jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: J H Zhang | Cunhao Lu | Jian Chen | Yaoji Deng
Source: Magnetochemistry (article, 2026-01-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Temperature-Corrected High-Frequency Non-Sinusoidal Excitation Core Loss Prediction Model Predicting core loss under high-frequency non-sinusoidal excitation is crucial for power electronics equipment design. Temperature significantly affects core loss, and traditional core loss prediction models typically incorporate temperature corrections to enable accurate loss estimation across varying temperatures. Based on the Modified Steinmetz Equation (nonT-MSE) model, this study considers the temperature effect by employing a combination of the Tanh function and a linear term to modify the three empirical parameters, with the Tanh function capturing the nonlinear saturation of the loss coefficient k with increasing temperature. This leads to the establishment of the temperature-corrected non-TMSE (T-MSE) model for predicting magnetic core loss under high-frequency non-sinusoidal excitation. During model derivation, training data undergo logarithmic transformation processing. Subsequently, with T-MSE empirical parameters as variables and the minimum mean squared error between T-MSE predicted values and experimental values as the objective function, a single-objective optimization model is established. Finally, the empirical parameters of T-MSE are calculated using the training data and the single-objective optimization model. Comparing the core loss experimental results of the four materials, the average MSE values for the T-MSE model, the nonT-MSE model, and the square-root temperature-corrected non-TMSE model proposed by Zeng et al. (Zeng) are 0.0082, 0.0459, and 0.0110, respectively; with average MAPE of 1.57%, 1.87%, and 2.17%, respectively; and average R2 of 0.9862, 0.9807, and 0.9731. Compared to the nonT-MSE model and the Zeng model, the T-MSE model demonstrated higher prediction accuracy. Mean squared error ; Function (biology) ; Core (optical fiber) ; Logarithm ; Nonlinear system ; Excitation ; Empirical modelling ; Mean absolute percentage error ; Term (time) Magnetic Properties and Applications ; Silicon Carbide Semiconductor Technologies ; Advanced DC-DC Converters
Authors: Xiaolong Mei | Yaguang Wang | Xiang Zhu | Xiaoming Liu
Source: Scientia Sinica Technologica (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

A machine learning-driven multi-objective optimization model for enhancing the mechanical properties of silica fume-modified magnesium phosphate cement concrete 摘要 硅灰在磷酸镁水泥混凝土中通过物理填充、化学活化和化学反应等多重作用, 可显著提升材料力学性能、体积稳定性和耐久性, 但会降低工作性并缩短凝结时间. 因此, 需要通过机器学习优化其掺量以实现性能平衡与力学性能最大化. 本研究基于634组多龄期数据(0.125/1/3/7/28/90天), 采用线性回归(LR)、决策树(DT)、随机森林(RF)、极端梯度提升(XGBoost)和人工神经网络(ANN)五种算法构建预测模型, 并通过SHapley加性解释(SHAP分析)确定最优硅灰掺量. 结果表明, 在28天养护时获得磷酸镁水泥混凝土最优力学性能(强度68±3 MPa), 对应配比为硅灰20~25%、水胶比0.25~0.30, 同时证实7天早强需增加硅灰至25~30%, 90天耐久性需控制水胶比≤0.28. 本研究为磷酸镁水泥混凝土的配比设计提供了重要的理论依据和实践指导. Cement ; Magnesium ; Phosphate ; Deformation (meteorology) ; Compression (physics) Magnesium Oxide Properties and Applications ; Concrete and Cement Materials Research ; Phosphorus and nutrient management
Authors: Janet Temitope Asifat | K. A. Obakhume | Olanrewaju Tolu Omigbodun
Source: Annals of Ecology and Environmental Science (article, 2026-03-10)
Field: Chemistry — Physical and Theoretical Chemistry

