jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Krzysztof Ciecieląg
Source: Advances in Science and Technology – Research Journal (article, 2025-10-25)
Field: Materials Science — General Materials Science

Research on the main and side surfaces after face milling of polymer composites The article presents research on glass and carbon fiber reinforced plastics.The main and side surfaces of the composites were machined using face milling.A milling cutter with polycrystalline diamond inserts was used for the research.The composite materials were tested under variable milling parameters for two types of surfaces and two polymer composites.A novelty presented in the article is the comparative study of the main and side surfaces of two composite materials using 3D topography maps and material bearing ratio for the same milling parameters.The paper also includes plots showing roughness parameters as a function of changing technological parameters for two composite machining areas.The research showed that a small feed per tooth and a high cutting speed have a positive effect on surface quality.The lowest roughness parameter value of Ra 0.72 m was achieved for the main machining of a glass fiber reinforced plastics with a feed per tooth of 0.05 mm/tooth.A high feed per tooth of 0.60 mm/tooth resulted in Ra roughness values of 2.32 m for the carbon fiber reinforced plastics.A high cutting speed of 600 m/min during the machining of the main surface of the glass fiber reinforced plastics allowed Ra=0.52 m to be achieved.In contrast, a low cutting speed resulted in a Ra roughness value of 1.85 m for the side machining of the glass fiber reinforced plastics.The paper also presents material bearing ratio and 3D topography maps, which also confirm that a low feed per tooth and high cutting speed have a positive effect on surface quality. Face (sociological concept) ; Polymer ; Composite number ; Work (physics) ; Deformation (meteorology) Material Properties and Applications ; Engineering Technology and Methodologies ; Polymer Science and Applications
Authors: Laura Slebioda | Bogna Zawieja
Source: Euphytica (article, 2025-01-15)
Field: Chemistry — Analytical Chemistry

Applying convolutional neural networks for mustard variety recognition Abstract The aim of this study was to develop and apply a Convolutional Neural Network (CNN) model to recognize and classify white mustard ( Sinapis Alba L. ) varieties, addressing the complex task of discriminating among 57 varieties. Utilizing a one-dimensional CNN model, the research focused on multivariate analysis based on a set of 15 traits. The CNN architecture included convolutional layers, batch normalization, pooling, flattening, dropout, and dense layers. The model demonstrated effectiveness in classifying varieties, achieving high accuracy and providing valuable insights into potential new varieties. Subset division, a new approach, was applied. Evaluation metrics, including accuracy, F1 score, precision, and recall, were calculated for eight subsets, confirming the model's robust performance. While this study uses mustard as an illustrative example, the method is not limited to this crop and can be extended to other agricultural crops, with potential modifications depending on the specific traits relevant to each crop. The approach contributes to agricultural advancements, offering a reliable tool for breeders to assess variety distinctness and streamline the testing process. The model’s ability to detect unknown varieties further enhances its utility in agricultural research covering a comprehensive and impactful advancement in variety classification. Biology ; Variety (cybernetics) ; Convolutional neural network ; Mustard Plant ; Computational biology ; Botany ; Artificial intelligence ; Computer science ; Brassica Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: Longgang Ma | Zhengzhong Wan | Zhencan Yang | X. Chen | Ruihua Zhang | Maoyuan Yin | Xinqing Xiao
Source: Eng—Advances in Engineering (article, 2025-07-10)
Field: Chemistry — Analytical Chemistry

Flexible Visible Spectral Sensing for Chilling Injuries in Mango Storage Mango, as an important economic crop in tropical and subtropical regions, suffers from chilling injuries caused by postharvest low-temperature storage, which seriously affect its quality and economic benefits. Traditional detection methods have limitations such as low efficiency and strong destructiveness. This study designs and implements a flexible visible light spectral sensing system based on visible light spectral sensing technology and low-cost environmentally friendly flexible circuit technology. The system is structured based on a perception-analysis-warning-processing framework, utilizing laser-induced graphene electroplated copper integrated with laser etching technology for hardware fabrication, and developing corresponding data acquisition and processing functionalities. Taking Yunnan Yumang as the research object, a three-level chilling injury label dataset was established. After Z-Score standardization processing, the prediction accuracy of the SVM (Support Vector Machine) model reached 95.5%. The system has a power consumption of 230 mW at 4.5 V power supply, a battery life of more than 130 days, stable signal transmission, and a monitoring interface integrating multiple functions, which can provide real-time warning and intervention, thus offering an efficient and intelligent solution for chilling injury monitoring in mango cold chain storage. Environmental science ; Remote sensing ; Computer science ; Horticulture ; Biology ; Geology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: Adrián Criollo | Darío Benavides | Paúl Arévalo | Luis I. Minchala | D. X. Morales
Source: Batteries (article, 2025-06-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Enhancing Grid Stability in Microgrid Systems with Vehicle-to-Grid Support and EDLC Supercapacitors Grid stability in microgrids represents a critical challenge, particularly with the increasing integration of variable renewable energy sources and the loss of systematic inertia. This study analyzes the use of vehicle-to-grid (V2G) technology and supercapacitors as complementary solutions to improve grid stability. A hybrid approach is proposed in which electric vehicles act as temporary storage units, supplying energy to regulate grid frequency. Supercapacitors, due to their rapid charging and discharging capabilities, are used to mitigate power fluctuations and provide immediate support during peak demand. The proposed management model integrates two strategies for frequency control, leveraging the linear relationship between power and frequency. Power smoothing is combined with Kalman filter-based frequency control, allowing for accurate estimation of the dynamic system state, even in the presence of noise or load fluctuations. This methodology improves grid stability and frequency regulation accuracy. A frequency variability analysis is also included, highlighting grid disturbance events related to renewable-energy penetration and demand changes. Furthermore, the effectiveness of the Kalman filter in improving grid stability control, ensuring an efficient dynamic response, is highlighted. The results obtained demonstrate that the combination of V2G and supercapacitors contributes significantly to reducing grid disturbances, optimizing energy efficiency, and enhancing system reliability. Supercapacitor ; Microgrid ; Grid ; Stability (learning theory) ; Computer science ; Automotive engineering ; Materials science ; Control (management) ; Electrochemistry ; Engineering ; Chemistry ; Mathematics ; Electrode Supercapacitor Materials and Fabrication ; Microgrid Control and Optimization ; Electric Vehicles and Infrastructure
Authors: Andrei K. Yudin | Morgan Cordell
Source: ChemRxiv (preprint, 2025-06-23)
Field: Chemistry — Physical and Theoretical Chemistry

Isoreactive Relations in Chemistry Isoelectronic and isolobal analogies are powerful tools for comparing the electronic structures of molecular fragments, but they do not necessarily capture similarities in chemical reactivity. In this perspective, we introduce the concept of isoreactive relations to address cases in which structurally distinct fragments support chemically productive outcomes despite dissimilar composition. This idea emerged from our studies on boryl migration, where replacing a hydride with a boron-containing substituent unexpectedly unlocked new reaction pathways. More broadly, isoreactive relations should provide a framework for identifying mechanistic parallels that extend beyond the scope of traditional electronic comparisons. By embracing this reactivity-centered perspective, it should become possible to uncover and experimentally validate unconventional fragment substitutions that lead to novel transformations. Chemistry History and advancements in chemistry
Authors: Sobia Wassan | Ok Hue Cho | Salman A. AlQahtani
Source: Legume Research - An International Journal (article, 2025-07-23)
Field: Chemistry — Analytical Chemistry

Detection and Classification of Soybean Wilting Across Progressive Stages using Convolutional Neural Network Method Background: Food security being one of the prominent global issues requires strategies to maximize plant productivity with due consideration to sustainability. In this regard, precision agricultural practices are incorporated and are quite promising. The use of technology i.e. integrating AI is rapidly changing the complete agricultural scenario. A quick identification of wilting ensures farmers take early action and remedies. An early detection of plant wilting in a real-time scenario can avoid huge food crop losses but the whole task is humongous. The images collected from open fields over large areas can be analyzed via various image processing techniques. Using the CNN model for identification of early images for quick response to plant care saves the time and effort of the farmers. CNN utilization allows prompt wilting detection and early corrective action to protect the crop. In this paper, CNN trained on image data allows high predictability of wilting detection at early stages in Soybean plants. Methods: A well-defined dataset of 6704 pictures of soybean plants from agricultural fields is considered and allocated appropriately to train, test and validate the CNN model. Using image preprocessing, resizing and rescaling of images is done to ensure consistency of image dimensions. Noise elimination from the images is done via a low-pass filtering method to preserve low-frequency information and the images are converted to grayscale. Using standard Python libraries, data augmentation is ensured and 9158 images across all classes are arranged for the model. The performance evaluation matrix indicating accuracy percentage, precision percentage and recall percentage is estimated. Result: The proposed CNN algorithm is first calibrated using a set of images from the dataset and then tested for an entirely different set of images not used earlier. The overall accuracy is 91%. The model promises unambiguous identification of wilting in soybean leaves by appropriately classifying images set in 5 orders using a substantially ample dataset. Early identification in real time can prove to be of utmost benefit to the agricultural community in terms of eradication of the causes and yield retention of the crop. Wilting ; Convolutional neural network ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Biology ; Agronomy Spectroscopy and Chemometric Analyses
Authors: Ondřej Kabot | Lukáš Klein | Zdeněk Slanina | Lukáš Prokop
Source: Scientific Data (article, 2025-08-05)
Field: Materials Science — Materials Chemistry

Partial vs. Corona Discharges in XLPE-Covered Conductors: High-Resolution Antenna Dataset for ML Applications Accurate differentiation between partial discharges (PD) and corona discharges in XLPE-covered conductors is crucial for power system diagnostics, yet remains limited by the lack of specialized, high-fidelity datasets for machine learning (ML) model development. This paper presents a high-resolution dataset (10<sup>7</sup> samples per 20 ms) acquired using a contactless dual-antenna system under controlled laboratory conditions simulating medium-voltage overhead distribution lines. The dataset includes 100 labeled measurements per class across five discharge types (PD, corona, mixed states, and high-impedance variants) and two background conditions (with and without high voltage), collected over a two-day campaign. By providing experimentally isolated signal types, this resource enables the development and benchmarking of ML models specifically tailored to the PD-corona classification challenge. Key applications include lightweight classification models for edge devices, synthetic data generation to augment limited training sets, and investigations into noise robustness, real-time monitoring, and explainable diagnostics. Through a controlled yet realistic acquisition design, the dataset supports the creation of advanced ML-based tools for non-invasive fault identification-enhancing diagnostic accuracy, mitigating insulation risks, and improving safety in critical power infrastructure. Computer science ; Electrical conductor ; High voltage ; Partial discharge ; Robustness (evolution) ; Corona discharge ; Corona (planetary geology) ; Voltage ; Remote sensing ; Real-time computing ; Electrical engineering ; Engineering ; Physics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Lightning and Electromagnetic Phenomena
Authors: Y. H. Yang | Chen Zhao | Liang-Peng Li | Yixin Lv | Beibei Yang | Xin Li | Ru Wang | Li Li
Source: Pharmaceuticals (article, 2025-06-02)
Field: Chemistry — Spectroscopy

Absolute Configuration and Chiroptical Properties of Flexible Drug Avapritinib <b>Background/Objective</b>: Avapritinib is an orally bioavailable tyrosine kinase inhibitor and was approved by the FDA in 2020 for gastrointestinal stromal tumor treatments. Although avapritinib is known to be chiral, its stereochemistry was initially established randomly. This study aims to develop a definitive method for determining avapritinib's absolute configuration and propose a universal methodology for stereochemical characterization of flexible chiral drugs. <b>Methods</b>: The absolute configuration of avapritinib was determined through an integrated approach combining chiral resolution, chiroptical spectroscopy and synthetic validation. Enantiomeric separation was achieved via chiral liquid chromatography, followed by comprehensive chiroptical characterization including electronic circular dichroism (ECD), specific optical rotation and optical rotatory dispersion. Conformational analysis and density functional theory (DFT) calculations correlated experimental spectra with theoretical predictions, facilitating definitive configurational assignment. The stereochemical determination were further verified through ECD derivatization and chemical synthesis. Finally, the enantiomers' kinase inhibition profiles against c-KIT D816V were quantitatively assessed. <b>Results</b>: Two enantiomers of avapritinib were resolved via chiral HPLC and a Chiralpak IG column. Through combined experimental ECD spectra and time-dependent DFT calculations employing the core extraction method, the <i>levo</i>-isomer was unambiguously determined as <i>S</i> configuration. This stereochemical assignment was confirmed by <i>p</i>-cyanobenzaldehyde derivatization and de novo synthesis. Biological evaluation revealed (<i>S</i>)-(-)-avapritinib exhibited superior c-KIT D816V inhibitory activity compared to its (<i>R</i>)-(+)-counterpart, a finding corroborated by molecular docking studies elucidating their differential target interactions. <b>Conclusions</b>: This study advances avapritinib stereochemical understanding and establishes a definitive protocol for its absolute configuration assignment, serving as a paradigm for flexible chiral drug characterization. Absolute configuration ; Drug ; Medicine ; Chemistry ; Pharmacology ; Stereochemistry Molecular spectroscopy and chirality ; Cancer therapeutics and mechanisms ; Axial and Atropisomeric Chirality Synthesis
Authors: Xize Dai | Haolun Che | Jian Hao
Source:  (review, 2025-01-10)
Field: Materials Science — Materials Chemistry

Applications of Physics-Informed Neural Networks in High-Voltage Insulation Engineering: A Review In High-Voltage Engineering (HVE), electrical insulation systems are essential to power assets. The rational design and preventative maintenance of insulation systems are two main research directions for power assets. In essence, the research of two directions is on exploring and representing high-voltage physics properties for insulation systems, such as space charge phenomena, electric field distribution, discharge and breakdown, and much more. Hence, a series of physical models and partial differential equations (PDEs) have been widely developed to characterize and describe these physical phenomena. Nevertheless, the parameters of these models are often difficult to solve and highly dependent on the numerical calculation techniques. Besides, deep learning as a pure data-driven technique also is used to conduct the insulation state inversion of power assets, but a large amount of high-quality training data is needed. Fortunately, Physics-Informed Neural Networks (PINNs) incorporate the dual advantages of physical models and a data-driven approach, which could be a promising solution. Driven by this fact, this work summarizes the recent research progress of PINN applications in three types of insulation systems (gas, solid, and liquid). Meanwhile, the challenges and outlook are discussed, offering inspiration for further development of PINNs in HVE. Artificial neural network ; Voltage ; High voltage ; Electrical engineering ; Computer science ; Materials science ; Engineering physics ; Engineering ; Mechanical engineering ; Systems engineering ; Artificial intelligence High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Power Transformer Diagnostics and Insulation
Authors: Peng Yin | Shilin Dong | Jiaoyang Yu | Zhihe Zhao | Yunxiang Hu
Source: International Journal of Nanomedicine (review, 2025-09-01)
Field: Materials Science — Biomaterials

The Emerging Roles of Nano Drug Delivery Systems in Treatment of Osteoporosis-Current Knowledge, Challenges and Future Perspectives Osteoporosis (OP) is a degenerative bone disease characterized by decreased bone mass and deterioration of bone tissue microstructure, which increases skeletal fragility and the risk of fractures. Currently, the drugs used clinically to treat OP are primarily classified into two types: bone resorption inhibitors and bone formation promoters. Although they demonstrate certain efficacy, most anti-OP medications do not specifically target bone tissue and may produce significant side effects. In recent years, bone-targeted therapies nano-drug delivery systems (NDDSs) for OP have gained attention due to their high drug loading capacity, strong targeting ability, ease of modification, and good biocompatibility. These characteristics effectively address the limitations of traditional therapies and have been widely applied in the treatment of OP. Therefore, this article systematically summarizes recent applications of NDDSs (including inorganic, organic, biogenic, and hybrid systems) in the treatment of OP, focusing on targeting design strategies, in vitro and in vivo efficacy validation, and clinical translation challenges, aiming to provide theoretical references for the development of the next generation of targeted nanomedicines. Drug delivery ; Osteoporosis ; Drug ; Bone disease ; Bone resorption ; Bone mass ; Nanomedicine ; Targeted drug delivery Nanoparticle-Based Drug Delivery ; Bone Tissue Engineering Materials ; Graphene and Nanomaterials Applications
Authors: Amine El Maraghi | Charles Kenzy | Habib El Alaoui El Abdallaou
Source: Current Chemistry Letters (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Waste recovered caffeine as a sustainable corrosion inhibitor and therapeutic lead: A combined DFT, MD &amp; docking approach This study highlights the dual functionality of caffeine extracted from spent coffee grounds as both a green corrosion inhibitor and a potential broad-spectrum therapeutic agent. Using DFT (B3LYP/6‑311G(d,p)), caffeine exhibited favorable electronic properties (ΔEgap = 4.42 eV, dipole moment = 5.21 D), supporting its strong adsorption capabilities. Molecular Dynamics simulations under acidic conditions confirmed stable, near-flat adsorption on Fe(110), Cu(111), and Al(111) surfaces, with significant interaction energies, indicating robust protective behavior. Simultaneously, molecular docking revealed promising binding affinities of caffeine to SARS-CoV-2 Mpro and Plasmodium falciparum kinases, suggesting antiviral and anti-malarial potential. This theoretical work demonstrates caffeine’s value as a sustainable, multifunctional molecule with applications in both corrosion control and drug discovery. Caffeine ; Adsorption ; Docking (animal) ; Molecular dynamics ; Corrosion inhibitor ; Corrosion ; Molecule Corrosion Behavior and Inhibition
Authors: Elif Meltem İşçimen | Mehmet Hayta
Source: Journal of Food Measurement & Characterization (article, 2025-04-28)
Field: Materials Science — Biomaterials

