jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Markus Mattenklott | Dennis Kaiser | Bianca Gasse
Source: BIO Web of Conferences (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Identification of inorganic fibres in workplace air by SEM-EDS When assessing fibre dusts in the air at workplaces, it is crucial to identify and classify the type of fibre in order to distinguish carcinogenic fibres from others from an occupational health and safety perspective.Up to now, this has been done using an error-prone method of product fibre identification.The aim of the project is to create a database for determining the type of fibre independently of reference materials from the sampled work areas and, based on this, to define identification criteria.Using available reference materials from the synthetic inorganic fibres used in work areas ( synthetic mineral fibres, high temperature wool, micro glass fibres, textile glass fibres, etc.), the range of element composition of the fibre types is determined by means of EDS analysis.In addition, systematic investigations are used to determine the quality of these analyses of fibres with diameters in the micrometre range, identify systematic problems and derive possible differentiation criteria.This database is supplemented by information from publications and manufacturer data, which have been structured for comparison with EDS analyses A scheme was established that allows fibres in airborne dusts to be classified largely independently of reference materials.It is important not only to look at individual particles separately, but also to classify the fibres found on the basis of previous grouping.The presented solution to the problem of identifying fibres shows the limits of manual classification.A further step will be the establishment of self-learning neural networks, which have already achieved successful results in initial tests in this area and provide constructive support for identification. Identification (biology) ; Materials science ; Composite material ; Polymer science ; Business ; Biology ; Botany Textile materials and evaluations ; Material Properties and Processing ; Recycling and Waste Management Techniques
Authors: Shuo Peng | Haoquan Wang | Peiyu Yang | Minjie Yuan | Chuanmin Wu | Zihao Peng | Yunlan Tan
Source:  (article, 2024-04-26)
Field: Chemistry — Analytical Chemistry

Research on the Identification and Prediction of Honey Pomelo Diseases and Pests Based on Deep Learning Early control of honey pomelo pests and diseases stands as a pivotal focus within contemporary honey pomelo farming practices. The ability to promptly identify and predict such pests and diseases empowers farmers to preemptively mitigate their impact, thereby averting potential economic losses. Traditional methods of pest and disease identification encompass manual observation, meteorological analysis, and chemical intervention. However, these approaches necessitate seasoned agricultural expertise and are susceptible to subjectivity and inherent limitations. Furthermore, they may engender issues of environmental contamination and exacerbate drug resistance. Consequently, this paper posits an automated method for pest and disease prediction and identification, grounded in deep learning principles. This method comprises two primary modules: pest species identification and pest occurrence pattern prediction, achieving a pest species identification rate of 96% and over 80% accuracy in pest prediction. Additionally, an automated identification system for honey pomelo pests and diseases has been developed, consolidating the aforementioned modules to enable real-time and automatic identification and prediction of honey pomelo pests and diseases for early warning purposes. Identification (biology) ; Artificial intelligence ; Computer science ; Machine learning ; Deep learning ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Songbo Wang | Siyuan Yang | Tim Stratford | Jun Su | Jiayi He | Jiali Yu
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — General Materials Science

Low-Code Automl Solutions for Predicting Glass Transition Temperatures of Structural Strengthening Adhesives Code (set theory) ; Adhesive ; Glass transition ; Transition (genetics) ; Forensic engineering ; Materials science ; Econometrics ; Environmental science ; Statistical physics ; Computer science ; Engineering ; Nanotechnology ; Chemistry ; Composite material ; Mathematics ; Physics ; Programming language Material Properties and Applications ; Structural Analysis of Composite Materials ; Polymer Science and Applications
Authors: Goranov Vitaly | Siutsou Ivan | Makhaniok Alexander | Lavrova Olga | Barballinardo Marianna | Dediu Valentin | Košťal. Yaroslav
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2024-10-10)
Field: Materials Science — Materials Chemistry

Novel route to assessment of iron nanoparticles toxicity. In this study, a new framework for the evaluation of the toxicity of superparamagnetic iron oxide nanoparticles (SPIONs) is proposed. To achieve this, in vitro and in vivo experimental data are analysed by consequent mathematical modelling and using parameters that were determined by the ML/AI approach. The framework takes into account the inherent complexity of nanoparticle toxicity, which differs from conventional chemical compounds considering time-dependent processes such as cellular uptake, intracellular transport and degradation of SPIONs. In vitro experiments with CaCo2 cells evaluate cytotoxicity, reactive oxygen species (ROS) production and expression of antioxidant enzymes over 96 hours. C. elegans is used for in vivo studies to evaluate interspecies extrapolation. Convolutional neural networks are trained to segment and classify cells based on nanoparticle uptake, allowing quantitative assessment of morphological changes. The results show a strong correlation between increased nanoparticle uptake, morphological alterations, increased intracellular iron levels and oxidative stress. The mathematical models are validated using experimental data and an IC50 value for in vitro and in vivo toxicity is calculated. Finally, the study discusses the potential for interspecies extrapolation and highlights the potential of the framework to reduce animal testing in future nanotoxicity assessments. Nanotoxicology ; In vivo ; Nanoparticle ; Intracellular ; Iron oxide nanoparticles ; In vitro toxicology ; In vitro Nanoparticles: synthesis and applications ; Genetics, Aging, and Longevity in Model Organisms ; Molecular Communication and Nanonetworks
Authors: Y Wang | Linlin Zhong
Source: Plasma Sources Science and Technology (article, 2024-10-01)
Field: Materials Science — Surfaces, Coatings and Films

DeepCSNet: a deep learning method for predicting electron-impact doubly differential ionization cross sections Abstract Electron-impact ionization cross sections of atoms and molecules are essential for plasma modeling. However, experimentally determining the absolute cross sections is not easy, and ab initio calculations become computationally prohibitive as molecular complexity increases. Existing artificial intelligence -based prediction methods suffer from limited data availability and poor generalization. To address these issues, we propose Deep Cross Section Network (DeepCSNet), a deep learning approach designed to predict electron-impact ionization cross sections using limited training data. We present two configurations of DeepCSNet: one tailored for specific molecules and another for various molecules. Both configurations can typically achieve a relative L 2 error less than 5%. The present numerical results, focusing on electron-impact doubly differential ionization cross sections, demonstrate DeepCSNet’s generalization ability, predicting cross sections across a wide range of energies and incident angles. Additionally, DeepCSNet shows promising results in predicting cross sections for molecules not included in the training set, even large molecules with more than 10 constituent atoms, highlighting its potential for practical applications. Electron ionization ; Ionization ; Differential (mechanical device) ; Atomic physics ; Electron ; Chemistry ; Physics ; Ion ; Nuclear physics ; Thermodynamics Electron and X-Ray Spectroscopy Techniques ; Ion-surface interactions and analysis ; Radiation Effects in Electronics
Authors: Seung-Chul Yoon | Zirak Khan
Source:  (article, 2024-06-06)
Field: Chemistry — Analytical Chemistry

Enhancing food safety: real-time detection of foreign materials using high-speed NIR hyperspectral imaging This study focuses on the development of real-time inline sensing technology using high-speed hyperspectral imaging (HSI) and high-performance Deep Learning (DL) for the detection of Foreign Materials (FMs) on chicken breast meat. HSI and DL are useful tools for assessing agricultural and food products' safety and quality features. However, most HSI-based DL models for food quality and safety assessment lack real-time sensing capabilities critical for industrial deployment, due to the high computational demands of both HSI and DL. Therefore, we propose near-infrared hyperspectral imaging (NIR-HSI) coupled with a DL model based on a semi-supervised generative adversarial network, suitable for high-speed food production applications such as real-time inline sensing for detecting FMs during poultry processing. A line-scan NIR-HSI camera (1000-1700 nm) is used for data acquisition. For real-time imaging and DL inferencing, the software system is implemented in C++. This technology will enable the rapid and accurate detection of FMs in hyperspectral images, suggesting applications in other food products. Hyperspectral imaging ; Computer science ; Remote sensing ; Computer vision ; Environmental science ; Artificial intelligence ; Geology Spectroscopy and Chemometric Analyses
Authors: Weihong Li | Zhongyi Fan | Lenan Zhang
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Materials Science — Metals and Alloys

Unveiling the Gas Evolution Dynamics in Electrochemical Processes via Acoustic Emission Analysis Gas bubble evolution plays a pivotal role in water electrolysis. Electrochemical gas evolution reactions operating at high current densities represent a promising future trend for improving efficiency. However, the lack of reliable bubble detection methods in electrochemical gas evolution systems, as well as the limited understanding of bubble behavior at different current levels, hinder a comprehensive understanding of the actual bubble dynamics under various voltage and current conditions. To address this challenge, a novel methodology that utilizes acoustic emission signals is proposed to evaluate bubble behavior in the electrolyzer. The dynamic variations in the amplitude and frequency of the acoustic emissions can be correlated with the different stages of bubble evolution. By combining this acoustic technique with high-speed optical imaging, the relationship between the acoustic signal variations and the characteristics of bubble release under different voltage and current levels can be unvealed. Furthermore, clustering algorithms from machine learning are employed to determine bubble acoustic characteristics under various conditions based on the acoustic variations, which allows for redefining the polarization curve. These findings significantly enhance the understanding of the actual bubble behavior under different voltage and current intensities, going beyond the traditional pool boiling curve-based approach. The results demonstrate that acoustic emission can serve as an effective and non-intrusive tool for monitoring bubble formation in gas evolution reactions, making it particularly valuable for applications in non-transparent electrolyzer cells. This facile operating approach offers a new perspective for studying and optimizing electrochemical gas evolution processes. Acoustic emission ; Electrochemistry ; Dynamics (music) ; Materials science ; Chemical physics ; Acoustics ; Physics ; Chemistry ; Electrode ; Physical chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Geoscience and Mining Technology ; Advancements in Solid Oxide Fuel Cells
Authors: K E Vipin | Prahallad Padhan
Source: arXiv (Cornell University) (preprint, 2024-07-27)
Field: Materials Science — Materials Chemistry

Advancements in Tuning Thermoelectric Properties: Insights from Hybrid Functional Studies, Strain Engineering, and Machine Learning Models Thermoelectric properties in topological insulator Bi2Se3 are explored with multifaceted strategies, i.e., hybrid functional with strain and artificial intelligence methodology. The assessment with the experimental band gap values recognises the limitations of conventional functional and the effectiveness of screened hybrid functionals. A thorough investigation into the impact of biaxial and uniaxial strain on thermoelectric parameters uncovers distinctive behaviours in n-type and p-type Bi2Se3 , providing insights into optimal strain conditions for improved performance. Furthermore, the studies on the role of topologically non-trivial surface states (TNSS) in thermoelectric properties reveal that TNSS significantly dominate electronic transport. Dual scattering time approximation elucidates the segregation of thermoelectric transport contributions from bulk and surface states, highlighting the importance of controlling the relaxation time ratio for enhanced thermoelectric performance. Additionally, the prediction of thermoelectric properties using Random Forest and Neural Networks models showcase impressive agreement with density functional theory predictions across varying temperatures, offering a powerful tool for understanding complex temperature-dependent trends in thermoelectric properties. In summary, this interdisciplinary study presents a unique approach to advancing the understanding and optimization of thermoelectric properties in Bi2Se3 . It provides a comprehensive framework for tailoring material behaviour for diverse thermoelectric applications. Thermoelectric effect ; Materials science ; Strain (injury) ; Strain engineering ; Mechanical engineering ; Computer science ; Nanotechnology ; Artificial intelligence ; Engineering ; Physics ; Optoelectronics ; Thermodynamics ; Biology Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Zsolt Divéki | Balázs Nagyillés | Gergely Nagy | Bálint Kiss | E. Cormier | Péter Földi | Katalin Varjú | Subhendu Kahaly | Mousumi Upadhyay Kahaly
Source:  (preprint, 2024-07-22)
Field: Chemistry — Spectroscopy

MIR laser CEP estimation using machine learning concepts in bulk high harmonic generation Monitoring the carrier-envelope phase (CEP) is of paramount importance for experiments involving few cycle intense laser fields. Common measurement techniques include f-2f interferometry or stereo-ATI setups. These approaches are adequate, but are challenging to implement on demand, at different locations as additional metrology tools, in intense few cycle laser-matter interaction experiments, such as those prevalent in sophisticated user beamlines. In addition there are inherent difficulties for CEP measured at non-conventional laser wavelengths (like e.g. mid infrared) and measurements above 10 kHz laser repetition rates, on single shot basis. Here we demonstrate both by simulations and by experiments a machine learning (ML) driven method for CEP estimation in the mid infrared, which is readily generalizable for any laser wavelength and possibly up to MHz repetition rates. The concept relies on the observation of the spectrum of high harmonic generation (HHG) in bulk material and the use of ML techniques to estimate the CEP of the laser. Once the ML model is trained, the method provides a way for cheap and compact real-time CEP tagging. This technique can complement the otherwise sophisticated monitoring of CEP, and is able to capture the complex correlation between the CEP and the observable HHG spectra. Estimation ; Laser ; Harmonic ; High harmonic generation ; Computer science ; Artificial intelligence ; Physics ; Optics ; Acoustics ; Engineering ; Systems engineering Spectroscopy and Laser Applications ; Laser Design and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Sojiro Yamatoko | Masashi Kishimoto | Yachao Guo | Hiroshi Iwai
Source: Doryoku, Enerugi Gijutsu Shinpojiumu koen ronbunshu/Doryoku, enerugi gijutsu no saizensen koen ronbunshu (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Generation of 3D microstructure of SOFC anodes using Generative Adversarial Network: To enhance the performance of solid oxide fuel cells (SOFCs), optimizing the porous microstructure of SOFC anodes is crucial, with a focus on improving gas transport and promoting electrochemical reactions occurring in the triple phase boundaries (TPBs). Previous approaches involved prototyping anodes with different porous microstructures and conducting observations using advanced three-dimensional imaging devices, which is time-consuming and labor-intensive. Therefore, there is growing interest in data-driven optimization methods utilizing machine learning to generate simulated microstructures on the computer and evaluate their performance. One of the challenges in this method is generating highly accurate simulated microstructures with arbitrary features. Many researchers have focused on generating simulated microstructures that resemble real structures, making it difficult to generate various kinds of microstructures separately. In this study, we employed generative adversarial network (GAN) to generate highly accurate simulated microstructures. Additionally, by incorporating a supplementary learning mechanism to reduce loss values for volume fraction and specific surface area, we aimed to control these pivotal parameters of SOFC anode microstructures. This research is expected to facilitate the identification of optimal microstructures with greater ease and efficiency. Microstructure ; Generative grammar ; Generative adversarial network ; Adversarial system ; Materials science ; Computer science ; Metallurgy ; Artificial intelligence ; Deep learning Machine Learning in Materials Science
Authors: Farshid Golnary | Mohsen Asghari
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Efficient Feature Extraction for Morphologically Complex Self-Assembled Porous Microstructures Using Computational Homology and Unsupervised Machine Learning Persistent homology ; Computer science ; Artificial intelligence ; Homology (biology) ; Unsupervised learning ; Microstructure ; Pattern recognition (psychology) ; Feature (linguistics) ; Materials science ; Biology ; Algorithm ; Metallurgy ; Biochemistry ; Amino acid Nanoporous metals and alloys ; Machine Learning in Materials Science ; Pickering emulsions and particle stabilization
Authors: Zsolt Divéki | Balázs Nagyillés | Gergely Nagy | Bálint Kiss | E. Cormier | Péter Földi | Katalin Varjú | Subhendu Kahaly | Mousumi Upadhyay Kahaly
Source:  (preprint, 2024-07-19)
Field: Chemistry — Spectroscopy

MIR laser CEP estimation using machine learning concepts in bulk high harmonic generation Monitoring the carrier-envelope phase (CEP) is of paramount importance for experiments involving few cycle intense laser fields. Common measurement techniques include f-2f interferometry or stereo-ATI setups. These approaches are adequate, but are challenging to implement on demand, at different locations as additional metrology tools, in intense few cycle laser-matter interaction experiments, such as those prevalent in sophisticated user beamlines. In addition there are inherent difficulties for CEP measured at non-conventional laser wavelengths (like e.g. mid infrared) and measurements above 10 kHz laser repetition rates, on single shot basis. Here we demonstrate both by simulations and by experiments a machine learning (ML) driven method for CEP estimation in the mid infrared, which is readily generalizable for any laser wavelength and possibly up to MHz repetition rates. The concept relies on the observation of the spectrum of high harmonic generation (HHG) in bulk material and the use of ML techniques to estimate the CEP of the laser. Once the ML model is trained, the method provides a way for cheap and compact real-time CEP tagging. This technique can complement the otherwise sophisticated monitoring of CEP, and is able to capture the complex correlation between the CEP and the observable HHG spectra. Estimation ; Laser ; High harmonic generation ; Harmonic ; Computer science ; Artificial intelligence ; Physics ; Optics ; Acoustics ; Engineering ; Systems engineering Spectroscopy and Laser Applications ; Laser Design and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Edward M. Kober | Jacob P. Tavenner | Colin M. Adams | Nithin Mathew
Source: arXiv (Cornell University) (preprint, 2024-02-06)
Field: Materials Science — Materials Chemistry

Strain Functionals: A Complete and Symmetry-adapted Set of Descriptors to Characterize Atomistic Configurations Extracting relevant information from atomistic simulations relies on a complete and accurate characterization of atomistic configurations. We present a framework for characterizing atomistic configurations in terms of a complete and symmetry-adapted basis, referred to as strain functionals. In this approach a Gaussian kernel is used to map discrete atomic quantities, such as number density, velocities, and forces, to continuous fields. The local atomic configurations are then characterized using nth order central moments of the local number density. The initial Cartesian moments are recast unitarily into a Solid Harmonic Polynomial basis using SO(3) decompositions. Rotationally invariant metrics, referred to as Strain Functional Descriptors (SFDs), are constructed from the terms in the SO(3) decomposition using Clebsch-Gordan coupling. A key distinction compared to related methods is that a minimal but complete set of descriptors is identified. These descriptors characterize the local geometries numerically in terms of shape, size, and orientation descriptors that recognize n-fold symmetry axes and net shapes such as trigonal, cubic, hexagonal, etc. They can easily distinguish between most different crystal symmetries using n = 4, identify defects (such as dislocations and stacking faults), measure local deformation, and can be used in conjunction with machine learning techniques for in situ analysis of finite temperature atomistic simulation data and quantification of defect dynamics. Strain (injury) ; Set (abstract data type) ; Symmetry (geometry) ; Statistical physics ; Mathematics ; Computer science ; Theoretical physics ; Physics ; Geometry ; Biology Machine Learning in Materials Science
Authors: Minsu Chong | Tae Gyu Rhee | Yeong Gwang Khim | Min‐Hyoung Jung | Young‐Min Kim | Hu Young Jeong | Heung‐Sik Kim | Young Jun Chang | Hyuk Jin Kim
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine-Learning-Empowered Identification of Initial Growth Modes for 2d Transition Metal Dichalcogenide Thin Films Identification (biology) ; Transition metal ; Thin film ; Materials science ; Transition (genetics) ; Metallurgy ; Nanotechnology ; Chemistry Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; 2D Materials and Applications