jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sanjay Hazarika | Harish Reddy C. | Karthick Mani Bharathi B | Bharath Kumar B M | Basanagouda Jekinakatti | Pawan Saini | S.P. Sharan | Kishan Kumar R
Source: Journal of Experimental Agriculture International (article, 2024-08-12)
Field: Materials Science — Biomaterials

Silk Revolution: Redefining Sericulture through Application of Precise Nanotechnologies Human civilization has been intertwined with sericulture—the art and science of producing silk for centuries. The application of nanotechnology to sericulture techniques has created new opportunities to improve the quantity, quality and use of silk in recent decades. This abstract examines the numerous applications of nanotechnology in sericulture, with particular attention on how it affects the quality of silk fibers, the procedures involved in producing silk and the creation of cutting-edge materials derived from silk. By providing comprehensive control over the properties of silk at the molecular and atomic levels, nanotechnology, defined as the manipulation of matter at the nano scale, has completely transformed the silk industry. Improving the health and growth of silkworms is one of the main application areas. Silver nanoparticles, for example have been used to fight illnesses and strengthen silkworm’s immune systems, boosting the insect’s resilience against illness and optimizing silk yield. Furthermore, silk fiber quality has increased largely in substantial measure by nanotechnology. The mechanical, thermal and optical characteristics of silk fibers have been altered by researchers by adding nanoparticles during the spinning process. For example, adding nanoparticles to silk can increase its tensile strength, making it more durable and appropriate for a variety of industrial uses. Similarly, silk can be given antibacterial qualities by adding nanoscale compounds, increasing its shelf life and possible uses in medical textiles. Furthermore, the production of sophisticated silk-based materials with distinctive functions has been made easier by nanotechnology. Nanocomposites, which consist of nanoparticles embedded in silk matrices, have unique characteristics like improved electrical conductivity, controlled drug release, and greater biocompatibility. Silk-based materials have emerged as a result of these developments, finding use both in wearable electronics and biomedical devices. SILK ; Sericulture ; Nanotechnology ; Bombyx mori ; Textile ; Materials science ; Applications of nanotechnology ; Composite material ; Chemistry Silk-based biomaterials and applications ; Silkworms and Sericulture Research ; Insect and Arachnid Ecology and Behavior
Authors: I.A. Khan | F. Guastavino | L. Della Giovanna | E. Torello
Source: Energies (article, 2024-09-12)
Field: Materials Science — Materials Chemistry

Partial Discharge Inception Voltage Monitoring of Enameled Wires under Thermal Stress over Time Electrical insulation is a critical component in electrical machines. The performance of the insulation system can be adversely affected by operating conditions that induce aging. Assessing the impact of environmental stresses is essential for predicting the failure of electrical insulation. Predicting maintenance to prevent service interruptions caused by insulation breakdown is a key objective. For type I insulating systems used in low-voltage and low-power rotating electrical machines, it has been demonstrated that partial discharges (PDs) are a contributing factor to electrical insulation breakdown. In fact, these insulating systems are not able to withstand the action of PD activity. The inception and evolution of PD activity is an indication of the poor conditions of the electrical insulating system, and this activity can be produced by the electronic power supply. The progressive reduction in partial discharge inception voltage (PDIV) is attributed to the deterioration of insulation properties induced by operational stresses. This study aims to evaluate and compare the effects of thermal stress on various types of enameled wires by collecting the PDIV values over time. In this paper, the authors analyze some particular effects of thermal stress as an aging factor. During the tests, an electrical stress was applied, which acted as a conditioning stress rather than one capable of producing degradation phenomena, as it was not high enough to initiate PD activity. In this research study, twisted pairs prepared from copper wires were evaluated. These wires were coated with various types of enamel and belonged to the thermal class of 200 °C. The samples were subjected to thermal aging tests at different temperatures. An electrical conditioning stress was also applied during all the tests and pertained to the same voltage, amplitude and frequency. The PDIV value pertaining to each sample was regularly measured to monitor its evolution over time. Partial discharge ; Voltage ; Thermal ; Stress (linguistics) ; Electrical engineering ; Nuclear engineering ; Materials science ; Environmental science ; Automotive engineering ; Engineering ; Meteorology ; Physics High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Thermal Analysis in Power Transmission
Authors: Tianlei Xu | Xi Pang | Zongliang Xie | Peng Liu | Zongren Peng
Source: High Voltage (article, 2024-09-27)
Field: Materials Science — Materials Chemistry

Research on interface charge behaviour and electrical threshold in layered epoxy/paper composites: From experiment to simulation Abstract Layered epoxy/paper composites that exhibit excellent insulating characteristics under elevated electrification and temperature conditions are essential components for power system insulation. Yet, inevitable charge accumulation occurs at hetero interfaces between layers due to interfacial polarisation and interface barrier effect, bringing about local electric field distortion and potential risk of partial discharge. A fundamental challenge is either to obtain accurate interface charge behaviours of in‐service multi‐layer insulating composites, or to construct verified simulating models for replacing experiments. Herein, a modified bipolar charge transport model to simulate interface charge behaviours in layered composites is proposed. With model parameters directly originated from equivalent experiments (e.g. conduction current measurement, thermally stimulated depolarisation current testing, and ultraviolet‐visible spectroscopy) of epoxy/paper composites, the simulated temperature‐dependent interface charge characteristics match well with pulsed electro‐acoustic results. Furthermore, electrical thresholds can also be accurately calculated using such models (maximum deviation of 8.44% from experimental results), providing references for optimised insulation structural design. Epoxy ; Composite material ; Materials science ; Interface (matter) ; Charge (physics) ; Physics High voltage insulation and dielectric phenomena ; Advanced Sensor and Energy Harvesting Materials ; Dielectric materials and actuators
Authors: Naifan Xue | Bei Li | Yuan Wang | Ning Yang | Ruicheng Yang | F. H. Zhang | Qingmin Li
Source: Polymers (article, 2024-02-09)
Field: Materials Science — Materials Chemistry

Spatial-Temporal Kinetic Behaviors of Micron-Nano Dust Adsorption along Epoxy Resin Insulator Surfaces and the Physical Mechanism of Induced Surface Flashover The advanced Gas Insulated Switchgear/Gas Insulated Lines (GIS/GIL) transmission equipment serves as an essential physical infrastructure for establishing a new energy power system. An analysis spanning nearly a decade on faults arising from extra/ultra-high voltage discharges reveals that over 60% of such faults are attributed to the discharge of metal particles and dust. While existing technical means, such as ultra-high frequency and ultrasonic sensing, exhibit effectiveness in online monitoring of particles larger than sub-millimeter dimensions, the inherent randomness and elusive nature of micron-nano dust pose challenges for effective characterization through current technology. This elusive micron-nano dust, likely concealed as a latent threat, necessitates special attention due to its potential as a "safety killer". To address the challenges associated with detecting micron-nano dust and comprehending its intricate mechanisms, this paper introduces a micron-nano dust adsorption experimental platform tailored for observation and practical application in GIS/GIL operations. The findings highlight that micron-nano dust's adsorption state in the electric field predominantly involves agglomerative adsorption along the insulator surface and diffusive adsorption along the direction of the ground electrode. The pivotal factors influencing dust movement include the micron-nano dust's initial position, mass, material composition, and applied voltage. Further elucidation emphasizes the potential of micron-nano dust as a concealed safety hazard. The study reveals specific physical phenomena during the adsorption process. Agglomerative adsorption results in micron-nano dust speckles forming on the epoxy resin insulator's surface. With increasing voltage, these speckles undergo an "explosion", forming an annular dust halo with deepening contours. This phenomenon, distinct from the initial adsorption, is considered a contributing factor to flashovers along the insulator's surface. The physical mechanism behind flashovers triggered by micron-nano dust is uncovered, highlighting the formation of a localized short circuit area and intense electric field distortion constituted by dust speckles. These findings establish a theoretical foundation and technical support for enhancing the safe operational performance of AC and DC transmission pipelines' insulation. Materials science ; Adsorption ; Nano- ; Nanotechnology ; Insulator (electricity) ; Epoxy ; Optoelectronics ; Composite material ; Chemistry High voltage insulation and dielectric phenomena ; Aerosol Filtration and Electrostatic Precipitation ; Lightning and Electromagnetic Phenomena
Authors: Valeriy Telichenko | Vladimir Rimshin | Ekaterina Ketsko
Source: E3S Web of Conferences (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Promising directions for the artificial intelligence development in the housing and utilities sector Modern technologies require the improvement of automation and labour savings. Therefore, successful construction companies are everywhere introducing artificial intelligence into their business, which actually optimizes any processes without human intervention. At the same time, the final product quality increases. Investing in high technology may be a daunting task for many businesses, but in the long run, reducing waste and material consumption will have a positive impact on profitability. Investments in the technologies development in the housing and communal services are increasing around the world, and households are increasingly switching to smart metering devices. Artificial intelligence technologies allow organizations in housing and communal services to reduce the operators cost and automate the most frequent communications with residents. The innovative technologies introduction for the development of housing and communal services is aimed primarily at optimizing the services range in accordance with the population needs and rationalizing their use in the context of sustainable territories development. The authors analyse the prospects for the housing and communal complex development in the digitalization period, explores the possibilities and directions for the introduction and application of artificial intelligence in the field of housing and communal services in Russia and abroad. Business ; Engineering ; Architectural engineering ; Artificial intelligence ; Computer science Structural mechanics and materials ; Environmental and Industrial Safety ; Construction Management and Sustainability
Authors: J.F. Zhang | Yao Lu | Yi Guo | Chengkai Wu | Hengjun Liu | Zhuoyi Yu | Jiayi Zhou
Source: Electronics (article, 2024-08-17)
Field: Chemistry — Analytical Chemistry

Lightweight and Optimized Multi-Label Fruit Image Classification: A Combined Approach of Knowledge Distillation and Image Enhancement In our research, we aimed to address the shortcomings of traditional fruit image classification models, which struggle with inconsistent lighting, complex backgrounds, and high computational demands. To overcome these challenges, we developed a novel multi-label classification method incorporating advanced image preprocessing techniques, such as Contrast Limited Adaptive Histogram Equalization and the Gray World algorithm, which enhance image quality and color balance. Utilizing lightweight encoder–decoder architectures, specifically MobileNet, DenseNet, and EfficientNet, optimized with an Asymmetric Binary Cross-Entropy Loss function, we improved model performance in handling diverse sample difficulties. Furthermore, Multi-Label Knowledge Distillation (MLKD) was implemented to transfer knowledge from large, complex teacher models to smaller, efficient student models, thereby reducing computational complexity without compromising accuracy. Experimental results on the DeepFruit dataset, which includes 21,122 images of 20 fruit categories, demonstrated that our method achieved a peak mean Average Precision (mAP) of 90.2% using EfficientNet-B3, with a computational cost of 7.9 GFLOPs. Ablation studies confirmed that the integration of image preprocessing, optimized loss functions, and knowledge distillation significantly enhances performance compared to the baseline models. This innovative method offers a practical solution for real-time fruit classification on resource-constrained devices, thereby supporting advancements in smart agriculture and the food industry. Image (mathematics) ; Distillation ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Computer vision ; Process engineering ; Chromatography ; Engineering ; Chemistry Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Shafaq Khan | Munir Majdalawieh | Boubakeur Boufama | Yajan Sharma | Ashwitha Basani
Source: Frontiers in Agronomy (article, 2024-10-22)
Field: Chemistry — Analytical Chemistry

Unlocking the potential of simulated hyperspectral imaging in agro environmental analysis: a comprehensive study of algorithmic approaches This study focuses on identifying and evaluating the severity of powdery mildew disease in tomato plants. The uniqueness of this work lies in combining the imaging and advanced deep learning methods to develop a technique that transforms Red Green Blue (RGB) images into Simulated Hyperspectral Images (SHSI) to perform spectral and spatial analysis for precise detection and assessment of powdery mildew severity, thereby enhancing disease management. Furthermore, this research evaluates three advanced pre-trained VGG16 models, ResNet50 and EfficientNet-B7 algorithms for image preprocessing and feature extraction. Extracted features are passed to a neural network generator model to convert RGB image features into SHSIs, providing insights into the spectrum. This method enables the image analysis to perform assessments from SHSIs for health classification using Normalized Difference Vegetation Index (NDVI) values, which are meticulously compared with accurate hyperspectral data using metrics like mean absolute error (MAE) and root mean squared error (RMSE). This strategy enhances precision farming, environmental monitoring, and remote sensing accuracy. Results show that ResNet50’s architecture offers a robust framework for this study’s spectral and spatial analysis, making it a suitable choice over VGG16 and EfficientNet-B7 for transforming RGB images into SHSI. These simulated hyperspectral images offer a scalable and affordable approach for precise assessment of crop disease severity. Hyperspectral imaging ; Computer science ; Remote sensing ; Environmental science ; Artificial intelligence ; Geology Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Jindai Liu | Fengshuang Liu | Jun Fu
Source: Agriculture (article, 2024-10-31)
Field: Chemistry — Analytical Chemistry

An Attention-Based Spatial-Spectral Joint Network for Maize Hyperspectral Images Disease Detection Maize is susceptible to pest disease, and the production of maize would suffer a significant decline without precise early detection. Hyperspectral imaging is well-suited for the precise detection of diseases due to its ability to capture the internal chemical characteristics of vegetation. However, the abundance of redundant information in hyperspectral data poses challenges in extracting significant features. To overcome the above problems, in this study we proposed an attention-based spatial-spectral joint network model for hyperspectral detection of pest-infected maize. The model contains 3D and 2D convolutional layers that extract features from both spatial and spectral domains to improve the identification capability of hyperspectral images. Moreover, the model is embedded with an attention mechanism that improves feature representation by focusing on important spatial and spectral-wise information and enhances the feature extraction ability of the model. Experimental results demonstrate the effectiveness of the proposed model across different field scenarios, achieving overall accuracies (OAs) of 99.24% and 97.4% on close-up hyperspectral images and middle-shot hyperspectral images, respectively. Even under the condition of a lack of training data, the proposed model performs a superior performance relative to other models and achieves OAs of 98.29% and 92.18%. These results proved the validity of the proposed model, and it is accomplished efficiently for pest-infected maize detection. The proposed model is believed to have the potential to be applied to mobile devices such as field robots in order to monitor and detect infected maize automatically. Hyperspectral imaging ; Joint (building) ; Joint disease ; Remote sensing ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Environmental science ; Geography ; Biology ; Medicine ; Engineering ; Pathology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Mookala Premasudha | B.S. Reddy | Kwon‐Koo Cho | Ahn Hyo-Jun | Jae-Kyung Sung | N.S. Reddy
Source: Batteries (article, 2024-12-31)
Field: Materials Science — Materials Chemistry

Classification of the Crystal Structures of Orthosilicate Cathode Materials for Li-Ion Batteries by Artificial Neural Networks The crystal structures of orthosilicate cathode materials play a critical role in determining the physical and chemical properties of Li-ion batteries. Accurate predictions of these crystal structures are essential for estimating key properties of cathode materials in battery applications. In this study, we utilized crystal structure data from density functional theory (DFT) calculations, sourced from the Materials Project, to predict monoclinic and orthorhombic crystal systems in orthosilicate-based cathode-based materials with Li–Si–(Fe, Mn, Co)–O compositions. An artificial neural network (ANN) model with a 6-22-22-22-1 architecture was trained on 85% of the data and tested on the remaining 15%, achieving an impressive accuracy of 97.3%. The model demonstrated strong predictive capability, with only seven misclassifications from 267 datasets, highlighting its robustness and reliability in predicting the crystal structure of orthosilicate cathodes. To enhance interpretability and model reliability, we employed the Index of Relative Importance (IRI) to identify critical features influencing predictions. Additionally, a user-friendly graphical user interface was also developed to facilitate rapid predictions, enabling researchers to explore structural configurations efficiently and accelerating advancements in battery materials research. Orthosilicate ; Cathode ; Materials science ; Ion ; Artificial neural network ; Chemical engineering ; Nanotechnology ; Tetraethyl orthosilicate ; Computer science ; Artificial intelligence ; Chemistry ; Electrical engineering ; Engineering ; Organic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electric Power Systems and Control
Authors: Xingyue Shi | Linming Zhou | Yuhui Huang | Yongjun Wu | Zijian Hong
Source: arXiv (Cornell University) (preprint, 2024-09-15)
Field: Materials Science — Materials Chemistry

Machine learning assisted screening of metal binary alloys for anode materials In the dynamic and rapidly advancing battery field, alloy anode materials are a focal point due to their superior electrochemical performance. Traditional screening methods are inefficient and time-consuming. Our research introduces a machine learning-assisted strategy to expedite the discovery and optimization of these materials. We compiled a vast dataset from the MP and AFLOW databases, encompassing tens of thousands of alloy compositions and properties. Utilizing a CGCNN, we accurately predicted the potential and specific capacity of alloy anodes, validated against experimental data. This approach identified approximately 120 low potential and high specific capacity alloy anodes suitable for various battery systems including Li, Na, K, Zn, Mg, Ca, and Al-based. Our method not only streamlines the screening of battery anode materials but also propels the advancement of battery material research and innovation in energy storage technology. Anode ; Binary number ; Materials science ; Metal ; Metallurgy ; Chemistry ; Mathematics ; Electrode ; Arithmetic Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Recycling and Waste Management Techniques
Authors: Xiaofeng Xiang | Dylan Soh | Scott T. Dunham
Source: The Journal of Physical Chemistry C (article, 2024-05-16)
Field: Materials Science — Materials Chemistry

Exploration of Deep Learning Models for Accelerated Defect Property Predictions and Device Design of Cubic Semiconductor Crystals In this work, we present an exploration of deep learning models for predicting defect properties in cubic phase semiconductors. The nature of impurity energy levels strongly influences the performance of semiconductors in a wide range of applications, such as solar cells, field effect transistors, and qubits for quantum computing. In this work, we employ two types of deep learning models, a crystal defect graph neural network and a chemical environment-encoded artificial neural network, to predict defect properties. The models are trained on a data set of charge-dependent defect formation energies obtained from density functional theory computations and descriptors based on elemental properties, defect local environment, and relevant semiconductor properties. We assess the models’ performance and showcase their capability in optimizing semiconductor devices, particularly when used in tandem with compositionally constrained thermodynamics and technology computer-aided design models. Semiconductor ; Property (philosophy) ; Materials science ; Engineering physics ; Computer science ; Optoelectronics ; Engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Electronic and Structural Properties of Oxides