jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yoshimichi Ohki | Naoshi Hirai
Source: IEEE Transactions on Dielectrics and Electrical Insulation (article, 2024-04-09)
Field: Materials Science — Materials Chemistry

Tools for Condition Monitoring of Polymeric Electrical Insulating Materials Polymeric electrical insulating materials are essential for most electrical apparatuses, from small electronic devices to large power equipment such as switchgear and electric cables. Therefore, their condition must be monitored adequately, and their degradation must be detected well before it becomes fatal. This report describes the condition monitoring tools, which are versatile and powerful but not so commonly used to analyze ordinary insulating polymers. Their features are also explained, showing concrete examples. The tools cover electrical or electromagnetic methods with frequencies from zero to the THz range and light absorption and emission. Tools to examine the surface hardness of polymers are also included. The points to be aware of when estimating the remaining life using the Arrhenius plot analysis are noted. Materials science ; Composite material ; Electronic engineering ; Engineering High voltage insulation and dielectric phenomena ; Sensor Technology and Measurement Systems
Authors: Nusrat Hossain | Muhammad Sabbir Hossain | Rakibul Islam | Raiyan Rahman
Source:  (article, 2024-10-17)
Field: Chemistry — Analytical Chemistry

Rice Grain Classification and Automated Quality Control Using Deep Learning Approaches In Bangladesh, rice is a staple food and a significant contributor to the country's economy as well.An accurate rice grain classification and identification are crucial for improving agricultural productivity, ensuring food security and facilitating trade.The quality inspection of rice grain holds significance for both local and export markets ensuring the maintenance of high standards.This study aims to revolutionize rice grain evaluation by employing Convolutional Neural Networks (CNNs) to enhance the classification and identification of various rice classes.The system commences with five pre-trained models ( MobileNet, VGG16, InceptionResNet, In-ceptionV3, Xception) .We obtained accuracy of 95% , 96% , 94% , 94% and 96% using VGG16 , MobileNet , InceptionV3 , InceptionResNet and Xception respectively.Additionally, a majority voting method is employed by the system to assemble the predictions derived from several models.We also used prominent image processing methods to process our images.The primary objective is to present an image processing-based approach that effectively categorizes diverse rice grain classes based on various parameters.To compile our extensive dataset we captured images of various rice grains under diverse lighting conditions.Our research unveils an accuracy of 98% . Quality (philosophy) ; Computer science ; Artificial intelligence ; Control (management) ; Grain quality ; Machine learning ; Agricultural engineering ; Engineering ; Agronomy Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: K. Grzybowska | Ż. Wojnarowska | A. Grzybowski | Marian Paluch
Source: International Journal of Molecular Sciences (article, 2024-03-11)
Field: Materials Science — Materials Chemistry

Fragility and Tendency to Crystallization for Structurally Related Compounds The present study was designed to investigate the physical stability of three organic materials with similar chemical structures. The examined compounds revealed completely different crystallization tendencies in their supercooled liquid states and were classified into three distinct classes based on their tendency to crystallize. (S)-4-Benzyl-2-oxazolidinone easily crystallizes during cooling from the melt; (S)-4-Benzylthiazolidine-2-thione does not crystallize during cooling from the melt, but crystallizes easily during subsequent reheating above <i>T</i><sub>g</sub>; and (S)-4-Benzyloxazolidine-2-thione does not crystallize either during cooling from the melt or during reheating. Such different tendencies to crystallize are observed despite the very similar chemical structures of the compounds, which only differ in oxide or sulfur atoms in one of their rings. We also studied the isothermal crystallization kinetics of the materials that were shown to transform into a crystalline state. Molecular dynamics and thermal properties were thoroughly investigated using broadband dielectric spectroscopy, as well as conventional and temperature-modulated differential scanning calorimetry in the wide temperature range. It was found that all three glass formers have the same dynamic fragility (<i>m</i> = 93), calculated directly from dielectric structural relaxation times. This result verifies that dynamic fragility is not related to the tendency to crystallize. In addition, thermodynamic fragility predictions were also made using calorimetric data. It was found that the thermodynamic fragility evaluated based on the width of the glass transition, observed in the temperature dependence of heat capacity, correlates best with the tendency to crystallize. Fragility ; Differential scanning calorimetry ; Supercooling ; Crystallization ; Glass transition ; Thermodynamics ; Relaxation (psychology) ; Isothermal process ; Materials science ; Atmospheric temperature range ; Heat capacity ; Crystallography ; Dielectric ; Chemistry ; Physical chemistry ; Organic chemistry ; Polymer Material Dynamics and Properties ; Thermal and Kinetic Analysis ; Chemical Thermodynamics and Molecular Structure
Authors: Melese Legesse Mitku | Abera Dessie Dagnaw | Derso Teju Geremew | Yeniewa Kerie Anagaw | Minichil Chanie Worku | Liknaw Workie Limenh | Yabibal Berie Tadesse | Asrat Elias Ergena
Source: SAGE Open Medicine (article, 2024-01-01)
Field: Chemistry — Organic Chemistry

In silico prediction of some pharmacokinetic, safety, biological activity and molecular docking studies of 1-piperazine indole hybrid with nicotinic amide and nicotinic acid and their analogues Overall, our findings offer valuable insights into the potential efficacy and safety of these compounds. It appears that almost all of the compounds have successfully passed the pharmacokinetic evaluations and integration of nicotinic acid into indole appears to be more beneficial than nicotinic amide regarding certain biological activities. Nicotinic agonist ; In silico ; Docking (animal) ; Pharmacology ; Chemistry ; Drug discovery ; Computational biology ; Combinatorial chemistry ; Biochemistry ; Medicine ; Biology ; Receptor Synthesis and Biological Evaluation ; Synthesis and biological activity ; Trypanosoma species research and implications
Authors: Safe ELdeen M.E. Mahmoud | Tarek M. Abdel‐Fattah | Mohamed E. Mahmoud | Eva Dı́az
Source: Nanomaterials (article, 2024-11-07)
Field: Materials Science — Materials Chemistry

Facile Doping and Functionalization of Molybdic Acid into Nanobiochar to Enhance Mercury Ion Removal from Water Systems Functionalized nanomaterials with surface-active groups have garnered significant research interest due to their wide-ranging applications, particularly in water treatment for removing various contaminants. This study focuses on developing a novel, multi-functional nanobiosorbent by synthesizing nanosized biochar from artichoke leaves (NBAL) and molybdic acid (MA). The resulting nanobiosorbent, MA@NBAL, is produced through a microwave-irradiation process, offering a promising material for enhanced environmental remediation. The characteristics of assembled MA@NBAL were evaluated from SEM-EDX, XPS, TGA, FT-IR, and zeta potential detection. The size of particles ranged from 18.7 to 23.7 nm. At the same time, the EDX analysis denoted the existence of several major elements with related percentage values of carbon (52.9%), oxygen (27.6%), molybdenum (8.8%), and nitrogen (4.5%) in the assembled MA@NBAL nanobiosorbent. The effectiveness of MA@NBAL in removing Hg(II) ions was monitored via the batch study method. The optimized maximum removal capacity of Hg(II) ions onto MA@NBAL was established at pH 6.0, 30.0 min equilibrium time, and 20 mg of nanobiosorbent, providing 1444.25 mg/g with a 10.0 mmol/L concentration of Hg(II). Kinetic studies revealed that the adsorption process followed a pseudo-second-order model, with R<sup>2</sup> values ranging from 0.993 to 0.999 for the two tested Hg(II) concentrations, indicating excellent alignment with the experimental data. This suggests that the chemisorption mechanism involves cation exchange and complex formation. Isotherm model evaluation further confirmed the adsorption mechanism, with the Freundlich model providing the best fit, yielding an R<sup>2</sup> of 0.962. This result indicates that Hg(II) adsorption onto the surface of MA@NBAL nanobiosorbent occurs on a heterogeneous surface with multilayer formation characteristics. The results of the temperature factor and computation of the thermodynamic parameters referred to endothermic behavior via a nonspontaneous process. Finally, the valid applicability of MA@NBAL nanobiosorbent in the adsorptive recovery of 2.0 and 5.0 µg/mL Hg(II) from contaminated real aquatic matrices was explored in this study, providing 91.2-98.6% removal efficiency. Surface modification ; Mercury (programming language) ; Doping ; Ion ; Chemistry ; Materials science ; Inorganic chemistry ; Nanotechnology ; Computer science ; Optoelectronics ; Organic chemistry ; Physical chemistry Covalent Organic Framework Applications ; Gas Sensing Nanomaterials and Sensors ; Adsorption and biosorption for pollutant removal
Authors: Xiao Cheng | Jenny Tempeler | Serhiy Danylyuk | Alexander Böker | Larisa Tsarkova
Source: Polymers (article, 2024-07-08)
Field: Materials Science — Materials Chemistry

Disclosing Topographical and Chemical Patterns in Confined Films of High-Molecular-Weight Block Copolymers under Controlled Solvothermal Annealing The microphase separation of high-molecular-weight block copolymers into nanostructured films is strongly dependent on the surface fields. Both, the chain mobility and the effective interaction parameters can lead to deviations from the bulk morphologies in the structures adjacent to the substrate. Resolving frustrated morphologies with domain period L<sub>0</sub> above 100 nm is an experimental challenge. Here, solvothermal annealing was used to assess the contribution of elevated temperatures of the vapor T<sub>v</sub> and of the substrate T<sub>s</sub> on the evolution of the microphase-separated structures in thin films symmetric of polystyrene-b-poly(2vinylpyridine) block copolymer (PS-PVP) with L<sub>0</sub> about 120 nm. Pronounced topographic mesh-like and stripe patterns develop on a time scale of min and are attributed to the perforated lamella (PL) and up-standing lamella phases. By setting T<sub>v</sub>/T<sub>s</sub> combinations it is possible to tune the sizes of the resulting PL patterns by almost 10%. Resolving chemical periodicity using selective metallization of the structures revealed multiplication of the topographic stripes, i.e., complex segregation of the component within the topographic pattern, presumably as a result of morphological phase transition from initial non-equilibrium spherical morphology. Reported results reveal approaches to tune the topographical and chemical periodicity of microphase separation of high-molecular-weight block copolymers under strong confinement, which is essential for exploiting these structures as functional templates. Copolymer ; Materials science ; Lamella (surface anatomy) ; Annealing (glass) ; Polystyrene ; Template ; Morphology (biology) ; Nanostructure ; Chemical physics ; Nanotechnology ; Chemical engineering ; Substrate (aquarium) ; Composite material ; Polymer ; Chemistry Block Copolymer Self-Assembly ; Rheology and Fluid Dynamics Studies ; Theoretical and Computational Physics
Authors: Zhengda He | Bin Ouyang
Source: Journal of Materials Chemistry A (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Charting the electronic structure for discovering low-cost intermetallic catalysts We utilize electronic-structure-based descriptors to screen stable and active intermetallic catalysts. Several novel catalysts are proposed for HER and ORR reactions and the effectiveness of descriptors has been thoroughly investigated. Intermetallic ; Catalysis ; Electronic structure ; Materials science ; Computer science ; Metallurgy ; Chemistry ; Computational chemistry ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Chemical Physics Studies
Authors: Benedikt Alkin | Andreas Fürst | Simon Schmid | Lukas Gruber | Markus Holzleitner | J. Brandstetter
Source: arXiv (Cornell University) (preprint, 2024-02-19)
Field: Materials Science — Materials Chemistry

Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators Neural operators, serving as physics surrogate models, have recently gained increased interest. With ever increasing problem complexity, the natural question arises: what is an efficient way to scale neural operators to larger and more complex simulations - most importantly by taking into account different types of simulation datasets. This is of special interest since, akin to their numerical counterparts, different techniques are used across applications, even if the underlying dynamics of the systems are similar. Whereas the flexibility of transformers has enabled unified architectures across domains, neural operators mostly follow a problem specific design, where GNNs are commonly used for Lagrangian simulations and grid-based models predominate Eulerian simulations. We introduce Universal Physics Transformers (UPTs), an efficient and unified learning paradigm for a wide range of spatio-temporal problems. UPTs operate without grid- or particle-based latent structures, enabling flexibility and scalability across meshes and particles. UPTs efficiently propagate dynamics in the latent space, emphasized by inverse encoding and decoding techniques. Finally, UPTs allow for queries of the latent space representation at any point in space-time. We demonstrate diverse applicability and efficacy of UPTs in mesh-based fluid simulations, and steady-state Reynolds averaged Navier-Stokes simulations, and Lagrangian-based dynamics. Transformer ; Physics ; Theoretical physics ; Quantum mechanics ; Voltage Machine Learning in Materials Science
Authors: I. D. Busov | М. А. Генаев | Е. Г. Комышев | V. S. Koval | Tatyana E. Zykova | Anastasiya Y. Glagoleva | Д. А. Афонников
Source: Vavilov Journal of Genetics and Breeding (article, 2024-07-11)
Field: Chemistry — Analytical Chemistry

A pipeline for processing hyperspectral images, with a case of melanin-containing barley grains as an example Analysis of hyperspectral images is of great interest in plant studies. Nowadays, this analysis is used more and more widely, so the development of hyperspectral image processing methods is an urgent task. This paper presents a hyperspectral image processing pipeline that includes: preprocessing, basic statistical analysis, visualization of a multichannel hyperspectral image, and solving classification and clustering problems using machine learning methods. The current version of the package implements the following methods: construction of a confidence interval of an arbitrary level for the difference of sample averages; verification of the similarity of intensity distributions of spectral lines for two sets of hyperspectral images on the basis of the Mann-Whitney U-criterion and Pearson's criterion of agreement; visualization in two-dimensional space using dimensionality reduction methods PCA, ISOMAP and UMAP; classification using linear or ridge regression, random forest and catboost; clustering of samples using the EM-algorithm. The software pipeline is implemented in Python using the Pandas, NumPy, OpenCV, SciPy, Sklearn, Umap, CatBoost and Plotly libraries. The source code is available at: https://github.com/igor2704/Hyperspectral_images. The pipeline was applied to identify melanin pigment in the shell of barley grains based on hyperspectral data. Visualization based on PCA, UMAP and ISOMAP methods, as well as the use of clustering algorithms, showed that a linear separation of grain samples with and without pigmentation could be performed with high accuracy based on hyperspectral data. The analysis revealed statistically significant differences in the distribution of median intensities for samples of images of grains with and without pigmentation. Thus, it was demonstrated that hyperspectral images can be used to determine the presence or absence of melanin in barley grains with great accuracy. The flexible and convenient tool created in this work will significantly increase the efficiency of hyperspectral image analysis. Hyperspectral imaging ; Isomap ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Cluster analysis ; Preprocessor ; Dimensionality reduction ; Visualization ; Principal component analysis ; Pipeline (software) ; Python (programming language) ; Image processing ; Image (mathematics) ; Nonlinear dimensionality reduction Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Remote Sensing in Agriculture
Authors: María del Mar Juez Lorenzo | Vladislav Kolarik | Khyati Sethia | Petr Strakoš
Source: High Temperature Corrosion of Materials (article, 2024-10-26)
Field: Materials Science — Materials Chemistry

Segmentation and Metallographic Evaluation of Aluminium Slurry Coatings Using Machine Learning Techniques Abstract Analysis of scanning electron microscope (SEM) images is crucial for characterising aluminide diffusion coatings deposited via the slurry route on steels, yet challenging due to various factors like imaging artefacts, noise, and overlapping features such as resin, precipitates, cracks, and pores. This study focuses on determining the thicknesses of the coating layers Fe 2 Al 5 and, if present, FeAl, pore characteristics, and chromium precipitate fractions after the heat treatment that forms the diffusion coating. A deep learning SEM image segmentation model utilising U-Net architecture is proposed. Ground truth data were generated using the trainable Weka segmentation plugin in ImageJ, manually refined for accuracy, and supplemented with synthetic data from Blender 3D software for data augmentation of a limited number of SEM label images. The deep learning model trained on a combination of synthetic and real SEM data achieved mean dice scores of 98.7% ± 0.2 for the Fe 2 Al 5 layer, 82.6% ± 8.1 for pores, and 81.48% ± 3.6 for precipitates when evaluated on manually labelled SEM data. The deep learning procedure was applied to evaluate a series of SEM images of diffusion coatings obtained with three different slurry compositions. The evaluation revealed that using a slurry without a rheology modifier may lead to a thicker partial Fe 2 Al 5 layer that is formed by inward diffusion. The relation between the outward and inward diffusion Fe 2 Al 5 layers was not affected by the coating thickness. The thinner diffusion coating presents lower pores and chromium precipitate fractions independently of the slurry selected. Aluminium ; Slurry ; Materials science ; Metallurgy ; Artificial intelligence ; Composite material ; Computer science Metal Alloys Wear and Properties ; Metallurgy and Material Forming ; Welding Techniques and Residual Stresses
Authors: Guofeng Qian | Zhen Hu | Michael D. Todd
Source: e-Journal of Nondestructive Testing (article, 2024-06-29)
Field: Materials Science — Metals and Alloys

Diagnostics and Prognostics of Pitting Corrosion in Large Civil infrastructure Using Multi-scale Simulation and Machine Learning Corrosion, particularly pitting corrosion, represents a prominent form of deterioration in large infrastructure, characterized by its gradual and continuous capacity-reducing nature. As structures age, the need for assessing and monitoring corrosion status becomes increasingly crucial for system life-cycle management and optimal maintenance. However, the intricacies of the corrosion process pose challenges to effective diagnosis and prognosis. These challenges include the insufficient integration of mechanical stress into existing corrosion simulations, the computational intensity of most physics-based corrosion simulations hindering probabilistic studies and real-time predictions, and the difficulty in detecting pitting corrosion due to its localized nature, making it impractical to measure individual pits. This paper introduces a comprehensive prognosis and diagnosis framework applied to a miter gate case study. The framework seamlessly integrates multi-scale simulation, including mesoscale Phase-Field (PF) simulation and macroscale structural analysis, with Machine Learning (ML) methods to enable real-time corrosion diagnosis and prognosis. It facilitates efficient structural health monitoring (SHM) through a digital twin (DT) for corrosion damage. The core components of this framework encompass the development of multiscale simulation for simulating pitting corrosion in large structures, the construction of ML-based surrogate models to expedite simulations, image-based diagnosis, and the updating engine for prognosis. Corrosion ; Prognostics ; Pitting corrosion ; Probabilistic logic ; Computer science ; Scale (ratio) ; Process (computing) ; Engineering ; Materials science ; Artificial intelligence ; Metallurgy ; Data mining Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Corrosion Behavior and Inhibition
Authors: Abderezak Guemache
Source: Brazilian Journal of Animal and Environmental Research (article, 2024-01-08)
Field: Chemistry — Organic Chemistry

Removal of a local antiseptic dye on two solid supports by adsorption In this research, we studied the adsorption of an Eosin B (EB) dye is a local antiseptic, available in pharmacies in the form of an active preparation on the surface of activated carbon and natural clay. Activated carbon and natural clay were characterized using various physicochemical techniques to determine the crystalline structures of the two adsorbents by DRX, and the chemical bonds by FTIR analysis, and finally, to know the maximum absorbance λmax of the dye by UV-Visible analysis. The effects of various experimental parameters, such as the effect of adsorbent mass, the initial concentration of eosin B is 1.610-4 molL-1, the contact time is 24 hours, and the pH 7 ~ 9, were studied. The removal of the dye increases with the decrease of the initial concentration of Eosin B (EB) and the contact time of the solution. The percentage of Eosin B (EB) removal increases accordingly, which arrives at 80% for activated carbon and 92% for natural clay. Langmuir and Freundlich adsorption models were used for the adsorption equilibrium descriptions. The data was very well corrected with these models. Monolayer adsorption capacities were equal to 50 mgg-1 at pH 8.3 and 25°C. Adsorption measurements show that the adsorption process is very fast and physical in nature. Adsorption ; Freundlich equation ; Activated carbon ; Langmuir ; Eosin ; Monolayer ; Chemistry ; Fourier transform infrared spectroscopy ; Absorbance ; Antiseptic ; Eosin Y ; Nuclear chemistry ; Chemical engineering ; Chromatography ; Organic chemistry ; Staining ; Catalysis Synthesis of Tetrazole Derivatives ; Adsorption and biosorption for pollutant removal ; Polymer Synthesis and Characterization