jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: CM Bindu | T. Farmaan | Aparna Kuna | Madeh Sadan | T Reshma
Source: Jordan Journal of Pharmaceutical Sciences (article, 2025-06-25)
Field: Materials Science — Biomaterials

Nano-Analytical Techniques in Pharmaceutical Analysis Nano-analytical techniques play a pivotal role in advancing pharmaceutical analysis by providing detailed insights into drug formulations, quality control processes, nanoparticle characterization, impurity detection, and emerging trends in the field. This abstract highlights the significance of nano-analytical tools in optimizing drug delivery systems, ensuring product quality and safety, characterizing nanoparticles, and detecting trace impurities. Key points include the importance of these techniques in enhancing drug stability, enabling targeted drug delivery, and facilitating personalized medicine. Furthermore, the abstract emphasizes the evolving landscape of nano-analytical methods, such as multimodal imaging and quantum-based sensors, and their potential for breakthroughs in real-time drug monitoring and precision medicine. The abstract calls for continued research and development efforts to advance instrumentation, explore novel applications, address technical challenges, foster collaboration, and enhance education and training programs in pharmaceutical analysis. Overall, nano-analytical techniques hold promise for revolutionizing drug development, improving healthcare outcomes, and paving the way for personalized therapies tailored to individual patient needs. Nano- ; Chemistry ; Traditional medicine ; Materials science ; Medicine ; Composite material Nanoparticle-Based Drug Delivery ; Biosensors and Analytical Detection ; Microfluidic and Capillary Electrophoresis Applications
Authors: Chenlei Xu | Tianhao Su | Jie Xiong | Yue Wu | Shuya Dong | Tian Jiang | Meilin He | Shuai Chen | Tong‐Yi Zhang
Source: Research Square (preprint, 2025-11-07)
Field: Materials Science — Materials Chemistry

KAN-Enhanced Contrastive Learning Accelerating Crystal Structure Identification from XRD Patterns Embedding ; Scalability ; Diffraction ; Projection (relational algebra) ; Identification (biology) ; Space (punctuation) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: Junjie Hu | Peng Wu | Xiangyu Li | Qi Li | Yingying Fang | Shiyi Wang | Fenglei Cao | Qi Ao | Lulu Cai | Guang Yang
Source: ChemRxiv (preprint, 2025-09-05)
Field: Materials Science — Materials Chemistry

Exploring the Potential of an End-to-End AI Design Framework for Photoresponsive Drug Delivery. In our effort to more reliably leverage large language models for designing effective photoresponsive molecular materials for drug delivery, we developed the Explanation CPO (XCPO), an end-to-end reinforcement learning fine-tuning workflow. The maturity of RLHF techniques has greatly stimulated the flourishing application of general-purpose LLMs across various scientific domains. Compared with representative RLHF approaches like CPO, our XCPO emphasizes the potential and effectiveness of a fully AI-driven, end-to-end fine-tuning process. In this workflow, the preference dataset for RLFT is derived from the combined output of AI models and XAI tools, whereas traditional RLHF pipelines require domain researchers to identify key features based on their expertise. This streamlined improvement via integration with XAI tools has not been previously reported. Based on our machine learning evaluation and computational chemistry validation, our end-to-end XCPO approach outperformed CPO by over 27\% on the MoE-based Qwen3Moe architecture, while the designed photoresponsive molecular materials aligned well with chemical intuition. Our end-to-end workflow compensates for the subjective influence of chemists' intuitive experience, enabling the model to iterate within a fully AI-driven process. End-to-end principle ; Drug delivery ; End user ; Drug ; Computer science ; Nanotechnology ; Artificial intelligence ; World Wide Web ; Materials science ; Pharmacology ; Medicine Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Shen Wang | Weiren Zhao | Minjia Zhou | Tanghao Liu | Yi'an Wang | Run Shi | Yunfan Wang | Zhuoqiong Zhang
Source: Progress in Photovoltaics Research and Applications (article, 2025-12-24)
Field: Materials Science — Materials Chemistry

Machine Learning for Stability Enhancement in Perovskite Solar Cells: A Pathway to Commercial Viability ABSTRACT Organic–inorganic hybrid perovskites hold significant promise for low‐cost, high‐efficiency, and scalable photovoltaic production. However, stability challenges of perovskite materials hinder their commercial viability. Although significant progress in enhancing stability has been achieved through compositional adjustments, additive engineering, and solvent‐based processing strategies, these methods often involve laborious and time‐consuming optimization processes. Machine learning (ML) is proving highly effective for accelerating the development and optimization of stable perovskite materials, reducing reliance on trial‐and‐error methods. This review outlines the fundamental ML workflow and highlights its applications in material screening, mechanism investigation, and characterization analysis in perovskite solar cells (PSCs) research. These key advancements underscore the utility of ML in systematically improving the durability of PSCs. Future integration of ML with high‐throughput experimentation is expected to further advance the development of efficient, stable, and commercially viable PSCs, contributing to sustainable energy solutions. Perovskite (structure) ; Workflow ; Stability (learning theory) ; Photovoltaic system ; Characterization (materials science) ; Scalability Machine Learning in Materials Science ; Perovskite Materials and Applications ; Chemical and Physical Properties of Materials
Authors: Frederik L. Johansen | Ulrik Friis-Jensen | Erik B. Dam | Kirsten M. Ø. Jensen | Rocío Mercado | Raghavendra Selvan
Source: ArXiv.org (preprint, 2025-02-04)
Field: Materials Science — Materials Chemistry

deCIFer: Crystal Structure Prediction from Powder Diffraction Data using Autoregressive Language Models Novel materials drive advancements in fields ranging from energy storage to electronics, with crystal structure characterization forming a crucial yet challenging step in materials discovery. In this work, we introduce \emph{deCIFer}, an autoregressive language model designed for powder X-ray diffraction (PXRD)-conditioned crystal structure prediction (PXRD-CSP). Unlike traditional CSP methods that rely primarily on composition or symmetry constraints, deCIFer explicitly incorporates PXRD data, directly generating crystal structures in the widely adopted Crystallographic Information File (CIF) format. The model is trained on nearly 2.3 million crystal structures, with PXRD conditioning augmented by basic forms of synthetic experimental artifacts, specifically Gaussian noise and instrumental peak broadening, to reflect fundamental real-world conditions. Validated across diverse synthetic datasets representative of challenging inorganic materials, deCIFer achieves a 94\% structural match rate. The evaluation is based on metrics such as the residual weighted profile ($R_{wp}$) and structural match rate (MR), chosen explicitly for their practical relevance in this inherently underdetermined problem. deCIFer establishes a robust baseline for future expansion toward more complex experimental scenarios, bridging the gap between computational predictions and experimental crystal structure determination. Autoregressive model ; Diffraction ; Powder diffraction ; Crystallography ; Computer science ; Materials science ; Econometrics ; Mathematics ; Optics ; Chemistry ; Physics X-ray Diffraction in Crystallography ; Crystallography and molecular interactions ; Crystallization and Solubility Studies
Authors: Danish Khan | Seema Sarwar Ghumro | Ammat ur Rehman Soomro | Dr. Yasir Mustafa | Wania Fatima
Source: Global Research Journal of Natural Science and Technology (article, 2025-01-11)
Field: Materials Science — Materials Chemistry

Engineering High-Performance Nanocatalysts for Sustainable Chemical Transformations: A Study of Structure–Activity Relationships and Catalytic Mechanisms Engineering high-performance nanocatalysts is crucial for advancing sustainable chemical transformations. This study systematically investigated the influence of structural features, defect engineering, and composition on the catalytic activity, selectivity, and stability of various nanocatalysts, including defect-rich ZnO, alloy Pt–Ni, core–shell Au@TiO₂, and N-doped Carbon–Co. Structural characterization revealed significant differences in particle size, surface area, crystal structure, and defect density, which directly affected catalytic performance. Catalytic evaluation in CO₂ reduction reactions demonstrated that alloy Pt–Ni exhibited the highest conversion rate (71.2%) and turnover frequency due to synergistic electronic interactions, while defect-rich ZnO showed enhanced selectivity attributed to oxygen vacancies. Core–shell Au@TiO₂ displayed superior stability over multiple reaction cycles, confirming the protective role of shell architectures. In contrast, N-doped Carbon–Co experienced significant activity loss due to carbon oxidation and higher activation energy, highlighting limitations in long-term durability. The study elucidated mechanistic insights, emphasizing that while defect engineering improved selectivity, combining alloy composition and structural protection was essential for optimal performance. These findings provide a comprehensive structure–activity framework for designing next-generation nanocatalysts capable of efficient, selective, and sustainable chemical transformations. The results have implications for green chemistry, CO₂ valorization, and the development of durable catalysts for industrial applications. Nanomaterial-based catalyst ; Catalysis ; Alloy ; Chemical stability ; Nanoparticle ; Chemical composition ; Characterization (materials science) ; Carbon fibers Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Electrocatalysts for Energy Conversion
Authors: Keting Huang | Jonathon Pipe | Alan Martin | Tianyuan Wang | Barnabas A. Franklin | Andy M. Tyrrell | Ian J. S. Fairlamb | Jihong Zhu
Source: ArXiv.org (preprint, 2025-10-22)
Field: Materials Science — Materials Chemistry

TARMAC: A Taxonomy for Robot Manipulation in Chemistry Chemistry laboratory automation aims to increase throughput, reproducibility, and safety, yet many existing systems still depend on frequent human intervention. Advances in robotics have reduced this dependency, but without a structured representation of the required skills, autonomy remains limited to bespoke, task-specific solutions with little capacity to transfer beyond their initial design. Current experiment abstractions typically describe protocol-level steps without specifying the robotic actions needed to execute them. This highlights the lack of a systematic account of the manipulation skills required for robots in chemistry laboratories. To address this gap, we introduce TARMAC - a Taxonomy for Robot Manipulation in Chemistry - a domain-specific framework that defines and organizes the core manipulations needed in laboratory practice. Based on annotated teaching-lab demonstrations and supported by experimental validation, TARMAC categorizes actions according to their functional role and physical execution requirements. Beyond serving as a descriptive vocabulary, TARMAC can be instantiated as robot-executable primitives and composed into higher-level macros, enabling skill reuse and supporting scalable integration into long-horizon workflows. These contributions provide a structured foundation for more flexible and autonomous laboratory automation. More information is available at https://tarmac-paper.github.io/ Robot ; Robotics ; Automation ; Reuse ; Taxonomy (biology) ; Laboratory automation Machine Learning in Materials Science ; Various Chemistry Research Topics ; Scientific Computing and Data Management
Authors: Zhen-Hao Guo | Dingcheng Duan | Youwei Liang | A. S. Patil | Pengtao Xie
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-11-10)
Field: Materials Science — Materials Chemistry

MetaboliteChat: A Unified Multimodal Large Language Model for Interactive Metabolite Analysis and Functional Insights Accurately characterizing the mechanisms and properties of metabolite molecules is essential for advancing metabolomics and systems biology. Yet most existing approaches are narrow, task-specific models that do not transfer across tasks and cannot express the richer, multimodal biological context of a metabolite in natural language. To address these challenges, we introduce MetaboliteChat, a multimodal ChatGPT-like large language model (LLM) that provides a unified and interactive framework for metabolite analysis. MetaboliteChat integrates molecular-graph and image reasoning with natural language understanding to generate comprehensive, free-form predictions about metabolite mechanisms and properties. The MetaboliteChat architecture consists of a graph neural network (GNN), a convolutional neural network (CNN), a large language model (LLM), and adapters, all trained end to end. This unified, multimodal design enables interactive reasoning over unseen metabolites, allowing the model to integrate structural and contextual cues and support discovery and translational insights across diverse biological systems. Context (archaeology) ; Natural language ; Convolutional neural network ; Natural (archaeology) ; Natural language understanding ; Question answering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Xuexue Pan | Yi Zhao | Huiling Zhou | Jianhui Zhang
Source: Electrocatalysis (article, 2025-12-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-Efficiency Aqueous Zinc-ion Capacitors Based On Mofs/cofs-derived Nanomaterials: Challenges and Future Prospects Aqueous zinc-ion capacitors have garnered significant attention recently due to their safety, environmental benefits, and high capacity. However, its practical application still faces many challenges. Metal–organic frameworks (MOFs) and Covalent organic frameworks (COFs)-derived nanomaterials have demonstrated outstanding potential as electrode materials due to their unique structure and adjustable properties. Here, the current application status of MOFs/COFs-derived nanomaterials in high-efficiency aqueous ZICs is reviewed and the structure and properties of these materials, as well as their synthesis methods and advantages in capacitors are explored. The key factors influencing the properties of aqueous ZICs, including material structure, morphology, electrochemical characteristics, stability, and cycle life, are further examined. Although there are current challenges in the controllability of material preparation, large-scale production, electrolyte and electrode interface, the future development of aqueous ZICs is promising through new material design and synthesis strategies, performance optimization, and interdisciplinary research. This paper examines the future research directions and application potential of MOFs/COFs-derived nanomaterials in waterborne ZICs. Nanomaterials ; Capacitor ; Aqueous solution ; Controllability ; Electrochemistry ; Electrolyte Supercapacitor Materials and Fabrication ; Advanced battery technologies research ; Advancements in Battery Materials
Authors: Morteza Maleki | SeyedAli Ghahari
Source: Preprints.org (preprint, 2025-09-03)
Field: Chemistry — Inorganic Chemistry

Symmetrical Catalysis and C-H Activation in Unsaturated Amides and Esters of Osmium: Applications in Pharmaceuticals Drug Synthesis We report the selective activation and transformation of unsaturated amides and esters at multinuclear osmium carbonyl clusters and clarify how these symmetry-enabled, cooperative reactions inform pharmaceutical synthesis. Treatment of the activated triosmium complex Os3(CO)10(NCCH3)2 with N,N-dimethylacrylamide (DMA) or methylacrylate (MA) furnishes eight structurally characterized products spanning Os2, Os3, Os4, Os5, and Os6 nuclearities. Single-crystal X-ray diffraction, IR, NMR, and MS data establish (i) β-C–H activation of the alkene unit to give bridg- ing μ-alkenyl/μ-hydride motifs, (ii) substrate–substrate coupling on the cluster to form a chelated DMA–DMA dimer, and (iii) carbonylation/decarbonylation events that reorganize Os–Os connectivity. The key intermediate HOs3(CO)10(μ-acryloyl) maps a stepwise sequence in which two or more metal centers share bond-making/bond-breaking—an intrinsic “symmetrical catalysis" element arising from the defined Os–Os edges. Solvent governs pathway selection: noncoordinating, higher-boiling media (e.g., heptane) favor direct C–H activation and dimerization, whereas CH2Cl2 enables gentler transformations that preserve cluster integrity and access higher nuclearities. Although the ransformations are stoichiometric, they recapitulate catalytic elementary steps central to late-stage functionalization and feedstock upgrading—concerted C–H cleavage, migratory insertion of CO, and controlled C–C coupling—and thus offer design rules for bimetallic/cluster catalysts targeting drug-like motifs (enamides, dienamides, and 1,4-dicarbonyl frameworks). By linking structure, mechanism, and chemoselectivity across a family of symmetric Os clusters, this work provides a mechanistic platform for translating cooperative multimetal activation into future catalytic processes relevant to API synthesis and process intensification. Osmium ; Catalysis ; Chemistry ; Drug ; Combinatorial chemistry ; Medicinal chemistry ; Stereochemistry ; Organic chemistry ; Pharmacology ; Medicine ; Ruthenium Asymmetric Hydrogenation and Catalysis ; Coordination Chemistry and Organometallics ; Asymmetric Synthesis and Catalysis
Authors: R.M. Kudaibergenova | A.R. Aitekova | N.S. Murzakasymova | A.S. Seitkan | A.N. Nurlybayeva
Source: SERIES CHEMISTRY AND TECHNOLOGY (article, 2025-12-26)
Field: Materials Science — Materials Chemistry

STRUCTURAL AND COMPOSITIONAL CHARACTERISTICS OF COBALT FERRITE NANOPARTICLES Spinel ferrites are a class of magnetic materials that have attracted significant scientific interest because of their diverse functional properties and broad application potential. Among them, cobalt ferrite (CoFe₂O₄) is considered one of the most promising materials, as it combines moderate magnetic hardness with stable magnetic behavior. These features make it suitable for devices such as sensors, inductors, transformers, and magnetic storage media. In addition, the ability of cobalt ferrite to exhibit variable oxidation states, together with its high permeability and strong electrochemical stability, gives it distinctive physicochemical characteristics. In this work, CoFe₂O₄ nanoparticles were synthesized by the sol–gel combustion technique. The crystalline and chemical structure of the samples was examined by X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDS), and Fourier-transform infrared spectroscopy (FTIR). XRD analysis confirmed the formation of a single-phase cobalt ferrite spinel structure (ICSD 1533163, space group Fd3m). EDS revealed the presence of only cobalt, iron, and oxygen, indicating the absence of impurity elements. The FTIR spectrum demonstrated characteristic absorption bands associated with the spinel lattice, confirming the successful synthesis of CoFe₂O₄. The spinel lattice of cobalt ferrite consists of tetrahedral (A) and octahedral (B) sites, typically described as (Fe³⁺)ₐ(Co²⁺Fe³⁺)ᵦO₄. This structural arrangement imparts unique properties, including high catalytic efficiency, strong saturation magnetization, structural stability, low solubility of metal ions, and a relatively large surface area. Such a combination of characteristics makes CoFe₂O₄ particularly attractive for environmental applications, including wastewater treatment, where its magnetic nature enables easy separation and reuse. Spinel ; Cobalt ; Ferrite (magnet) ; Nanoparticle ; Impurity ; Infrared spectroscopy ; Absorption spectroscopy ; Magnetic nanoparticles ; Fourier transform infrared spectroscopy Magnetic Properties and Synthesis of Ferrites ; Pigment Synthesis and Properties ; Chemical and Physical Properties of Materials
Authors: David Moss
Source:  (article, 2025-08-23)
Field: Materials Science — Materials Chemistry

2D graphene oxide thin films for photonic and electronic integrated circuits As a derivative of graphene, graphene oxide (GO) was initially developed by chemists to emulate some of the key properties of graphene, but it was soon recognized as a unique material in its own right, addressing an application space that is not accessible to chemical vapor deposition based materials. Over the past decade, GO has emerged as a new frontier material with tremendous advances in its material fabrication and quality control over its properties. These in turn have led to rapid progress in GO based photonics, electronics, and optoelectronics concepts and devices, evoking new science and paving the way for many technological breakthroughs with exceptional performance. Here, we review the unique fundamental characteristics of GO, its thin film fabrication methods, patterning techniques, and mechanisms for tuning its material properties. This latter capability in particular has enabled novel advanced functional photonic, electronic, and optoelectronic devices. Understanding these insights is essential for designing and tailoring GO properties for state-of-the-art applications including solar energy harvesting, energy storage, medical diagnosis, image displays, and optical communications. We conclude by discussing the open challenges and exciting opportunities of this field, together with future prospects for major technological advancements and breakthroughs. Graphene ; Oxide ; Materials science ; Electronic circuit ; Optoelectronics ; Photonics ; Nanotechnology ; Thin film ; Integrated circuit ; Electrical engineering ; Engineering Graphene research and applications