jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alexey Rulev | Nobumoto Nagasawa | Heng Li | Hongxin Wang | Stephen P. Cramer | Qianli Chen | Yoshitaka Yoda | Artur Braun
Source: Preprints.org (preprint, 2025-09-11)
Field: Materials Science — Materials Chemistry

&lt;em&gt;Operando&lt;/em&gt; NRVS on LiFePO&lt;sub&gt;4&lt;/sub&gt; Battery with &lt;sup&gt;57&lt;/sup&gt;Fe Phonon DOS The vibration properties of materials play a role in their conduction of elec-tric charges. Relevant in this respect are also ionic conductors such as electrodes and solid electrolytes. The vibration properties are typically assessed with infrared and Raman spectroscopy, and inelastic neutron scattering, which all allow for the deriva-tion of the phonon density of states (PDOS) in part of full portion of the Brioullin zone. A novel method is nuclear resonant vibration spectroscopy (NRVS), which produces the element specific PDOS from Mössbauer active isotopes in a compound. We have employed NRVS operando on a pouch cell battery containing a Li57FePO4 electrode and thus could derive the PDOS of the 57Fe in the electrode during charging and discharg-ing. The spectra reveal reversible vibrational changes associated with the two-phase conversion between LiFePO₄ and FePO₄, as well as signatures of metastable intermedi-ate states. We demonstrate how the NRVS data can be used to tune the atomistic sim-ulations to accurately reconstruct the full vibration structures of the battery materials in operando conditions. Unlike optical techniques, NRVS provides bulk-sensitive, ele-ment-specific access to the full phonon spectrum under realistic operando conditions. These results establish NRVS as a powerful method to probe lattice dynamics in work-ing batteries and to advance the understanding of ion transport and phase transfor-mation mechanisms in electrode materials. