jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Di Sun | Xuesong Feng | Qing Miao | Xinyuan Zhu | Jinghui Yang | Youfu Wang
Source: Advanced Healthcare Materials (article, 2025-12-21)
Field: Materials Science — Materials Chemistry

High‐Entropy Metal–Organic Cages as Multienzyme Mimetics for the Mitigation of Acute Kidney Injury Precise morphological and compositional regulation of high-entropy nanoparticles (HENPs) represents a fundamental prerequisite for advancing their applications in catalysis and electronics. Conventional synthesis approaches, however, face challenges stemming from intrinsic elemental segregation dynamics and the unpredictable atomic configurations within individual nanoparticles. To address this, we introduce an isostructural substitution strategy for constructing HENPs, utilizing predefined molecular scaffolds to guide multielemental integration. By employing a calixarene-based octahedral metal-organic cage (MOC) featuring six well-defined M<sub>4</sub>O clusters (totaling 24 metal sites) as a structural template, we systematically incorporate five distinct metallic elements (Mg, Mn, Co, Ni, Zn) through solvothermal synthesis, yielding high-entropy MOC (HEMOC) nanoobjects in gram scale. These engineered HEMOCs preserve the octahedral skeleton and structural uniformity, maintain readily accessible catalytic centers with tunable stoichiometry, and exhibit exceptional aqueous-phase stability. Significantly, the diverse heterometallic M<sub>4</sub>O clusters within HEMOCs function as cluster nanozymes, demonstrating amplified multienzyme-mimetic catalytic activity for scavenging reactive oxygen species. Demonstrating favorable biocompatibility profiles in biological evaluations, the optimized HEMOCs successfully attenuate inflammation in acute kidney injury models, suggesting therapeutic potential for oxidative stress-associated pathologies. Isostructural ; Biocompatibility ; Catalysis ; Octahedron ; Nanoparticle ; Reactive oxygen species ; Acute kidney injury ; Function (biology) Advanced Nanomaterials in Catalysis ; Supramolecular Chemistry and Complexes ; Nanocluster Synthesis and Applications
Authors: Maxwell Thatyana | Masixole Sihlahla | Nomvano Mketo
Source: Environmental Science and Pollution Research (review, 2025-11-27)
Field: Chemistry — Inorganic Chemistry

Removal of pharmaceutical pollutants by adsorption onto novel metal–organic frameworks The growing presence of new pollutants in the environment could lead to both direct and indirect water contamination, endangering human and marine health life. Pharmaceutical contaminants are becoming more prevalent in the environment due to their persistence and therefore there is urgent attention to develop necessitating technological interventions to eliminate them. Metal-organic frameworks (MOFs) are a type of crystalline material with a large surface area, significant pore size, and strong chemical structure in environmental settings. Research interest in MOFs has increased recently, as they have proven to be effective in removing pharmaceutical pollutants through the adsorption process. This review highlights the effectiveness of various MOF architectures in removing pharmaceutical pollutants from aqueous environments. The review further elaborates on different synthesis methods, functionalization techniques, and adsorption mechanisms in detail. The study reveals that while there has been significant research on the adsorption of antibiotics and anti-inflammatory agents from water using MOFs, there is a lack of information on other types of pharmaceutical agents that also pose risks to human health in the environment. Moreover, the study reports for the first time detailed literature on the adsorptive removal of antibiotics and anti-inflammatory pollutants from water during the period 2015 to 2024. The current review suggests that more attention should be given to phytonanoparticle-MOF nanocomposites for pharmaceutical waste removal, as this could lead to innovative solutions for water purification systems. Pollutant ; Adsorption ; Water pollutants ; Human health ; Contamination ; Water treatment ; Surface water Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Covalent Organic Framework Applications
Authors: Raphaël Vangheluwe | Carine Clavaguéra | Megan Truong | Dominik Domin | H. Pham | Mihai‐Cosmin Marinica | Nguyen‐Thi Van‐Oanh
Source: ChemPhysChem (article, 2025-09-03)
Field: Materials Science — Materials Chemistry

Accelerating the Structure Exploration of Diverse Bi–Pt Nanoclusters via Physics‐Informed Machine Learning Potential and Particle Swarm Optimization Bimetallic Bi-Pt nanoclusters exhibit diverse structural motifs, including core-shell, Janus, and mixed alloy configurations, due to the unique bonding characteristics between Bi and Pt atoms. Using density functional theory refinements from ChIMES physically machine-learned potential and CALYPSO particle swarm optimization global searches, 34 Bi20-Pt20 nanoclusters are systematically classified. The results reveal that Bi atoms predominantly occupy surface sites, driven by charge transfer effects. Cohesive energy trends alone prove insufficient for structure differentiation, necessitating a data-driven approach employing principal component analysis and K-means clustering. Furthermore, vibrational, electronic, and infrared spectral analyses provide additional insights into structure-property relationships. The findings offer an original framework for the automated classification and analysis of bimetallic nanoclusters, enhancing the understanding of their stability and functional properties. Nanoclusters ; Bimetallic strip ; Cluster analysis ; Density functional theory ; Particle swarm optimization ; Materials science ; Chemical physics ; Nanotechnology ; Physics ; Computer science ; Chemistry ; Computational chemistry ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Chuanzhe Wang | Junshan Li | Yongfa Zhu
Source: Science for energy and environment. (article, 2025-08-28)
Field: Chemistry — Organic Chemistry

Research Progress in Photocatalytic Organic Transformation: From Materials to Applications Photocatalytic organic transformation, as a mild, green, and environmentally friendly synthesis method, has attracted increasing attention as a sustainable development scheme to address the global energy crisis and environmental problems. At present, a variety of materials have been used to develop various photocatalytic organic conversion reactions. The development of high-efficiency photocatalytic materials, which necessitates a comprehensive understanding of their catalytic mechanisms, is essential to enhancing the performance of photocatalytic organic transformation. However, only a few studies summarize and discuss the results of research on materials and mechanisms involved in photocatalytic organic transformation. In this review, we aim to cover the development of photocatalysts, including metal and its oxide materials, organic semiconductors, metal-organic frameworks, covalent-organic frameworks, and other materials. Then, the general mechanisms, unique advantages, and strategies in photocatalysis synthesis are discussed. In addition, it also provides an overview of applications in various organic reactions, such as oxidation, reduction, coupling, cyclization, and asymmetric synthesis. Finally, we summarize the challenges and prospects of material design and reaction mechanisms in photocatalytic organic synthesis in the future. Photocatalysis ; Transformation (genetics) ; Materials science ; Nanotechnology ; Process engineering ; Environmental science ; Chemistry ; Engineering ; Organic chemistry ; Catalysis Chemical Synthesis and Reactions ; Advanced Photocatalysis Techniques
Authors: Akash Balakrishnan | Frency P. Mathew | Milan Tom Joseph | Mona Mary Varghese | K. S. Aryamol | Mahendra Chinthala | Natarajan Rajamohan | Bo Weng
Source: Journal of Materials Chemistry A (article, 2025-12-22)
Field: Materials Science — Biomaterials

Engineered cellulose-supported photocatalysts for clean energy and environmental remediation: progress and prospects The growing global demand for sustainable energy and environmental remediation has accelerated interest in efficient cellulose-supported photocatalysts. Clean energy ; Sustainable energy ; Environmental remediation ; Clean technology ; Energy consumption ; Energy (signal processing) ; Energy demand Advanced Cellulose Research Studies ; Advanced Photocatalysis Techniques ; TiO2 Photocatalysis and Solar Cells
Authors: Huilin Li | Rui Gao | Xiaochun Hu | Mengmeng Gao | Mingfei Pan
Source: Foods (review, 2025-09-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Noble Metal-Based Nanocomposites for Surface-Enhanced Raman Spectroscopy Detection of Food Contaminants Public health concerns related to food contaminants, including biotoxins, pesticide and veterinary drug residues, illegal additives, foodborne pathogens, and heavy metals, have garnered significant public attention in recent years. Consequently, there is an urgent need to develop rapid and accurate technologies to detect these harmful substances. Surface-enhanced Raman spectroscopy (SERS), due to its characteristics of high sensitivity and specificity enabling the detection of food contaminants within complex matrices, has attracted widespread interest. This review focuses on the application of noble metal-based nanocomposites as SERS-active substrates for food contaminant detection. It particularly highlights the structure-performance relationships of metallic nanomaterials, including gold and silver nanoparticles (e.g., nanospheres, nanostars, nanorods), bimetallic structures (e.g., Au@Ag core-shell), as well as metal-nonmetal composite nanomaterials such as semiconductor-based, carbon-based, and porous framework-based materials. All of which play a crucial role in achieving effective Raman signal enhancement. Furthermore, the significant applications in detecting various contaminants and distinct advantages in terms of the sensitivity and selectivity of noble metal-based nanomaterials are also discussed. Finally, this review addresses current challenges associated with SERS technology based on noble metal-based nanomaterials and proposes corresponding strategies alongside future perspectives. Raman spectroscopy ; Surface-enhanced Raman spectroscopy ; Nanocomposite ; Contamination ; Materials science ; Metal ; Noble metal ; Nanotechnology ; Chemical engineering ; Spectroscopy ; Nanoparticle ; Environmental chemistry ; Chemistry ; Raman scattering ; Metallurgy ; Optics ; Biology Gold and Silver Nanoparticles Synthesis and Applications ; Melamine detection and toxicity ; Mercury impact and mitigation studies
Authors: Donald Muringaniza | Laurencia Zulu | Linia Gedi Marazani | Piwai Tshuma | Gift Mehlana
Source: Materials Advances (article, 2025-11-08)
Field: Chemistry — Inorganic Chemistry

Recent progress in synthesis and applications of monolithic metal–organic frameworks In this review, we explore the recent advances in the synthetic methods of monolithic MOFs and their emerging applications. Challenges and opportunities are also highlighted. Emerging technologies ; Context (archaeology) ; Key (lock) ; Field (mathematics) Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Magnesium Oxide Properties and Applications
Authors: Maxim Moraru | Weiyi Xia | Zhuo Ye | Feng Zhang | Yan Yao | Ying Wai Li | Cai‐Zhuang Wang
Source: The Journal of Open Source Software (article, 2025-11-17)
Field: Materials Science — Materials Chemistry

exa-AMD: A Scalable Workflow for Accelerating AI-Assisted Materials Discovery and Design exa-AMD is a Python-based application designed to accelerate the discovery and design of functional materials by integrating AI/ML tools, materials databases, and quantum mechanical calculations into scalable, high-performance workflows.The execution model of exa-AMD relies on Parsl (Babuji et al., 2019), a task-parallel programming library that enables a flexible execution of tasks on any computing resource from laptops to supercomputers.exa-AMD provides the following key-features: Workflow ; Scalability ; Workflow management system ; Workflow technology Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Catalysis and Oxidation Reactions
Authors: Harrison J. Cassady | E. H. Martin | Yifan Liu | Debjyoti Bhattacharya | Maria F. Rochow | Brock A. Dyer | Wesley F. Reinhart | Valentino R. Cooper | Michael A. Hickner
Source: ACS Applied Materials & Interfaces (article, 2025-03-09)
Field: Materials Science — Materials Chemistry

Database of Nonaqueous Proton-Conducting Materials This work presents the assembly of 48 papers, representing 74 different compounds and blends, into a machine-readable database of nonaqueous proton-conducting materials. SMILES was used to encode the chemical structures of the molecules, and we tabulated the reported proton conductivity, proton diffusion coefficient, and material composition for a total of 3152 data points. The data spans a broad range of temperatures ranging from -70 to 260 °C. To explore this landscape of nonaqueous proton conductors, DFT was used to calculate the proton affinity of 18 unique proton carriers. The results were then compared to the activation energy derived from fitting experimental data to the Arrhenius equation. It was found that while the widely recognized positive correlation between the activation energy and proton affinity may hold among closely related molecules, this correlation does not necessarily apply across a broader range of molecules. This work serves as an example of the potential analyses that can be conducted using literature data combined with emerging research tools in computation and data science to address specific materials design problems. Materials science ; Proton ; Database ; Nanotechnology ; Computer science ; Nuclear physics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Xiaoying Hu | Ziqi Chen | Daniel Adu-Ampratwum | Xia Ning
Source: Journal of Chemical Information and Modeling (article, 2025-07-29)
Field: Materials Science — Materials Chemistry

log-RRIM: Yield Prediction via Local-to-Global Reaction Representation Learning and Interaction Modeling Accurate prediction of chemical reaction yields is crucial for optimizing organic synthesis, potentially reducing time and resources spent on experimentation. With the rise of artificial intelligence (AI), there is growing interest in leveraging AI-based methods to accelerate yield predictions without conducting in vitro experiments. We present log-RRIM, an innovative graph transformer-based framework designed for predicting chemical reaction yields. A key feature of log-RRIM is its integration of a cross-attention mechanism that focuses on the interplay between reagents and reaction centers. This design reflects a fundamental principle in chemical reactions: the crucial role of reagents in influencing bond-breaking and formation processes, which ultimately affect reaction yields. log-RRIM also implements a local-to-global reaction representation learning strategy. This approach initially captures detailed molecule-level information and then models and aggregates intermolecular interactions. Through this hierarchical process, log-RRIM effectively captures how different molecular fragments contribute to and influence the overall reaction yield, regardless of their size variations. log-RRIM shows superior performance in our experiments, especially for medium to high-yielding reactions, proving its reliability as a predictor. The framework's sophisticated modeling of reactant-reagent interactions and precise capture of molecular fragment contributions make it a valuable tool for reaction planning and optimization in chemical synthesis. The data and codes of log-RRIM are accessible through https://github.com/ninglab/Yield_log_RRIM. Reagent ; Yield (engineering) ; Computer science ; Representation (politics) ; Biological system ; Intermolecular force ; Chemistry ; Artificial intelligence ; Molecule ; Materials science ; Organic chemistry ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Bioinformatics and Genomic Networks
Authors: Chen Zhang | Siyuan Lv | Haojie Gong | Qianxi Cheng | Jianxiu Guo | Zheng Duanmu | Hang Xiao
Source: Applied Physics Letters (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

Inverse design of high-performance piezoelectric semiconductors via advanced crystal representation and large language models The inverse design of solid-state materials with targeted properties represents a significant challenge in materials science, particularly for piezoelectric semiconductors where both structural symmetry and electronic properties must be carefully controlled. Here, we employ the simplified line-input crystal-encoding system representation combined with the MatterGPT framework for discovering potential piezoelectric semiconductors. By training on a curated dataset of 1556 piezoelectric materials from the Materials Project database, our model learns to generate crystal structures with targeted piezoelectric properties through an autoregressive sampling process. Starting from approximately 5000 generated structures, we implemented a comprehensive screening workflow incorporating structural validity, thermodynamic stability, and property verification. This approach identified several promising candidates from 4100 reconstructed structures, each representing compounds unrecorded in existing databases. Among these, the most notable material demonstrated a piezoelectric stress coefficient of 25.9 C/m2 in the e[1,6] direction. Additionally, these materials demonstrate suitable bandgaps ranging from 1.63 to 3.61 eV, suggesting potential applications in high-sensitivity sensors and high-temperature electronics. Our work demonstrates the effectiveness of combining crystal structure language encoding with generative models for accelerating the discovery of functional materials with targeted properties. Piezoelectricity ; Semiconductor ; Inverse ; Materials science ; Representation (politics) ; Crystal (programming language) ; Computer science ; Optoelectronics ; Mathematics ; Composite material ; Geometry ; Programming language Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Thermoelectric Materials and Devices
Authors: Brock A. Stenfors | Jamie A. Cadge | Santeri Aikonen | Guilian Luchini | Jessica Wahlers | Kevin Koh | Mikko Muuronen | Maximilian Menche | Mark Pfeifle | Angus B. Keto | Robert S. Paton | Matthew S. Sigman | Olaf Wiest
Source: ChemRxiv (preprint, 2025-07-14)
Field: Chemistry — Organic Chemistry

Conformation Dependent Features of Bisphosphine Ligands Phosphines are a ubiquitous class of ligand in transition-metal (TM) catalyzed reactions. Data-driven methods using feature databases have shown promise in expediting the selection of optimal ligands for various catalytic processes. However, feature development can be challenging as many phosphine-based ligands, exemplified by bisphosphines, have diverse structural features resulting from their conformationally dependent reactivity. We present here an automated conformational analysis workflow to overcome this challenge in which a conformational ensemble of surrogate palladium-bisphosphine complexes was constructed and featurized. The resulting database contains 78 features for 147 commercially available bisphosphines, including ferrocene and atropisomeric ligands. The conformational dependence of the features is discussed and then compared in multivariate linear regression (MLR) models for an enantioselective Hayashi-Heck reaction, which resulted in improved performance using conformation-dependent features. Chemistry ; Stereochemistry Inorganic and Organometallic Chemistry ; Asymmetric Hydrogenation and Catalysis ; Synthetic Organic Chemistry Methods
Authors: Steve Dave Wansi Wendji | Rajmund G. Piotrowski | Carlo Massobrio | Mauro Boero | Christine Tugène | Firas Shuaib | David Hamani | Pierre‐Marie Geffroy | Philippe Thomas | Assil Bouzid | Olivier Masson | Gaëlle Delaizir | Guido Ori
Source: Journal of Non-Crystalline Solids (article, 2025-02-21)
Field: Materials Science — Ceramics and Composites

Enhanced structural description of sodium vanadium phosphate glasses: A combined experimental and molecular dynamics study Vanadium ; Sodium ; Molecular dynamics ; Phosphate ; Dynamics (music) ; Materials science ; Chemistry ; Metallurgy ; Physics ; Computational chemistry ; Biochemistry Glass properties and applications ; Crystal Structures and Properties ; Luminescence Properties of Advanced Materials
Authors: Mingren Cheng | Moyuan Cao | Xian‐He Bu
Source: Advanced Engineering Materials (article, 2025-06-15)
Field: Chemistry — Inorganic Chemistry

Shaping Metal–Organic Frameworks Toward Practical Applications: From 0D to 3D Over the past three decades, characterized by structural diversity, large specific surface area, and ultrahigh porosity, metal–organic frameworks (MOFs) have demonstrated broad application prospects in fields such as gas adsorption and separation, catalysis, energy, and sensing. Based on the progress of fundamental research, the shaping of MOFs has increasingly drawn attention. MOFs are typically obtained in the form of crystalline powders, which pose challenges for integration, handling, and storage. The shaping of MOFs is a complex process that requires craftsmanship and also involves numerous scientific issues. In this perspective, the shaping of MOFs through various processing techniques is introduced in sequence from 0D to 3D. In addition, the impacts of different techniques and shaped structures on practical applications are discussed. Finally, current challenges and prospects are also addressed, aiming to boost the practical applications of MOFs. Materials science ; Nanotechnology ; Metal-organic framework ; Systems engineering ; Engineering ethics ; Engineering physics ; Engineering ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Machine Learning in Materials Science