jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jesus Tovar-Amézquita | Cristian Rincón-Guio | Francy Elaine Torres-Suarez | Magda Melissa Flórez | Claudia Patricia Ortíz | Fleming Martínez | Daniel Ricardo Delgado
Source: Molecules (article, 2024-10-28)
Field: Materials Science — Materials Chemistry

Thermodynamic Assessment of the Pyrazinamide Dissolution Process in Some Organic Solvents Pyrazinamide is a first line drug used for the treatment of tuberculosis, a pathology that caused the death of more than 1.3 million people in the world during 2022, according to WHO, being a drug of current interest due to its relevance in pharmaceutical and medical sciences. In this context, solubility is one of the most important physicochemical parameters in the development and/or optimization of new pharmaceutical forms, so the present work aims to present a thermodynamic study of the solubility of pyrazinamide in nine organic solvents of pharmaceutical interest. Using the shake-flask method and UV/Vis spectrophotometry, the solubility of this drug was determined at 9 temperatures; the maximum solubility was obtained in dimethyl sulfoxide at 318.15 K (x2=0.0816±0.004) and the minimum in cyclohexane at 283.15 K (1.73±0.05×10-5). From the apparent solubility data, the thermodynamic functions of solution and mixing were calculated, indicating an endothermic process. In addition, the solubility parameter of pyrazinamide was calculated using the Hoftyzer-van Krevelen (32.90 MPa<sup>1/2</sup>) and Bustamante (28.14 MPa<sup>1/2</sup>) methods. The maximum solubility was reached in dimethyl sulfoxide and the minimum in cyclohexane. As for the thermodynamic functions, the entropy drives the solution process in all cases. In relation to the solubility parameter, it can be analyzed that the mathematical models offer approximations; however, the experimental data are still primordial at the time of inferring any process. Dissolution ; Pyrazinamide ; Chemistry ; Process (computing) ; Environmental chemistry ; Organic solvent ; Organic chemistry ; Chemical engineering ; Biochemistry ; Computer science Crystallization and Solubility Studies ; Chemistry and Chemical Engineering ; Thermal and Kinetic Analysis
Authors: Patil Rajvardhan Kiran | Md Yeasin | Pramod Aradwad | T. V. Arunkumar | Roaf Ahmad Parray
Source: Future Postharvest and Food (article, 2024-06-01)
Field: Chemistry — Analytical Chemistry

Advancing mango quality assurance: Non‐destructive detection of spongy tissue using visible near‐infrared spectroscopy and machine learning classification Abstract The renowned Alphonso mango, celebrated in India for its exquisite taste, saffron hue, pleasing texture, and prolonged shelf life, holds significant global commercial appeal. Unfortunately, the widespread issue of spongy tissue (ST) disorder in Alphonso mangoes results in a soft, corky texture, affecting up to 30% of mangoes in a single batch. This challenge leads to the outright rejection of affected mangoes during export due to delayed disorder identification. The current evaluation method involves destructive cutting, causing substantial fruit loss, and lacks assurance for the overall batch quality. This study addresses these challenges by focusing on the utilization of visible near‐infrared spectroscopy as a non‐invasive method to assess the internal quality of mangoes. Additionally, it introduces innovative classification models for automated binary categorization (Healthy vs. ST affected). Through preprocessing and principal component analysis of spectral reflectance data for feature extraction and wavelength optimization, successful wavelength ranges of 650–970 nm were identified, effectively distinguishing between healthy and damaged mangoes. Various machine learning models used notably, linear discriminant analysis, support vector machine, and logistic regression exhibited strong discriminative capabilities with higher accuracy reaching 99%. This non‐destructive approach addresses critical challenges in the mango export industry, offering early detection of internal disorders and minimizing postharvest losses. Postharvest ; Artificial intelligence ; Preprocessor ; Linear discriminant analysis ; Computer science ; Support vector machine ; Pattern recognition (psychology) ; Quality assurance ; Feature extraction ; Principal component analysis ; Local binary patterns ; Quadratic classifier ; Mathematics ; Machine learning ; Engineering ; Horticulture ; Biology Spectroscopy and Chemometric Analyses ; Date Palm Research Studies ; Postharvest Quality and Shelf Life Management
Authors: Xiaoyang Luo | Rong Guo | Qiwen Zhang | Xingchang Tang
Source: Symmetry (article, 2024-02-05)
Field: Materials Science — Materials Chemistry

