion Channels through the Light of the Computational Microscope The optical control and investigation of neuronal activity can be achieved and carried out with photoswitchable ligands. Such compounds are designed in a modular fashion, combining a known ligand of the target protein and a photochromic group, as well as an additional electrophilic group for tethered ligands. Such a design strategy can be optimized by including structural data. In addition to experimental structures, computational methods (such as homology modeling, molecular docking, molecular dynamics and enhanced sampling techniques) can provide structural insights to guide photoswitch design and to understand the observed light-regulated effects. This review discusses the application of such structure-based computational methods to photoswitchable ligands targeting voltage- and ligand-gated ion channels. Structural mapping may help identify residues near the ligand binding pocket amenable for mutagenesis and covalent attachment. Modeling of the target protein in a complex with the photoswitchable ligand can shed light on the different activities of the two photoswitch isomers and the effect of site-directed mutations on photoswitch binding, as well as ion channel subtype selectivity. The examples presented here show how the integration of computational modeling with experimental data can greatly facilitate photoswitchable ligand design and optimization. Recent advances in structural biology, both experimental and computational, are expected to further strengthen this rational photopharmacology approach. Photoswitch ; Homology modeling ; Docking (animal) ; Ligand (biochemistry) ; Chemistry ; Rational design ; Virtual screening ; Ion channel ; Molecular dynamics ; Biophysics ; Nanotechnology ; Materials science ; Biology ; Computational chemistry ; Biochemistry ; Receptor Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Retinal Development and Disorders
Authors: M. C. Girish Baabu | M. C. Padma
Source: Indonesian Journal of Electrical Engineering and Computer Science (article, 2021-07-01)
Field: Chemistry — Analytical Chemistry

Semantic feature extraction method for hyperspectral crop classification &lt;span&gt;Hyperspectral imaging (HSI) is composed of several hundred of narrow bands (NB) with high spectral correlation and is widely used in crop classification; thus induces time and space complexity, resulting in high computational overhead and Hughes phenomenon in processing these images. Dimensional reduction technique such as band selection and feature extraction plays an important part in enhancing performance of hyperspectral image classification. However, existing method are not efficient when put forth in noisy and mixed pixel environment with dynamic illumination and climatic condition. Here the proposed Sematic Feature Representation based HSI (SFR-HSI) crop classification method first employ Image Fusion (IF) method for finding meaningful features from raw HSI spectrally. Second, to extract inherent features that keeps spatially meaningful representation of different crops by eliminating shading elements. Then, the meaningful feature set are used for training using Support vector machine (SVM). Experiment outcome shows proposed HSI crop classification model achieves much better accuracies and Kappa coefficient performance. &lt;/span&gt; Hyperspectral imaging ; Pattern recognition (psychology) ; Artificial intelligence ; Support vector machine ; Computer science ; Feature extraction ; Feature (linguistics) ; Feature selection ; Overhead (engineering) ; Set (abstract data type) ; Spectral bands ; Mathematics ; Remote sensing ; Geography Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Remote Sensing and Land Use
Authors: Christian Hazael Pérez-Beltrán | Víctor M. Zúñiga-Arroyo | J.M. Andrade | Luis Cuadros-Rodrı́guez | Guadalupe Pérez‐Caballero | Ana M. Jiménez–Carvelo
Source: Chemosensors (article, 2021-02-27)
Field: Chemistry — Analytical Chemistry

A Sensor-Based Methodology to Differentiate Pure and Mixed White Tequilas Based on Fused Infrared Spectra and Multivariate Data Treatment Mexican Tequila is one of the most demanded import spirits in Europe. Its fast-raising worldwide request makes counterfeiting a profitable activity affecting both consumers and legal distillers. In this paper, a sensor-based methodology based on a combination of infrared measurements (IR) and multivariate data analysis (MVA) is presented. The case study is about differentiating two categories of white Tequila: pure Tequila (or ‘100% agave’) and mixed Tequila (or simply, Tequila). The IR spectra were treated and fused with a low-level approach. Exploratory data analysis was performed using PCA and partial least squares (PLS), whilst the authentication analyses were carried out with PLS-discriminant analysis (DA) and soft independent modeling for class analogy (SIMCA) models. Results demonstrated that data fusion of IR spectra enhanced the outcomes of the authentication models capable of differentiating pure from mixed Tequilas. In fact, PLS-DA presented the best results which correctly classified all fifteen commercial validation samples. The methodology thus presented is fast, cheap, and of simple application in the Tequila industry. Partial least squares regression ; Agave ; Linear discriminant analysis ; Multivariate statistics ; Artificial intelligence ; Authentication (law) ; Computer science ; Multivariate analysis ; Pattern recognition (psychology) ; Mathematics ; Statistics ; Data mining ; Machine learning ; Geography Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Takuma Hori
Source: Scientific Reports (article, 2021-11-22)
Field: Materials Science — Materials Chemistry

Structural optimization of silicon thin film for thermoelectric materials The method to optimize nanostructures of silicon thin films as thermoelectric materials is developed. The simulated annealing method is utilized for predicting the optimized structure. The mean free path and thermal conductivity of thin films, which are the objective function of optimization, is evaluated by using phonon transport simulations and lattice dynamics calculations. In small systems composed of square lattices, the simulated annealing method successfully predicts optimized structure corroborated by an exhaustive search. This fact indicates that the simulated annealing method is an effective tool for optimizing nanostructured thin films as thermoelectric materials. Thermoelectric effect ; Materials science ; Thin film ; Annealing (glass) ; Thermoelectric materials ; Silicon ; Simulated annealing ; Mean free path ; Thermal conductivity ; Phonon ; Nanostructure ; Nanotechnology ; Optoelectronics ; Computer science ; Condensed matter physics ; Composite material ; Optics ; Thermodynamics ; Algorithm ; Physics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies
Authors: S B Mallikarjuna | Palaiahnakote Shivakumara | Vijeta Khare | Vinay Kumar N | M Basavanna | Umapada Pal | B. Poornima
Source: Malaysian Journal of Computer Science (article, 2021-07-31)
Field: Chemistry — Analytical Chemistry

CNN BASED METHOD FOR MULTI-TYPE DISEASED ARECANUT IMAGE CLASSIFICATION Arecanut image classification is a challenging task to the researchers and in this paper a new combined approach of multi-gradient images and deep convolutional neural networks for multi-type arecanut image classification is presented. To enhance the fine details in arecanut images affected by different diseases, namely, rot, split and rot-split, we propose to explore multiple-Sobel masks for convolving with the input image. Although, the images suffer from distortion due to disease infection, this masking operation helps to enhance the fine details. We believe that the fine details provide vital clues for classification of normal, rot, split and rot-split images. To extract such clues, we explore the combination of multi-gradient and AlexNet by feeding enhanced images as input. Implementation results on the four-class dataset indicate that the approach proposed is superior in terms of classification rate, recall, precision and F-measures. The same conclusion can be drawn from the results of comparative study of proposed method with the existing methods. Computer science ; Artificial intelligence ; Convolutional neural network ; Pattern recognition (psychology) ; Image (mathematics) ; Masking (illustration) ; Distortion (music) ; Sobel operator ; Contextual image classification ; Computer vision ; Image processing ; Edge detection ; Bandwidth (computing) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI