jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Botao Rui
Source: Applied and Computational Engineering (article, 2024-05-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electrical characteristics and reliability of gallium oxide devices study via TCAD simulation As the Ga2O3 MOSFET is difficult to prepare and less studied, the use of computers to simulate the device is a great help in research. This paper introduces how to use Silvaco TCAD to simulate Ga2O3 MOSFET, and explores its role in the study of the electrical characteristics and reliability of Ga2O3 MOSFET. It is found that the field plate, medium and fin shape of the device can affect the breakdown voltage and on-resistance respectively, and the reliability of the self-heating effect and switching loss is studied. These experiments prove that TCAD has strong customizability, can accurately simulate the performance of the component, has a guiding role in the experiment and can improve the experiment efficiency. This facilitates further optimization of the electrical characteristics and reliability of Ga2O3 MOSFETs through modification of the device structure, resulting in lower power consumption, heat generation and better durability of electrical devices equipped with Ga2O3 MOSFETs. Reliability (semiconductor) ; MOSFET ; Materials science ; Power MOSFET ; Technology CAD ; Power semiconductor device ; Durability ; Reliability engineering ; Breakdown voltage ; Electronic engineering ; Optoelectronics ; Voltage ; Engineering physics ; Power (physics) ; Electrical engineering ; Engineering ; Transistor ; Engineering drawing ; Physics Ga2O3 and related materials ; GaN-based semiconductor devices and materials ; ZnO doping and properties
Authors: Abir Kebir | Hossein Emadi | Dorcas Eyinla | Humza Bin Navaid
Source: Journal of energy technologies and policy (article, 2024-10-01)
Field: Chemistry — Spectroscopy

Methane Detection using Optical Gas Imaging: Passive Infrared Technology Methane (CH 4 ) is the dominant constituent of natural gas and plays a significant role in climate change as a powerful greenhouse gas. CH 4 lingers 30 times more than CO 2 in the atmosphere and has a greater potency in trapping heat. Human activities such as Oil and Gas production, underground mining, and landfill activities contribute to 25% to 33% of global warming worldwide. Traditional methods often lack real-time and accurate monitoring capabilities. To reduce the effect of methane emissions on global warming, several research works have demonstrated the efficiency of Passive Infrared Optical Gas Imaging in methane detection and quantification, which is widely used in Leak Detection and Repair programs (LDAR) as a non-destructive and non-invasive method. The combination of Machine Learning procedures such as Faster Region Convolutional Neural Network (Faster R-CNN) along with thermal infrared cameras was proven to be effective for methane detection and quantification. However, challenges remain in accurately determining emission rates. Recent studies have also revealed that methane emissions estimates provided by agencies like the Environmental Protection Agency (EPA) are often significantly underestimated. Recently, several detection projects demonstrated that the estimates the Environment Protection Agency (EPA) provided are underestimated. This paper explores the integration of machine learning models with optical gas imaging, examining methodologies, results, and limitations in methane detection across the petroleum industry, underground mining, and landfill sectors. Future research directions include improving detection algorithms to enhance accuracy under varying environmental conditions, developing lower-cost sensor technologies for widespread deployment, and addressing regulatory challenges to ensure consistent and reliable methane reporting practices. This addition outlines promising future research areas and emphasizes key technical and regulatory challenges that must be addressed to advance methane monitoring capabilities. Keywords: Greenhouse gas emissions, methane detection and quantification, Optical Gas Imaging, Infrared Technology, Faster R-CNN. DOI : 10.7176/JETP/14-3-05 Publication date : October 30th 2024 Infrared ; Methane ; Materials science ; Optics ; Optoelectronics ; Remote sensing ; Physics ; Chemistry ; Geology Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Advanced Chemical Sensor Technologies
Authors: Fayzulla Nurkulov | J. Ziyamukhamedov | Lutfillo Yuldoshaliyevich BAKIROV | Erkin Rakhmatov | U. Shoazimova
Source: E3S Web of Conferences (article, 2024-01-01)
Field: Materials Science — General Materials Science

Study of modified thermoplastic elastomers based on polyethylene chlorinated with thiokol rubbers In this paper has been examined the mechanical properties of thermoelastoplastic polymer materials derived from polyethylene chlorinated with secondary polyethylene. In order to ensure the good performance of the thermoelastoplasts obtained in the external environment, attempts were made to increase their resistance to external influences by modifying them with 1% mass of polysulfide rubbers, and resistance to deformation was determined when studying the properties of the polymer material under UV light for 10 hours. Thermoplastic elastomer ; Elastomer ; Materials science ; Composite material ; Polymer science ; Polyethylene ; Thermoplastic ; Polymer ; Copolymer Material Properties and Applications ; Environmental and Industrial Safety ; Polymer Science and PVC
Authors: Anita Caracciolo | Tommaso Ferri | G. Borghi | Marco Carminati | Nicoletta Protti | S. Altieri | C. Fiorini
Source: Radiotherapy and Oncology (article, 2024-05-01)
Field: Materials Science — Materials Chemistry

1763: A compact gamma-ray detector coupled with a pinhole collimator for real-time dose monitoring in BNCT Collimator ; Pinhole (optics) ; Detector ; Gamma ray ; Pinhole camera ; Optics ; Nuclear medicine ; Physics ; Medical physics ; Radiochemistry ; Materials science ; Nuclear physics ; Medicine ; Chemistry Boron and Carbon Nanomaterials Research ; Boron Compounds in Chemistry ; Machine Learning in Materials Science
Authors: Yuxiao Mei | Ziyang Guo | Yue Sun | Zhichao Liu | Haitao Wang
Source: Matter (article, 2024-08-01)
Field: Chemistry — Spectroscopy

Transmembrane anion transport mediated by the well-designed electron-rich resorcin[4]arenes Transmembrane protein ; Chemistry ; Ion ; Electron transport chain ; Ion transporter ; Biophysics ; Chemical engineering ; Combinatorial chemistry ; Organic chemistry ; Biochemistry ; Engineering ; Receptor ; Biology Molecular Sensors and Ion Detection ; Supramolecular Chemistry and Complexes ; DNA and Nucleic Acid Chemistry
Authors: O. M. Klyuchko | Iryna Morozova | Michail Klyuchko | Nataliia Burceva
Source: Electronics and Control Systems (article, 2024-03-29)
Field: Materials Science — Materials Chemistry

Modeling of Ferric Nanoparticles Fe3+ Interactions with thin Films of Organic Substances The results of solving the problem of modeling the interaction of iron Fe3+ nanoparticles with thin bilayer membranes were represented. The purpose of the work was to create a physical model of the behavior and interaction of Fe3+ iron nanoparticles, which are located near thin films of organic substances. For this, physical model systems with thin films - BPM (bilayer phospholipid membranes) and BLM (bilayer lipid membranes) were considered and analyzed, and iron nanoparticles were introduced into these systems. To fulfill the given task, the data search methods ("data mining") obtained by the gas-liquid chromatography were used, as well as ones obtained in experiments with registration of transmembrane electric currents through artificial two-layer BPM and BLM membranes, and partially ones from the experiments on natural membranes with technique of voltage clamp. Since the applied FeCl3 undergoes hydrolysis, the formed hydroxyl groups interact with BLM membranes, also participating in the sum of interaction effects. Thus, it was concluded about the important role of hydroxyl groups in the mechanism of anionic selectivity of BLM, which is a confirmation of previously obtained research data. A number of effects with participation of ionophores, which increase the ion permeability of artificial BLM membranes, were also considered; they can bind metal ions due to ion-dipole interaction. The described system with a membrane and iron (III) ions located near it can be used to study a number of phenomena on both artificial and natural membranes, for example, when iron nanoparticles were absorbed by human body from the environment. For all considered cases, known physical and physico-chemical regularities and the corresponding developed mathematical apparatus were verified. Namely, analytical expressions based on electrostatic interactions of electric charges associated with Fe3+ ions and localized on membrane surfaces were valid for describing the considered effects. The considered model membranes are on the border between living and non-living nature. At the same time, these systems are simple systems to which the laws of physics and physical chemistry are applicable. And, in some cases, when developing some technical hybrid systems, they can play the role of biosensors. Due to their simplicity, such models can be reproduced quite easily in the conditions of experiments, substances testing with analytical purpose in extreme situations. Therefore, such simple, reliable, tested methods can be used in wartime situations with limited laboratory resources. Nanoparticle ; Ferric ; Chemical engineering ; Thin film ; Chemistry ; Nanotechnology ; Materials science ; Inorganic chemistry ; Engineering Material Properties and Failure Mechanisms ; Characterization and Applications of Magnetic Nanoparticles
Authors: Yangyang Zheng | Huayi Wei | Shuhao Cao | Ruchi Guo
Source: arXiv (Cornell University) (preprint, 2024-11-08)
Field: Materials Science — Materials Chemistry

$γ$-deepDSM for interface reconstruction: operator learning and a Learning-Automated FEM package In this work, we propose an Operator Learning (OpL) method for solving boundary value inverse problems in partial differential equations (PDEs), focusing on recovering diffusion coefficients from boundary data. Inspired by the classical Direct Sampling Method (DSM), our operator learner, named $γ$-deepDSM, has two key components: (1) a data-feature generation process that applies a learnable fractional Laplace-Beltrami operator to the boundary data, and (2) a convolutional neural network that operates on these data features to produce reconstructions. To facilitate this workflow, leveraging FEALPy \cite{wei2024fealpy}, a cross-platform Computed-Aided-Engineering engine, our another contribution is to develop a set of finite element method (FEM) modules fully integrated with PyTorch, called Learning-Automated FEM (LA-FEM). The new LA-FEM modules in FEALPy conveniently allows efficient parallel GPU computing, batched computation of PDEs, and auto-differentiation, without the need for additional loops, data format conversions, or device-to-device transfers. With LA-FEM, the PDE solvers with learnable parameters can be directly integrated into neural network models. Interface (matter) ; Finite element method ; Operator (biology) ; Computer science ; Artificial intelligence ; Engineering drawing ; Computational science ; Engineering ; Structural engineering ; Parallel computing ; Chemistry Machine Learning in Materials Science
Authors: Maria M. Savanović | Milinko Perić | Andrijana Bilić | Stevan Armaković | Sanja J. Armaković
Source:  (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Use of Dataexplorer Online for Data Processing in the Determination of Active Components of Drug This paper aimed to demonstrate the use of the newly developed Data-Explorer Online software for statistical analysis to evaluate the performance parameters of two different analytical techniques.Namely, HPLC-DAD (High Performance Liquid Chromatography Diode Array Detection) and spectrophotometry were used to measure the concentration of metoprolol in commercial drug tablets.The tools used for methods performance evaluation in the analysis of metoprolol were DataExplrorer Online.The online software tool DataExplorer (available at https://dataexplorer.online) was developed to support the analysis of large amounts of data and the validation of analytical methods.In addition, DataExplorer Online enables the assessment of normal distribution, which is unavoidable before ANOVA analysis.Depending on the number of examined factors, it offers One, Two, Three, and Multivariate ANOVA analysis.Therefore, DataExprorer Online is suitable for specific needs that may arise in different experimental studies.Statistical analysis in DataExprorer Online, which included checking for normal distribution and applying One way ANOVA, indicated that HPLC-DAD and spectrophotometric methods are suitable for routine analysis of metoprolol in drug tablets.Overall, results showed that HPLC-DAD and spectrophotometry contribute to precise and accurate analytical processes. Computer science ; Drug ; Information retrieval ; Pharmacology ; Medicine Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Analytical Methods in Pharmaceuticals
Authors: Ostap Kutyansky | Volodymyr Pasternak
Source: Measuring Equipment and Metrology (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

MEAT QUALITY RESEARCH USING CLASSIFICATION ALGORITHMS The food industry is going through constant improvements and is subject to analyzing consumer needs, product quality research is essential to striking this balance. In this regard, meat quality, the most essential food category, should be studied with unbiased methods that give precise and correct results. Classification algorithms are considered one of the main components of developing an objective and reliable method of meat quality assessment. Such algorithms imply meat analysis and classification automation with many parameters in mind, which eventually gives a chance to make quick and correct decisions concerning its quality. Quality (philosophy) ; Computer science ; Product (mathematics) ; Automation ; Algorithm ; Data mining ; Machine learning ; Mathematics ; Engineering Spectroscopy and Chemometric Analyses ; Advanced Scientific Research Methods ; Advanced Chemical Sensor Technologies
Authors: Peter A. Crozier
Source: Microscopy and Microanalysis (article, 2024-07-01)
Field: Materials Science — Surfaces, Coatings and Films

Exploring Spatiotemporal Limits for Atomic Resolution In Situ Electron Microscopy In situ ; Electron microscope ; Resolution (logic) ; Materials science ; Electron ; Microscopy ; Nanotechnology ; Physics ; Optics ; Computer science ; Artificial intelligence ; Nuclear physics Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Force Microscopy Techniques and Applications
Authors: Yidong Tian | Cheng Zhang
Source:  (preprint, 2024-01-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ferrite Core Loss Prediction with Direct Data Interpolation Method This paper proposes a direct data interpolation method for predicting ferrite core loss in power electronics. The core idea is to accurately construct the B-H loop shape given arbitrary Hfield waveforms. Unlike conventional loss models like the Steinmetz equation and its variations, or recent neural network methods like MagNet that leverage periodic B-H waveforms and frequencies, this method employs a data segmentation and reconstruction process to fully exploit the distinctive features of the B-H loop under varying conditions. The source data is from Princeton University’s MagNet open-source datasets. Interpolation (computer graphics) ; Ferrite core ; Leverage (statistics) ; Waveform ; Computer science ; Magnet ; Exploit ; Artificial neural network ; Segmentation ; Core (optical fiber) ; Algorithm ; Electronic engineering ; Artificial intelligence ; Engineering ; Electrical engineering ; Telecommunications ; Image (mathematics) Magnetic Properties and Applications ; Power Transformer Diagnostics and Insulation ; Silicon Carbide Semiconductor Technologies
Authors: Alexander H. Berger | Laurin Lux | Suprosanna Shit | Ivan Ezhov | Georgios Kaissis | Martin J. Menten | Daniel Rueckert | Johannes C. Paetzold
Source: arXiv (Cornell University) (preprint, 2024-03-11)
Field: Materials Science — Materials Chemistry

Cross-domain and Cross-dimension Learning for Image-to-Graph Transformers Direct image-to-graph transformation is a challenging task that involves solving object detection and relationship prediction in a single model. Due to this task's complexity, large training datasets are rare in many domains, making the training of deep-learning methods challenging. This data sparsity necessitates transfer learning strategies akin to the state-of-the-art in general computer vision. In this work, we introduce a set of methods enabling cross-domain and cross-dimension learning for image-to-graph transformers. We propose (1) a regularized edge sampling loss to effectively learn object relations in multiple domains with different numbers of edges, (2) a domain adaptation framework for image-to-graph transformers aligning image- and graph-level features from different domains, and (3) a projection function that allows using 2D data for training 3D transformers. We demonstrate our method's utility in cross-domain and cross-dimension experiments, where we utilize labeled data from 2D road networks for simultaneous learning in vastly different target domains. Our method consistently outperforms standard transfer learning and self-supervised pretraining on challenging benchmarks, such as retinal or whole-brain vessel graph extraction. Transformer ; Graph ; Computer science ; Artificial intelligence ; Theoretical computer science ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Brain Tumor Detection and Classification
Authors: A. J. Romero | Joe Villa‐Medina | Jorge Ropero | Néstor Cubillán
Source: The Canadian Journal of Chemical Engineering (article, 2024-10-02)
Field: Chemistry — Analytical Chemistry

Moisture content determination along production line of ibuprofen soft gelatin capsule manufacturing by near infrared spectroscopy and ensemble deep neural networks Abstract In the manufacturing process of ibuprofen soft gelatin capsules, controlling moisture content at each production line stage, including in their components—gelatin, fill content, and shell—is vital to ensure quality and stability. This study developed and assessed an analytical method for rapid and non‐destructive moisture determination using near‐infrared spectroscopy (NIRS) coupled with deep neural networks (DNN) on all stages of the ibuprofen production line. The NIRS‐DNN classifier models were able to distinguish between the three components, achieving accuracy scores of up to 99%. The DNN models for moisture quantification also demonstrated high accuracy, with R 2 values exceeding 0.997 across all production stages and prediction errors to those of previously reported models. A significant advantage of the NIRS‐DNN approach was its ability to maintain accuracy over a wide moisture concentration range, from 7% to 48%. The ensemble model, NIRS‐EDNN, seamlessly integrated classification and quantification, revealing its potential for real‐time process control in soft gel manufacturing. The comprehensive sampling approach ensured a diverse representation of moisture content, thereby enhancing the understanding of its impact on the final product stability, demonstrating that this methodology is potentially applicable to any soft gelatin capsule tracking worldwide. Gelatin ; Water content ; Ibuprofen ; Moisture ; Spectroscopy ; Infrared spectroscopy ; Line (geometry) ; Production line ; Materials science ; Chemistry ; Composite material ; Organic chemistry ; Medicine ; Engineering ; Mathematics ; Mechanical engineering ; Pharmacology ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Meat and Animal Product Quality
Authors: Brandon Willnecker | Mervlyn Moodley
Source: arXiv (Cornell University) (preprint, 2024-11-27)
Field: Materials Science — Materials Chemistry

An Adaptive Genetic Algorithm for determining optimal structures for atomic clusters The implementation of adaptive genetic algorithms (AGA) for optimization problems has proven to be superior than many other methods due to its nature of producing more robust and high quality solutions. Considering the complexity involved in many-body simulations, a novel AGA is proposed for applications to such systems and is specifically used to determine the lowest energy structures of various sized atomic clusters. For demonstrative purposes, we apply our method to various sized Lennard-Jones clusters and show that our results are more accurate than those found in the literature employing different methods. Genetic algorithm ; Algorithm ; Computer science ; Mathematical optimization ; Mathematics ; Machine learning X-ray Diffraction in Crystallography
Authors: Hanyi Liu | Jun Zhang
Source: Physics of Fluids (article, 2024-04-01)
Field: Materials Science — Surfaces, Coatings and Films

Bounce and contact mode regimes for drop impact on smooth surfaces: The influence of gas kinetics and electrostatics When liquid drops impact on solid surfaces, an air layer forms in between the drop and the surface, acting as a cushion to mitigate the impact. In this work, we focus on delineating the bounce and contact mode regimes of impacting drops on smooth surfaces, specifically discerning whether drops rebound from the air layer or make contact with the solid surfaces, and pinpointing the precise contact modes between the drop and solid surfaces by resolving the gas film evolution and rupture. Our simulation model incorporates gas kinetics and electrostatics effects, both of which have been validated by experiments documented in the literature or theoretical models regarding thin film instabilities. We undertake a comprehensive review and categorization of the contact modes and elucidate how they change under different conditions of impact velocities, ambient pressures, and electric field intensities. We also provide some perspectives on the regime map for the lubricated surfaces, which contains an unresolved issue that the critical Weber number for bouncing-wetting transition is significantly reduced compared to the solid smooth surfaces like mica. These insights have noteworthy practical implications offering guidance for a wide range of scenarios, from normal-pressure environments to low-pressure conditions at high altitudes, encompassing high electric field conditions such as nanogenerators as well as low electric field conditions resembling glass surfaces with static electricity. Electric field ; Drop (telecommunication) ; Mechanics ; Wetting ; Electrostatics ; Solid surface ; Physics ; Contact angle ; Nanotechnology ; Chemical physics ; Thermodynamics ; Mechanical engineering ; Materials science ; Engineering Surface Modification and Superhydrophobicity ; Fluid Dynamics and Heat Transfer ; Advanced Sensor and Energy Harvesting Materials