Prediction of Mechanical Properties of Cold-Rolled Steel Based on Improved Graph Attention Network The prediction of mechanical properties of cold-rolled steel is very important for the quality control, process optimization, and cost control of cold-rolled steel, but it is still a challenging task to predict accurately. For the existing graph structure of graph attention networks, it is difficult to effectively establish the complex coupling relationship and nonlinear causal relationship between variables. At the same time, it is considered that the process of cold-rolled steel has typical full-flow process characteristics and the graph attention network makes it difficult to extract the path information between the central node and its higher-order neighborhood. The neural Granger causality algorithm is used to extract the latent relationship between variables, and the basic graph structure of mechanical property prediction data is constructed. Secondly, the node embedding layer is added before the graph attention network, which leverages the symmetry nature of Node2vec method by incorporating both breadth-first and depth-first exploration strategies. This ensures a balanced exploration of diverse paths in the graph, capturing not only local structures but also higher-order relationships. The combined graph attention networks are then able to effectively capture the symmetry path information between nodes and dependencies between variables. The accuracy and superiority of this method are verified by experiments in real cold-rolled steel production cases. Graph ; Computer science ; Algorithm ; Data mining ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Yue Sun | Nikhita Sai Nayani | Yixiang Xu | Zhanfeng Xu | Jun Yang | Yiming Feng
Source: ACS Food Science & Technology (article, 2024-06-03)
Field: Chemistry — Analytical Chemistry

Rapid and Nondestructive Determination of Oil Content and Distribution of Potato Chips Using Hyperspectral Imaging and Chemometrics Conventional techniques used to measure oil content in the food are laborious, rely on chemical agents, and have a negative environmental impact. In this study, near-infrared hyperspectral imaging was used as a rapid and nondestructive tool to determine the oil content and its distribution in commercial flat-cooked and batch-cooked potato chips. By evaluating various algorithmic models, such as partial least-squares regression (PLSR), ridge regression, random forest, gradient boosting, and support vector regression, in combination with preprocessing methods like multiplicative scattering correction, standard normal variable (SNV) transform, Savitzky–Golay filtering, normalization, and baseline correction, the most effective preprocessing method and model combination was determined to be SNV-PLSR. Moreover, by employing the optimized PLSR model, a highly accurate oil content prediction model was developed, achieving a coefficient of determination (R2) of 0.95. To identify the wavelengths that contributed most significantly to the model’s predictive power, variable importance in projection (VIP) analysis was utilized. A dimensionally reduced PLSR model using only 68 selected wavelengths was developed based on the VIP analysis. This simplified model maintained similar performance to that of the full-spectrum model while using a smaller data set. The model was also used to apply the hyperspectral images of potato chips at the pixel level to visualize the oil distribution in potato chips with the intent to provide a real-time approach to quality control for the potato chip industry. Chemometrics ; Hyperspectral imaging ; Chemical imaging ; Chromatography ; Chemistry ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: D. Dardas
Source: Materials (review, 2024-08-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Survey of Applicable Methods for Determining Viscoelastic Effects in Ferroelectric and Antiferroelectric Chiral Liquid Crystals Viscosity, elasticity, and viscoelastic properties are one of the most fundamental properties of liquid crystalline materials; the main problem in determining these properties is the multitude of physical parameters needed to determine the values of elasticity and viscosity constants. In this paper, a number of different measurement methods for the complete characterization of viscoelastic properties for smectic liquid crystalline materials and their mixtures are analyzed, both theoretically and experimentally. The way in which viscoelastic material constants are determined depends mainly on the application/purpose of the materials under study. The subject of this work was to review the methods used to determine viscoelastic effects in ferroelectric and antiferroelectric chiral liquid crystals, their mixtures, composite materials, and even in dielectric systems, which would bear the hallmark of a universal method allowing the application of sufficiently low electric fields. In the case of chiral liquid crystals with ferroelectric and antiferroelectric phases and their subphases, the following assumption applies: fulfilment of Hooke's law (in the case of elastic coefficients) and preservation of laminar flow (in the case of viscosity coefficients). Antiferroelectricity ; Ferroelectricity ; Viscoelasticity ; Liquid crystal ; Materials science ; Composite material ; Dielectric ; Optoelectronics Liquid Crystal Research Advancements ; Surfactants and Colloidal Systems ; Optical Polarization and Ellipsometry
Authors: Yiwei Zhang | Chengcheng Guo | Yahui Huang | Ruizhi Zhang | Jian Zhang | Guoqiang Luo | Qiang Shen
Source: Materials (article, 2024-01-28)
Field: Materials Science — Materials Chemistry

