jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tiantian Min | Zhongna Zhang | Lan Chen | Jingan Li
Source: Materials (review, 2025-01-22)
Field: Materials Science — Surfaces, Coatings and Films

Recent Advances in Barnacle-Inspired Biomaterials in the Field of Biomedical Research As a marine fouling organism, barnacles secrete a cement whose proteins self-assemble into stable nanofibers, conferring exceptional underwater adhesion and curing properties. The barnacle cement proteins (BCPs) are of significant interest in biomedicine due to their adhesiveness, water resistance, stability, and biocompatibility, making them ideal for developing novel biomaterials. Additionally, BCPs have wound-healing acceleration and antibacterial properties, offering new insights for antimicrobial biomaterial development. Recently, barnacle-inspired materials have seen extensive research and notable progress in biomedicine. As the understanding of barnacle cement and its adhesion mechanisms deepens, their medical applications are expected to expand. This review summarizes the latest advancements of barnacle biomimetic materials in biomedicine, including their use in adhesives, tissue engineering, drug delivery, and hemostasis, highlighting their characteristics, applications, and potential research directions, and providing a comprehensive reference for the field. Barnacle ; Nanotechnology ; Biomedicine ; Biocompatible material ; Biomaterial ; Materials science ; Engineering ; Biology ; Biomedical engineering ; Bioinformatics ; Ecology Polymer Surface Interaction Studies ; Marine Biology and Environmental Chemistry ; Bone Tissue Engineering Materials
Authors: Andekuba Andezai | Jude O. Iroh
Source: Energies (article, 2025-01-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Review: Overview of Organic Cathode Materials in Lithium-Ion Batteries and Supercapacitors Organic materials have emerged as promising candidates for cathode materials in lithium-ion batteries and supercapacitors, offering unique properties and advantages over traditional inorganic counterparts. This review investigates the use of organic compounds as cathode materials in energy storage devices, focusing on their application in lithium-ion batteries and supercapacitors. The review covers various types of organic materials, organosulfur compounds, organic free radical compounds, organic carbonyl compounds, conducting polymers, and imine compounds. The advantages, challenges, and ongoing developments in this area are examined and the potential of organic cathode materials to achieve higher energy density, improved cycling stability, and environmental sustainability is highlighted. The comprehensive analysis of organic cathode materials provides insights into their electrochemical performance, electrode reaction mechanisms, and design strategies such as molecular structure modification, hybridization with inorganic components, porous architectures, conductive additives, electrolyte optimization, binder selection, and electrode architecture to improve their efficiency and performance. In addition, future research in the field of organic cathode materials should focus on addressing current limitations such as low energy density, cycling stability, poor discharge capability, potential safety concerns and improving their performance. To do this, it will be necessary to improve structural stability, conductivity, cycle life, and capacity fading, explore new redox-active organic compounds, and pave the way for the next generation of high-performance energy storage devices. For organic cathode materials to be commercially viable, it is also essential to develop scalable and economical manufacturing processes. Supercapacitor ; Lithium (medication) ; Cathode ; Materials science ; Organic radical battery ; Ion ; Nanotechnology ; Electrochemistry ; Chemistry ; Electrode ; Engineering ; Electrical engineering ; Organic chemistry ; Psychology ; Physical chemistry Supercapacitor Materials and Fabrication
Authors: Yiyuan Cheng | Haoran Pang | Wenjun Zhang
Source: Journal of the American Chemical Society (article, 2025-03-29)
Field: Chemistry — Organic Chemistry

Exquisite Complex Reaction Cascade in the Natural 1,2,4-Triazine Assembly 1,2,4-Triazine ring is a scaffold widely found in biologically active compounds, but how nature makes it remains enigmatic. In this study, we unveil the complex enzymatic and nonenzymatic cascade reactions that assemble the 1,2,4-triazine moiety found in the structures of the natural products pseudoiodinine and toxoflavin. Through biochemical studies, isotope labeling, and the application of substrate analogues, we propose a plausible pathway for the 1,2,4-triazine assembly from a common precursor in riboflavin biosynthesis. This process involves four two-electron oxidation steps, C-N bond formation, decarboxylation, and the N-N bond forming step catalyzed by a metal-dependent WD40-repeat (WDR) protein. This study thus not only provides the first biocatalytic route for the 1,2,4-triazine assembly but also identifies a previously unrecognized catalytic role of a large WDR protein family. Chemistry ; Cascade ; Triazine ; Cascade reaction ; Combinatorial chemistry ; Stereochemistry ; Polymer chemistry ; Organic chemistry ; Chromatography ; Catalysis Synthesis and Characterization of Heterocyclic Compounds ; Chemical Reaction Mechanisms ; Chemical Reactions and Isotopes
Authors: Qingqing Zuo | Xin Tang | Yintao Li | Yuping Bao | Min Chen | Pengfei Ji
Source: Small (article, 2025-04-27)
Field: Chemistry — Inorganic Chemistry

Design and Combinatorial Synthesis of Biomimetic Multivariate Metal–Organic Frameworks for Efficient Drug Loading Multivariate metal-organic frameworks (MOFs) covering diverse iso-reticular materials while encompassing tunable function through the spatial composition of the organic functional groups are an excellent starting point for developing sequence-dependent materials. There remains a demand for synthesizing an extensive library of multivariate MOFs for function-targeted selection, similar to the process of directed evolution in improving the function of proteins by selecting the best mutant from a large mutant library. The design of biomimetic multivariate MOFs (bMTV-MOFs) is reported that incorporate the panel of functionality presented in amino acids to enable the complexity and adjustability of proteins to the structure of MOFs. A total of 1089 MOFs, ranging from single-component to multivariate structures, are synthesized in a combinatorial manner for the delivery of the verteporfin, a photosensitive drug. Tertiary bMTV-MOFs demonstrated superiority in the adsorption and release of verteporfin. In vitro studies on various cancer cell lines demonstrated a 23 to 68-fold enhancement in efficacy for photodynamic therapy using the privileged A<sub>1</sub>F<sub>6</sub>H<sub>3</sub> or A<sub>3</sub>D<sub>1</sub>H<sub>6</sub> bMTV-MOFs over pristine MOFs. Metal-organic framework ; Multivariate statistics ; Materials science ; Rational design ; Nanotechnology ; Combinatorial chemistry ; Adsorption ; Chemistry ; Computer science ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Dendrimers and Hyperbranched Polymers ; Machine Learning in Materials Science
Authors: Naohiro Matsugaki | Toshiya Senda
Source: Journal of Synchrotron Radiation (article, 2025-03-24)
Field: Materials Science — Materials Chemistry

Advances in macromolecular crystallography at the Photon Factory: automation from crystallization to structural determination The Structural Biology Research Center (SBRC) and the Photon Factory at the High Energy Accelerator Research Organization (KEK) have played a key role in advancing macromolecular crystallography (MX) and have developed advanced experimental systems in the MX field. Key innovations include a long-wavelength MX beamline for native single-wavelength anomalous diffraction phasing (BL-1A), a crystal-shaping machine, an automated crystal-centering system and a fully automated diffraction data-acquisition system. In addition to the beamline technologies, the SBRC has developed a fully automated protein crystallization and monitoring system (PXS/PXS2). The crystallization plate prepared by PXS2 can be mounted directly onto an in situ data-acquisition system at BL-17A. These technologies have transformed experimental workflows, enabling high-throughput structure determination and supporting drug discovery. Furthermore, the SBRC can integrate advanced imaging techniques, including MX, cryogenic electron microscopy (cryo-EM) and small-angle X-ray scattering (SAXS), under one roof. This interdisciplinary approach facilitates hybrid structural analysis by combining techniques such as MX and SAXS or MX and cryo-EM. Crystallization ; Automation ; Macromolecule ; Factory (object-oriented programming) ; Chemistry ; Crystallography ; Materials science ; Computer science ; Engineering ; Biochemistry ; Mechanical engineering ; Organic chemistry Enzyme Structure and Function ; Various Chemistry Research Topics
Authors: Xiang Chen | Mingkang Liu | Shun‐Gao Yin | Yuchen Gao | Nan Yao | Qiang Zhang
Source: Angewandte Chemie (article, 2025-04-17)
Field: Materials Science — Materials Chemistry

Uni‐Electrolyte: An Artificial Intelligence Platform for Designing Electrolyte Molecules for Rechargeable Batteries Abstract Electrolytes are an essential part of rechargeable batteries, such as lithium batteries. However, electrolyte innovation is facing grand challenges due to the complicated solution chemistry and infinite molecular space (&gt;10 60 for small molecules). This work reported an artificial intelligence (AI) platform, namely Uni‐Electrolyte, for designing advanced electrolyte molecules, which mainly includes three parts, i.e., EMolCurator, EMolForger, and EMolNetKnittor. New molecules can be designed by combining high‐throughput screening and generative AI models from more than 100 million alternative molecules in the EMolCurator module. The molecular properties, including frontier molecular orbital information, formation energy, binding energy with a Li ion, viscosity, and dielectric constant, can be adopted as the screening parameters. The EMolForger and EMolNetKnittor modules can predict the retrosynthesis pathway and solid electrolyte interphase (SEI) formation mechanism for a given molecule, respectively. With the assistance of advanced AI methods, the Uni‐Electrolyte is strongly supposed to discover new electrolyte molecules and chemical principles, promoting the practical application of next‐generation rechargeable batteries. Electrolyte ; Nanotechnology ; Materials science ; Molecule ; Chemical engineering ; Chemistry ; Computer science ; Electrode ; Engineering ; Organic chemistry ; Physical chemistry Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Fuel Cells and Related Materials
Authors: Tiantong Zhang | Hong Cheng | Nian Yao | Jinli Zhang | Qingbiao Li | You Han
Source: Chinese Journal of Chemical Engineering (article, 2025-06-06)
Field: Materials Science — Materials Chemistry

Application of generative artificial intelligence in catalysis Generative grammar ; Artificial intelligence ; Catalysis ; Computer science ; Engineering ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Di Zhang | Xue Jia | Heng Liu | Yuhang Wang | Songbo Ye | Qiuling Jiang | Yuan Wang | Zhongyuan Guo | Linda Zhang | Li Wei | Weijie Yang | Hui Liu | Shun Zhao | Haoxiang Xu | Daojian Cheng | Yusuke Hashimoto | Takaaki Tomai | Hao Li
Source: AI Agent (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

Cloud synthesis: a global closed-loop feedback powered by autonomous AI-driven catalyst design agent The Digital Catalysis Platform (DigCat) pioneers a revolutionary framework for cloud-based synthesis and global closed-loop feedback, redefining catalyst research through advanced automation and collaboration. By integrating &gt; 400,000 experimental and &gt; 400,000 structural data points with cutting-edge AI tools, DigCat streamlines catalyst discovery and optimization into a seamless, automated, and data-driven workflow. Through its five-step process - ranging from material design to pH-dependent microkinetic modeling - the platform delivers unparalleled accuracy and efficiency. Accessible globally via the cloud, DigCat enables researchers worldwide to leverage its robust computational capabilities, driving the development of next-generation catalysts with enhanced stability and performance. Leverage (statistics) ; Automation ; Process (computing) ; Cloud computing ; Ranging ; Catalysis Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Ammonia Synthesis and Nitrogen Reduction
Authors: Ji Kim
Source: Journal of Chemical Education (article, 2025-06-12)
Field: Materials Science — Materials Chemistry

Integrating Artificial Intelligence (AI) Chatbots and Green Chemistry Principles in the Synthesis of Cyclohexene This activity presents a redesigned undergraduate organic chemistry laboratory activity that integrates green chemistry principles and generative artificial intelligence (AI) tools to enhance student learning. Conducted at a two-year college, the activity challenged students to synthesize cyclohexene from cyclohexanol using safer, more sustainable catalysts. Students utilized AI chatbots─such as ChatGPT, Gemini, and Microsoft Copilot─to explore greener alternatives to traditional reagents, aligning their experimental choices with the 12 Principles of Green Chemistry and evaluating their selections with ChemForward and instructor feedback. Catalysts including Amberlyst-15 and dried orange peels were tested, with varying yields, emphasizing the practical challenges and benefits of eco-conscious substitutions. The activity fostered skills in digital literacy, sustainability, and scientific inquiry while promoting awareness of the ethical implications of AI in laboratory decision-making. Survey responses and student reflections showed increased engagement and a deeper understanding of green chemistry. This approach offers a replicable, cost-effective model for embedding sustainability and technology into STEM education while encouraging evidence-based reasoning and critical thinking. Chemistry ; Cyclohexene ; Nanotechnology ; Organic chemistry ; Materials science ; Catalysis Machine Learning in Materials Science ; Various Chemistry Research Topics
Authors: Rohit Dahule | Kenji Oqmhula | Ryo Maezono | Kenta Hongo
Source: ACS Applied Polymer Materials (article, 2025-02-02)
Field: Materials Science — Materials Chemistry

Physics-Informed Data-Driven Discovery of Polymer Crystals with High Thermal Conductivity High thermal conductivity (HTC) in polymers is a vital thermophysical property that significantly enhances industrial applications, such as modern electronics and electrical systems. Because of multiple degrees of freedom in polymer synthesis, it is impractical to explore HTC polymers solely through experimentation; hence, a predictive model for HTC is necessary. In this context, we emphasize polymer crystals, which are thought to represent the highest possible thermal conductivity bounds for real polymers. Here, the HTC polymer crystals were computationally discovered by using a data-driven method incorporating physics-informed screening with first-principles phonon calculations. Additionally, a less computationally demanding physical quantity associated with thermal conductivity was selected for the polymer crystals, and its correlations were investigated. A physics-informed descriptor was used to perform the high-throughput virtual screening of 1073 polymer crystal structures obtained from the polymer genome datasets. Through this strategy, we identified polymer crystals of polymethylenimine (PMI), poly(methylene oxide) (PMO), and polyamide (PA), with lattice thermal conductivities (LTCs) at 300 K of 21.81, 94.95, and 65.27 W/(m K), respectively. Moreover, the LTC of PMO exceeded 100 W/(m K) in the temperature range of 100–270 K. Additionally, the obtained results were further analyzed by examining phonon lifetimes, group velocity, mean free paths, and modal heat capacities of the polymer crystals, offering deeper insight into the fundamental mechanisms behind their thermal conductivity. Thermal conductivity ; Polymer ; Materials science ; Phonon ; Context (archaeology) ; Thermal ; Nanotechnology ; Chemical physics ; Condensed matter physics ; Thermodynamics ; Physics ; Composite material Machine Learning in Materials Science ; Thermal properties of materials ; Fuel Cells and Related Materials
Authors: Chendruru Geya Sree | Wesley Wei-Wen Hsiao | Adhimoorthy Saravanan | Balamurugan Devadas | Karel Bouzek
Source: Materials Science and Engineering R Reports (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Emerging trends and smart integration of wireless and artificial intelligence in 2D hybrid materials-based biosensors Flexibility (engineering) ; Biosensor ; Wearable computer ; Scalability ; Wireless ; Smart material ; Emerging technologies ; Wearable technology 2D Materials and Applications ; MXene and MAX Phase Materials ; Gas Sensing Nanomaterials and Sensors
Authors: Kengqiang Zhong | Feng Yu | Di Zhang | Hao Li | Donghua Xie | Ting‐Ting Li | Yun Zhang | Yuan Li | Zhenyu Wu | Guo‐Ping Sheng
Source: Angewandte Chemie (article, 2025-01-31)
Field: Chemistry — Organic Chemistry

Data‐Driven Accelerated Discovery Coupled with Precise Synthesis of Single‐Atom Catalysts for Robust and Efficient Water Purification Abstract The development of advanced catalysts frequently employs trial‐and‐error methods and is lack of highly controlled synthesis, resulting in unsatisfactory development efficiency and performance. Here we propose a data‐driven prediction coupled with precise synthesis strategy to accelerate the development of single‐atom catalysts (SACs) for efficient water purification. The data‐driven approach enables the rapid screening and prediction of high‐performance SACs from 43 metals‐N 4 structures comprising transition and main group metal elements, followed by validation and structural modulation for improved performance through a highly controllable hard‐template method. Impressively, a well‐designed Fe‐SAC with a high loading of Fe‐pyridine‐N 4 sites (~3.83 wt %) and highly mesoporous structure, exhibits ultra‐high decontamination performance (rate constant of 100.97 min −1 g −2 ), representing the best Fenton‐like activities for sulfonamide antibiotics to date. Furthermore, the optimized Fe‐SAC shows excellent robust environmental resistance and cyclic stability with almost 100 % degradation efficiency of sulfonamide antibiotics for 100‐h continuous operation. Density functional theory calculations reveal that Fe‐pyridine‐N 4 sites can reduce the energy barrier of intermediate O* formation, the rate‐determining step, resulting in highly selective generation of singlet oxygen. The integration of data‐driven method with precise synthesis strategy provides a novel paradigm for the rapid development of high‐performance catalysts for environmental field as well as other important fields including sustainable energy and catalysis. Catalysis ; Chemistry ; Combinatorial chemistry ; Atom (system on chip) ; Nanotechnology ; Materials science ; Computer science ; Organic chemistry ; Parallel computing Nanomaterials for catalytic reactions ; Catalytic Processes in Materials Science ; Advanced Photocatalysis Techniques
Authors: Xiaoqian Nan | Kei Hayashi | Zhicheng Huang | Yuzuru Miyazaki
Source: Science Advances (article, 2025-06-27)
Field: Materials Science — Materials Chemistry

Data-driven approach for potential iron-based half-Heusler thermoelectrics with chemical bonding characteristics Chemical bonding influences various physical properties and holds promise for guiding the discovery of high-performance materials. In <i>XYZ</i> half-Heusler (HH) compounds, complex interactions between constituent atoms, featuring both covalent and ionic characters, are supposed to affect mechanical and thermoelectric behavior. We use a data-driven approach based on first-principles calculations to identify promising HH compounds and explore the correlations between bonding-related features and functional properties. Our analysis suggests that enhanced <i>Y</i>─<i>Z</i> bonding correlates with greater bulk modulus (<i>B</i>), larger Grüneisen parameter, and enhanced power factor. Tungsten-iron-lead stands out with excellent <i>B</i> = 162.4 gigapascals and a low lattice thermal conductivity of ~7.7 watts per meter per kelvin, leading to a figure of merit of ~0.52 (at 526 kelvin) without any nanostructuring, surpassing that of vanadium-iron-antimony by ~80.8%. These findings highlight how chemical bonding characteristics, interpreted from electronic structure and orbital-resolved bonding analysis, provide in-depth insights into the structure-to-property correlation to accelerate the screening out of potential thermoelectrics. Thermoelectric materials ; Chemical bond ; Materials science ; Ionic bonding ; Valence (chemistry) ; Thermoelectric effect ; Vanadium ; Nanotechnology ; Rietveld refinement ; Covalent bond ; Chemical physics ; Figure of merit ; Crystal structure ; Chemistry ; Thermal conductivity ; Crystallography ; Thermodynamics ; Metallurgy ; Optoelectronics ; Physics ; Ion ; Composite material Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties ; Machine Learning in Materials Science
Authors: Jesús Barrio | Arne Thomas
Source: Advanced Science (review, 2025-09-12)
Field: Materials Science — Materials Chemistry

Synthesis of Functional Materials Using N‐heterocyclic Amines Beyond Melamine Melamine, a nitrogen-rich heterocycle with a 1,3,5-triazine core and three exocyclic amines, has played a central role in materials chemistry, enabling the synthesis of supramolecular assemblies, covalent organic polymers, and carbon nitride semiconductors. However, structurally related N-heterocyclic amines, differing in ring substitution or heterocycle, are far less applied in such fields despite their potential to overcome limitations associated with melamine-based materials. In this review, recent advances in the synthesis of covalent and carbon-based materials derived from melamine analogues, including 6-Phenyl-1,3,5-triazine-2,4-diamine, 6-Methyl-1,3,5-triazine-2,4-diamine and 2,4,6-Triaminopyrimidine are highlighted. It is focused on how variations in heterocycle identity and functional group substitution influence monomer reactivity, condensation behavior, and the resulting material properties, such as chemical composition, dimensionality, porosity and morphology. Diverse applications of such materials are discussed, from fluorescent sensing platforms to CO<sub>2</sub> capture and photo-electrocatalysis, however, the primary emphasis lies on synthetic strategies and structure-property relationships. It is concluded with a critical perspective on how these alternative building blocks, when leveraging sustainable synthesis methods coupled with emerging AI-guided materials discovery, may enable the targeted design of next-generation functional materials. Melamine ; Covalent bond ; Supramolecular chemistry ; Monomer ; Functional group ; Porous medium ; Carbon nitride ; Carbon fibers ; Covalent organic framework Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques