jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Saisai Yang | Qingyi Huang | Jing Yuan | Liangyu Zha | Kai Tang | Yuhang Yang | Ning Wang | Yucheng Wei | Liyao Li | Wentao Ye | Hao Chen | Tao Zhang | Junlin Zhou | Haobo Wang | Gang Chen | Junbo Zhao
Source: ArXiv.org (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

TableGPT-R1: Advancing Tabular Reasoning Through Reinforcement Learning Tabular data serves as the backbone of modern data analysis and scientific research. While Large Language Models (LLMs) fine-tuned via Supervised Fine-Tuning (SFT) have significantly improved natural language interaction with such structured data, they often fall short in handling the complex, multi-step reasoning and robust code execution required for real-world table tasks. Reinforcement Learning (RL) offers a promising avenue to enhance these capabilities, yet its application in the tabular domain faces three critical hurdles: the scarcity of high-quality agentic trajectories with closed-loop code execution and environment feedback on diverse table structures, the extreme heterogeneity of feedback signals ranging from rigid SQL execution to open-ended data interpretation, and the risk of catastrophic forgetting of general knowledge during vertical specialization. To overcome these challenges and unlock advanced reasoning on complex tables, we introduce \textbf{TableGPT-R1}, a specialized tabular model built on a systematic RL framework. Our approach integrates a comprehensive data engineering pipeline that synthesizes difficulty-stratified agentic trajectories for both supervised alignment and RL rollouts, a task-adaptive reward system that combines rule-based verification with a criteria-injected reward model and incorporates process-level step reward shaping with behavioral regularization, and a multi-stage training framework that progressively stabilizes reasoning before specializing in table-specific tasks. Extensive evaluations demonstrate that TableGPT-R1 achieves state-of-the-art performance on authoritative benchmarks, significantly outperforming baseline models while retaining robust general capabilities. Our model is available at https://huggingface.co/tablegpt/TableGPT-R1. Reinforcement learning ; Forgetting ; Table (database) ; Code (set theory) ; Domain (mathematical analysis) ; Natural language understanding ; SQL ; Language model Machine Learning in Materials Science ; Topic Modeling ; Machine Learning and Data Classification
Authors: Sultan Kabylkanov | Azamat Burumbayev | Armat Zhakan | SADYK ZH.O.
Source: Ķ. Žu̇banov atyndaġy Aķtôbe ôṇìrlìk memlekettìk universitetìnìṇ habaršysy. (article, 2025-05-14)
Field: Materials Science — Materials Chemistry

EFFECTIVE METHODS FOR OBTAINING CHROMIUM-MANGANESE LIGATURE IN LABORATORY CONDITIONS Бұл мақалада отандық шикізаттарды пайдалана отырып, хроммарганецті лигатураның зертханалық жағдайда технологиясын әзірлеу көрсетілген. Зерттеу жұмысының негізгі мақсаты – хром және марганец кендерінен жоғары сапалы лигатура алу процесін оңтайландыру және оның химиялық құрамын жақсарту болып табылады. Хроммарганецті лигатура болат өндірісінде беріктік, коррозияға төзімділік және тозуға қарсы қасиеттерін жақсартатын маңызды қосымша материал ретінде қолданылады. Зерттеу жұмысы жоғары температуралы Тамман пешінде жүргізілді. Эксперименттерде хром және марганец кендері шикізат ретінде, ал ферросиликоалюминий қорытпасы тотықсыздандырғыш ретінде қолданылды. Алынған лигатураның химиялық құрамы зерттеліп, оның өндірістік мақсаттарға сәйкес келетіндігі анықталды. Зерттеу нәтижелері хроммарганецті лигатураны тиімді алу үшін оңтайлы температуралық және уақыттық параметрлерді белгілеп, оның сапасын арттыруға мүмкіндік берді. Бұл жұмыс болашақта металлургиялық өндірістерде хроммарганецті лигатураны өндірудің тиімді әдістерін енгізуге мүмкіндік береді. Сонымен қатар, Қазақстанның табиғи қорларын тиімді пайдалану арқылы отандық металлургия саласында жаңа технологиялық жетістіктерге қол жеткізуге жол ашады. Manganese ; Chromium ; Ligature ; Environmental science ; Metallurgy ; Materials science ; Medicine Thermal and Kinetic Analysis ; Inorganic and Organometallic Chemistry ; Advanced materials and composites
Authors: Jing Fu | Zhifan Lin | Jian Luo | Yufan Zheng | Yuhao Liu | Bin Cao | Fanghui Yin | Liming Wang
Source: Materials (article, 2025-08-15)
Field: Materials Science — Materials Chemistry

Understanding the Interfacial Behavior of Cycloaliphatic-like Epoxy Resin with Optical Fibers: Insights from Experiments and Molecular Simulations Optical fiber composite insulators are essential for photoelectric current measurement, yet insulation failure at embedded optical fiber interfaces remains a major challenge to long-term stability. This study proposes a strategy to replace conventional silicone rubber with cycloaliphatic-like epoxy resin (CEP) as the shed-sheathing material. Three optical fibers with distinct outer coatings, ethylene-tetrafluoroethylene copolymer (ETFE), thermoplastic polyester elastomer (TPEE), and epoxy acrylate resin (EA), were evaluated for their interfacial compatibility with CEP. ETFE, with low surface energy and weak polarity, exhibited poor wettability with CEP, resulting in an interfacial tensile strength of 0 MPa, pronounced dye penetration, and rapid electrical tree propagation. Its average interfacial breakdown voltage was only 8 kV, and the interfacial leakage current reached 35 μA after hygrothermal aging. In contrast, TPEE exhibited high surface energy and strong polarity, enabling strong bonding with CEP, yielding an average interfacial tensile strength of approximately 46 MPa. Such a strong interface effectively suppressed electrical tree growth, increased the average interfacial breakdown voltage to 27 kV, and maintained the interfacial leakage current below 5 μA even after hygrothermal aging. EA exhibited moderate interfacial performance. Mechanism analysis revealed that polar ester and ether groups in TPEE enhanced interfacial electrostatic interactions, restricted the mobility of CEP molecular chain segments, and increased charge traps. These synergistic effects suppressed interfacial charge transport and improved insulation strength. This work offers valuable insight into structure-property relationships at fiber-resin interfaces and provides a useful reference for the design of composite insulation materials. Materials science ; Composite material ; Epoxy ; Ultimate tensile strength ; ETFE ; Polyester ; Elastomer ; Wetting High voltage insulation and dielectric phenomena ; Advanced Fiber Optic Sensors ; Semiconductor materials and devices
Authors: Jorge Benjamín Diaz-López | Diana De la Cruz-Gumeta | Gabriela Alvarado-Arguello | Oscar Rico-Domínguez | Evelyn Valdez-Rodríguez | Karina Hernández-Ovalle | María Celina Luján-Hidalgo | Rosa Isela Cruz‐Rodríguez | Rocío Meza‐Gordillo
Source: Journal of Materials Science Materials in Engineering (article, 2025-07-04)
Field: Materials Science — Materials Chemistry

Optimization of silver nanoparticles synthesis using Elaeis guineensis Jacq. residue extract and their antibacterial activity against Pseudomonas aeruginosa Abstract This study explores the green synthesis of silver nanoparticles (Ag NPs) using oil palm ( Elaeis guineensis ) residue as a reducing agent. The synthesis was optimized by analyzing the effects of pH, silver nitrate (AgNO 3 ) concentration, and extract-to-AgNO 3 ratios using a Taguchi L9 design. The highest yield theorist (72%) was achieved under the conditions of pH 10, 100 mM AgNO 3 concentration, and a 2:3 extract-to-AgNO 3 ratio. The synthesized Ag-NPs were characterized through UV–Vis (400–450 nm), Fourier-transform infrared spectroscopy (FT-IR, 1370 cm -1 , attributed to the nitro group), dynamic light scattering (DLS, 10.07 nm average particle size with a hydrodynamic diameter (Dh) of 235.82 nm in a neutral pH), zeta potential (− 18.33 mV), scanning electron microscopy (SEM), and X-ray diffraction (XRD). Antimicrobial testing against Pseudomonas aeruginosa revealed antibacterial activity, making these nanoparticles a promising alternative to traditional antibiotics. Graphical Abstract Pseudomonas aeruginosa ; Antibacterial activity ; Residue (chemistry) ; Elaeis guineensis ; Silver nanoparticle ; Chemistry ; Botany ; Food science ; Bacteria ; Biology ; Nanoparticle ; Materials science ; Organic chemistry ; Palm oil ; Nanotechnology Nanoparticles: synthesis and applications ; Moringa oleifera research and applications ; African Botany and Ecology Studies
Authors: Pratibha Singh | Babu Nand Choudhary
Source: Journal of Biochemistry International (article, 2025-04-02)
Field: Chemistry — Organic Chemistry

Exploring Novel Quinoxaline Derivatives as Potent Antiviral Agents: Synthesis and Biological Insights The present study explores about synthesis and biological Insights of Novel Quinoxaline Derivatives as Potent Antiviral Agents. Viral infections are a big public health issue and we need antiviral agents. There has been a surge in interest regarding quinoxaline derivatives during recent years owing to the large array of therapeutic qualities they possess. A variety of analyses conducted between the years 2020 to 2024 have proved to be useful for any biological application including anticancer, antimicrobial, antiviral, anti-inflammatory and even antidiabetic. Due to their ability to treat a wide range of conditions, these compounds are promising candidates for subsequent drug design. The results of this study indicate that several quinoxaline-based compounds have promising antiviral activity against various viruses. The compounds showed the highest activity against influenza and herpes simplex viruses, which are major causes of morbidity worldwide. The presence of the quinoxaline ring, along with appropriate substitutions, appears to enhance antiviral activity. Additionally, the compounds were non-toxic to host cells, making them potential candidates for further development. In this study, we report quinoxaline derivatives as promising broad-spectrum antiviral agents with a special focus on their activity against respiratory viruses. This is an important area and it is in the general outline of the systematic review. Quinoxaline ; Combinatorial chemistry ; Chemistry ; Computational biology ; Biology ; Organic chemistry Synthesis and Biological Evaluation ; Synthesis and Reactions of Organic Compounds
Authors: Michael Thanga | Lalhmangaihzuali | Jugabrat Sarma | Zonunmawii | Aroos Rauf Rafiqu | Mohammad Hayat Bhat
Source: Archives of Current Research International (review, 2025-09-17)
Field: Materials Science — Biomaterials

A Review on Recent Advances in Silk Sericin, Extraction, Properties, Applications and Future Perspectives Silk sericin, traditionally considered a waste by-product of the silk industry has recently gained attention as a multifunctional biomaterial with applications across biomedical, cosmetic, nutraceutical and environmental sectors. Its unique amino acid composition and bioactive properties including antioxidant, antimicrobial, anti-inflammatory and moisturizing effects make it a promising candidate for next-generation products. The aim of this review is to consolidate recent advances in the extraction methods, structural properties and diverse applications of silk sericin, while highlighting its translational potential from laboratory research to industrial implementation. Special emphasis is given to sustainable recovery approaches and its role in the circular bioeconomy aligning with global priorities in green chemistry and waste valorization. In addition, the review identifies research gaps such as standardization of large-scale extraction, regulatory approval challenges and the need for long-term safety studies. By addressing these gaps future research can accelerate the transition of sericin from a discarded effluent into a high value biomaterial that bridges sericulture with modern healthcare, industry and sustainability. Standardization ; Nutraceutical ; Sustainability ; Biomaterial ; Sericin ; Sericulture Silk-based biomaterials and applications ; Phytochemical compounds biological activities ; Biochemical and Structural Characterization
Authors: Razvan Rotaru | Elena Ungureanu | Bogdan M. Tofănică | Ovidiu C. Ungureanu | Maria E. Fortună
Source: Inorganics (article, 2025-11-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

New Electromagnetic Shielding Materials Based on Viscose/Maghemite/Goethite/Polysiloxane In this study, we present a convenient approach for the preparation of viscose, maghemite, goethite, and poly(methylhydro-dimethyl)siloxane hybrid materials possessing electromagnetic shielding properties, thermal stability, strong magnetization, and very good hydrophobicity. The chemical compositions, morphologies, thermal properties, magnetic measurements, wettability, and dielectric properties of the prepared composites and pristine precursors were thoroughly investigated by Fourier transform infrared spectroscopy (FTIR), scanning and transmission electron microscopy (SEM and TEM), thermal degradation (TG, DTG, and DTA), magnetic measurements (magnetization, thermomagnetic curves, relative magnetic permeability), and dielectric spectrometry. Moreover, the electromagnetic shielding properties of pristine viscose and the final composite were assessed. Electromagnetic shielding ; Dielectric ; Thermomagnetic convection ; Permittivity ; Scanning electron microscope ; Thermal ; Fourier transform infrared spectroscopy ; Electromagnetic radiation Electromagnetic wave absorption materials ; Radiation Shielding Materials Analysis ; Polymer Nanocomposites and Properties
Authors: Rajesh Kushwaha | Sandeep Pokharia | Nagraj Mani | Albela Minj
Source: Journal of Scientific Research and Reports (article, 2025-01-06)
Field: Chemistry — Organic Chemistry

Quantum Chemical Calculation and in silico Molecular Modelling Studies on Some Multi-targeting Anti-Inflammatory Inhibitors Problem Statement: Inflammation plays a key role in many chronic diseases, including autoimmune disorders and arthritis. There are some of the natural compounds, which possess anti-inflammatory properties and offer significant therapeutic potential. Computer-aided molecular design helps to discover the Quantitative Structure Activity Relationship (QSAR) of the molecules. Aims: This study aims to provide a deeper understanding of the electronic structure and reactivity behaviour of natural compounds to design novel leads with enhanced biological activity. Methodology: Natural anti-inflammatory molecules from plant and marine sources were identified through an extensive literature review. Compounds such as Oleocanthal, Viridicatin, Liquiritin, and Nobiletin were selected for analysis. Their electronic structures were studied using Density Functional Theory (DFT) with the B3LYP functional and 6-311G(d,p) basis set to calculate geometric and electronic parameters, reactivity descriptors, and molecular electrostatic potential (MEP) maps. Physicochemical properties and drug-likeness were assessed through in silico ADMET studies. Finally, molecular docking simulations were performed to evaluate the binding affinities and interactions of the selected compounds with key inflammatory targets, including IL-17, MAPK, TNF-α, and MMP-9. Results: The DFT calculations revealed critical insights into the electronic distribution and reactivity patterns of the compounds, identifying potential interaction sites. ADMET predictions confirmed favourable drug-likeness and safety profiles, while docking studies highlighted significant binding affinities, particularly for Liquiritin, which demonstrated the highest binding efficiency among the tested compounds. Conclusion: By integrating quantum chemical calculations and molecular modelling, this study provides a comprehensive framework for exploring the QSARs of natural anti-inflammatory compounds. The findings contribute to the rational design and discovery of novel anti-inflammatory drugs with improved therapeutic potential. In silico ; Quantum chemical ; Anti-inflammatory ; Computational biology ; Chemistry ; Computational chemistry ; Pharmacology ; Biology ; Biochemistry ; Molecule ; Organic chemistry ; Gene Synthesis and biological activity ; Computational Drug Discovery Methods ; Synthesis of β-Lactam Compounds
Authors: Xiaopeng Wang | Songyuan Li | Zhen‐Yu Yin | Qi Zhang | Lei Deng | Yiqin Peng | Yan Mi
Source: Micromachines (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

The Influence of Pulsed Superimposed DC Electric Field Synergistically Inducing Orientation Arrangement of BNNSs on Thermal Properties of Epoxy Composites Modern power systems require better heat dissipation and thermal stability, but traditional low-filler composites cannot significantly enhance thermal conductivity. To address this issue, electric field induction technology orientation can efficiently orient boron nitride nanosheets (BNNSs), thereby improving the thermal conductivity of epoxy composites composed of BNNSs as the thermally conductive filler. In this study, an innovative approach employing a pulsed superimposed direct current (DC) electric field to synergistically induce filler orientation is used to construct efficient thermally conductive channels. The study found that the thermal conductivity of the composite prepared by superimposing an 8 kV/mm pulsed electric field on a 30 V/mm DC electric field is about 0.474 W/(m·K), which is 34.66% higher than that prepared by only a pulsed-induced field and 17.5% higher than the theoretical superposition value. Similarly, the composite prepared by superimposing a 4 kV/mm pulsed electric field on a 70 V/mm DC electric field increased to about 0.464 W/(m·K), which is 27.47% higher than that prepared by only a DC-induced field and 12.4% higher than the theoretical superposition value. These results indicate that the superimposed electric field treatment synergistically improves the thermal conductivity of the composite. Compared to other materials, composites prepared using the superimposed pulsed and DC electric field induction also exhibit superior thermal stability. This strategy effectively addresses the issue of material thermal aging caused by insufficient thermal conductivity, providing innovative ideas and a solid theoretical foundation for material design and thermal management. Electric field ; Boron nitride ; Electrical conductor ; Thermal conductivity ; Composite number ; Superposition principle ; Thermal ; Epoxy Thermal properties of materials ; Carbon Nanotubes in Composites ; Fiber-reinforced polymer composites
Authors: Fuquan Gao | Shuyan Wang | Xiaomin Yu
Source: Beverage Plant Research (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Machine learning in tea industry: data-driven approaches for quality and sustainability Tea holds significant cultural, economic, and nutritional value globally, yet the industry faces persistent challenges including quality inconsistency, climate change impacts, labor shortages, and inefficient conventional production methods. Machine learning (ML) has emerged as a powerful solution to address these challenges through data-driven decision-making, automation, and resource optimization across the entire production chain. This review systematically examines ML applications throughout tea industry, from cultivation and harvesting to processing and quality assessment. We analyze the development of comprehensive ML pipelines encompassing data acquisition, feature engineering, model development, optimization, evaluation, and deployment. Key technological advancements include automated monitoring systems for enhanced productivity, non-destructive spectroscopic and imaging techniques for improved quality assessment, and precision resource management for sustainable production. Despite its transformative potential, widespread ML adoption faces implementation barriers, including limited data availability, scalability issues, and integration with established practices. To overcome these barriers, we highlight strategic approaches such as advanced preprocessing techniques and domain-specific feature engineering to mitigate data limitations, resource-efficient ML architectures tailored for constrained environments, and user-centered interfaces that effectively bridge computational insights with traditional expertise. By synthesizing theoretical frameworks with practical implementation strategies, this review provides researchers, industry stakeholders, and practitioners with essential knowledge to advance sustainable and efficient tea industry through targeted ML integration. Quality (philosophy) ; Sustainability ; Scalability ; Resource efficiency ; Production (economics) ; Transformative learning ; Resource (disambiguation) ; Data pre-processing Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies
Authors: Mantas Baksys | Stefan Zetzsche | Olivier Bouissou | Rémi Delmas | Soonho Kong | Holden, Sean B.
Source: ArXiv.org (preprint, 2025-12-11)
Field: Materials Science — Materials Chemistry

ATLAS: Automated Toolkit for Large-Scale Verified Code Synthesis Large language models have become proficient at generating functional code, but ensuring the output truly matches the programmer's intent remains difficult. Testing improves trust, yet for safety-critical applications, formal verification provides the only true guarantees through machine-checked proofs. However, verified code remains scarce compared to mainstream languages or mathematical theorem proving, limiting LLM capabilities in this domain. We present ATLAS, an automated pipeline that synthesizes verified programs to address this data bottleneck. Applied to the TACO dataset of Python solutions to LeetCode-style problems, ATLAS generates 2.7K verified Dafny programs, each with high-quality specifications and machine-checked proofs. Through task decomposition, we extract 19K training examples. Fine-tuning Qwen 2.5 7B Coder on this data improves performance from 32.4% to 56.9% on DafnyBench and from 15.8% to 65.8% on DafnySynthesis, demonstrating that synthetic data generation is a viable path to scaling LLM capabilities for formal verification. Python (programming language) ; Limiting ; Program code ; Code (set theory) ; Code generation ; Software ; Pipeline (software) ; Debugging ; Task (project management) Machine Learning in Materials Science ; Logic, programming, and type systems ; Scientific Computing and Data Management
Authors: Jiaxin Tan | Edgar Acosta
Source: Liquids (article, 2025-09-08)
Field: Chemistry — Organic Chemistry

Bifunctional HLD–NAC for Clove Oil Microemulsions Clove oil is an essential oil used in food and pharmaceutical applications, with a market value of 300+ million dollars per year. Microemulsions have been used as effective clove oil delivery vehicles and could also be used to develop new extraction processes from clove buds. Eugenol, the main component of clove oil, is a polar oil that behaves as a surfactant and as an oil. This bifunctional behavior makes formulating clove oil microemulsions a challenging task. Here, we used a version of the Hydrophilic–Lipophilic Difference (HLD) + Net-Average Curvature (NAC) model that incorporates the bifunctional polar oil model to predict and fit the phase behavior of lecithin (surfactant) + polyglycerol-10 caprylate (hydrophilic linker) microemulsions using mixtures of heptane and clove oil as the oil phase. Using HLD-NAC parameters from the literature, the predicted HLD-NAC curves reproduced the expected phase transitions and the trends in Eugenol segregation toward the surfactant layer. Using these literature parameters as an initial guess to fit the experimental phase volumes produced accurate calculated phase volumes, and predicted interfacial tensions. This work demonstrates the application of heuristics and databases of HLD-NAC parameters in predicting the complex phase behavior of surfactant–oil–water (SOW) systems. Microemulsion ; Bifunctional ; Heptane ; Pulmonary surfactant ; Phase (matter) ; Eugenol ; Lecithin ; Distillation Surfactants and Colloidal Systems ; Food Chemistry and Fat Analysis ; Proteins in Food Systems