jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Avadhesh Kumar Yadav
Source: ChemistrySelect (article, 2025-08-01)
Field: Materials Science — Materials Chemistry

Superionic Glasses: Potential and Challenges in Solid‐State Batteries and Fuel Cells Abstract Superionic glasses are gaining significant attention as advanced solid electrolytes for solid‐state batteries (SSBs) and fuel cells due to their high ionic conductivity, structural flexibility, and ease of processing. Unlike their crystalline counterparts, these amorphous materials enable isotropic ion transport and better mechanical adaptability, making them suitable for next‐generation energy storage and conversion systems. Their inherent advantages position them as strong candidates to replace conventional liquid electrolytes and rigid ceramics. However, several critical challenges hinder their widespread adoption, including poor interface stability, the risk of dendrite formation, and sensitivity to moisture. These issues can compromise the long‐term performance and safety of devices. To address these challenges, researchers are exploring solutions such as protective coatings, composite electrolytes, and chemical modifications to enhance the electrochemical and environmental stability of superionic glasses. This review provides a comprehensive overview of their fundamental properties, current applications in electrochemical devices, and recent progress in overcoming key limitations. Furthermore, the development roadmap from research to commercialization is discussed, highlighting both emerging and commercial glass‐based solid electrolytes. With continued innovation and optimization, superionic glasses hold the potential to play a pivotal role in the advancement of safe, efficient, and scalable solid‐state energy technologies. Fuel cells ; Solid-state ; Materials science ; Nanotechnology ; Engineering physics ; Chemical engineering ; Engineering Advancements in Solid Oxide Fuel Cells ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: Mateusz Pokora | Jakub Goclon | Johannes T. Margraf | Chiara Panosetti | Artem Samtsevych | Piotr Paneth
Source: ChemPhysChem (article, 2025-05-05)
Field: Chemistry — Inorganic Chemistry

Low‐Cost Periodic Calculations of Metal‐Organic Frameworks: A GFN1‐xTB Perspective Semiempirical extended tight-binding (GFN1-xTB) and semilocal density functional theory (DFT)(Perdew-Becke-Ernzerhof (PBE)+D3) calculations are performed to evaluate the structural and electronic properties of five metal-organic frameworks (MOFs): rigid MOF-5(Zn), IRMOF(II)-74(Mg), ZIF-8(Zn), and flexible MIL-53(Al) and MIL-53(Fe). It is found that GFN1-xTB exhibits a similar performance to that of DFT in terms of accuracy of lattice vector preservation. Structural integrity is further supported by the low average root-mean-square displacement (RMSD) of the atomic positions, which remains below 0.3 Å. Consequently, the textural properties are also well preserved by GFN1-xTB, showing good agreement with those obtained from DFT. GFN1-xTB molecular dynamics (MD) simulations exhibit structural stability and correctly predict structural responses to temperature, which is fully consistent with experimental results. In addition, based on MD trajectories, this study constructs time-averaged X-ray diffraction patterns that closely aligned with experimental data. More importantly, GFN1-xTB performs exceptionally well at predicting the bandgap. Overall, GFN1-xTB offers almost semilocal DFT accuracy with significantly higher computational efficiency, making it a valuable tool for describing the geometric, textural, dynamic, and selected electronic properties of MOFs. Structural stability ; Molecular dynamics ; Density functional theory ; Metal-organic framework ; Lattice (music) ; Computational chemistry ; Materials science ; Diffraction ; Metal ; Band gap ; Electronic structure ; Chemistry ; Chemical physics ; Crystallography ; Physical chemistry ; Physics Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Magnetism in coordination complexes
Authors: Zhenyao Fang | Ting-Wei Hsu | Qimin Yan
