jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Joyjit Ghosh | Nishat Sarmin Rupanty | Tasneem Noor | Tanvir Rahman Asif | Tarikul Islam | Vladimir Reukov
Source: RSC Advances (review, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Functional coatings for textiles: advancements in flame resistance, antimicrobial defense, and self-cleaning performance The continuous evolution of textile technologies has led to innovative functional coatings that enhance protective textiles by integrating flame retardancy, antimicrobial efficacy, and self-cleaning properties. These multifunctional coatings address the growing demand for high-performance materials in healthcare, military, and industrial applications. This study reviews advancements in coating techniques, including dip-coating, spray-coating, sol-gel processes, and layer-by-layer assembly, highlighting their effectiveness in imparting durability, thermal stability, and biological activity to textile substrates. The incorporation of bioactive materials such as chitosan, silver nanoparticles, and plant-derived antimicrobials has demonstrated enhanced pathogen resistance and prolonged fabric functionality. Furthermore, recent developments in phosphorus-based flame retardants and photocatalytic self-cleaning agents, including titanium dioxide and silica nanoparticles, have contributed to the sustainability of functional textiles by reducing environmental impact. Challenges remain in achieving compatibility among diverse functional components while maintaining mechanical integrity and user comfort. Scalability and cost-efficiency also present barriers to commercialization, necessitating cross-disciplinary collaboration among material scientists, engineers, and regulatory experts. Future research should focus on biodegradable alternatives, smart-responsive coatings, and advanced nanomaterial integration to enhance the longevity and eco-friendliness of protective textiles. As industry standards shift towards sustainability, functional coatings are poised to redefine textile applications, offering tailored solutions that balance safety, performance, and environmental responsibility. This review underscores the transformative potential of multifunctional textile coatings and their role in advancing next-generation protective fabrics. Antimicrobial ; Materials science ; Nanotechnology ; Chemistry ; Organic chemistry Flame retardant materials and properties ; Dyeing and Modifying Textile Fibers ; Photopolymerization techniques and applications
Authors: R. D. SHARMA | Mitsuhiro Arisawa | Shinobu Takizawa | Mohamed S. H. Salem
Source: Organic Chemistry Frontiers (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Remodelling molecular frameworks <i>via</i> atom-level surgery: recent advances in skeletal editing of (hetero)cycles Skeletal editing enables atom-level modifications to molecular skeletons. This review highlights advances in single-atom insertions, deletions, and transmutations in (hetero)cyclic systems, offering a comprehensive view on cutting-edge strategies. Chemistry ; Atom (system on chip) ; Embedded system ; Computer science Machine Learning in Materials Science ; Radiopharmaceutical Chemistry and Applications ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Yeonghun Kang | Wonseok Lee | Taeun Bae | Seunghee Han | Huiwon Jang | Jihan Kim
Source: Journal of the American Chemical Society (article, 2025-01-21)
Field: Materials Science — Materials Chemistry

Harnessing Large Language Models to Collect and Analyze Metal–Organic Framework Property Data Set This research focused on the efficient collection of experimental metal-organic framework (MOF) data from scientific literature to address the challenges of accessing hard-to-find data and improving the quality of information available for machine learning studies in materials science. Utilizing a chain of advanced large language models (LLMs), we developed a systematic approach to extract and organize MOF data into a structured format. Our methodology successfully compiled information from more than 40,000 research articles, creating a comprehensive and ready-to-use data set. Specifically, data regarding MOF synthesis conditions and properties were extracted from both tables and text and then analyzed. Subsequently, we utilized the curated database to analyze the relationships between synthesis conditions, properties, and structure. Through machine learning, we identified the existence of a gap between simulation data and experimental data, and further analysis revealed the factors contributing to this discrepancy. Additionally, we leveraged the extracted synthesis condition data to develop a synthesis condition recommender system. This system suggests optimal synthesis conditions based on the provided precursors, offering a practical tool to refine synthesis strategies. This underscores the importance of experimental datasets in advancing MOF research. Chemistry ; Property (philosophy) ; Data set ; Set (abstract data type) ; Programming language ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Dan Wu | Longfei Hu | Xiaokang Liu | Tong Liu | Xiangyu Zhu | Qiquan Luo | Huijuan Zhang | Linlin Cao | Jinlong Yang | Zheng Jiang | Tao Yao
Source: Nature Communications (article, 2025-01-16)
Field: Materials Science — Materials Chemistry

Time-resolved spectroscopy uncovers deprotonation-induced reconstruction in oxygen-evolution NiFe-based (oxy)hydroxides Transition-metal layered double hydroxides are widely utilized as electrocatalysts for the oxygen evolution reaction (OER), undergoing dynamic transformation into active oxyhydroxides during electrochemical operation. Nonetheless, our understanding of the non-equilibrium structural changes that occur during this process remains limited. In this study, utilizing in situ energy-dispersive X-ray absorption spectroscopy and machine learning analysis, we reveal the occurrence of deprotonation and elucidate the role of incorporated iron in facilitating the transition from nickel-iron layered double hydroxide (NiFe LDH) into its active oxyhydroxide. Our findings demonstrate that iron substitution promotes deprotonation process within NiFe LDH, resulting in the preferential removal of protons from the specific bridged hydroxyl group (Ni<sup>2+</sup>-OH-Fe<sup>3+</sup>) linked to edge-sharing [NiO<sub>6</sub>] and [FeO<sub>6</sub>] octahedron. This deprotonation behavior drives the formation of high-valence Ni<sup>3+δ</sup> species (0 <δ < 1), which subsequently serve as the active sites, thereby ensuring efficient oxygen evolution activity. This approach offers high-resolution insights of dynamic structural evolution, overcoming the limitations of extended acquisition times and advancing our understanding of OER mechanisms. Deprotonation ; Spectroscopy ; Oxygen evolution ; Oxygen ; Materials science ; Chemistry ; Physics ; Physical chemistry ; Ion ; Organic chemistry Copper-based nanomaterials and applications ; Advancements in Battery Materials ; Electronic and Structural Properties of Oxides
Authors: Amir Omranpour | Jan Elsner | K. Nikolas Lausch | Jörg Behler
Source: ACS Catalysis (article, 2025-01-15)
Field: Materials Science — Materials Chemistry

Machine Learning Potentials for Heterogeneous Catalysis The production of many bulk chemicals relies on heterogeneous catalysis. The rational design or improvement of the required catalysts critically depends on insights into the underlying mechanisms on the atomic scale. In recent years, substantial progress has been made in applying advanced experimental techniques to complex catalytic reactions in operando, but in order to achieve a comprehensive understanding, additional information from computer simulations is indispensable in many cases. In particular, ab initio molecular dynamics (AIMD) has become an important tool to explicitly address the atomistic level structure, dynamics, and reactivity of interfacial systems, but the high computational costs limit applications to systems consisting of at most a few hundred atoms for simulation times of up to tens of picoseconds. Rapid advances in the development of modern machine learning potentials (MLP) now offer a promising approach to bridge this gap, enabling simulations of complex catalytic reactions with ab initio accuracy at a small fraction of the computational costs. In this Perspective, we provide an overview of the current state of the art of applying MLPs to systems relevant for heterogeneous catalysis along with a discussion of the prospects for the use of MLPs in catalysis science in the years to come. Computer science ; Biochemical engineering ; Molecular dynamics ; Ab initio ; Nanotechnology ; Chemistry ; Computational chemistry ; Materials science ; Engineering Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Zhongkang Han | Wen Liu | Yi Gao
Source: JACS Au (review, 2025-03-28)
Field: Materials Science — Materials Chemistry

Advancing the Understanding of Oxygen Vacancies in Ceria: Insights into Their Formation, Behavior, and Catalytic Roles Oxygen vacancies (O<sub>V</sub>'s) in ceria (CeO<sub>2</sub>) are critical structural and electronic features that underpin ceria's remarkable oxygen storage capacity, redox catalytic performance, and wide-ranging applications in catalysis, solid oxide fuel cells, and gas sensors. These vacancies, which result from the removal of oxygen atoms, enable dynamic oxygen exchange between the solid and its environment, profoundly influencing ceria's catalytic properties. The intricate surface structures of ceria play a key role in determining its properties and its interactions with supported metal catalysts. Over the past decade, advancements in state-of-the-art in situ characterizations, first-principles calculations, and emerging machine learning frameworks have significantly enhanced our understanding of the formation mechanisms, behaviors, and catalytic roles of O<sub>V</sub>'s. This perspective highlights recent experimental and theoretical progress in ceria surface research, emphasizing the dynamic interplay between surface structures and reactive environments. Additionally, the perspective addresses key challenges in elucidating ceria's defect chemistry and explores opportunities to tailor its properties using multiscale modeling and AI-driven methodologies. Catalysis ; Oxygen ; Materials science ; Nanotechnology ; Chemical engineering ; Chemistry ; Engineering Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Chunsheng Yan
Source: iScience (review, 2025-05-29)
Field: Chemistry — Analytical Chemistry

A review on spectral data preprocessing techniques for machine learning and quantitative analysis Spectroscopic techniques are indispensable for material characterization, yet their weak signals remain highly prone to interference from environmental noise, instrumental artifacts, sample impurities, scattering effects, and radiation-based distortions (e.g., fluorescence and cosmic rays). These perturbations not only significantly degrade measurement accuracy but also impair machine learning-based spectral analysis by introducing artifacts and biasing feature extraction. This review provides a systematic evaluation of critical spectral preprocessing methods-encompassing cosmic ray removal, baseline correction, scattering correction, normalization, filtering and smoothing, spectral derivatives, and advanced techniques like 3D correlation analysis-highlighting their theoretical underpinnings, performance trade-offs, and optimal application scenarios. The field is undergoing a transformative shift driven by three key innovations: context-aware adaptive processing, physics-constrained data fusion, and intelligent spectral enhancement. These cutting-edge approaches enable unprecedented detection sensitivity achieving sub-ppm levels while maintaining >99% classification accuracy, with transformative applications spanning pharmaceutical quality control, environmental monitoring, and remote sensing diagnostics. Preprocessor ; Spectral analysis ; Computer science ; Data science ; Artificial intelligence ; Machine learning ; Physics ; Spectroscopy Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Remote-Sensing Image Classification
Authors: Hongguang Liao | Hu Sheng | Yang Hu | Lei Wang | Shinya Tanaka | Ichigaku Takigawa | Wei Li | Hailong Fan | Jian Ping Gong
Source: Nature (article, 2025-08-06)
Field: Materials Science — Materials Chemistry

Data-driven de novo design of super-adhesive hydrogels Data-driven methodologies have transformed the discovery and prediction of hard materials with well-defined atomic structures by leveraging standardized datasets, enabling accurate property predictions and facilitating efficient exploration of design spaces<sup>1-3</sup>. However, their application to soft materials remains challenging because of complex, multiscale structure-property relationships<sup>4-6</sup>. Here we present a data-driven approach that integrates data mining, experimentation and machine learning to design high-performance adhesive hydrogels from scratch, tailored for demanding underwater environments. By leveraging protein databases, we developed a descriptor strategy to statistically replicate protein sequence patterns in polymer strands by ideal random copolymerization, enabling targeted hydrogel design and dataset construction. Using machine learning, we optimized hydrogel formulations from an initial dataset of 180 bioinspired hydrogels, achieving remarkable improvements in adhesive strength, with a maximum value exceeding 1 MPa. These super-adhesive hydrogels hold immense potential across diverse applications, from biomedical engineering to deep-sea exploration, marking a notable advancement in data-driven innovation for soft materials. Self-healing hydrogels ; Adhesive ; Chemistry ; Computer science ; Nanotechnology ; Materials science ; Polymer chemistry Machine Learning in Materials Science ; Software Engineering Research ; Advanced Polymer Synthesis and Characterization
Authors: Dandan Xiang | Qinchuan He | Cong-Min Fan | Yiqun Wang | Xuemin Yin | Changcong Wang
Source: Materials Today Nano (article, 2025-05-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Preparation and 3D network structure optimization of SiC and SiC@Fe3Si nanofibers for enhanced electromagnetic wave absorption Absorption (acoustics) ; Materials science ; Nanofiber ; Nanotechnology ; Composite material Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies
Authors: Veeranjaneya Reddy Lebaka | P. Ravi | Madhava C. Reddy | Thummala Chandrasekhar | Tapas Kumar Mandal
Source: Nanomaterials (review, 2025-05-17)
Field: Materials Science — Materials Chemistry

Zinc Oxide Nanoparticles in Modern Science and Technology: Multifunctional Roles in Healthcare, Environmental Remediation, and Industry Zinc oxide nanoparticles (ZnO NPs) have garnered significant attention across various scientific and technological domains due to their unique physicochemical properties, including high surface area, photostability, biocompatibility, and potent antimicrobial activity. These attributes make ZnO NPs highly versatile, enabling their application in biomedicine, environmental science, industry, and agriculture. They serve as effective antimicrobial agents in medical treatments and as catalysts in environmental purification processes, owing to their ability to generate reactive oxygen species (ROS) and exhibit photocatalytic activity under UV light. Moreover, ZnO NPs are being increasingly employed in advanced drug delivery systems and cancer therapies, highlighting their potential in modern medicine. Their growing popularity is further supported by their ease of synthesis, cost-effectiveness, and capacity for diverse functionalization, which expand their utility across multiple sectors. This review focuses on research from the past five years (2020-2025) on the practical uses of ZnO nanoparticles in the biomedical, environmental, industrial, and agricultural fields. It also highlights current trends, existing challenges, and future perspectives. By examining these aspects, the article provides a comprehensive understanding of the versatile roles of ZnO NPs and their emerging significance in science and technology. Nanotechnology ; Biomedicine ; Biocompatibility ; Materials science ; Bioinformatics Nanoparticles: synthesis and applications ; Advanced Nanomaterials in Catalysis
Authors: Zhe Fang | Su Xu | Rui Cao | Mingli Jiao | Keyi Liu | Quan Diao | Shao Kang Guan | Yu Jia
Source: Coatings (article, 2025-05-15)
Field: Materials Science — Materials Chemistry

Interfacial Adsorption Mechanisms of Arginine, Glutamic Acid, Aspartic Acid, and Valine on Magnesium and Magnesium Alloy Surfaces: A First-Principles Investigation Elucidating the interfacial interaction mechanisms between biomolecules and metal surfaces is crucial for designing functionalized biomedical materials. This study employs first-principles calculations based on density functional theory (DFT) to investigate the adsorption behaviors of arginine (Arg), glutamic acid (Glu), aspartic acid (Asp), and valine (Val) on magnesium (Mg) and Mg alloy surfaces. The adsorption behaviors of four kinds of amino acids on Mg and Mg alloy surfaces were analyzed through optimized adsorption configurations, adsorption energies (Eads), bond lengths, projected densities of states (PDOSs), and differential charge densities. The calculated results of Eads followed the order of Arg &gt; Glu &gt; Asp &gt; Val, driven by functional group spatial configurations and electron transfer efficiency. Alloying elements facilitated charge redistribution on the Mg and Mg alloy surfaces, enhancing the interaction between amino acids and the alloy surfaces. Notably, the guanidino group of Arg exhibited exceptional adsorption stability and multi-dentate bonding, increasing electron donation to the Mg(0001) surface, achieving the highest Eads (−1.67 eV). This work provides insights into the structure–activity relationships between amino acids and Mg and Mg alloy surfaces, offering a foundation for designing biomolecule-derived functional coatings and strategies for improving the biocompatibility of Mg and Mg alloy implants. Magnesium ; Aspartic acid ; Valine ; Arginine ; Glutamic acid ; Magnesium alloy ; Adsorption ; Chemistry ; Materials science ; Amino acid ; Metallurgy ; Biochemistry ; Organic chemistry Layered Double Hydroxides Synthesis and Applications ; Phosphorus and nutrient management ; Hydrogen Storage and Materials
Authors: Zi‐Ming Ye | Yi Xie | Kent O. Kirlikovali | Shengchang Xiang | Omar K. Farha | Banglin Chen
Source: Journal of the American Chemical Society (review, 2025-02-07)
Field: Chemistry — Inorganic Chemistry

Architecting Metal–Organic Frameworks at Molecular Level toward Direct Air Capture Escalating carbon dioxide (CO<sub>2</sub>) emissions have intensified the greenhouse effect, posing a significant long-term threat to environmental sustainability. Direct air capture (DAC) has emerged as a promising approach to achieving a net-zero carbon future, which offers several practical advantages, such as independence from specific CO<sub>2</sub> emission sources, economic feasibility, flexible deployment, and minimal risk of CO<sub>2</sub> leakage. The design and optimization of DAC sorbents are crucial for accelerating industrial adoption. Metal-organic frameworks (MOFs), with high structural order and tunable pore sizes, present an ideal solution for achieving strong guest-host interactions under trace CO<sub>2</sub> conditions. This perspective highlights recent advancements in using MOFs for DAC, examines the molecular-level effects of water vapor on trace CO<sub>2</sub> capture, reviews data-driven computational screening methods to develop a molecularly programmable MOF platform for identifying optimal DAC sorbents, and discusses scale-up and cost of MOFs for DAC. Chemistry ; Metal-organic framework ; Environmental chemistry ; Nanotechnology ; Organic chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Membrane Separation and Gas Transport
Authors: Jinyang Jiang | Han Wang | Junlin Lin | Fengjuan Wang | Zhiyong Liu | Liguo Wang | Zongjin Li | Yali Li | Yunjian Li | Zeyu Lu
Source: Nature Communications (article, 2025-03-28)
Field: Materials Science — Biomaterials

Nature-inspired hierarchical building materials with low CO2 emission and superior performance Conventional cement-based materials are faced with significant challenges, including large carbon emissions, high density, and quasi-brittleness. Here, inspired by hierarchical porous structures existing in nature, we develop a low carbon, lightweight, strong and tough cement-based material (LLST), which is obtained by a rapid gelation of hydrogel as skeleton and subsequent deposition of cement hydrates as a skin. As a result, the LLST exhibits hierarchical structure consisting of sponge-like micropores (1 ~ 50 μm) and nanopores (5 ~ 100 nm), without detrimental macropores that compromise light weight, strength, and toughness. Compared with the normal cement paste, LLST displays a 54% reduction in density, 145% and 1365% improvement in specific compressive strength and fracture energy, with only 51% carbon emission. These properties are further investigated with machine learning force field molecular dynamics along with well-tempered metadynamics simulations, indicating that strong chemical bonding is generated at the atomic level between functional groups in the hydrogel and Ca ion released from cement hydration. These findings not only demonstrate a strategy for developing lightweight building materials with low-carbon emission and remarkable mechanical properties, but also provide valuable insights for realizing the coexistence of light weight, strength and toughness by tailoring the hierarchical pore structure. Materials science ; Brittleness ; Carbon fibers ; Polyetherimide ; Toughness ; Composite material ; Cement ; Porosity ; Compressive strength ; Macropore ; Fracture toughness ; Nanotechnology ; Polymer ; Chemistry Calcium Carbonate Crystallization and Inhibition ; Concrete and Cement Materials Research ; Supercapacitor Materials and Fabrication
Authors: Chuang Wang | Xingxing Cheng | Kai Luo | K. Nandakumar | Zhiqiang Wang | Meng Ni | Xiaotao Bi | Jiansheng Zhang | Chunbo Wang
Source: Materials Science and Engineering R Reports (review, 2025-05-02)
Field: Chemistry — Inorganic Chemistry

A guided review of machine learning in the design and application for pore nanoarchitectonics of carbon materials Nanotechnology ; Materials science Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Fuel Cells and Related Materials