Digital and Data Mining Approaches for Evaluating Water Pollution The importance of digital and social media data for evaluating water pollution cannot be overemphasied.It enables the water pollution forecasts, hence the quality of data of information gathered from social media platforms must be maintained, especially for desert regions with intermittent but heavy rainfall and a significant risk of polluted areas.This study explored the digital and data mining, and social media data mining to evaluate water pollution.It was revealed that earlier studies concentrated more on creating a framework for social media activity in water pollution, especially in places that flood.This study examines reports and records of water pollution from a variety of social media sites, including YouTube, Flickr, Facebook, Instagram, and Twitter, for the first time.It verified data on water pollution from social media.Based on the results of many machine learning classifiers, social media data was validated to predict and evaluate water pollution. Water pollution ; Pollution ; Water resources ; Water quality ; Identification (biology) History and advancements in chemistry ; Water Quality and Pollution Assessment ; Advanced Computational Techniques in Science and Engineering
Authors: Jin Hua Wang | Fengping Luo | Yiheng Chen | Denghuang Chen | Bowen Zhang | Yuxin Liu | Yuntao Wang | Yunbiao Zhao | Sheng Mao | Chen, Mohan | Hong-Bo Zhou | Jianming Xue | Yugang Wang | Chenxu Wang
Source: ArXiv.org (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Stress-driven dynamic evolution of core-shell structured cavities with H and He in BCC-Fe under fusion conditions Understanding the dynamic behavior of microstructures formed under fusion conditions is critical for designing high-performance structural materials for fusion reactors. Under fusion conditions, cavities of core-shell structures are formed due to the interaction between irradiation-induced vacancies and H and He atoms produced via transmutation. In this study, thermodynamic analysis and molecular dynamics simulations are combined to investigate the atomic-scale mechanisms and dynamic response of core-shell cavities formed in BCC-Fe under applied stress/strain fields. The thermodynamic analysis provides both the foundational reference for cavity structures under fusion neutron irradiation and the initial configurations for atomistic simulations. Building on this framework, atomic-scale simulations demonstrate that H and He play a decisive role in the stress-strain response and the evolution of elastic-plastic deformation within the cavities. In core-shell configurations, H atoms serve a function analogous to that in He-filled cavities, synergistically interacting with He to induce cavity deformation under mechanical loading. Fusion ; Deformation (meteorology) ; Molecular dynamics ; Fusion power ; Function (biology) ; Microstructure Fusion materials and technologies ; Microstructure and mechanical properties ; Hydrogen Storage and Materials
Authors: Görkem Aksoy | Çağla Çelik Yoldaş | Fatma Dalkılıç | Cemile Yılmaz | Nimet Temur | Nilay Ildız | İsmail Öçsoy
Source: İnönü Üniversitesi Sağlık Hizmetleri Meslek Yüksek Okulu Dergisi (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

SYNTHESIS OF CLOXACILLIN@AG NANOPARTICLES WITH ENHANCED ANTIBACTERIAL ACTIVITY BY PHOTO-REDUCTION METHOD The increase in antimicrobial resistance necessitates the development of new therapeutics. In this context, silver nanoparticles (AgNPs) show considerable promise due to their multiple antimicrobial mechanisms, including cell wall disruption, oxidative stress, and DNA damage, as well as their relatively low toxicity. The effectiveness of AgNPs depends on their size, shape, and surface modifications; their combination with ligands can also produce synergistic effects. In this study, cloxacillin sodium (a β-lactam antibiotic) was employed as a ligand to synthesize Clox@AgNPs under UV (311 nm) irradiation using a photo-reduction method. The characteristic absorbance peak, morphology, surface charge, and diameter of the Clox@AgNPs were analyzed using UV–visible spectroscopy, scanning transmission electron microscopy, fourier-transform infrared spectroscopy, dynamic light scattering and zeta potential analyses. The antibacterial activity of the Clox@AgNPs against methicillin-resistant Staphylococcus aureus (MRSA) was then evaluated to enhance the efficacy of existing antibiotics. This approach may provide a valuable contribution to future antimicrobial strategies. Cloxacillin ; Zeta potential ; Antimicrobial ; Dynamic light scattering ; Nanoparticle ; Antibacterial activity ; Absorbance ; Ligand (biochemistry) Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Nanoplatforms for cancer theranostics
Authors: Tiziana Spina | R. Tebano | Christian-Éric Bruzek | Luca Soldati | Alberto Barutti | Umberto Melaccio | Georg Gamper | Pietro Corsaro | Gabriele Bino | Mohsen Abdolahi | Mohammad Yazdani-Asrami | Wenjuan Song | Stéphane Holé
Source: IEEE Transactions on Applied Superconductivity (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

CABLEGNOSIS Project: Ageing Studies on Insulating Materials and Superconducting Wires for Cable Applications To ensure power cables' lifetime and to prove their reliability, standardized type and PQ tests are carried out, e.g., withstand voltage test. Usually, polymeric materials (typically polyethylene) are used as dielectric materials. Standard testing procedures integrate extrapolations using the analytical ageing laws mostly related to thermal and voltage ageing to investigate the impact of thermal stress and excessive temperature on lifetime of the cable insulation. Superconducting (SC) cables are cooled and maintained at a cryogenic temperature, below 77 K. In such conditions, one can expect different ageing mechanisms to be dominant rather than thermal ageing, and consequently ageing laws might be different that will impact standard type tests. To perceive the ageing law and guide the future process of manufacturing, and developing techniques for prognostic health management of SC cables as well as to predict when maintenance might be needed, ASG superconductors, University of Glasgow (UofG) and École supérieure de physique et de chimie industrielles de la ville de Paris (ESPCI) are involved in work packages related to the ageing mechanisms affecting both insulation materials and SC wires within the framework of CABLEGNOSIS project. This article presents insights on the ageing mechanisms at cryogenic temperature, an innovative prediction methodology based artificial intelligence and strategies to accelerate the ageing and acquire accurate physical information up to breakdown. Ageing ; Voltage ; Thermal ; Work (physics) ; Superconductivity ; Stress (linguistics) High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Thermal Analysis in Power Transmission
Authors: Elham Gorgannejad | Qian Liu | Catherine Rui Jin Findlay | Mohammad Nadimi | Alex Chun-Te Ko | Pankaj Bhowmik | Jitendra Paliwal
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-03-24)
Field: Chemistry — Analytical Chemistry

Generalizable Cysteine Quantification in Pea Cultivars from SERS Spectra Using AI Abstract Rapid quantification of sulfur-containing amino acids, particularly cysteine, in legumes is critical for assessing nutritional quality, supporting breeding program screening, and ensuring consistency in quality control processes. However, conventional methods, such as high-performance liquid chromatography (HPLC), are time-consuming and resource-intensive for high-throughput applications. This study evaluated artificial intelligence models for predicting cysteine concentration from surface-enhanced Raman spectroscopy (SERS) spectra of pea extracts. SERS spectra were acquired from 20 cultivars grown at three geographically distinct locations, with HPLC-measured cysteine concentrations as a ground truth reference. Linear regression, partial least squares regression, support vector regression, random forest regression, and a one-dimensional convolutional neural network (1D-CNN) were compared using within-cultivar splits and leave-one-cultivar-out (LOCO) evaluation. The 1D-CNN achieved RMSE 0.008 g/100 g within cultivars and maintained performance under LOCO, while other models showed limited generalization. Shapley Additive Explanations highlighted informative bands in the 630–760 cm −1 range, and noise modeling optimized scan-count selection. Cultivar ; Partial least squares regression ; Cysteine ; Convolutional neural network ; Noise (video) ; Random forest ; Support vector machine ; Chemometrics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Biosensors and Analytical Detection
Authors: Qi Liu | Xirui Chen | Kenian Wang | Yifan Wu | Xiangcai Zou | Ying Li
Source: Interdisciplinary medicine (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Aggregation‐induced emission luminogens in in vitro diagnostics: Advancing biomarker detection in body fluids Abstract Rapid, sensitive, and accurate detection of ingredients in body fluids is crucial for monitoring health status and diagnosing diseases. Fluorescent biosensors have emerged as a user‐friendly tool for biomarker detection, because of their high sensitivity, rapid response, and adaptability. Among the various signal probes used in fluorescent biosensors, aggregation‐induced emission (AIE) luminogens (AIEgens) stand out as a highly promising candidate owing to their unique property of enhanced emission in the aggregated or solid state. This review outlines the sensing mechanisms and design strategies of AIE probes for constructing AIE‐based fluorescent biosensors. It provides a comprehensive overview of recent advancements in AIE probes for detecting various ingredients in body fluids, including blood, urine, saliva, throat swabs, and sweat. The efficacy of AIEgen‐based fluorescent biosensors in these applications is highlighted, focusing on the design of probes, response mechanisms, and luminescence principles. Finally, the current challenges of AIEgen‐based biosensors and future research perspectives are discussed, aiming to inspire the development of AIE probes and improve the sensitivity and accessibility of AIEgen‐based fluorescent biosensors. Biosensor ; Fluorescence ; Aggregation-induced emission ; Biomarker Luminescence and Fluorescent Materials ; Carbon and Quantum Dots Applications ; Molecular Sensors and Ion Detection
Authors: Ved Sharma
Source: ChemRxiv (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

Design of experiments (DOE) approach for optimizing ZnS nanoparticle morphology via displacement synthesis The controlled tailoring of zinc sulfide (ZnS) nanoparticle morphology is essential for achieving predictable optical, electronic, and catalytic performance in semiconductor applications.Displacement synthesis offers a simple and scalable route to ZnS formation; however, morphological outcomes are highly sensitive to the interacting reaction parameters, including precursor concentration, temperature, pH, reaction time, and stirring conditions.This study proposes a structured Design of Experiments (DOE) framework to systematically optimize the morphology of ZnS nanoparticles produced via displacement reactions.A multivariate experimental matrix based on factorial and response surface methodologies was employed to quantify the individual and interaction effects of critical process variables.Morphological characteristics, including particle size, aspect ratio, and agglomeration tendency, were correlated with statistical models to identify optimal synthesis windows.Analysis of variance (ANOVA) confirmed the statistical significance of key factors and their coupled effects, while predictive regression models demonstrated strong agreement with experimental observations.The optimized conditions enabled reproducible synthesis of narrowly distributed ZnS nanoparticles with controlled structural features.The proposed DOE-guided strategy reduces experimental redundancy, enhances process reproducibility, and provides a scalable pathway for morphology-driven functional tuning of ZnS nanomaterials.This methodology can be extended to other metal chalcogenide systems where precise nanostructure control is required. Nanoparticle ; Design of experiments ; Morphology (biology) ; Displacement (psychology) ; Deformation (meteorology) Quantum Dots Synthesis And Properties ; Gold and Silver Nanoparticles Synthesis and Applications ; Chemical and Physical Properties of Materials
Authors: Diego F. Monroy-Macias | Luis Alejandro Martínez-Chávez | Ana A. Feregrino-Pérez | Karen Esquivel
Source: Mundo Nano Revista Interdisciplinaria en Nanociencia y Nanotecnología (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Arabidopsis thaliana: una década como modelo de estudio de los efectos negativos de nanopartículas metálicas En la última década, Arabidopsis thaliana se ha consolidado como un organismo modelo clave para estudiar los efectos adversos de nanopartículas metálicas (MNPs) en plantas. Este artículo presenta una revisión sistemática de literatura científica reciente (2015-2025), enfocada en los efectos fisiológicos, moleculares y ecológicos provocados por diferentes tipos de MNPs, como Ag, Au, Cu, ZnO y TiO₂, sobre esta especie vegetal. Se recopilan hallazgos sobre inhibición del crecimiento, alteraciones en la fotosíntesis, retraso en la floración, estrés oxidativo, desequilibrios hormonales y cambios en la expresión génica. Asimismo, se discuten los impactos indirectos a través de la alteración de la microbiota del suelo. La revisión destaca que los efectos tóxicos de las MNPs pueden depender de factores como el tamaño, la forma, la carga superficial y la concentración. Se concluye que A. thaliana representa una herramienta predictiva útil para evaluar el riesgo ecológico de las MNPs y apoyar el desarrollo de marcos regulatorios en nanotecnología agrícola. Arabidopsis thaliana ; Context (archaeology) ; Human health ; Arabidopsis Nanoparticles: synthesis and applications ; Microplastics and Plastic Pollution ; Polymer-Based Agricultural Enhancements
Authors: Maialen Terceño Carrillo | Laura Razquin‐Bobillo | Sergio Carrasco | Patricia Horcajada | Garikoitz Beobide | Jose Angel Garcia | Pablo Salcedo‐Abraira | Andoni Zabala‐Lekuona | Antonio Rodríguez Diéguez | Javier Cepeda
Source: Materials Chemistry Frontiers (article, 2026-01-01)
Field: Chemistry — Inorganic Chemistry

Vacuum-responsive and water-soluble lanthanide( <scp>iii</scp> )/2-oxonicotinate coordination polymers with high photoluminescence efficiency Coordination polymers showing vacuum-induced photoluminescence linked to partial dehydration, supported by periodic DFT calculations, are characterized. PL behavior in aqueous solution and NIR emission are also evaluated. Photoluminescence ; Polymer ; Aqueous solution ; Polymer solution ; Aggregation-induced emission Metal-Organic Frameworks: Synthesis and Applications ; Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes
Authors: G. Kim | Yunsong Lee | Seungjun Kim | Yong Ju Lee | Do Young Lee | Tai-Ju Lee | Hyoung Jin Kim
Source: Polymers (article, 2026-02-10)
Field: Materials Science — Biomaterials

Allomorphic Transformation of Cellulose for Enhancing Enzymatic Accessibility In recent decades, lignocellulosic biomass has attracted increasing attention as a sustainable alternative to fossil-fuel-based resources. However, the compact and highly crystalline structure of cellulose remains a major limitation to its effective utilization. In this study, the allomorphic transformation of cellulose was induced through chemical treatments using sodium hydroxide (NaOH) and ethylenediamine (EDA), enabling the conversion of native cellulose I into cellulose II and cellulose III, respectively. The resulting changes in the crystalline structure were systematically investigated using X-ray diffraction and Raman spectroscopy. Both NaOH- and EDA-treated celluloses exhibited enhanced enzymatic digestibility compared to untreated cellulose, consistent with the observed modifications in the crystal structure. Nevertheless, some results indicate that crystalline structure is not an absolute determining factor, but rather one of several parameters, including specific surface area, particle size, and degree of polymerization. Cellulose ; Sodium hydroxide ; Hemicellulose ; Crystal structure ; Lignocellulosic biomass ; Biomass (ecology) ; Enzymatic hydrolysis ; Crystal (programming language) Advanced Cellulose Research Studies ; Biofuel production and bioconversion ; Catalysis for Biomass Conversion
Authors: Peixin Xie | Wufan Wang | Yangdong Zheng | Kuilong Song | Fei Zhu | Weian Huang | Yuchen Zhou | Weiqiang Chen | Shan Jiang | Longnan Li | Wei Li
Source: Chinese Science Bulletin (Chinese Version) (article, 2026-02-01)
Field: Chemistry — Spectroscopy

Research Progress in Spectral-Polarization-Intensity Multi-Dimensional Light Field Detectors 光场作为由波长、偏振、强度、相位等维度构建的多维光信息载体，携带远超传统二维图像的物质指纹与场景细节。随着信息维度的提升与应用场景的复杂化，发展能够在单一器件中同时获取光谱、偏振、强度等多维信息的高集成度、智能化探测器，已成为光电子领域的重要趋势。光谱、偏振与光强的协同探测，不仅能够极大提升信息获取效率与系统集成度，同时为遥感侦察、环境监测、自然资源勘探、机器视觉、医疗诊断、化学分析、光通信等应用提供更丰富的光学指纹。实现这一目标的关键在于突破传统分立式光学系统的局限，发展基于光场编码理论-新型器件架构-计算解码的多维到高维探测技术。本文从光场多维信息的表征与编码理论出发，系统综述了通过分时编码与空间结构化编码两种主要路径实现光谱、偏振、强度多维信息融合探测的研究进展；在此基础上，进一步阐明应用空间和频率色散多层膜结构、无序光子芯片、空间色散超表面等创新实现高维光场信息单次同步获取的高效率探测技术。本文深入剖析了各类探测器的物理机制、编码策略与重构算法，并对其技术优势、当前挑战与发展前景进行了总结与展望，以期为下一代集成化、智能化、高效率光场探测系统的设计提供理论基础与技术支撑。 Detector ; Field (mathematics) ; Noise (video) ; Particle detector Spectroscopy and Laser Applications ; Optical Systems and Laser Technology ; Optical Polarization and Ellipsometry
Authors: Calvin Tagne-Kaegom | Alexandre Llopez | Boris Croes | Geoffroy Kremer | B. Kierren | D. Malterre | Luc Moreau | Fotié Jaurès Ngoufo | Stefano Curiotto | Pierre Müller | Patrick Le Fèvre | Julien Rault | François Bertran | Andrés Saúl | Frédéric Leroy | Fabien Cheynis | Yannick Fagot-Revurat
Source: Physical review. B./Physical review. B (article, 2026-02-03)
Field: Materials Science — Materials Chemistry

Spin texture of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>α</mml:mi> <mml:mtext>−</mml:mtext> <mml:mi>GeTe</mml:mi> </mml:mrow> </mml:math> in the ultrathin regime International audience Texture (cosmology) ; Spin (aerodynamics) ; Ferromagnetism ; Permalloy ; Magnetization Phase-change materials and chalcogenides ; Topological Materials and Phenomena ; Semiconductor Quantum Structures and Devices