Source: Scientific Data (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Dataset of tensorial optical and transport properties of materials from the Wannier function method The discovery of functional materials for energy harvesting and electronic applications require accurate predictions of their optical and transport properties. While several existing datasets contain the optical and transport properties calculated from first-principles calculations, the amount of material entries is often limited and those properties are often reported in scalar form. Comprehensive datasets for the tensorial properties, particularly optical properties, still remain inadequate, which prevents from capturing the anisotropy effect in materials. Therefore, in this work we present the largest-to-date dataset of tensorial optical properties (optical conductivity, shift current) and the dataset of tensorial transport properties (electrical conductivity, thermal conductivity, Seebeck coefficient, thermoelectric figure of merit zT) for 7301 materials, calculated from the Wannier function method. The quality of the Wannier functions were validated by the maximal spread of the Wannier functions and by the comparison with the band structures from first-principles calculations. These results contribute to the systematic study the functional properties for diverse materials and can benefit future data-driven discovery of candidate materials for optoelectronic and thermoelectric applications. Function (biology) ; Wannier function ; Materials science ; Geology ; Computer science ; Physics ; Condensed matter physics ; Biology Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques ; Advanced Semiconductor Detectors and Materials
Authors: Saad Ahmed | Seema Inayat | Iram Javed
Source: Synthesis and Sintering (article, 2025-06-17)
Field: Materials Science — Biomaterials

Synthesis methods and characterization of iron oxide nanoparticles: A biomedical perspective Iron oxide nanoparticles (IONPs) have emerged as pivotal materials in nanomedicine due to their unique magnetic, catalytic, and biological properties. This review examines a variety of synthesis methods: chemical (co-precipitation, sol-gel, thermal decomposition, microemulsion), physical (ball milling, laser ablation, arc discharge, physical vapor deposition, spray pyrolysis), and biological (plant-mediated, microbial, and biomolecule-assisted) and discusses how these techniques influence nanoparticle size, crystallinity, and surface functionality. We also detail characterization techniques, such as SEM, TEM, XRD, DLS, and FTIR, that are critical for optimizing IONP performance in biomedical settings. Despite considerable progress, issues with reproducibility, scale-up, and biocompatibility remain. Future efforts should focus on standardizing protocols, integrating real-time monitoring, and conducting extensive safety assessments to facilitate the clinical translation and large-scale production of IONPs for diverse applications. Characterization (materials science) ; Perspective (graphical) ; Nanoparticle ; Nanotechnology ; Iron oxide nanoparticles ; Materials science ; Iron oxide ; Computer science ; Metallurgy ; Artificial intelligence Nanoparticle-Based Drug Delivery
Authors: Yusuf Shaidu | Mit H. Naik | Steven G. Louie | Jeffrey B. Neaton
Source: npj Computational Materials (article, 2025-08-24)
Field: Materials Science — Materials Chemistry

Transferable dispersion-aware machine learning interatomic potentials for multilayer transition metal dichalcogenide heterostructures Abstract Stacking atomically thin transition metal dichalcogenides (TMDs) into heterostructures enables exploration of exotic quantum phases, particularly through twist-angle-controlled moiré superlattices. These structures exhibit novel electronic and optical behaviors driven by atomic-scale structural reconstruction. However, studying such systems with DFT is computationally demanding due to their large unit cells and van der Waals (vdW) interactions between layers. To address this, we develop a transferable neural network potential (NNP) that includes long-range vdW corrections up to 12Å with minimal overhead. Trained on vdW-corrected DFT data for Mo- and W-based TMDs with S, Se, and Te, the NNP accurately models monolayers, bilayers, heterostructures, and their interaction with h-BN substrates. It reproduces equilibrium structures, energy landscapes, phonon dispersions, and matches experimental atomic reconstructions in twisted WS 2 and MoS 2 /WSe 2 systems. We demonstrate that our NNP achieves DFT-level accuracy and high computational efficiency, enabling large-scale simulations of TMD-based moiré superlattices both with and without substrates. Heterojunction ; Transition metal ; Materials science ; Dispersion (optics) ; Interatomic potential ; Chemical physics ; Metal ; Optoelectronics ; Nanotechnology ; Condensed matter physics ; Chemistry ; Optics ; Metallurgy ; Physics ; Computational chemistry ; Molecular dynamics 2D Materials and Applications ; Machine Learning in Materials Science ; Perovskite Materials and Applications
Authors: Nargish Parvin | Mohammad Aslam | Md Najib Alam | Tapas Kumar Mandal
Source: Micromachines (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Advances in NIR-II Fluorescent Nanoprobes: Design Principles, Optical Engineering, and Emerging Translational Directions Fluorescent nanoprobes operating in the NIR-II window have gained considerable attention for biomedical imaging because of their deep-tissue penetration, reduced scattering, and high spatial resolution. Their tunable optical behavior, flexible surface chemistry, and capacity for multifunctional design enable sensitive detection and targeted visualization of biological structures in vivo. This review highlights recent advances in the design and optical engineering of four widely studied NIR-II nanoprobe families: quantum dots, carbon dots, upconversion nanoparticles, and dye-doped silica nanoparticles. These materials were selected because they offer well-defined architectures, controllable emission properties, and substantial mechanistic insight supporting discussions of imaging performance and translational potential. Particular focus is placed on emerging strategies for activatable, targeted, and ratiometric probe construction. Recent efforts addressing biosafety, large-scale synthesis, optical stability, and early preclinical validation are also summarized to clarify the current progress and remaining challenges that influence clinical readiness. By outlining these developments, this review provides an updated and focused perspective on how engineered NIR-II nanoprobes are advancing toward practical use in biomedical imaging and precision diagnostics. Nanoprobe ; Photon upconversion ; Optical imaging ; Quantum dot ; Translational research Carbon and Quantum Dots Applications ; Nanoplatforms for cancer theranostics ; Luminescence Properties of Advanced Materials
Authors: David Loffreda | Nathalie Tarrat | Corinne Lacaze‐Dufaure | Franck Rabilloud | Katia Fajerwerg | Myrtil L. Kahn | Vincent Collière | Christine Lepetit | Pierre Fau
Source: Nano Today (article, 2025-02-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Competition between ordered morphologies of functionalized silver nanoparticles elucidated by a joint experimental and multiscale theoretical study A multiscale approach, combining density functional theory models of functionalized silver nanoparticles and extended surfaces, is introduced to predict the competition between ordered nanoparticles at experimentally relevant size. An original theoretical descriptor, named synthesis energy, is defined and validated by high resolution transmission electron microscopy characterizations of silver nanoparticles synthesized in a solvated amine and amidine medium. Microscopy images show that icosahedral clusters cover 90 % of statistics around 7 nm, with very minority face-centered cubic particles (1 %). This trend is counter-intuitive compared to face-centered cubic morphologies reported in the literature. The ab initio models show a larger stability for octylamine over methyl amidine ligands on small silver nanoparticles (1.5 nm). At larger size (2.5 nm), the octylamine-covered silver nanocluster models indicate a clear preference for icosahedra. To reach the experimental size, the computed synthesis energy is decomposed into several contributions and shows the prevalence of the clean nanoparticle cohesion energy normalized by its surface area, over the ligand adsorption strength, the precursor dissociation and the adhesion of the cluster on the support. A mathematical model fitting ab initio data and predicting this cohesion energy at any size, relevantly captures the competition between morphologies, by showing a net preference for icosahedra at 7 nm, in agreement with experiments. • Predominance of functionalized icosahedral Ag nanoparticles with unexpected quasi-absence of octahedral forms in experiments. • Synthesis energy as a new theoretical descriptor of relative stability order between functionalized silver nanoparticles. • Synthesis energy decomposed into precursor dissociation, nanoparticle cohesion, ligand adsorption, nanoparticle adhesion. • Cohesion energy normalized to the surface area of silver nanoparticles as the main contribution of synthesis energy. • Development of a multiscale mathematical model showing the predominance of Ag icosahedra in the experimental range of sizes. Competition (biology) ; Joint (building) ; Nanoparticle ; Nanotechnology ; Materials science ; Chemical engineering ; Polymer science ; Engineering ; Structural engineering ; Ecology ; Biology Gold and Silver Nanoparticles Synthesis and Applications ; nanoparticles nucleation surface interactions ; Nanoparticles: synthesis and applications
Authors: Ying Shirley Meng | Qingsong Deng | Lin Zhang | Yibo Feng | Lei Fan | Yuxin Liu | Danmin Liu | Cong Wang
Source: Colloids and Interfaces (article, 2025-10-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A SERS Substrate for Ultrafast Photosynthetic Au Nanoparticle Growth on WO3 Nanowires The practical adoption of surface-enhanced Raman scattering (SERS) technology is often hampered by the high cost, complex fabrication, and poor reproducibility of conventional substrates, which typically rely on noble metals or inefficient semiconductors. Herein, we address key challenges in the practical commercialization of surface-enhanced Raman scattering (SERS) technology by reporting a facile, scalable, and environmentally benign strategy for fabricating a hybrid SERS substrate. This approach integrates Au nanoparticles (NPs) with hydrothermally synthesized WO3 nanowires through a green photoreduction process, which is rapid, organic-solvent-free, and amenable to large-scale production. The design of the Au/WO3 nanocomposite capitalizes on the synergistic effect between electromagnetic (EM) enhancement from Au NPs and chemical mechanism (CM) enhancement via charge transfer involving the WO3 semiconductor. This synergy empowers the substrate with exceptional SERS activity, enabling the sensitive detection of Rhodamine 6G (R6G) down to 10−11 M and yielding an enhancement factor (EF) of 4.09 × 106. More importantly, this EM-CM synergy proves critical for detecting molecules with weak affinity, such as the nerve agent simulant dimethyl methylphosphonate (DMMP), achieving a significant signal enhancement of 102–103 times, which is notably challenging for conventional plasmonic substrates. Beyond sensitivity, the substrate exhibits excellent reproducibility and operational stability, which are paramount for real-world applications. This work presents a nanohybrid strategy that successfully balances scalability, stability, and sensitivity, offering a reliable and cost-effective pathway for advancing SERS technologies toward practical implementation. Rhodamine 6G ; Raman scattering ; Substrate (aquarium) ; Nanoparticle ; Nanowire ; Plasmon ; Raman spectroscopy Gold and Silver Nanoparticles Synthesis and Applications ; Copper-based nanomaterials and applications ; Quantum Dots Synthesis And Properties
Authors: Haishen Huang | Xiaoying Liu | Li Sun | Zhenzhen Shang | Tingyan Zhou | Ping Li | Bo Wu
Source: Coatings (article, 2025-05-13)
Field: Materials Science — Materials Chemistry

First-Principles Investigation of the Structural, Magnetic, and Electronic Properties of Janus MXene Material CrScCO2 This study employed first-principles density functional theory (DFT) to systematically investigate the influence of oxygen (–O) functional groups on the structural, magnetic, and electronic properties of Janus MXene CrScC. Nine distinct CrScCO2 configurations with varying oxygen adsorption sites were examined. All configurations exhibited robust ferromagnetic ordering, with total magnetic moments ranging from 1 to 3 μB, predominantly contributed by Cr atoms. Notably, the majority of the configurations exhibited half-metallic behavior, characterized by fully spin-polarized conduction channels and half-metallic gaps spanning 0.23–1.54 eV, with one configuration approaching a spin-gapless semiconductor characterized by a minimal bandgap (&lt;0.1 eV). The ground-state configuration demonstrated strong performance, featuring a 100% spin polarization ratio and a wide half-metallic gap of 0.44 eV, indicating significant potential for spintronic applications. Phonon spectrum calculations confirmed the dynamic stability of the half-metallic ground-state structure, while binding energy analysis highlighted the enhanced stability of the oxygen-functionalized system compared to pristine CrScC. These results demonstrate that –O functional groups play a key role in modulating the magnetism and electronic properties of CrScC, offering versatility for various spintronic device applications. Janus ; Materials science ; Nanotechnology MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Memory and Neural Computing
Authors: Sanjay Sharma | G. Santhosh | G. P. Nayaka | S. Shiva Samhitha | Rekha Pilliadugula | B. Shivamurthy | S. C. Gurumurthy | M. A. Ruiz-Robles | D. Rojas | Srivathsava Surabhi
Source: npj Materials Degradation (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Advanced zinc-polymer composites for marine corrosion protection and self-healing Steel corrosion in saltwater costs over $2.5 trillion annually. This review highlights advancements in zinc and polymer-based coatings for marine protection. It covers traditional methods and emerging technologies like nanoparticle-enhanced alloys, conducting polymers, metal organic frameworks (MOFs), and 2D materials. These offer improved resistance, self-healing, and intelligent protection with a future focus on sustainability and extended service life for marine infrastructure. Sustainability ; Corrosion ; Focus (optics) ; Marine biodiversity ; Emerging technologies Corrosion Behavior and Inhibition ; Polymer composites and self-healing ; Concrete Corrosion and Durability
Authors: Francisco Márquez
Source: Preprints.org (preprint, 2025-09-01)
Field: Materials Science — Materials Chemistry

MXenes in Solid-State Batteries: Multifunctional Roles from Electrodes to Electrolytes and Interfacial Engineering MXenes, a rapidly emerging family of two-dimensional transition metal carbides and nitrides, have attracted considerable attention in recent years for their potential in next-generation energy storage technologies. In solid-state batteries (SSBs), they combine metallic-level conductivity (&amp;amp;gt;10³ S cm⁻¹), adjustable surface terminations, and mechanical resilience, which makes them suitable for diverse functions within the cell architecture. Current studies have shown that MXene-based anodes can deliver reversible lithium storage with Coulombic efficiencies approaching ~98% over 500 cycles, while their use as conductive additives in cathodes significantly improves electron transport and rate capability. As interfacial layers or structural scaffolds, MXenes effectively buffer volume fluctuations and suppress lithium dendrite growth, contributing to extended cycle life. In solid polymer and composite electrolytes, MXene fillers have been reported to increase Li⁺ conductivity to the 10⁻³–10⁻² S cm⁻¹ range and enhance Li⁺ transference numbers (up to ~0.76), thereby improving both ionic transport and mechanical stability. Beyond established Ti-based systems, double transition metal MXenes (e.g., Mo₂TiC₂, Mo₂Ti₂C₃) and hybrid heterostructures offer expanded opportunities for tailoring interfacial chemistry and optimizing energy density. Despite these advances, large-scale deployment remains constrained by high synthesis costs (often exceeding USD 200–400 kg⁻¹ for Ti₃C₂Tₓ at lab scale), restacking effects, and stability concerns, highlighting the need for greener etching processes, robust quality control, and integration with existing gigafactory production lines. Addressing these challenges will be crucial for enabling MXene-based SSBs to transition from laboratory prototypes to commercially viable, safe, and high-performance energy storage systems. MXenes ; Electrolyte ; Electrode ; Materials science ; Solid-state ; Nanotechnology ; Chemical engineering ; Chemistry ; Engineering physics ; Engineering MXene and MAX Phase Materials ; Nanomaterials for catalytic reactions ; Ammonia Synthesis and Nitrogen Reduction
Authors: Zeng Jin | Jiatong Gui | Siddharth Deshpande
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

A structural similarity based data-mining algorithm for modeling multi-reactant heterogeneous catalysts First-principles-based Density Functional Theory (DFT) simulations are powerful tools for studying heterogeneous catalyst systems. However, their high computational cost and large configuration space hinder their application in understanding multi-reactant catalysis on geometrically diverse surfaces. This work introduces an innovative similarity algorithm that quantifies the structural differences between atomic configurations to address this challenge. The quantification effectively identifies structurally dissimilar configurations with minimal human intervention. Consequently, data mining the configurational phase-space through this similarity algorithm drastically reduces the number of DFT simulations required to identify stable atomic models relevant to key multi-reactant chemistries. In this work, the similarity algorithm is utilized to understand CO*-OH* co-adsorption at varying adsorbate coverages on a stepped Pt surface by DFT simulating only 2% of possible unique configurations. Furthermore, the versatility of the similarity algorithm is showcased by analyzing bidentate adsorption on a stepped Pt surface. This work serves as a crucial steppingstone towards understanding important multi-reactant heterogeneous catalytic chemistries. Similarity (geometry) ; Catalysis ; Computer science ; Algorithm ; Structural similarity ; Data mining ; Chemistry ; Artificial intelligence ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science