jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hao Qin | H. Shi | Tingting Guo | Geng Chen | Weicai Li | Jingbo Zhao | Jing Zhang | Yu Zhu | Ruiman Han | Wangqiao Chen | Tiejun Lv | Guanghui Li | Yongsheng Chen
Source: Advanced Materials (article, 2025-09-07)
Field: Materials Science — Materials Chemistry

Gbits/s‐Level Encrypted Spectral Wireless Communication Enabled by High‐Performance Flexible Organic Hyperspectrometer The exponential growth of data in the information era has pushed conventional optical communication technology to its limitations, including inefficient spectral utilization, slow data rate, and inherent security vulnerabilities. Here, a transformative high-speed organic spectral wireless communication (SWC) technology enabled by a flexible, miniaturized, and high-performance organic hyperspectrometer is proposed that integrates ultrahigh-speed data transmission with hardware-level encryption. By synergistically combining organic photodetector arrays with tunable responsivities and spectral-tunable organic filters, the organic hyperspectrometer achieves a broad spectral detection range of 400 to 900 nm, resolution of 1.08 nm, accuracy of 0.60 nm, and response time of 684 ns. Unlike prior optical wireless communication systems, the organic hyperspectrometer-based SWC platform uniquely decodes high-speed encrypted data at the hardware level, which is a breakthrough in secure, high-speed, and high-capacity communication. Harnessing the full visible-to-near-infrared spectrum, the SWC system achieves a theoretical data rate of 9.1 Gbits s<sup>-1</sup>, ranking as the highest-speed organic optical communication system. Furthermore, the intrinsic flexibility and bandgap-tunability of organic materials enable unparalleled portability, adaptability, and scalability of organic SWC, establishing a scalable framework for terahertz-scale data transmission. These advancements mark a pivotal leap toward secure, high-speed, and ultracompact optical networks for the future data- and AI-driven era. Encryption ; Decodes ; Scalability ; Wireless ; Data transmission ; Optical wireless ; Transmission (telecommunications) ; Visible light communication ; Flexibility (engineering) Luminescence and Fluorescent Materials ; Polydiacetylene-based materials and applications ; Organic Light-Emitting Diodes Research
Authors: Marvin Alberts | Nina Hartrampf | Teodoro Laino
Source: Analytical Chemistry (article, 2025-07-16)
Field: Chemistry — Spectroscopy

From Spectra to Structure: AI-Powered <sup>31</sup> P NMR Interpretation Phosphorus-31 nuclear magnetic resonance (<sup>31</sup>P NMR) spectroscopy is a powerful technique for characterizing phosphorus-containing compounds in diverse chemical environments. However, spectral interpretation remains a time-consuming and expertise-dependent task, relying on reference tables and empirical comparisons. In this study, we introduce a data-driven approach that automates <sup>31</sup>P NMR spectral analysis, providing rapid and accurate predictions of the local phosphorus environments. By leveraging a curated data set of experimental and synthetic spectra, our model achieves a Top-1 accuracy of 53.64% and a Top-5 accuracy of 77.69% at predicting the local environment around a phosphorus atom. Furthermore, it demonstrates robustness across different solvent conditions and outperforms expert chemists by 25% in spectral assignment tasks. The models, data sets, and architecture are openly available, facilitating seamless adoption in chemical laboratories engaged in structure elucidation, with the goal of advancing <sup>31</sup>P NMR spectral analysis and interpretation. Chemistry ; Interpretation (philosophy) ; NMR spectra database ; Spectral line ; Analytical Chemistry (journal) ; Computational chemistry ; Physical chemistry ; Nuclear magnetic resonance ; Organic chemistry ; Physics ; Quantum mechanics Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Advanced NMR Techniques and Applications
Authors: Nabil Fikri | Youssef Hattafi | Lamyae Mardi | Fahed El Amarty | Farah El Hassani | Lahcen Benaabidate | Abderrahim Lahrach
Source: Journal of Environmental Engineering and Science (article, 2025-03-22)
Field: Materials Science — Biomaterials

Production and application of biochar: a bibliometric–bibliographic study from 2004 to 2023 Biochar is a carbon-rich material that has attracted considerable interest and has generated an important literature across various academic disciplines in the past decade. Environmental applications of biochar include climate change mitigation, waste management, soil fertility improvement, and contaminant remediation. In this study, a bibliometric analysis was performed to visualise the current research status and emerging trends in the field of biochar production and application research. A total of 33 718 documents related to the topic of biochar were collected from the Scopus database, covering the period from 2004 to December 2023. These publications were analysed using the bibliometric tools provided by the Bibliometrix package R and VOSviewer. The analysis focused on publication output, subject areas, authors, journals, institutions, and countries. The leading subject area is Environmental Science, and most articles are published in the Journal of Environmental Science and Pollution Research. China and the USA are the leading countries contributing in the number of publications, with China producing 17 096 publications and the USA 4255 publications. Based on the keyword co-occurrence analyses, in the last 5 years, searches on biochar are mainly focused on ‘biochar for adsorption’, ‘Biochar for global warming mitigation’, and ‘Biochar for heavy metals immobilisation’. Finally, the literature review highlighted several underexplored areas in biochar research, mainly using artificial intelligence to determine optimal production parameters for specific biochar applications and the scarcity of thorough assessment of the entire biochar life cycle. This review offers essential insights for stakeholders and researchers on the most extensively studied topics related to biochar production and applications, helping to identify areas that have received less attention. Biochar ; Environmental science ; Production (economics) ; Environmental chemistry ; Chemistry ; Waste management ; Pyrolysis ; Engineering ; Economics Clay minerals and soil interactions ; Soil Carbon and Nitrogen Dynamics ; Geochemistry and Geologic Mapping
Authors: Mei Bie | Wei Zhao | Dong He | Yan Gu | Shiwei Yuan | Kai Song
Source: Sustainability (article, 2025-12-01)
Field: Materials Science — Biomaterials

From Agricultural Waste to Recyclable Biodegradable Packaging: A Systematic and Bibliometric Review Amidst the dual global pressures of plastic pollution and resource scarcity, the transition to a circular economy has become an imperative. The valorization of biomass waste from agricultural, food, and animal processing into biodegradable packaging materials presents a key strategy to address this challenge. This review aims to systematically construct a comprehensive knowledge framework for the field, addressing the thematic fragmentation and methodological limitations of existing literature through integrated cross-stream analysis, combined bibliometric and technological assessment, and identification of emerging research frontiers. We begin with a bibliometric analysis to delineate the field’s evolutionary trajectory since 2008, global collaboration networks, core research themes, and emerging frontiers, revealing a clear progression from environmental impact assessment to functional material innovation. Subsequently, this review delves into the complete technological chain, from the green extraction of bio-based materials from three major waste streams to the comparison of traditional and advanced film fabrication methods. We then elaborate on the critical performance evaluation dimensions, including mechanical, barrier, biodegradable, safety, and functional properties, and summarize current applications in sectors such as food and medicine. Finally, we critically assess the core challenges related to cost, performance stability, and large-scale production, and provide a systematic outlook on future research directions, particularly the development of high-performance, multifunctional, and intelligent materials. This review offers a comprehensive and data-driven reference framework for researchers and industry stakeholders in the field. Circular economy ; Agriculture ; Resource (disambiguation) ; Identification (biology) ; Construct (python library) ; Environmental pollution ; Natural resource Nanocomposite Films for Food Packaging ; biodegradable polymer synthesis and properties ; Consumer Packaging Perceptions and Trends
Authors: Movaffaq Kateb | Sahar Safarian
Source: Machine Learning with Applications (article, 2025-11-06)
Field: Materials Science — Materials Chemistry

Machine learning-driven predictive modeling of temperature-dependent mechanical properties in austenitic stainless steels This work demonstrates that modern tree‑based models can effectively model complex, temperature-dependent mechanical responses, including highly nonlinear and even non-monotonic trends, in austenitic stainless steel and highlights limitations of composition‑only empirical models. To ensure robust model evaluation, we employed multiple validation strategies including repeated random train and test partitions and leave-one-out cross-validation. While one might assume that steel grade is fully captured by its composition, local assessments within narrower compositional ranges reveal different feature importance rankings than those observed in the full dataset. Grade-specific (AISI 304, 316, 321 and 347) feature importance analysis offered deeper insights into local alloy behavior and demonstrated the advantage of disaggregated modeling in avoiding misleading conclusions. Clustering and SHAP analyses further revealed a temperature-sensitive role of nitrogen, which strengthens the alloy through interstitial and fine precipitate mechanisms at lower temperatures but loses effectiveness at elevated temperatures due to precipitate coarsening. This highlights how data-driven methods can uncover metallurgically consistent, temperature-dependent strengthening behaviors not captured by simpler models. Our results confirm that temperature governs the mechanical performance of austenitic stainless steels, with other features contributing marginally, particularly for UTS. Additionally, the model achieved a notably high score for elongation, highlighting the critical role of testing temperature in addressing the long-standing challenge of poor elongation predictions in composition-only or composition-processing models. This suggests that low accuracy in previous studies is more likely due to dataset limitations rather than shortcomings of tree-based models. Austenite ; Alloy ; Work (physics) ; Austenitic stainless steel ; Nonlinear system ; Cluster analysis Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels
Authors: Xinde Wang | Zhirui Chen | Peng‐Jie Guo | Ze-Feng Gao | Cheng Mu | Zhong-Yi Lu
Source: ArXiv.org (preprint, 2025-07-22)
Field: Materials Science — Materials Chemistry

Perovskite-R1: A Domain-Specialized LLM for Intelligent Discovery of Precursor Additives and Experimental Design Perovskite solar cells (PSCs) have rapidly emerged as a leading contender in next-generation photovoltaic technologies, owing to their exceptional power conversion efficiencies and advantageous material properties. Despite these advances, challenges such as long-term stability, environmental sustainability, and scalable manufacturing continue to hinder their commercialization. Precursor additive engineering has shown promise in addressing these issues by enhancing both the performance and durability of PSCs. However, the explosive growth of scientific literature and the complex interplay of materials, processes, and device architectures make it increasingly difficult for researchers to efficiently access, organize, and utilize domain knowledge in this rapidly evolving field. To address this gap, we introduce Perovskite-R1, a specialized large language model (LLM) with advanced reasoning capabilities tailored for the discovery and design of PSC precursor additives. By systematically mining and curating 1,232 high-quality scientific publications and integrating a comprehensive library of 33,269 candidate materials, we constructed a domain-specific instruction-tuning dataset using automated question-answer generation and chain-of-thought reasoning. Fine-tuning the QwQ-32B model on this dataset resulted in Perovskite-R1, which can intelligently synthesize literature insights and generate innovative and practical solutions for defect passivation and the selection of precursor additives. Experimental validation of several model-proposed strategies confirms their effectiveness in improving material stability and performance. Our work demonstrates the potential of domain-adapted LLMs in accelerating materials discovery and provides a closed-loop framework for intelligent, data-driven advancements in perovskite photovoltaic research. Scalability ; Photovoltaic system ; Domain (mathematical analysis) ; Toolbox ; Work (physics) ; Selection (genetic algorithm) ; Scientific discovery Machine Learning in Materials Science
Authors: Yi Shi | Weimin Zhong | Xin Peng | Kaixun He | Dong Xue
Source: Chemical Engineering Science (article, 2025-02-12)
Field: Materials Science — Materials Chemistry

Molecular management in continuous catalytic reforming operations by enhanced aromatics production through transformer-driven entropy maximization reconstruction Maximization ; Entropy maximization ; Catalysis ; Entropy production ; Entropy (arrow of time) ; Transformer ; Chemistry ; Process engineering ; Mathematical optimization ; Computer science ; Mathematics ; Engineering ; Thermodynamics ; Organic chemistry ; Principle of maximum entropy ; Physics ; Electrical engineering ; Artificial intelligence ; Voltage Machine Learning in Materials Science ; Process Optimization and Integration ; Catalysis and Oxidation Reactions
Authors: Alper Durmaz | İbrahim Mizan Kahyaoğlu | Erdi Can Aytar | Ferdane Danışman Kalındemirtaş | Esra Sert | Ayşe Erol‐Bozkurt | Selcan Karakuş
Source: ChemistryOpen (article, 2025-01-29)
Field: Materials Science — Materials Chemistry

In Vitro, Anti‐Colon Cancer Activity of Green Dumbbell‐Shaped <i>Rhododendron luteum</i>‐Based Carbon Dots Colorectal cancer is the second most common cause of cancer-related deaths worldwide and the third most common cancer overall. In this study, we investigate the anti-colon cancer potential of phytochemically, and thermally synthesised novel green carbon dots based on Rhododendron luteum (RL-CDs). A new synthesis method was used to produce carbon dots obtained from the Rhododendron luteum (RL) plant in an environmentally friendly manner. The green RL were characterized using Fourier-transform infrared spectroscopy (FTIR), UV-Vis spectroscopy, transmission electron microscopy (TEM), and artificial intelligence (AI)-based TEM analysis. The FTIR spectrum showed peaks corresponding to the hydroxyl (-OH) vibration of polyphenols at 3500 cm<sup>-1</sup>, the C=O vibration of cellulose derivatives at 1728 cm<sup>-1</sup>, and the C-O stretching of primary alcohol at 1041 cm<sup>-1</sup>. Two UV absorption peaks at roughly 253 nm (UV-C range), and 320 nm (UV-B range) were observed. The size of the green RL was measured to be less than 50 nm, and its morphology was characterized as dumbbell-shaped through TEM analysis. In-vitro studies were performed with HCT116 colon cancer, MCF-7 breast cancer, and normal HUVEC cells. The results demonstrated that the RL-CDs exhibited selective cytotoxic activity against HCT116 colon cancer cells. The results show that the RL extract stimulates cancer cell death by decreasing the CD44/24 ratio, and increasing apoptotic activity. These observations suggest that green RL-CDs could be an effective anticancer agent in colon cancer therapy, investigating their potential in this direction could be a promising way for future research. Fourier transform infrared spectroscopy ; Cancer ; Colorectal cancer ; Chemistry ; Materials science ; Cancer cell ; Nuclear chemistry ; Nanotechnology ; Medicine ; Chemical engineering ; Internal medicine Carbon and Quantum Dots Applications ; Curcumin's Biomedical Applications ; Nanocluster Synthesis and Applications
Authors: Giovanni Nicodemo | G. Zullo | Antonio Cammi | L. Luzzi | D. Pizzocri
Source: Journal of Nuclear Materials (article, 2025-06-10)
Field: Materials Science — Materials Chemistry

Data assimilation for accelerated development of fuel performance codes: Towards automatic implementation of material properties correlations Developing fuel performance codes requires lengthy technical and legal verification procedures. This work proposes an approach to automate and facilitate these processes by using a data assimilation method. The method considered is Gaussian process regression, which allows for exploiting experimental data to correct/update material property correlations in fuel performance codes. As a demonstration of this method, the correlation for the single-atom xenon diffusivity is updated in the SCIANTIX code with recent lower-length scale results, without the need to modify the SCIANTIX code itself. The goal is to demonstrate the validity of this approach for data-driven developments of physics-based fuel performance codes. • A Gaussian Processes is applied to nuclear fuel modelling. • The method allows updating existing correlations with new data. • The output of the method is usable for online calculations. • Demonstrative case on xenon diffusivity update. • The method is extendable to cover a full set of models and properties. Data assimilation ; Assimilation (phonology) ; Computer science ; Materials science ; Nuclear engineering ; Environmental science ; Physics ; Engineering ; Meteorology ; Linguistics Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Machine Learning in Materials Science
Authors: Yongqing Zhao | Wen‐Hua Li | Xin‐Gui Tang | Zhongbao Jian | Yongxi Liang | Renkai Zhao | Kai‐Jie Chen | Yanping Jiang | Xiaobin Guo | Kai Yan
Source: Journal of Physics and Chemistry of Solids (article, 2025-05-12)
Field: Materials Science — Materials Chemistry

Significant enhancement of the negative electrocaloric effect in Mn-doped PbHfO3 films Electrocaloric effect ; Doping ; Materials science ; Condensed matter physics ; Optoelectronics ; Ferroelectricity ; Physics Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Electronic and Structural Properties of Oxides
Authors: Ankit Jain | Yagyank Srivastava | Amey G. Gokhale | Nidheesh Virakante | Hardik L. Kagdada
Source: Journal of the Indian Institute of Science (article, 2025-10-21)
Field: Materials Science — Materials Chemistry

Higher-Order Thermal Transport Theory for Phonon Thermal Transport in Semiconductors Using Lattice Dynamics Calculations and the Boltzmann Transport Equation Boltzmann equation ; Phonon ; Thermal conductivity ; Transport theory ; Lattice (music) ; Thermal ; Convection–diffusion equation ; Transport phenomena Thermal properties of materials ; Machine Learning in Materials Science ; Advancements in Semiconductor Devices and Circuit Design
Authors: Shahrukh Abid | Ching‐Chi Hsu | Song‐Jeng Huang | Mahnaz Mahmoudi Belvirdi
Source: Advanced Engineering Materials (article, 2025-12-21)
Field: Materials Science — Biomaterials

Explainable Artificial Intelligence‐Integrated Optimization and Experimental Validation of Mechanical Performance in Mg‐Based Composites The development of magnesium‐based metal matrix composites (MMCs) for biomedical implants is often hindered by the high cost and time associated with conventional trial‐and‐error optimization. In this study, we present an explainable ML framework to accelerate the design and prediction of mechanical performance in magnesium‐based alloys reinforced with HAP and a hybrid system of HAP and GNP. An ensemble of regression models was evaluated, with XGBoost demonstrating superior generalization capability, achieving R 2 values of 0.955 for yield strength, 0.954 for ultimate tensile strength, and 0.939 for % elongation. To ensure model transparency, SHAP was integrated to interpret feature contributions at both global and local levels. The analysis revealed that ECAP and weight percentage of reinforcement are the most influential factors, with nonlinear optimal ranges identified for weight percentage of reinforcement, particle size, and reinforcement. Guided by these insights, a new AZ91‐matrix composite incorporating a hybrid HAP and GNP reinforcement was designed and fabricated via stir casting. Experimental validation demonstrated excellent agreement between model predictions and measured mechanical responses, confirming the high‐fidelity framework. Residual analysis confirmed model robustness and homoscedasticity. This work establishes a transparent, data‐driven framework that bridges predictive modeling with physical interpretability, enabling the rational design of high‐performance, biodegradable implants with reduced reliance on extensive experimentation. Robustness (evolution) ; Composite number ; Ultimate tensile strength ; Reinforcement ; Residual ; Nonlinear system ; Generalization ; Tensile testing ; Matrix (chemical analysis) Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Cellular and Composite Structures
Authors: Eric T. Dziekonski
Source: International Journal of Mass Spectrometry (article, 2025-09-05)
Field: Chemistry — Spectroscopy

Two-dimensional tandem mass spectrometry in a linear quadrupole ion trap using consecutive product ion sweeps Chemistry ; Quadrupole ion trap ; Tandem ; Ion ; Ion trap ; Tandem mass spectrometry ; Quadrupole ; Top-down proteomics ; Mass spectrometry ; Selected reaction monitoring ; Analytical Chemistry (journal) ; Atomic physics ; Chromatography ; Aerospace engineering ; Organic chemistry Mass Spectrometry Techniques and Applications ; Microfluidic and Capillary Electrophoresis Applications ; Analytical Chemistry and Chromatography
Authors: Hyungyong Kim | Shao Gong Ju | Jaewon Cho | Eun Young Lee | Kyung-Ryul Lee | Yong‐Il Kim | Miso Kim
Source: Chemical Engineering Journal (article, 2025-06-10)
Field: Materials Science — Materials Chemistry

Fe2O3-assisted (1–x)(K, Na)NbO3–xSrZrO3 ceramics for X8R multilayer ceramic capacitors Ceramic ; Capacitor ; Ceramic capacitor ; Materials science ; Composite material ; Chemical engineering ; Electrical engineering ; Engineering ; Voltage Ferroelectric and Piezoelectric Materials ; Microwave Dielectric Ceramics Synthesis ; Multiferroics and related materials