jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mehmet Emin Kılıç | Puru Jena
Source: Small (article, 2025-01-05)
Field: Materials Science — Materials Chemistry

Superatoms as Superior Catalysts: ZrO versus Pd Single-atom catalysts are the focus of studies for over a decade due to their enhanced reactivity at smaller sizes. However, they have limitations as they offer only one active site, which may not be sufficient for reactions requiring the co-adsorption of multiple reactants. Additionally, atoms can migrate on a substrate and coalesce, resulting in decreased reactivity. Here, an alternate path, a single-superatom catalyst is provided. Superatoms are clusters of atoms that mimic the chemistry of atoms even if they do not contain a single atom whose chemistry they mimic. Motivated by an experimental paper on the photoelectron-spectroscopy of negatively charged ions where ZrO is found to mimic properties of a Pd atom, first the reaction of Pd and ZrO with small molecules in the gas-phase is studied and found that ZrO not only mimics the chemistry of Pd, but is able to activate these molecules more strongly than Pd. A detailed first-principles study of CO<sub>2</sub> reduction (CO<sub>2</sub>-RR) and hydrogen evolution reactions (HER) on Pd and ZrO supported on graphene, Au(111), and Cu(111) surfaces shows that superatoms are indeed superior catalysts. The ability to design numerous superatoms by varying size and composition offers a promising new paradigm for catalyst design and synthesis. Catalysis ; Materials science ; Chemical engineering ; Nanotechnology ; Chemistry ; Organic chemistry ; Engineering Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Toshikazu Ono | Yousuke Ooyama
Source: Dalton Transactions (review, 2025-01-01)
Field: Chemistry — Organic Chemistry

Axial and helical chirality in multinuclear group 13 complexes: pathways to functional optical materials Main-group element complexes have emerged as promising functional dyes owing to their unique photophysical properties and potential applications in sensors, luminescent devices, and photocatalysis. Among these, multinuclear main-group complexes that incorporate multiple elements within a single ligand have garnered significant attention, particularly for their ability to exhibit chirality with optical functions. Axial and helical chirality, resulting from unique coordination geometries, represent a critical frontier in the design of functional materials. These complexes enable diverse functionalities, including circular dichroism and circularly polarized luminescence. This Frontier article highlights recent advances in the synthesis of multinuclear main-group element complexes with chirality, focusing on their structural uniqueness and photochemical characteristics. Particular emphasis is placed on group 13 element complexes, including boron(III), aluminum(III), gallium(III), and indium(III), which exhibited unique chiral properties and photophysical behaviors. Key topics include the design strategies for chiral multinuclear frameworks, their photophysical properties, and their integration into advanced functional materials. Chirality (physics) ; Group (periodic table) ; Chemistry ; Crystallography ; Materials science ; Physics ; Quantum mechanics ; Organic chemistry ; Chiral symmetry Synthesis and Properties of Aromatic Compounds ; Molecular spectroscopy and chirality ; Axial and Atropisomeric Chirality Synthesis
Authors: Jiahui Liu | Marek W. Urban
Source: Materials Horizons (review, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Living dynamic polymeric materials Many materials exhibit static and functional properties. This article elaborates on how the last few decades of material research have demonstrated the apparent similarities between the equilibrium thermodynamics of static materials and the biological world utilizing non-equilibria biological processes. Dynamic polymer networks involving covalent and non-covalent multi-stimulus materials at nano-, micro-, and macro-scales and their biological counterparts at molecular, cellular, and species levels are addressed in the context of opportunities and challenges governing their development involving non-equilibria processes. Materials science ; Nanotechnology Polymer composites and self-healing ; Advanced Materials and Mechanics ; Advanced Sensor and Energy Harvesting Materials
Authors: Frédéric A. Perras | Songlin Wang | Jacob Mayer | Mita Halder | Alexander L. Paterson | Damien B. Culver | Chad M. Rienstra
Source: Analytical Chemistry (article, 2025-02-17)
Field: Chemistry — Spectroscopy

Removal of Homogeneous Broadening from <sup>1</sup>H-Detected Multidimensional Solid-State NMR Spectra <sup>1</sup>H-detected magic-angle spinning (MAS) NMR experiments have revolutionized the NMR studies of biological and inorganic solids by providing unparalleled sensitivity and resolution. Despite these gains, homogeneous broadening, originating from the incomplete removal of homonuclear dipolar interactions under fast MAS, remains highly prevalent and limits the achievable resolution. In direct analogy to super-resolution microscopy methods, we show that resolution beyond that currently achievable by fast MAS alone can be obtained by experiment-driven deconvolution. Following the acquisition of a single 2D NMR spectrum to measure the frequency-dependent homogeneous lineshapes, any number of <sup>1</sup>H-detected spectra can be enhanced in resolution, yielding comparable spectra as obtained with twice the MAS frequency. The versatility of this approach is demonstrated in the enhancement of single- and double-quantum homonuclear correlation spectra, in addition to heteronuclear correlation spectra acquired on a surface organometallic complex and the protein GB1. Chemistry ; Homogeneous ; Spectral line ; NMR spectra database ; Solid-state ; Solid-state nuclear magnetic resonance ; Nuclear magnetic resonance spectroscopy ; Analytical Chemistry (journal) ; Nuclear magnetic resonance ; Physical chemistry ; Stereochemistry ; Thermodynamics ; Chromatography ; Physics Advanced NMR Techniques and Applications ; NMR spectroscopy and applications ; Advanced MRI Techniques and Applications
Authors: Jiamin Zhang | Yanzhe Li | Chuanqi Li | Xiancheng Mei | Jian Zhou
Source: Materials (article, 2025-07-01)
Field: Chemistry — Inorganic Chemistry

Application of Soft Computing Represented by Regression Machine Learning Model and Artificial Lemming Algorithm in Predictions for Hydrogen Storage in Metal-Organic Frameworks Metal-organic frameworks (MOFs) have been extensively studied for hydrogen storage due to their unique properties. This paper aims to develop several regression-based machine learning models to predict the hydrogen storage capacity of MOFs, including artificial neuron network (ANN), support vector regression (SVR), random forest (RF), extreme learning machine (ELM), kernel extreme learning machine (KELM), and generalized regression neural network (GRNN). An improved population-based metaheuristic optimization algorithm, the artificial lemming algorithm (ALA), is employed to select the hyperparameters of these machine learning models, enhancing their performance. All developed models are trained and tested using experimental data from multiple studies. The performance of the models is evaluated using various statistical metrics, complemented by regression plots, error analysis, and Taylor graphs to further identify the most effective predictive model. The results show that the ALA-RF model obtains the best performance in predicting hydrogen storage, with optimal values of coefficient of determination (R<sup>2</sup>), root mean square error (RMSE), Willmott's index (WI), and weighted average percentage error (WAPE) in both training and testing phases (0.9845 and 0.9840, 0.2719 and 0.2828, 0.9961 and 0.9959, and 0.0667 and 0.0714, respectively). Additionally, pressure is identified as the most significant feature for predicting hydrogen storage in MOFs. These findings provide an intelligent solution for the selection of MOFs and optimization of operational conditions in hydrogen storage processes. Extreme learning machine ; Support vector machine ; Machine learning ; Artificial intelligence ; Artificial neural network ; Random forest ; Mean squared error ; Hyperparameter ; Soft computing ; Feature selection ; Computer science ; Algorithm ; Mathematics ; Statistics Metal-Organic Frameworks: Synthesis and Applications ; Catalysis and Hydrodesulfurization Studies ; Carbon dioxide utilization in catalysis
Authors: Hassan Harb | Sarah N. Elliott | Logan Ward | Ian Foster | Stephen J. Klippenstein | Larry A. Curtiss | Rajeev S. Assary
Source: Scientific Data (article, 2025-01-29)
Field: Materials Science — Materials Chemistry

Accurate Dehydrogenation Enthalpies Dataset for Liquid Organic Hydrogen Carriers This contribution presents a comprehensive extension of the QM9 dataset (originally at 133 K molecules) with the calculation of G4MP2 enthalpies for 9,841 molecules, featuring up to nine heavy atoms. We present QM9-LOHC, a (de)hydrogenation dataset of 10,373 reactions, including a minimum of 5.5% weight hydrogen storage capacity in line with the Department of Energy standards for Liquid Organic Hydrogen Carriers (LOHC). By utilizing the accurate quantum chemical method G4MP2 we expand the QM9 database and explore new avenues for the exploration of hydrogen storage technologies (electrochemical LOHCs, alkali metal-LOHCs, and mixtures of LOHCs). The QM9-LOHC dataset, with its focus on reactions that vary only by hydrogen saturation levels, provides a needed data resource for advancing the design and optimization of both conventional and innovative LOHC systems, and high-fidelity data for molecular discovery. Dehydrogenation ; Hydrogen ; Chemistry ; Organic chemistry ; Catalysis Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Hybrid Renewable Energy Systems
Authors: Keith G. Andrews
Source: Beilstein Journal of Organic Chemistry (article, 2025-02-24)
Field: Materials Science — Biomaterials

Beyond symmetric self-assembly and effective molarity: unlocking functional enzyme mimics with robust organic cages The bespoke environments in enzyme active sites can selectively accelerate chemical reactions by as much as 10<sup>19</sup>. Macromolecular and supramolecular chemists have been inspired to understand and mimic these accelerations and selectivities for applications in catalysis for sustainable synthesis. Over the past 60+ years, mimicry strategies have evolved with changing interests, understanding, and synthetic advances but, ubiquitously, research has focused on use of a molecular "cavity". The activities of different cavities vary with the subset of features available to a particular cavity type. Unsurprisingly, without synthetic access to mimics able to encompass more/all of the functional features of enzyme active sites, examples of cavity-catalyzed processes demonstrating enzyme-like rate accelerations remain rare. This perspective will briefly highlight some of the key advances in traditional cavity catalysis, by cavity type, in order to contextualize the recent development of robust organic cage catalysts, which can exploit stability, functionality, and reduced symmetry to enable promising catalytic modes. Chemistry ; Enzyme ; Combinatorial chemistry ; Nanotechnology ; Biochemistry Supramolecular Self-Assembly in Materials ; Supramolecular Chemistry and Complexes ; Molecular Sensors and Ion Detection
Authors: Cherly Firdharini | İbrahim Yıldız | Halima Alnaqbi | Manzar Abbas
Source: ChemSystemsChem (article, 2025-08-13)
Field: Materials Science — Biomaterials

Minimal Designer Peptides for Dynamic Homotypic Coacervate‐Based Protocell Models Abstract Homotypic coacervates, formed of a single component, are notable for compartmentalization and could serve as artificial cells for our understanding of living cells. Recently, small designer biomolecules have been investigated for liquid–liquid phase separation (LLPS), like intrinsically disordered proteins (IDPs), allowing them to make coacervate droplets spontaneously through associative molecular interactions. In this context, we highlight the recent developments in the reductionist approach for designer biomolecules, particularly amino acid derivatives, dipeptides, and bioinspired polypeptides, which undergo coacervation to create biomimetic protocells. Weak non‐covalent molecular interactions usually drive the self‐coacervation of biomolecules, and their structure‐function properties are crucial for phase separation. Besides this, we discuss the essential parameters required for promising applications of protocell formation to mimic living cells, including the catalytic ability for enzymatic reactions and the sequestration of micro‐ and macro‐molecules. Finally, we provide some perspective and conclude that simple coacervates formed from small peptide building blocks undergo phase separation to form protocells. Protocell ; Coacervate ; Chemistry ; Computer science ; Computational biology ; Biochemical engineering ; Engineering ; Biology ; Biochemistry ; Membrane Supramolecular Self-Assembly in Materials ; RNA Research and Splicing ; Block Copolymer Self-Assembly
Authors: Ruiyu Zhang | Xiaoxin Zheng | Wenhong Fan | Xiangrui Wang | Tianhui Zhao | Xiaoli Zhao | Willie J.G.M. Peijnenburg | Martina G. Vijver | Ying Wang
Source: Environmental Science Nano (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Fate models of nanoparticles in the environment: a critical review and prospects Herein, we systematically reviewed the environmental fate models of nanoparticles from the perspective of mathematical methods and proposed an optimal modeling process for simulating the fate of nanoparticles. Nanoparticle ; Nanotechnology ; Computer science ; Biochemical engineering ; Materials science ; Engineering Nanoparticles: synthesis and applications ; Water Quality and Pollution Assessment ; Heavy metals in environment
Authors: Sasan Amariamir | Janine George | Philipp Benner
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

SynCoTrain: a dual classifier PU-learning framework for synthesizability prediction SynCoTrain is a PU-learning framework using dual GNN classifiers to predict material synthesizability. It leverages co-training to mitigate model bias and enhance generalizability. Artificial intelligence ; Classifier (UML) ; Computer science ; Dual (grammatical number) ; Machine learning ; Philosophy ; Linguistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallization and Solubility Studies
Authors: Julia Chrzan | Anna K. Drabczyk | Izabela Siemińska | Monika Baj‐Krzyworzeka | Katarzyna Ewa Greber | Jolanta Jaśkowska | Damian Kułaga | Krzesimir Ciura
Source: Molecules (article, 2025-06-02)
Field: Chemistry — Organic Chemistry

Green and Efficient Synthetic Protocol for 1,3,5-Triazine Derivatives with Anticancer Potential Against Colorectal Cancer Colorectal cancer (CRC) remains a major global health challenge, necessitating the development of more effective and environmentally sustainable treatments. This study presents a novel green synthetic protocol for 1,3,5-triazine derivatives with anticancer potential, employing both microwave-assisted and ultrasound-assisted methods. The synthesis was optimized using 4-chloro-<i>N</i>-(2-chlorophenyl)-6-(morpholin-4-yl)-1,3,5-triazin-2-amine as the key intermediate, with sodium carbonate, TBAB, and DMF providing optimal yields under microwave conditions. To enhance sustainability, a modified sonochemical method was also developed, enabling efficient synthesis in aqueous media with a minimal use of organic solvents. A series of nine morpholine-functionalized derivatives were synthesized and evaluated for cytotoxic activity against SW480 and SW620 colorectal cancer cell lines. Compound <b>11</b> demonstrated superior antiproliferative activity (IC₅₀ = 5.85 µM) compared to the reference drug 5-fluorouracil, while compound <b>5</b> showed promising dual-line activity. In silico ADME analysis supported the drug likeness of the synthesized compounds, and biomimetic chromatography analysis confirmed favorable physicochemical properties, including lipophilicity and membrane affinity. These results underscore the potential of the developed protocol to produce bioactive triazine derivatives through an efficient, scalable, and environmentally friendly process, offering a valuable strategy for future anticancer drug development. ADME ; Chemistry ; Combinatorial chemistry ; Lipophilicity ; Green chemistry ; Organic chemistry ; Biochemistry ; Molecule ; In vitro Synthesis and Characterization of Heterocyclic Compounds ; Synthesis of Tetrazole Derivatives ; Synthesis and biological activity
Authors: Zhenming Li | Kang Qin | Yang Zhang | Peng Yang | Yue Lou | Mingfeng Li
Source: Processes (article, 2025-04-08)
Field: Chemistry — Analytical Chemistry

PSO-Optimized Data-Driven and Mechanism Hybrid Model to Enhance Prediction of Industrial Hydrocracking Product Yields Under Data Constraints The accurate prediction of hydrocracking product yields is crucial for optimizing resource allocation and improving production efficiency. However, the prediction of product flowrates in hydrocracking units often faces challenges due to insufficient data and weak correlations between input and output variables. This study proposes a hybrid framework combining a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model, mechanism modeling, and Particle Swarm Optimization (PSO) to address these issues. The CNN-LSTM captures spatiotemporal dependencies in operational data, while the mechanism model incorporates domain-specific physical constraints. The hybrid model is structured in both series and parallel configurations, with PSO optimizing key hyperparameters to enhance its predictive performance. The results demonstrate significant improvements in prediction accuracy, with determination coefficients (R2s) reaching 0.896 (kerosene), 0.879 (residue), 0.899 (heavy naphtha), and 0.78 (light naphtha). Shapley Additive Explanations (SHAP) and Mutual Information Coefficient (MIC) analyses highlight the mechanism model’s role in improving feature interpretability. This study underscores the efficacy of integrating kinetics modeling, deep learning, and metaheuristic optimization for complex industrial processes under data constraints, offering a robust approach to enhance hydrocracking yield prediction. Mechanism (biology) ; Product (mathematics) ; Computer science ; Process engineering ; Engineering ; Mathematics Petroleum Processing and Analysis ; Coal Combustion and Slurry Processing
Authors: Venishaa Sethumadhavan | Nonappa Nonappa
Source: Dalton Transactions (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Atomically precise noble metal nanoclusters for engineering self-assembled two-dimensional materials Two-dimensional nanomaterials derived from noble metals exhibit unique optoelectronic properties with potential applications in advanced nanodevices, catalysis, imaging, biomedicine, and sensing. Over the years, atomically precise noble metal nanoclusters have emerged as promising building blocks for self-assembled discrete and hierarchical colloidal superstructures. Various approaches, including templated growth, air-water interfaces, and non-covalent interactions between surface ligand functional groups, have been utilized to promote the self-assembly of nanoclusters. However, until recently, the self-assembly of nanoclusters into ultrathin, free-standing, two-dimensional materials across length scales has remained challenging. In this perspective article, approaches for the fabrication of self-assembled gold and silver nanocluster-based films, 2D nanosheets, colloidal crystals and macroscopic membranes are discussed. We present the self-assembly principles and insights into how such assemblies enable altered optoelectronic properties, mechanical performance, catalytic activities, enhanced sensing, and device performance. Finally, we highlight how nanocluster-based two-dimensional assemblies offer the emergence of new optoelectronic properties, still retaining the intrinsic properties of individual building blocks. Nanoclusters ; Nanotechnology ; Materials science ; Nanomaterials ; Noble metal ; Self-assembly ; Fabrication ; Nanostructure ; Nanoparticle ; Metal Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Élie Benchimol | Jacopo Tessarolo | Andrew Tarzia | Haeri Lee | Abdelaziz Jouaiti | Mir Wais Hosseini | Giovanni M. Pavan | Guido H. Clever
Source: Chem (article, 2025-10-15)
Field: Chemistry — Organic Chemistry

Adjacent backbone interactions control self-sorting of chiral heteroleptic Pd3A2B4 isosceles triangles and Pd4A4C4 pseudo-tetrahedra International audience Isosceles triangle ; Control (management) ; Topology (electrical circuits) ; Chirality (physics) Supramolecular Chemistry and Complexes ; Crystallography and molecular interactions ; Supramolecular Self-Assembly in Materials
Authors: Xiaoxu Huang | Xiaobo Li | Qun‐Yan Wu | Wei‐Qun Shi
Source: Industrial Chemistry and Materials (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Experimental and theoretical progress on the reduction of Np(<scp>vi</scp>) with salt-free reagents in the PUREX process This review summarizes the reduction reaction of Np( vi ) to Np( v ) by hydrazine, hydroxylamine, aldehydes, oximes, hydroxamic acids and their derivatives as well as the reaction kinetics, reaction mechanism and electronic structures. PUREX ; Reduction (mathematics) ; Chemistry ; Salt (chemistry) ; Reagent ; Process (computing) ; Radiochemistry ; Computer science ; Chromatography ; Mathematics ; Organic chemistry Radioactive element chemistry and processing
Authors: Tihomir Borko | Gordana Bilić | Katarina Žbulj | Helena Otmačić Ćurković
Source: Coatings (article, 2025-01-15)
Field: Materials Science — Materials Chemistry

Individual and Joint Effect of Oleic Acid Imidazoline and CeCl3 on Carbon Steel Corrosion in CO2-Saturated Brine Solution In production and transportation systems of the oil industry, brine solutions contain high concentrations of chloride and dissolved CO2, which is a very corrosive medium to which carbon steel is exposed. Therefore, finding new effective and environmentally friendly corrosion inhibitors is of great importance. The effect of CeCl3 (in concentrations from 5 mg dm−3 to 20 mg dm−3) and oleic acid imidazoline (IOA) (in concentrations from 5 mg dm−3 to 20 mg dm−3) separately and their mixtures (in concentrations from 5 mg dm−3 to 15 mg dm−3 of CeCl3 and from 5 mg dm−3 to 20 mg dm−3 of IOA) as corrosion inhibitors of AISI 1018 carbon steel corrosion in simulated brine solution saturated with CO2 at 60 °C were examined by means of weight-loss testing, electrochemical measurements (polarization resistance, linear polarization with Tafel extrapolation, electrochemical impedance spectroscopy) and surface analyses (scanning electron microscopy with energy-dispersive X-ray spectroscopy analyses, Raman spectroscopy and X-ray diffraction). All test methods showed a higher efficiency of compounds′ mixtures (from 62.77% to 97.94%) and a higher degree of corrosion protection compared to the action of individual compounds (efficiency from 3.43% to 94.61% for IOA and from 57.58% to 96.27% for CeCl3). Imidazoline, a common corrosion inhibitor in CO2-saturated systems, most likely forms a surface film with voids via its adsorption on steel surface, while cerium carbonate tends to fill these voids by creating a more compact film. In this way, a denser and thicker surface film is formed. Brine ; Imidazoline receptor ; Corrosion ; Carbon steel ; Joint (building) ; Oleic acid ; Materials science ; Metallurgy ; Chemistry ; Organic chemistry ; Engineering ; Medicine ; Structural engineering ; Internal medicine Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals