jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jie Fu | Jinyang Kang | T. K. Kwei | Zhiwen Huang | Ling Yuan Kong | Kai Xie | Qiu‐Hong Zhu | Guo‐Hao Zhang | Guo‐Hong Tao | Ling He
Source: Chemical Communications (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Covalent organic frameworks for radioactive iodine capture: structure and functionality The adsorption of radioactive iodine is a critical concern in nuclear safety and environmental protection due to its hazardous nature and long half-life. Covalent organic frameworks (COFs) have emerged as promising materials for capturing radioactive iodine owing to their tunable porosity, high surface area, and versatile functionalization capabilities. This review provides a comprehensive overview of the application of COFs in the adsorption of radioactive iodine. We begin by discussing the sources, properties, and hazards of radioactive iodine, as well as traditional capture techniques and their limitations. We then delve into the intrinsic structures of COFs, focusing on their porosity, conjugated frameworks, and hydrogen bonding, which are pivotal for effective iodine adsorption. The review further explores various functionalization strategies, including electron-rich COFs, flexible COFs, ionic COFs, COF nanosheets, and quasi-3D COFs, highlighting how these modifications enhance the adsorption performance. Finally, we conclude with an outlook on future research directions and potential applications, underscoring the significance of continued innovation in this field. This review aims to provide valuable insights for researchers and practitioners seeking to develop advanced materials for the efficient capture of radioactive iodine. Radioactive iodine ; Covalent bond ; Iodine ; Radiochemistry ; Chemistry ; Organic chemistry ; Medicine Covalent Organic Framework Applications ; Radioactive element chemistry and processing ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Zeba Tabassum | Madhuri Girdhar | Abhinav Anand | Neelam Kumari | B.S. Sood | Tabarak Malik | Anil Kumar | Anand Mohan
Source: Materials Advances (article, 2025-01-01)
Field: Materials Science — Biomaterials

Trash to treasure: advancing resource efficiency using waste-derived fillers as sustainable reinforcing agents in bioplastics The escalating environmental challenges posed by different waste sources, including agricultural residues and industrial byproducts, necessitate innovative solutions for waste utilization. Bioplastic ; Treasure ; Waste management ; Resource efficiency ; Resource recovery ; Resource (disambiguation) ; Environmental science ; Natural resource economics ; Business ; Engineering ; Economics ; Wastewater ; Computer science ; Ecology ; Archaeology ; Geography biodegradable polymer synthesis and properties ; Natural Fiber Reinforced Composites ; Microplastics and Plastic Pollution
Authors: Alireza Ghafarollahi | Markus J. Buehler
Source: Proceedings of the National Academy of Sciences (article, 2025-01-24)
Field: Materials Science — Materials Chemistry

Automating alloy design and discovery with physics-aware multimodal multiagent AI The design of new alloys is a multiscale problem that requires a holistic approach that involves retrieving relevant knowledge, applying advanced computational methods, conducting experimental validations, and analyzing the results, a process that is typically slow and reserved for human experts. Machine learning can help accelerate this process, for instance, through the use of deep surrogate models that connect structural and chemical features to material properties, or vice versa. However, existing data-driven models often target specific material objectives, offering limited flexibility to integrate out-of-domain knowledge and cannot adapt to new, unforeseen challenges. Here, we overcome these limitations by leveraging the distinct capabilities of multiple AI agents that collaborate autonomously within a dynamic environment to solve complex materials design tasks. The proposed physics-aware generative AI platform, AtomAgents, synergizes the intelligence of large language models (LLMs) and the dynamic collaboration among AI agents with expertise in various domains, including knowledge retrieval, multimodal data integration, physics-based simulations, and comprehensive results analysis across modalities. The concerted effort of the multiagent system allows for addressing complex materials design problems, as demonstrated by examples that include autonomously designing metallic alloys with enhanced properties compared to their pure counterparts. Our results enable accurate prediction of key characteristics across alloys and highlight the crucial role of solid solution alloying to steer the development of advanced metallic alloys. Our framework enhances the efficiency of complex multiobjective design tasks and opens avenues in fields such as biomedical materials engineering, renewable energy, and environmental sustainability. Computer science ; Flexibility (engineering) ; Process (computing) ; Artificial intelligence ; Data science ; Generative grammar ; Domain (mathematical analysis) ; Deep learning ; Key (lock) ; Systems engineering ; Machine learning ; Human–computer interaction ; Engineering Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Junfan Xia | Yaolong Zhang | Bin Jiang
Source: Chemical Society Reviews (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

The evolution of machine learning potentials for molecules, reactions and materials Recent years have witnessed the fast development of machine learning potentials (MLPs) and their widespread applications in chemistry, physics, and material science. By fitting discrete <i>ab initio</i> data faithfully to continuous and symmetry-preserving mathematical forms, MLPs have enabled accurate and efficient atomistic simulations in a large scale from first principles. In this review, we provide an overview of the evolution of MLPs in the past two decades and focus on the state-of-the-art MLPs proposed in the last a few years for molecules, reactions, and materials. We discuss some representative applications of MLPs and the trend of developing universal potentials across a variety of systems. Finally, we outline a list of open challenges and opportunities in the development and applications of MLPs. Computer science ; Molecule ; Nanotechnology ; Cognitive science ; Chemistry ; Materials science ; Psychology ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Kais Iben Nassar | S. Soreto Teixeira | M.P.F. Graça
Source: Gels (review, 2025-08-19)
Field: Materials Science — Materials Chemistry

Sol–Gel-Synthesized Metal Oxide Nanostructures: Advancements and Prospects for Spintronic Applications—A Comprehensive Review Spintronics, an interdisciplinary field merging magnetism and electronics, has attracted considerable interest due to its potential to transform data storage, logic devices, and emerging quantum technologies. Among the materials explored for spintronic applications, metal oxide nanostructures synthesized via sol-gel methods offer a unique combination of low-cost processing, structural tunability, and defect-mediated magnetic control. This comprehensive review presents a critical overview of recent advances in sol-gel-derived magnetic oxides, such as Co-doped ZnO, La<sub>1-x</sub>Sr<sub>x</sub>MnO<sub>3</sub>, Fe<sub>3</sub>O<sub>4</sub>, NiFe<sub>2</sub>O<sub>4</sub>, and transition-metal-doped TiO<sub>2</sub>, with emphasis on synthesis strategies, the dopant distribution, and room-temperature ferromagnetic behavior. Key spintronic functionalities, including magnetoresistance, spin polarization, and magnetodielectric effects, are systematically examined. Importantly, this review differentiates itself from the prior literature by explicitly connecting sol-gel chemistry parameters to spin-dependent properties and by offering a comparative analysis of multiple oxide systems. Critical challenges such as phase purity, reproducibility, and defect control are also addressed. This paper concludes by outlining future research directions, including green synthesis, the integration with 2D materials, and machine-learning-assisted optimization. Overall, this work bridges sol-gel synthesis and spintronic material design, offering a roadmap for advancing next-generation oxide-based spintronic devices. Spintronics ; Nanotechnology ; Materials science ; Nanostructure ; Sol-gel ; Oxide ; Metal ; Ferromagnetism ; Metallurgy ; Physics ZnO doping and properties ; Copper-based nanomaterials and applications ; Magnesium Oxide Properties and Applications
Authors: Nicholas E. Charron | Klara Bonneau | Aldo S. Pasos-Trejo | Andrea Guljas | Yaoyi Chen | Félix Musil | Jacopo Venturin | Daria Gusew | Iryna Zaporozhets | Andreas Krämer | Clark Templeton | Atharva Kelkar | Aleksander E. P. Durumeric | Simon Olsson | Adrià Pérez | Maciej Majewski | Brooke E. Husic | Ankit Patel | Gianni De Fabritiis | Frank Noé | Cecilia Clementi
Source: Nature Chemistry (article, 2025-07-18)
Field: Materials Science — Materials Chemistry

Navigating protein landscapes with a machine-learned transferable coarse-grained model The most popular and universally predictive protein simulation models employ all-atom molecular dynamics, but they come at extreme computational cost. The development of a universal, computationally efficient coarse-grained (CG) model with similar prediction performance has been a long-standing challenge. By combining recent deep-learning methods with a large and diverse training set of all-atom protein simulations, we here develop a bottom-up CG force field with chemical transferability, which can be used for extrapolative molecular dynamics on new sequences not used during model parameterization. We demonstrate that the model successfully predicts metastable states of folded, unfolded and intermediate structures, the fluctuations of intrinsically disordered proteins and relative folding free energies of protein mutants, while being several orders of magnitude faster than an all-atom model. This showcases the feasibility of a universal and computationally efficient machine-learned CG model for proteins. Transferability ; Metastability ; Molecular dynamics ; Force field (fiction) ; Chemistry ; Folding (DSP implementation) ; Protein folding ; Atom (system on chip) ; Computer science ; Set (abstract data type) ; Protein structure prediction ; Biological system ; Field (mathematics) ; Process (computing) ; Artificial intelligence ; Statistical physics ; Protein structure ; Machine learning ; Computational chemistry ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Enzyme Structure and Function
Authors: Ting Liang | Yali Huang | Leiwenxuan Yang | Yunfeng Chai | Zhenxia Hao | Hongping Chen | Guicen Ma
Source: Small (article, 2025-02-16)
Field: Materials Science — Materials Chemistry

Hollow Mn/Co‐MOF as a Powerful Oxidase‐Like Nanozyme for Detection of Total Antioxidant Capacity and Black Tea Fermentation Degree The detection of total antioxidant capacity (TAC) in teas and its vital indicator for monitoring the fermentation degree during black tea processing are of great significance for intelligent manufacturing of the tea industry. Here, highly dispersed manganese species on Co-MOF with hollow structures (HS-Mn/Co-MOF) nanozyme with superior oxidase-like activity is constructed for colorimetric detection of ascorbic acid (AA) and TAC. Owing to the Mn doping and unique hollow structures, the HS-Mn/Co-MOF nanozyme possesses more surface active oxygen species, negative surface charges and rapid mass transport, thus resulting in 20-times higher catalytic activity and 30-times higher reaction rate than that of pure Co-MOF. Thanks to the remarkable oxidase-like catalytic activity, the HS-Mn/Co-MOF nanozyme-based colorimetric sensor exhibits a wide linear range of 0.01-120 µm, along with a low detection limit of 3 nm for AA detection. Additionally, the determination of TAC levels in real tea samples is further achieved and different tea types can be effectively distinguished by utilizing HS-Mn/Co-MOF nanozyme. Identification and prediction of the fermentation quality during the black tea fermentation process using nanozyme combined with machine learning are realized for the first time. Ascorbic acid ; Catalysis ; Detection limit ; Chemistry ; Fermentation ; Manganese ; Antioxidant ; Antioxidant capacity ; Black tea ; Chromatography ; Biochemistry ; Organic chemistry ; Food science Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Carbon and Quantum Dots Applications
Authors: Nibras Hijazi | Anastasiya Bavykina | Irina Yarulina | Tuiana Shoinkhorova | Enrique V. Ramos–Fernández | Jorge Gascón
Source: Chemical Society Reviews (review, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Chemical engineering of zeolites: alleviating transport limitations through hierarchical design and shaping Zeolites are indispensable catalysts in a wide range of industrial applications due to their well-defined microporous structures and exceptional shape-selective properties. However, their practical use is often constrained by diffusion limitations, which can hinder reactant accessibility, influence product selectivity, and accelerate catalyst deactivation. This review critically examines strategies to alleviate these diffusion constraints, focusing on hierarchical structuring, nanozeolite synthesis, and advanced shaping techniques. We discuss fundamental diffusion theories, experimental characterization methods, and emerging methodologies that enhance mass transport in zeolites. By bridging fundamental principles with industrial applications, this review provides a comprehensive overview of how tailored zeolite architectures can optimize catalytic performance, paving the way for more efficient and sustainable processes. Structuring ; Biochemical engineering ; Nanotechnology ; Process engineering ; Computer science ; Zeolite ; Systems engineering ; Materials science ; Engineering ; Chemistry ; Catalysis ; Business ; Organic chemistry Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Catalysis and Hydrodesulfurization Studies
Authors: Naveen Thanjavur | Laxmi Bugude | Young‐Joon Kim
Source: Biosensors (review, 2025-01-10)
Field: Materials Science — Materials Chemistry

Integration of Functional Materials in Photonic and Optoelectronic Technologies for Advanced Medical Diagnostics Integrating functional materials with photonic and optoelectronic technologies has revolutionized medical diagnostics, enhancing imaging and sensing capabilities. This review provides a comprehensive overview of recent innovations in functional materials, such as quantum dots, perovskites, plasmonic nanomaterials, and organic semiconductors, which have been instrumental in the development of diagnostic devices characterized by high sensitivity, specificity, and resolution. Their unique optical properties enable real-time monitoring of biological processes, advancing early disease detection and personalized treatment. However, challenges such as material stability, reproducibility, scalability, and environmental sustainability remain critical barriers to their clinical translation. Breakthroughs such as green synthesis, continuous flow production, and advanced surface engineering are addressing these limitations, paving the way for next-generation diagnostic tools. This article highlights the transformative potential of interdisciplinary research in overcoming these challenges and emphasizes the importance of sustainable and scalable strategies for harnessing functional materials in medical diagnostics. The ultimate goal is to inspire further innovation in the field, enabling the creation of practical, cost-effective, and environmentally friendly diagnostic solutions. Nanotechnology ; Photonics ; Computer science ; Systems engineering ; Scalability ; Risk analysis (engineering) ; Materials science ; Engineering ; Medicine Quantum Dots Synthesis And Properties ; Nanoplatforms for cancer theranostics ; Luminescence and Fluorescent Materials
Authors: Abbas Aziz | Huma Shaikh | Amna Abbas | Kissa E. Zehra | Bilal Javed
Source: Microscopy Research and Technique (review, 2025-01-08)
Field: Materials Science — Materials Chemistry

Microscopic Techniques for Nanomaterials Characterization: A Concise Review Nanomaterials have been gaining interest due to their remarkable properties at the nanoscale. The surface area of particles becomes high at the nanoscale because of this virtue, they have been used in a bundle of applications like electronics, biomedical, agriculture, wastewater treatment, semiconductor industry, cosmetics, drug delivery, paints, and so forth. The morphology (size and shape) of nanomaterials plays an important role because each application requires the appropriate morphology for better performance. Generally, there are a few microscopic techniques used to characterize nanomaterial morphology, AFM (atomic force microscopy), TEM (transmission electron microscopy), SEM (scanning electron microscopy), and others. In this review, the principles, operations, advantages, and limitations of these microscopic techniques for nanomaterial morphology characterization have been briefly discussed. The existing difficulties and path forward for the development of these techniques have also been highlighted. Nanomaterials ; Nanotechnology ; Characterization (materials science) ; Materials science ; Nanoscopic scale ; Atomic force microscopy ; Cosmetics ; Chemistry Nanoparticles: synthesis and applications ; Nanotechnology research and applications ; Graphene and Nanomaterials Applications
Authors: Jingyi Liu | Jun Sun | Yasong Wang | Xin Liu | Yingjie Zhang | Haijun Fu
Source: Foods (review, 2025-06-19)
Field: Chemistry — Analytical Chemistry

Non-Destructive Detection of Fruit Quality: Technologies, Applications and Prospects Fruit quality testing plays a crucial role in the advancement of fruit industry, which is related to market competitiveness, consumer satisfaction and production process optimization. In recent years, nondestructive testing technology has become a research hotspot due to its outstanding advantages. In this paper, the principle, application, advantages and disadvantages of optical, acoustic, electromagnetics, dielectric properties research and electronic nose non-destructive testing technology in fruit quality testing are systematically reviewed. These technologies can detect a variety of chemical components of fruit, realize the assessment of maturity, damage degree, disease degree, and are suitable for orchard picking, quality grading, shelf life prediction and other fields. However, there are limitations to these techniques. The optical, acoustic and electronic nose technologies are susceptible to environmental factors, the electromagnetic technology has defects in the detection of complex molecules and fruit internal quality, and the dielectric characteristics are greatly affected by the shape and state of the sample surface. In the future, efforts should be made to enhance the implementation of non-destructive testing technology in the fruit industry through technology integration, optimization algorithm, cost reduction, and expansion of industrial chain application, so as to help the premium growth of the fruit industry. Electronic nose ; Computer science ; Quality assurance ; Nondestructive testing ; Quality (philosophy) ; Process engineering ; Engineering ; Biochemical engineering ; Artificial intelligence ; Operations management Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Huayun Shi | Rafael C. Marchi | Peter J. Sadler
Source: Angewandte Chemie International Edition (review, 2025-01-13)
Field: Materials Science — Materials Chemistry

Advances in the Design of Photoactivatable Metallodrugs: Excited State Metallomics Photoactivatable metal complexes offer the prospect of novel drugs with low side effects and new mechanisms of action to combat resistance to current therapy. We highlight recent progress in the design of platinum, ruthenium, iridium, gold and other transition metal complexes, especially for applications as anticancer and anti-infective agents. In particular, understanding excited state chemistry related to identification of the bioactive species (excited state metallomics/pharmacophores) is important. Photoactivatable metallodrugs are classified here as photocatalysts, photorelease agents and ligand-activated agents. Their activation wavelengths, cellular mechanisms of action, experimental and theoretical metallomics of excited states and photoproducts are discussed to explore new strategies for the design and investigation of photoactivatable metallodrugs. These photoactivatable metallodrugs have potential in clinical applications of Photodynamic Therapy (PDT), Photoactivated Chemotherapy (PACT) and Photothermal Therapy (PTT). Chemistry ; Nanotechnology ; Photodynamic therapy ; Ruthenium ; Excited state ; Biochemistry ; Materials science ; Organic chemistry Nanocluster Synthesis and Applications ; Click Chemistry and Applications ; Nanoplatforms for cancer theranostics
Authors: Teng Li | Yan‐Ting Chen | Xiaobin Zhang | Rongrong Du | Lijuan Ma | Ya-Qian Lan
Source: Chemical Society Reviews (review, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Asymmetric heterogeneous catalysis using crystalline porous materials Asymmetric catalysis has emerged as a pivotal strategy in the synthesis of chiral compounds, offering significant advantages in selectivity and efficiency. In recent years, heterogeneous catalysis has become a focal point in the fields of organic synthesis and materials science due to continuous advancements in science and technology, especially the use of crystalline porous materials (CPMs) as catalysts. This review summarizes recent advances in using CPMs, such as metal-organic frameworks (MOFs), covalent organic frameworks (COFs) and zeolites, as promising supports for asymmetric catalysts. These materials provide high surface areas, tunable porosity, and the ability to host active catalytic sites, which enhance reaction rates and selectivity. In this review, we summarize the stereostructural properties of chiral CPMs to guide the future design of asymmetric heterogeneous catalysts and the study of catalytic mechanisms. Moreover, we discuss various strategies for incorporating catalytic moieties into these frameworks, including direct synthesis, post-synthesis modification and induced synthesis methods. Additionally, we highlight recent examples where CPMs have been successfully applied in asymmetric transformations, examining their mechanistic insights and the role of substrate diffusion in achieving high enantioselectivity. This review concludes with a perspective on the challenges and future directions in this rapidly evolving field, emphasizing the need for further integration of advanced artificial intelligence techniques and design principles to optimize the synthesis and catalytic performance of chiral CPMs. Porosity ; Catalysis ; Porous medium ; Materials science ; Heterogeneous catalysis ; Nanotechnology ; Chemical engineering ; Chemistry ; Organic chemistry ; Composite material Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Surface Chemistry and Catalysis