The effect of ultrasound amplitude and black cumin seed oil nanoemulsion on edible film properties produced from orange peel pectin Abstract The purpose of current research is to explore the effect of different ultrasound amplitudes to increase the commercial value and performance of edible films prepared from orange peel pectin (OP). It also aims to determine the effects of using nanoemulsions of black cumin seed essential oil (BCSO) on antimicrobial activities and the total phenolic content (TPC) of films. The physical and structural characteristics of films (pictures, SEM, micrographs, FTIR, and XRD) as well as their antibacterial properties and TPC values were examined. The use of OP increased the antimicrobial properties of films compared to commercial pectin (CP). Additionally, the TPC value and antimicrobial properties of films containing 5 and 10% BCSO nanoemulsion were positively affected by increasing the applied ultrasound amplitude. The highest TPC value was determined as 3.62 ± 0.23 and 3.49 ± 0.25 for samples applied 100% amplitude containing 10 and 5% BCSO, respectively. On the contrary, the mechanical properties were negatively affected when the ultrasonic amplitude was increased to 100% amplitude because of tiny cavities in the film matrix. The results proved that it would be appropriate to produce food-grade edible films with low-intensity amplitude applications. In addition, the physical and structural qualities of the films were not adversely affected by the addition of BCSO nanoemulsions. Pectin ; Orange (colour) ; Materials science ; Food science ; Chemistry Nanocomposite Films for Food Packaging ; Polysaccharides and Plant Cell Walls ; Microencapsulation and Drying Processes
Authors: LI Ben | CHEN Fangfang | Wang Fei | ZHENG Zansheng | ZOU Yibo | 虞益挺 Yu Yiting
Source: Optics and Precision Engineering (article, 2025-01-01)
Field: Chemistry — Spectroscopy

微小型MEMS-FPI近红外光谱仪 针对传统近红外光谱仪器体积庞大且成本高昂的现状，基于滨松MEMS-FPI近红外光谱传感器C14272，成功研发了工作波长为1350-1650 nm的微小型化近红外光谱仪。该系统采用模块化架构，整合高效信号获取与数据处理单元，显著降低系统成本。通过系统性能评估，包括波长精度、光谱分辨率、动态范围等指标，并通过温度特性补偿算法有效抑制了温度漂移。实验结果表明：该光谱分析系统具备优异的稳定性与重现性，光谱分辨率约为15.2 nm。结合卷积神经网络算法，构建的葡萄糖溶液浓度预测模型在检测未知样本时表现出高准确性（<italic>R</italic>²&gt;0.99）和低误差率（RMSE&lt;0.2%）。所研制的微小型化近红外光谱仪为生物医学检测与食品质量分析提供了经济高效的解决方案。 Telmatology ; Metamorphic petrology Spectroscopy and Laser Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Optical Polarization and Ellipsometry