jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: HU Dan, CUI Yuting, ZHOU Haihe, LIU Yingli
Source: SHILAP Revista de lepidopterología (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Optimization of Process Parameters Using Improved Dung Beetle Algorithm As an important foundation of materials genome engineering, the establishment of materials database and the optimization of process parameter have an important impact on materials research and development. To address current challenges such as traditional relational databases being inadequate for storing multi-source, high-dimensional, and heterogeneous alloy data, and the urgent need to shift from the traditional trial-and-error experimentation to a data-driven research paradigm, this paper proposes to establish an alloy graph database based on ontology and graph data models. Based on this foundation, an improving dung beetle optimization (IDBO) algorithm is introduced to optimize combinations of process parameters. The algorithm enhances global search capability by initializing the population using inverse learning and updating dung beetle position using a variable spiral search strategy. To avoid local optima, Cauchy mutation perturbation and hybrid strategies are fused around the optimal solution to generate new candidates. To verify the performance of the proposed IDBO algorithm, it was tested on 23 classical benchmark functions, with results showing that IDBO algorithm outperforms other algorithms and significantly improves the convergence speed and optimization accuracy. Furthermore, when applied to the optimization of alloy process parameters, the IDBO algorithm not only demonstrated superior performance compared to other optimization algorithms but also successfully identified the optimal parameter combinations, validating its superiority in process parameter optimization. Dung beetle ; Population ; Initialization ; Benchmark (surveying) ; Graph ; Process (computing) ; Optimization algorithm ; Convergence (economics) ; Meta-optimization Machine Learning in Materials Science ; Process Optimization and Integration ; Catalysts for Methane Reforming
Authors: Abayneh Lemma | Tamra Legron-Rodriguez
Source: Journal of Chemical Education (article, 2025-09-01)
Field: Chemistry — Physical and Theoretical Chemistry

No Such Things as Sodium and Chlorine Atoms: A Debate with Copilot One can find expressions such as “building blocks of matter”, “sodium and chlorine atoms in sodium chloride”, “atoms make up all substances”, “bromine replaced chlorine atoms in SN2 substitution”, or “the number of sodium atoms in the reactant and product sides” in books, articles, and personal communications. Some argue that such expressions create obstacles for students in understanding fundamental chemistry concepts like the particulate nature of matter, colligative properties, physical and chemical changes, and more advanced topics such as emergence and supervenience. Given students’ wide use of artificial intelligence (AI) tools such as Copilot and ChatGPT, we examined the nature of expressions AI generates to such prompts and drew ontological and conceptual implications. We developed a prompting protocol and used it for a debate with Copilot. We noted that Copilot generates similar types of misleading expressions. However, as the debate continued, Copilot adjusted its responses in a more adaptive way, gradually refining its explanations. Maintaining such an adaptive correction can be challenging for students. So, it is crucial to help students develop the desired evaluative habits through course assignments and discussions. Chlorine ; Sodium ; Chemistry ; Engineering physics ; Aeronautics ; Astrobiology ; Environmental science ; Engineering ; Physics ; Organic chemistry History and advancements in chemistry
Authors: Weihua Wang | Chenxin Gong | Mingming Chen | Nan Zhang | Jingqing Zhang | Jing Shi | Hongxing Liu | Jiawei Teng | Jianqiang Wang | Yongfeng Hu
Source: Advanced Science (article, 2025-11-14)
Field: Chemistry — Inorganic Chemistry

Application of Synchrotron Radiation Based X‐Ray Diffraction in Zeolite Research: Advanced Analysis from Atomic Structure to Dynamic Behavior Zeolites, as crystalline materials with regular pore channels, are widely utilized in energy, environmental, and advanced manufacturing sectors. Characterizing zeolites is crucial for understanding their structure and properties, which are essential for various applications. Synchrotron Radiation-based X-ray Diffraction (SR-XRD) has become an advanced tool in zeolite research, providing higher resolution, faster scans, and more precise structural information than laboratory X-ray diffraction methods. This technique allows for detailed studies, from atomic structures to dynamic behaviors, particularly in understanding structural evolution during synthesis and monitoring changes in the framework during reactions. Moreover, SR-XRD has made significant contributions to catalytic research by revealing structural alterations during catalytic processes and identifying active sites. However, SR-XRD still faces challenges in data interpretation and other technological limitations. To overcome these, integrating SR-XRD with other techniques and using AI-assisted analysis are expected to further advance zeolite characterization and catalytic research. Zeolite ; Synchrotron radiation ; Characterization (materials science) ; Diffraction ; Synchrotron ; X-ray crystallography Zeolite Catalysis and Synthesis ; X-ray Diffraction in Crystallography ; Advanced NMR Techniques and Applications
Authors: Ashutosh Sengar
Source: Preprints.org (preprint, 2025-08-13)
Field: Materials Science — Biomaterials

Comprehensive Developments in Targeted Drug Delivery Using Liposomes Nanoparticles and Vesicular Systems Targeted drug delivery systems have progressed from basic liposomal carriers of the past to cutting-edge nanocarriers and microrobotic platforms with precise high precision navigation and controlled release. The present article is an elaborate review of the fundamental design principles, classification, and mode of action of liposomes and new developments in nanoparticle-based delivery systems such as polymeric nanoparticles, mesoporous silica nanocarriers, and hybrid lipid-polymer platforms. Less complex vesicular delivery systems, such as proniosomes and pH-sensitive carriers, are described in terms of enhanced stability, bioavailability, and patient compliance. Bioinspired carriers, specifically extracellular vesicles, possess inherent targeting competency and immune tolerability, and hybrid methods and plant-derived molecules offer additional therapeutic modes. Insertion of magnetic microrobots and programable lipid nanoparticles introduces an active and autonomous dimension to drug delivery with target spatial and temporal control. But their evolution into a clinical product entails overcoming stability, mass production, regulatory approval, and long-term safety. In the future, the union of artificial intelligence, real-time biosensing, and adaptive smart carriers promises the emergence of a new generation of personalized nanomedicine. The review highlights the interdisciplinarity of the area and encourages consideration to the revolutionary promise of developing next-generation targeted drug delivery systems by uniting biomimicry, engineering, and computational intelligence. Liposome ; Drug delivery ; Nanotechnology ; Targeted drug delivery ; Nanoparticle ; Business ; Chemistry ; Materials science Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; RNA Interference and Gene Delivery
Authors: Haoyang Wu | Anna C. Doner | Hao‐Wei Pang | William H. Green
Source: The Journal of Physical Chemistry A (article, 2025-04-20)
Field: Chemistry — Organic Chemistry

Toward Accurate Quantum Mechanical Thermochemistry: (2) Optimal Methods for Enthalpy Calculations from Comprehensive Benchmarks of 284 Model Chemistries Accurate and efficient computations of the standard enthalpies of formation (Δ<i>H</i><sub>f</sub><sup>°</sup>) for small organic molecules are crucial for diverse chemical engineering and scientific applications. Building on part 1 of this work [<i>J. Phys. Chem. A</i> <b>2024</b>, 128, 21, 4335-4352], we systematically benchmark 284 model chemistries for Δ<i>H</i><sub>f</sub><sup>°</sup> computations. These methods span semiempirical approaches, density functional theory (DFT), wave function theory, and composite schemes. We derive Petersson- and Melius-type bond-additivity corrections (BACs) for each method using a curated database of 421 reference species. We further validate the top-performing methods using an independent test set of 500 species, including ions, radicals, and other challenging cases. Across nearly all methods and species, BACs significantly improve accuracy, especially for neutral singlet species. Composite schemes coupling moderate-level DFT geometries with local coupled-cluster single-point energies strike an excellent balance between cost and accuracy, often approaching chemical accuracy (≤1 kcal/mol). Notably, DLPNO-CCSD(T)-F12d/cc-pVTZ-F12//ωB97X-D/def2-TZVPD with Petersson BAC attains the benchmark-best mean absolute error (MAE) of 0.57 kcal/mol. Switching to DLPNO-CCSD(T)-F12d/cc-pVDZ-F12//GFN2-xTB reduces the computational cost by an order of magnitude, with only a modest increase in MAE (0.96 kcal/mol). Although carefully tuned model chemistries can also benefit charged and open-shell species, the scarcity of robust reference data in these areas highlights the need for broader, high-accuracy thermochemistry datasets. Overall, this benchmark provides practical guidance on selecting optimal model chemistries to efficiently compute accurate Δ<i>H</i><sub>f</sub><sup>°</sup> under varied computational constraints and molecular complexities, laying a foundation for large-scale, high-throughput thermochemical calculations that will support data-driven discovery and industrial applications. Thermochemistry ; Enthalpy ; Standard enthalpy change of formation ; Thermodynamics ; Computational chemistry ; Chemistry ; Physics Chemical Thermodynamics and Molecular Structure ; Thermal and Kinetic Analysis ; Various Chemistry Research Topics
Authors: Yueting Wu | Yanlong Li
Source: ACS Applied Materials & Interfaces (review, 2025-08-09)
Field: Materials Science — Biomaterials

Nanocellulose Functional Materials for Applications in Intelligent Winter Sports: Recent Progress and Prospects The dual goals of digital transformation and carbon neutrality have driven the interest in high-performance functional materials and environmental sustainability associated with their applications. As a new type of natural material, nanocellulose (NC) has a unique nanomorphology, high crystallinity, excellent mechanical properties, and a high specific surface area that enables the realization of fine nano/microscale structures. Therefore, NC is expected to replace traditional petrochemical sources in the production of various high-value-added functional materials, especially those that have applications in intelligent winter sports. Intelligent winter sports is the intelligent transformation and upgrading of winter sports by using modern information technology. Among them, the combination of artificial intelligence and functional materials has brought revolutionary changes in the field of winter sports health monitoring, monitoring, and sports performance improvement. However, to date, applications of NC in intelligent winter sports have not been extensively researched. Therefore, this report comprehensively summarizes the applications of NC in intelligent winter sports. First, the different forms of NC involved in applications in intelligent winter sports and the emerging strategies for the synthesis of functional materials are introduced. Second, the performance advantages of NC in intelligent winter sports are examined. Specifically, its practical and potential applications in human thermal management, detection of stimulants, action monitoring, and motion bioelectrical signal monitoring were considered. Finally, constructive views on the development of NC functional materials and their challenges and prospects in applications in intelligent winter sports are proposed. Bionics ; Computer science ; Nanotechnology ; Systems engineering ; Materials science ; Artificial intelligence ; Engineering Advanced Cellulose Research Studies ; Nanocomposite Films for Food Packaging ; Advanced Sensor and Energy Harvesting Materials
Authors: Ying Xu | Xuan Tao | Liubin Li | Yao Chen | Rong He | Xingrong Ju | Zhigao Wang
Source: Critical Reviews in Food Science and Nutrition (review, 2025-07-29)
Field: Materials Science — Materials Chemistry

Advances in natural biomacromolecule-based Janus materials: preparation, applications and future prospects in food science This review provides an in-depth exploration of Janus materials based on natural biomacromolecules (polysaccharides, proteins, and lipids). The unique asymmetric structure and multifunctionality of Janus materials endow them with broad application potential in food science and beyond. In terms of preparation, the review details several cutting-edge methods, including Pickering emulsions, asymmetric acylation, one-pot <i>in situ</i> methods, microfluidics, electrospinning, and electrospraying, which collectively support the efficient synthesis and functionalization of Janus materials. Regarding applications, these materials can serve as antimicrobial agents to extend food shelf-life, as thickeners and emulsifiers to enhance food stability and texture, and in the precise delivery of bioactive substances. Although previous studies have touched on the applications of Janus materials in food science, a comprehensive and systematic review focusing on those derived from natural biomacromolecules has been lacking. This review fills that gap, providing a crucial theoretical foundation. However, several challenges remain for the widespread application of Janus materials, including difficulties in scaling up production processes, issues with the repeatability and long-term stability of products, and complex regulatory requirements. Future research directions should focus on developing eco-friendly preparation strategies and addressing safety and regulatory concerns through interdisciplinary collaboration. Additionally, AI-driven material design holds promise for accelerating the optimization and innovation of Janus materials, thereby promoting their extensive application in food science and related fields to enhance food safety and quality. Nanotechnology ; Janus ; Biochemical engineering ; Natural materials ; Data science ; Chemistry ; Polymer science ; Materials science ; Engineering ; Computer science Pickering emulsions and particle stabilization ; Algal biology and biofuel production ; Proteins in Food Systems
Authors: Xiaochen Zhou | Weihua Wu | Han Gu | Pei Zhang | Bowen Fu | Yu Li | Z. Zhou | Jiwei Zhai
Source: Materials Science in Semiconductor Processing (article, 2025-06-08)
Field: Materials Science — Materials Chemistry

Recent progress in performance optimization of Sb-Te materials for data storage by doping modification and multilayer structure Materials science ; Doping ; Computer data storage ; Nanotechnology ; Chemical engineering ; Process engineering ; Optoelectronics ; Computer science Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Advanced Semiconductor Detectors and Materials
Authors: Qi Xin | Meishuai Liu | Ruonan Wang | Peng Du | Jia Song | Libo Liu | Chun Li | Qi Wang | Yongping Bao
Source: Trends in Food Science & Technology (article, 2025-12-16)
Field: Materials Science — Biomaterials

Multifunctional whey protein composite films: Material design and AI integration for next-generation food packaging Food packaging ; Active packaging ; Whey protein ; Food industry ; Microplastics ; Food products ; Food safety Nanocomposite Films for Food Packaging ; Proteins in Food Systems ; Postharvest Quality and Shelf Life Management
Authors: Behrouz Nemati | Mohammadreza Kamali | Sara Zandi | Tejraj M. Aminabhavi | Raf Dewil | Maria Elisabete V. Costa | Isabel Capela
Source: Chemical Engineering Journal (article, 2025-10-09)
Field: Materials Science — Materials Chemistry

Simplified hydrogen storage in nanocomposites optimized by artificial intelligence Hydrogen storage ; Nanocomposite ; Nanomaterials ; Adsorption ; Porosity ; Energy storage ; Tube (container) ; Titanium ; Specific surface area Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Graphite, nuclear technology, radiation studies
Authors: Pengliang Qin | Linhui Zhao
Source: IEEE Transactions on Instrumentation and Measurement (article, 2025-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

A Novel Data-Driven Framework Under Limited Sensors for In-Pack Cell Temperature Estimation The battery pack consists of numerous cells, with only a limited number of temperature sensors placed on the surface of selected cells. This limitation hinders effective estimation and monitoring of the temperature for all cells within the pack. In comparison to model-based methods, data-driven methods avoid considering the complex heat generation and transfer mechanisms in batteries. However, conventional data-driven-based temperature estimation methods fail to fully utilize the limited measurable temperatures for obtaining more accurate estimations. This work designs a novel framework based on measurable voltage information to guide data-driven modeling for estimating in-pack cell temperatures. The designed framework employs correlation between measured voltages to identify a reference cell equipped with a temperature sensor that exhibits similar internal temperature states as the target cell. Consequently, this reference cell provides valuable modeling information for data-driven temperature estimation of the target cell. Furthermore, two specific methods of direct and indirect data-driven temperature estimation modeling are presented based on this framework. Extensive experimental results from various packs under different working conditions demonstrate that the proposed methods can achieve superior accuracy with maximum temperature estimation errors below 2.5 °C. Temperature measurement ; Estimation ; Computer science ; Engineering ; Physics ; Systems engineering thermodynamics and calorimetric analyses ; Advanced Control Systems Optimization ; Advanced Battery Technologies Research
Authors: Yang Liu | Quan Li | Jinqi Zhu | Bo Zhang | Jia Guo
Source: Electronics (article, 2025-09-24)
Field: Materials Science — Materials Chemistry

A Robust AI Framework for Safety-Critical LIB Degradation Prognostics: SE-VMD and Dual-Branch GRU-Transformer Lithium-ion batteries (LIBs) are critical components in safety-critical systems such as electric vehicles, aerospace, and grid-scale energy storage. Their degradation over time can lead to catastrophic failures, including thermal runaway and uncontrolled combustion, posing severe threats to human safety and infrastructure. Developing a robust AI framework for degradation prognostics in safety-critical systems is essential to mitigate these risks and ensure operational safety. However, sensor noise, dynamic operating conditions, and the multi-scale nature of degradation processes complicate this task. Traditional denoising and modeling approaches often fail to preserve informative temporal features or capture both abrupt fluctuations and long-term trends simultaneously. To address these limitations, this paper proposes a hybrid data-driven framework that combines Sample Entropy-guided Variational Mode Decomposition (SE-VMD) with K-means clustering for adaptive signal preprocessing. The SE-VMD algorithm automatically determines the optimal number of decomposition modes, while K-means separates high- and low-frequency components, enabling robust feature extraction. A dual-branch architecture is designed, where Gated Recurrent Units (GRUs) extract short-term dynamics from high-frequency signals, and Transformers model long-term trends from low-frequency signals. This dual-branch approach ensures comprehensive multi-scale degradation feature learning. Additionally, experiments with varying sliding window sizes are conducted to optimize temporal modeling and enhance the framework’s robustness and generalization. Benchmark dataset evaluations demonstrate that the proposed method outperforms traditional approaches in prediction accuracy and stability under diverse conditions. The framework directly contributes to Artificial Intelligence for Security by providing a reliable solution for battery health monitoring in safety-critical applications, enabling early risk mitigation and ensuring operational safety in real-world scenarios. Prognostics ; Robustness (evolution) ; Cluster analysis ; Benchmark (surveying) ; Adaptability ; Sliding window protocol ; Thermal runaway ; Degradation (telecommunications) High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Semiconductor materials and devices
Authors: David Balcells
Source: ChemRxiv (preprint, 2025-03-13)
Field: Materials Science — Materials Chemistry

Co-Intelligent Design of Catalysis Research with Large Language Models: Hype or Reality? Large language models are making a strong impact on broad fields of science and technology. Initial concerns like hallucinations have been largely mitigated in the latest models, enhancing their reliability. Co-intelligence, the synergy between human and artificial intelligences, has a strong potential in research when using large language models. This analysis focused on the question: Can large language models collaborate with chemists in the design of catalysis research? In a conversation where the author made a few prompts giving minimal guidance, GPT proved its ability to co-design a research project at different levels of detail. Besides a few, non-critical flaws, the project concept and workflows were provided by the model, making a major contribution to the research design. The assessment of the project by a committee including other models (DeepSeek and Gemini) was also investigated. Computer science Machine Learning in Materials Science