Accurate Finite Element Simulations of Dynamic Behaviour: Constitutive Models and Analysis with Deep Learning Owing to the challenge of capturing the dynamic behaviour of metal experimentally, high-precision numerical simulations have become essential for analysing dynamic characteristics. In this study, calculation accuracy was improved by analysing the impact of constitutive models using the finite element (FE) model, and the deep learning (DL) model was employed for result analysis. The results showed that FE simulations with these models effectively capture the elastic-plastic response, and the ZA model exhibits the highest accuracy, with a 26.0% accuracy improvement compared with other models at 502 m/s for Hugoniot elastic limit (HEL) stress. The different constitutive models offer diverse descriptions of stress during the elastic-plastic response because of temperature effects. Concurrently, the parameters related to the yield strength at quasi-static influence the propagation speed of elastic waves. Calculation show that the yield strength at quasi-static of 6061 Al adheres to y = ax + b for HEL stress. The R-squared (R<sup>2</sup>) and mean absolute error (MAE) values of the DL model for HEL stress predictions are 0.998 and 0.0062, respectively. This research provides a reference for selecting constitutive models for simulation under the same conditions. Constitutive equation ; Finite element method ; Stress (linguistics) ; Yield (engineering) ; Materials science ; Structural engineering ; Mechanics ; Statistical physics ; Engineering ; Physics ; Composite material High-Velocity Impact and Material Behavior ; Fatigue and fracture mechanics ; Non-Destructive Testing Techniques
Authors: Mohammad Rusydi Fatahillah | Philippe M. Heynderickx | Kevin M. Van Geem | Dirk Poelman | Geraldine J. Heynderickx
Source: Industrial & Engineering Chemistry Research (article, 2024-12-05)
Field: Materials Science — Materials Chemistry

Reactive Flow CFD Simulation for VOC Abatement: Model Validation Using Experimental Data for Propane Total Oxidation A two-dimensional computational fluid dynamics (CFD) model for an annular fixed-bed chemical reactor, incorporating a radial bed porosity correlation, has been developed. The model was first validated against experimental data for the total oxidation of propane on a CuO-CeO2/γ-Al2O3 catalyst using Langmuir–Hinshelwood kinetics, yielding a mean absolute relative error of 13%. The model accurately reflects the effects of the increased space time and oxygen inlet partial pressure on propane conversion across various temperatures. Following validation, the CFD model was employed to investigate propane total oxidation on a Co/ZSM-5 catalyst using Mars–van Krevelen kinetics, derived from different experimental setups, with the results qualitatively matching experimental trends under both dry and humid conditions. Finally, the model was used to compare the performance of the two catalysts under identical conditions, revealing that CuO-CeO2/γ-Al2O3 outperforms Co/ZSM-5, with the performance difference narrowing at higher temperatures. Propane ; Computational fluid dynamics ; Flow (mathematics) ; Environmental science ; Chemistry ; Process engineering ; Computer science ; Thermodynamics ; Mechanics ; Organic chemistry ; Engineering ; Physics Catalytic Processes in Materials Science ; Industrial Gas Emission Control ; Odor and Emission Control Technologies
Authors: Yongfeng Tian | Lu Zhao | Xiaofeng Shen | Shan‐Zhai Shang | Yonghua Pan | Gaofeng Dong | Wangtu Huo | Dong‐Lai Zhu | Xianghu Tang
Source: Frontiers in Chemistry (article, 2024-03-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Self-assembled core-shell nanoparticles with embedded internal standards for SERS quantitative detection and identification of nicotine released from snus products Surface enhanced Raman spectroscopy (SERS) is a unique analytical technique with excellent performance in terms of sensitivity, non-destructive detection and resolution. However, due to the randomness and poor repeatability of hot spot distribution, SERS quantitative analysis is still challenging. Meanwhile, snus is a type of tobacco product that can release nicotine and other components in the mouth without burning, and the rapid detection technique based on SERS can reliably evaluate the amount of nicotine released from snus, which is of great significance for understanding its characteristics and regulating its components. Herein, the strategy was proposed to solve the feasibility of SERS quantitative detection based on self-assembled core-shell nanoparticles with embedded internal standards (EIS) due to EIS signal can effectively correct SERS signal fluctuations caused by different aggregation states and measurement conditions, thus allowing reliable quantitative SERS analysis of targets with different surface affinity. By means of process control, after the Au nanoparticles (Au NPs) were modified with 4-Mercaptobenzonitrile (4-MBN) as internal standard molecules, Ag shell with a certain thickness was grown on the surface of the AuNP@4-MBN, and then the Au@4-MBN@Ag NPs were used to regulate and control the assembly of liquid-liquid interface. The high-density nano-arrays assembled at the liquid-liquid interface ensure high reproducibility as SERS substrates, and which could be used for SERS detection of nicotine released from snus products. In addition, time-mapping research shows that this method can also be used to dynamically monitor the release of nicotine. Moreover, such destruction-free evaluation of the release of nicotine from snus products opens up new perspectives for further research about the impact of nicotinoids-related health programs. Materials science ; Detection limit ; Nanoparticle ; Drug detection ; Snus ; Raman spectroscopy ; Nanotechnology ; Surface-enhanced Raman spectroscopy ; Repeatability ; Chemistry ; Chromatography ; Raman scattering ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Electrochemical Analysis and Applications
Authors: N. Kvashin | N. Anento | G. Bonny | A. Serra | L. Malerba
Source: Journal of Nuclear Materials (article, 2024-10-09)
Field: Materials Science — Materials Chemistry

On the capabilities of k-ART over MD for the study of the kinetics of small point defect clusters in α-Fe Molecular Dynamics simulations, while contributing to the understanding of the mechanisms of diffusion and interactions of point defects and their clusters, are inherently limited in their temporal scope (few nanoseconds). This constraint becomes particularly evident when studying the dynamics of vacancies at low temperatures, where their jump frequency is exceedingly low, posing challenges for accurate reproduction. Additionally, the size of the simulation box imposes constraints, influencing the representation of the system and potentially affecting the accuracy of results. A relatively new kinetic activation-relaxation technique (k-ART) efficiently resolves the limitations of MD simulations, such as computation time and system dimensionality, without the need for a priori knowledge of the simulated system. This technique enables simulations lasting up to several seconds and encompassing systems with higher dimensions. In this paper we check the validity of k-ART to reproduce accurately the migration mechanisms and energies of point defects and small clusters, previously obtained by MD and validated experimentally. We point out the advantages and difficulties of using AKMC with k-ART. Kinetics ; Point (geometry) ; Materials science ; Chemistry ; Physics ; Mathematics ; Geometry Fusion materials and technologies ; Advanced Materials Characterization Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: Sina Tajmiri | Waseem Haider | Ishraq Shabib
Source: Corrosion and Materials Degradation (article, 2024-08-29)
Field: Materials Science — Metals and Alloys

Effect of Heating Rate on Microstructure and Corrosion Resistance of Quenched and Tempered 8620 Low Carbon Alloy Steel In the process of heat treatment of 8620 low alloy steel, the heating rate is a critical parameter that must be carefully controlled to achieve the desired combination of microstructural features, mechanical properties, and corrosion resistance while simultaneously ensuring process efficiency and cost-effectiveness. This study investigates, for the first time, the microstructural evolution and electrochemical properties of 8620 steel under identical quenching and tempering heat treatment routes with slow-rate (SR) and fast-rate (FR) heating rates. Microstructural analysis revealed martensitic phases for SR, while FR exhibited a dual-phase microstructure containing ferrite. Upon tempering, for both samples, the martensite transformed into tempered martensite, with tempered (Temp) FR exhibiting around 50% smaller ferrite grains. Mechanical testing indicated that SR had 17% higher hardness than FR, although hardness decreased after tempering by 22% (SR) and 17% (FR). All electrochemical tests indicated that the as-quenched SR exhibited significantly superior corrosion resistance than FR. For instance, the polarization resistance of SR was 440 Ω higher than that of the FR samples. Tempering resulted in a considerable decrease in corrosion resistance for Temp SR, whereas Temp FR improved. Electrochemical characterization revealed Temp FR displayed close-to-ideal capacitive behavior and low double-layer capacitance, indicating enhanced overall corrosion resistance. Microstructure ; Materials science ; Alloy ; Corrosion ; Metallurgy ; Carbon fibers ; Carbon steel ; Composite material Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; Welding Techniques and Residual Stresses
Authors: Antoine Castagnède | Laura Filion | Frank Smallenburg
Source: The Journal of Chemical Physics (article, 2024-07-12)
Field: Materials Science — Materials Chemistry

Fast event-driven simulations for soft spheres: from dynamics to Laves phase nucleation Conventional molecular dynamics (MD) simulations struggle when simulating particles with steeply varying interaction potentials due to the need to use a very short time step. Here, we demonstrate that an event-driven Monte Carlo (EDMC) approach was first introduced by Peters and de With [Phys. Rev. E 85, 026703 (2012)] and represents an excellent substitute for MD in the canonical ensemble. In addition to correctly reproducing the static thermodynamic properties of the system, the EDMC method closely mimics the dynamics of systems of particles interacting via the steeply repulsive Weeks-Chandler-Andersen (WCA) potential. In comparison to time-driven MD simulations, EDMC runs faster by over an order of magnitude at sufficiently low temperatures. Moreover, the lack of a finite time step in EDMC circumvents the need to trade accuracy against the simulation speed associated with the choice of time step in MD. We showcase the usefulness of this model to explore the phase behavior of the WCA model at extremely low temperatures and to demonstrate that spontaneous nucleation and growth of the Laves phases are possible at temperatures significantly lower than previously reported. Nucleation ; Molecular dynamics ; Statistical physics ; Monte Carlo method ; Event (particle physics) ; Phase (matter) ; Laves phase ; Hard spheres ; Physics ; Canonical ensemble ; Kinetic Monte Carlo ; SPHERES ; Materials science ; Thermodynamics ; Mathematics ; Quantum mechanics ; Statistics Material Dynamics and Properties ; Theoretical and Computational Physics ; Phase Equilibria and Thermodynamics
Authors: Dian Yu | Shaozhen Lou | Xiangnian Ou | Ping Yu | Huigao Duan | Yueqiang Hu
Source: Scientific Reports (article, 2024-10-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Polarization Independent Dynamic Beam Steering based on Liquid Crystal Integrated Metasurface Digital Micromirror Devices, extensively employed in projection displays offer rapid, polarization-independent beam steering. However, they are constrained by microelectromechanical system limitations, resulting in reduced resolution, limited beam steering angle and poor stability, which hinder further performance optimization. Liquid Crystal on Silicon technology, employing liquid crystal (LC) and silicon chip technology, with properties of high resolution, high contrast and good stability. Nevertheless, its polarization-dependent issues lead to complex system and low efficiency in device applications. This paper introduces a hybrid integration of metallic metasurface with nematic LC, facilitating a polarization-independent beam steering device capable of large-angle deflections. Employing principles of geometrical phase and plasmonic resonances, the metallic metasurface, coupled with an electronically controlled LC, allows for dynamic adjustment, achieving a maximum deflection of ± 27.1°. Additionally, the integration of an LC-infused dielectric grating for dynamic phase modulation and the metasurface for polarization conversion ensures uniform modulation effects across all polarizations within the device. We verify the device's large-angle beam deflection capability and polarization insensitivity effect in simulations and propose an optimization scheme to cope with the low efficiency of individual diffraction stages. Beam steering ; Polarization (electrochemistry) ; Beam (structure) ; Optics ; Liquid crystal ; Computer science ; Materials science ; Physics ; Optoelectronics ; Chemistry Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis