jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: N. Nikolov
Source:  (article, 2025-11-12)
Field: Materials Science — Polymers and Plastics

DEVELOPMENT OF A POWDER FORMULATION FOR EFFECTIVE REMOVAL OF TOXIC CHEMICALS FROM TEXTILE MATERIAL The liquid toxic chemical in the textile material is retained by capillary forces in the pores of fibers with a diameter of less than 20 μm, therefore the main part of the liquid phase of TC cannot be removed by degassing powder compositions consisting of microparticles. The aim of the work is to develop a powder formulation that allows degassing porous textile materials contaminated with TC droplets. The development of such a formulation is based on the fact that in order to increase the proportion of the removed liquid phase of TC from the porous structure of the material, it is necessary to reduce the particle size of the degassing powder so that their diameter is smaller than the diameter of the pores formed by the fibers.. Textile ; Liquid phase ; Porosity ; Capillary action ; Raw material ; Porous medium ; Particle size Polymer Foaming and Composites ; Aerogels and thermal insulation ; Textile materials and evaluations
Authors: Zhejun Kuang | Yunkai Li | Yang Bao | Shengyang Zhou | Zhao Yang Dong | Weidi Wang | Guan Ning Lin | Han Wang | Zhe Liu
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-09-17)
Field: Materials Science — Materials Chemistry

Automating RNA-Ligand Interaction Modeling via a Self-Improving LLM Agent Abstract Precise modeling of RNA-ligand interactions is essential for understanding RNA functionality and designing RNA-targeted therapeutics. Current computational approaches largely focus on predicting discrete binding sites, limiting their applicability to complex RNA regions that may harbor multiple or diffuse ligand binding motifs. Here, we present RLAgent, an interactive agent framework designed to predict ligand interactions at the RNA region level, enabling higher-resolution and more flexible modeling than conventional site-centric approaches. RLAgent reframes the RNA-ligand prediction workflow as a dialogue-driven process. Through a natural language interface, users can interactively configure modeling preferences without writing code. A locally hosted large language model (LLM) acts as the core orchestration agent, automating all key components of the modeling pipeline, including data validation, feature encoding, model training, evaluation, and visualization. This agent-based design lowers technical barriers and enhances reproducibility, making RNA-ligand prediction more accessible for both computational and experimental researchers. Workflow ; Orchestration ; Key (lock) ; Limiting ; Feature (linguistics) ; Focus (optics) ; Modeling language ; Computational model Machine Learning in Materials Science ; Statistical and Computational Modeling ; Computational Drug Discovery Methods
Authors: Sangshuang Li | Huaimin Wang
Source: Wiley Interdisciplinary Reviews Nanomedicine and Nanobiotechnology (article, 2025-11-01)
Field: Materials Science — Biomaterials

Peptide Hydrogels for Postoperative Tumor Therapy: Enhancing Immune Modulation and Local Drug Delivery Postoperative tumor cavities provide a transient but targetable immune niche where local biomaterials can suppress residual disease and complement systemic therapy. Self-assembling peptidic hydrogels are uniquely suited for this setting because their molecular programmability, supramolecular order, and tissue-conforming mechanics enable both precision drug delivery and immune modulation. In this review, we organize recent advances into three emerging strategies: (i) immunoactive carriers that remain immunologically inert while delivering innate agonists, nucleic acids, or antibodies with spatial and temporal precision; (ii) intrinsically immunomodulatory matrices whose chirality, supramolecular order, and mechanics regulate dendritic-cell activation, macrophage polarization, and memory formation; and (iii) drug-as-hydrogelator formats that integrate cytotoxic debulking and myeloid reprogramming within a single filament network. We highlight design elements such as sequence-encoded motifs, stimulus-responsive linkers, chemokine programming, and logic-gated release systems, and we discuss translational considerations including cavity geometry, surgical workflow, and safety liabilities. Together, these strategies reframe the resection cavity as a programmable immune interface rather than a passive drug reservoir, pointing toward material-centric frameworks that could transform postoperative oncology. This article is categorized under: Biology-Inspired Nanomaterials > Peptide-Based Structures Therapeutic Approaches and Drug Discovery > Emerging Technologies. Drug delivery ; Immune system ; Self-healing hydrogels ; Innate immune system ; Cancer immunotherapy ; Targeted drug delivery ; Immunotherapy ; Drug Supramolecular Self-Assembly in Materials ; Nanoplatforms for cancer theranostics ; Hydrogels: synthesis, properties, applications
Authors: Mohammad Hossein Nargesi | Kamran Kheiralipour | Jalal Khodaei
Source: Scientific Reports (article, 2025-11-12)
Field: Chemistry — Analytical Chemistry

Intelligent detection of potassium oxide in potassium sulfate fertilizer using hyperspectral imaging and artificial neural networks Determining potassium oxide in chemical fertilizers is essential for maintaining soil health. Due to difficulty, time-consuming, high cost, and requiring chemical agents of laboratory methods, hyperspectral imaging was applied to predict potassium oxide in potassium sulfate based on its visible and near-infrared reflectance. After acquiring the hyperspectral images, 453.32, 669.95, 778.19, 891.55, 663.34, 847.72, and 869.32 nm were identified as the effective wavelengths. Subsequently, mean, minimum, maximum, variance, median, and standard deviation features were extracted and subjected to classification. Finally, the correlation coefficient of the prediction model based on the artificial neural network method was obtained as 0.92. The results suggest applying hyperspectral imaging to assess the quality of potassium fertilizer. Hyperspectral imaging ; Potassium ; Potassium sulfate ; Correlation coefficient ; Sulfate ; Artificial neural network Spectroscopy and Chemometric Analyses ; Soil Geostatistics and Mapping ; Water Quality Monitoring and Analysis
Authors: Christopher Baker | Karen Rafferty | Mark Price
Source: Big Data and Cognitive Computing (article, 2025-11-30)
Field: Materials Science — Materials Chemistry

Large Language Models in Mechanical Engineering: A Scoping Review of Applications, Challenges, and Future Directions Following PRISMA-ScR guidelines, this scoping review systematically maps the landscape of Large Language Models (LLMs) in mechanical engineering. A search of four major databases (Scopus, IEEE Xplore, ACM Digital Library, Web of Science) and a rigorous screening process yielded 66 studies for final analysis. The findings reveal a nascent, rapidly accelerating field, with over 68% of publications from 2024 (representing a year-on-year growth of 150% from 2023 to 2024), and applications concentrated on front-end design processes like conceptual design and Computer-Aided Design (CAD) generation. The technological landscape is dominated by OpenAI’s GPT-4 variants. A persistent challenge identified is weak spatial and geometric reasoning, shifting the primary research bottleneck from traditional data scarcity to inherent model limitations. This, alongside reliability concerns, forms the main barrier to deeper integration into engineering workflows. A consensus on future directions points to the need for specialized datasets, multimodal inputs to ground models in engineering realities, and robust, engineering-specific benchmarks. This review concludes that LLMs are currently best positioned as powerful ‘co-pilots’ for engineers rather than autonomous designers, providing an evidence-based roadmap for researchers, practitioners, and educators. Bottleneck ; Process (computing) ; Scarcity ; Engineering design process ; Reliability (semiconductor) ; Conceptual model Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Artificial Intelligence in Healthcare and Education
Authors: Nik Dennler | J. Sommerhäuser | Andreas T. Güntner
Source:  (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

MP28 - Enhancing Selectivity Through Fast Heater Cycles with MOx Sensors Selectivity ; Detector ; Work (physics) ; MOX fuel ; Process (computing) Transition Metal Oxide Nanomaterials ; Gas Sensing Nanomaterials and Sensors ; CCD and CMOS Imaging Sensors
Authors: Yaolong Hou | Chang Han | Yidan Fan | Xiangxue Zhang | Yuxuan Xiong | Bo Zhang | Sanhe Wan
Source: Buildings (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

Energy Performance Evaluation and Optimization of a Residential SOFC-CGS in a Typical Passive-Designed Village House in Xi’an, China Due to the increasingly severe energy crisis and extreme climate conditions in recent years, the development and use of alternative clean energy sources have become increasingly important. This study evaluates the energy performance of applying residential solid oxide fuel cells (SOFCs) in a typical passive-designed residential village house in Xi’an. Furthermore, the study integrates photovoltaic (PV) systems and storage batteries with a solid oxide fuel cell co-generation system (SOFC-CGS) to enhance its overall energy performance. The results show that when the SOFC-CGS operates independently, it can provide stable electricity. However, due to its limited capacity, it only meets 43% of the total energy demand and cannot fully satisfy the heating requirements. In this energy supply scenario, the SOFC-CGS heating efficiency reaches 25%, the power generation efficiency reaches 42%, and the overall efficiency reaches 67%. After integrating the PV battery system with the SOFC-CGS, the addition of photovoltaic and battery systems boosts the energy self-sufficiency rate by 32 percent, reaching 75%. In other words, this clean energy combination can cover 75% of the household’s traditional energy consumption. In addition, the heating efficiency increases by 2 percentage points to 27%, the power generation efficiency rises by 4 percent to 46%, and the overall system efficiency improves by 6 percent to reach 73%. Furthermore, the utilization rate of the photovoltaic battery system also rises from 25% to 73%: an increase of 48 percent. Therefore, according to the analysis results, integrating PV and storage batteries with the SOFC-CGS proves to be a profitable and efficient solution for application in passive-designed village houses in Xi’an. Photovoltaic system ; Battery (electricity) ; Efficient energy use ; Energy storage ; Electricity generation ; Solid oxide fuel cell ; Energy (signal processing) ; Power (physics) ; Solar energy Advancements in Solid Oxide Fuel Cells ; Thermodynamic and Exergetic Analyses of Power and Cooling Systems ; Adsorption and Cooling Systems
Authors: Jane Greenberg | Scott McClellan | Addy Ireland | Robert Sammarco | Colton Gerber | Christopher B. Rauch | Mat Kelly | John Kunze | Yuan An | Eric S. Toberer
Source: ArXiv.org (preprint, 2025-12-10)
Field: Materials Science — Materials Chemistry

Human-in-the-Loop and AI: Crowdsourcing Metadata Vocabulary for Materials Science Metadata vocabularies are essential for advancing FAIR and FARR data principles, but their development constrained by limited human resources and inconsistent standardization practices. This paper introduces MatSci-YAMZ, a platform that integrates artificial intelligence (AI) and human-in-the-loop (HILT), including crowdsourcing, to support metadata vocabulary development. The paper reports on a proof-of-concept use case evaluating the AI-HILT model in materials science, a highly interdisciplinary domain Six (6) participants affiliated with the NSF Institute for Data-Driven Dynamical Design (ID4) engaged with the MatSci-YAMZ plaform over several weeks, contributing term definitions and providing examples to prompt the AI-definitions refinement. Nineteen (19) AI-generated definitions were successfully created, with iterative feedback loops demonstrating the feasibility of AI-HILT refinement. Findings confirm the feasibility AI-HILT model highlighting 1) a successful proof of concept, 2) alignment with FAIR and open-science principles, 3) a research protocol to guide future studies, and 4) the potential for scalability across domains. Overall, MatSci-YAMZ's underlying model has the capacity to enhance semantic transparency and reduce time required for consensus building and metadata vocabulary development. Metadata ; Crowdsourcing ; Transparency (behavior) ; Vocabulary ; Scalability ; Controlled vocabulary ; Standardization ; Annotation Machine Learning in Materials Science ; Research Data Management Practices ; Scientific Computing and Data Management
Authors: Jason Liang
Source:  (article, 2025-11-14)
Field: Materials Science — Materials Chemistry

Self-Transcendence: Achieving AGI via Chaotic Dynamics and Thermodynamic Attractors This position paper addresses the "Architectural Trap", a fundamental constraint in deep learning where systems effectively optimize parameters within a fixed architecture but cannot discover radically superior structural designs. This trap constitutes a barrier within a non-continuous, high-dimensional search space that gradient-based and conventional search methods are ill-equipped to cross. We propose "Self-Transcendence" (ST) as a novel framework for artificial intelligence, defined by an agent's capacity to 1) model its own cognitive limitations, 2) evaluate its architecture via this internal model, and 3) execute discontinuous, non-local architectural modifications through controlled, chaotic dynamics. The central hypothesis is that this mechanism emerges from a synthesis of two physical principles: a thermodynamic "pull" and a chaotic "push". The "pull" is identified as the thermodynamic imperative for open, dissipative systems to self-organize into more stable, energy-rich "attractor states". These states are superior at modeling their environment to minimize "surprise", a concept formalized in the Free-Energy Principle [10]. This connects to active research in dissipation-driven adaptation [8] and its modern application in thermodynamic AI. The "push" is the mechanism of deterministic chaos, which provides a structured, efficient means of exploring the vast, discontinuous architectural phase space [37]. This aligns with recent findings that optimal deep learning performance often occurs at the edge of chaos [21]. Guided by principles of Self-Organized Criticality [24, 3], the system makes a "thermodynamic bet": it risks short-term instability (the chaotic push) to jump a local energy barrier, allowing it to "roll down" to the more powerful, thermodynamically-favored attractor (the pull). We argue this framework provides a viable mechanism for true Recursive Self-Improvement (RSI), moving beyond mere parameter-tuning to enable the structural, paradigm-shifting leaps required for Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI). Chaotic ; Attractor ; Dissipative system ; Phase space ; Edge of chaos ; State space ; Criticality ; Constraint (computer-aided design) ; Position (finance) Machine Learning in Materials Science ; Modular Robots and Swarm Intelligence ; Neural Networks and Reservoir Computing
Authors: Vasyl Duhanets | Ruslana Semenyshena | П. П. Федірко | Vitaliy Pukas | Mykola Volynkin
Source: Naukovij žurnal «Tehnìka ta energetika» (article, 2025-02-04)
Field: Materials Science — Materials Chemistry

Application of automated welding processes in the restoration of pipelines of power facilities The purpose of the study was to investigate the efficiency of automated welding processes in the restoration of pipelines of power facilities, with an emphasis on improving reliability and reducing repair time. The study used the analysis of technical literature, practical cases and the results of experimental implementation of automated welding technologies at power facilities. It was established that automated welding processes ensure high quality of welded joints in pipelines of power facilities, which significantly reduces the likelihood of defects. It was confirmed that the use of modern welding technologies helps to increase the corrosion resistance of joints and their ability to withstand high loads. Process automation has proven effective in ensuring stable welding performance, even under difficult operating conditions. It turned out that automated systems reduce the impact of the human factor, increasing the safety and reliability of work. The study also showed that automated technologies contribute to the rational use of energy resources during welding, minimise the cost of operating equipment, and ensure compliance of restored pipelines with modern technical and environmental standards. The introduction of these technologies helps to optimise repair work, reduce their duration and increase the durability of pipelines. This confirmed the feasibility of using automated welding processes to restore the infrastructure of energy facilities. It was determined that automated processes can ensure high welding accuracy, which is critical for the operation of pipelines under high pressure. The study showed that such technologies help to reduce material consumption by minimising the number of defects. In addition, the introduction of automation has proven to be an effective tool for improving the productivity of repair work and ensuring stable operation of energy systems in the long term Welding ; Pipeline transport ; Automation ; Reliability (semiconductor) ; Reliability engineering ; Process (computing) ; Manufacturing engineering ; Work (physics) ; Computer science ; Engineering ; Mechanical engineering ; Power (physics) Material Properties and Failure Mechanisms ; Engineering and Environmental Studies ; Engineering Diagnostics and Reliability
Authors: Zhenran Xu | Xue Yang | Y. Wang | Qin Hu | Zhenyun Wu | Longyue Wang | Wei Luo | Kaifu Zhang | Baotian Hu | Min Zhang
Source: ArXiv.org (preprint, 2025-06-05)
Field: Materials Science — Materials Chemistry

ComfyUI-Copilot: An Intelligent Assistant for Automated Workflow Development We introduce ComfyUI-Copilot, a large language model-powered plugin designed to enhance the usability and efficiency of ComfyUI, an open-source platform for AI-driven art creation. Despite its flexibility and user-friendly interface, ComfyUI can present challenges to newcomers, including limited documentation, model misconfigurations, and the complexity of workflow design. ComfyUI-Copilot addresses these challenges by offering intelligent node and model recommendations, along with automated one-click workflow construction. At its core, the system employs a hierarchical multi-agent framework comprising a central assistant agent for task delegation and specialized worker agents for different usages, supported by our curated ComfyUI knowledge bases to streamline debugging and deployment. We validate the effectiveness of ComfyUI-Copilot through both offline quantitative evaluations and online user feedback, showing that it accurately recommends nodes and accelerates workflow development. Additionally, use cases illustrate that ComfyUI-Copilot lowers entry barriers for beginners and enhances workflow efficiency for experienced users. The ComfyUI-Copilot installation package and a demo video are available at https://github.com/AIDC-AI/ComfyUI-Copilot. Workflow ; Debugging ; Usability ; Workflow technology ; Flexibility (engineering) ; Workflow management system ; Workflow engine Machine Learning in Materials Science ; Scientific Computing and Data Management ; Generative Adversarial Networks and Image Synthesis
Authors: X. Xiao | Hanzhang Zhao
Source:  (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

From A and B to A+B: Can Large Language Models Solve Compositional Math Problems? Large language models (LLMs) have demonstrated strong performance in solving math problems, and there is growing research on evaluating their robustness.Unlike previous studies that create problem variants by adding perturbations to a single problem, this paper focuses on the interaction between problems.Specifically, we combine two original problems with a logical connection to get a new math problem, and measure the LLM's performance on it to evaluate its compositional generalization, which is an important and essential reasoning capability in human intelligence.The result of experiments that cover 14 different LLMs shows that even when the mathematical essence remains unchanged, a simple form of combination can significantly reduce the performance of LLMs, revealing the limitation of their generalization ability.Additionally, we propose an automated pipeline with 98.2% accuracy to assist in annotating datasets (1 manual, 2 synthetic).The extensive experiments conducted on these datasets further verify the conclusion and obtain some important findings.Finally, we analyze the impact of factors such as difficulty and length on LLMs' performance, offering insights for future research.Code and Algebra over a field ; Calculus (dental) ; Component (thermodynamics) ; Natural language Machine Learning in Materials Science ; Language and cultural evolution ; Ferroelectric and Negative Capacitance Devices
Authors: Aastha Aastha | Leonardo de Macêdo Filho | Michael Woolman | Vladimir Ignatchenko | Alexander Keszei | Gabriela Remite-Berthet | Alireza Mansouri | Thomas Kislinger
Source: Clinical Proteomics (article, 2025-11-28)
Field: Chemistry — Spectroscopy

Comparative evaluation of analytical methods for CSF proteomics Cerebrospinal fluid (CSF) provides a unique window into brain pathology, yet challenges in unbiased mass-spectrometric (MS) discovery persist due to sample complexity and the need for optimized analytical workflows. Multiple laboratory workflows have been developed for CSF proteomics, each with distinct advantages for specific applications. To interrogate which laboratory workflow is most suitable for this biological matrix, we benchmarked five orthogonal sample-preparation strategies- MStern, Proteograph™ nanoparticle enrichment (Seer), N-glycopeptide capture (N-Gp), and two extracellular-vesicle (EV) fractions isolated by differential ultracentrifugation (P20- and P150-EV)- in CSF from 19 patients with central nervous system lymphoma. The protocols span a practical spectrum of input volume (6000-50 µL), hands-on time, and reagent cost, enabling informed method selection for translational applications. In total we performed 82 LC-MS/MS experiments and detected over 38,000 unique peptides and more than 3000 proteins across all modalities. Seer achieved the best proteomic depth (~ 17,000 unique peptides) across samples, followed by P20-EV (~ 9,000), MStern (~ 5,500), P150-EV (~ 5,000), and N-Gp (~ 1,000). None of the methods introduced systematic bias in peptide or protein isoelectric point or hydrophobicity, yet each selectively highlighted distinct biological niches: P20-EVs favoured mitochondrial signatures, N-Gp capture lysosomal and plasma membrane signatures and Seer enhanced nuclear representation. These findings demonstrate that no single protocol suffices for every research question; instead, workflow selection should align with sample-volume constraints, budget and biological question. Our comparative framework empowers investigators to match CSF proteomics strategies to specific neuro-oncological objectives, thereby accelerating the translation of CSF biomarkers into clinically actionable assays. Proteomics ; Workflow ; Cerebrospinal fluid ; Proteome ; Cerebrospinal fluid proteins ; Selection (genetic algorithm) ; Quantitative proteomics ; Biological fluids ; Protocol (science) Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; S100 Proteins and Annexins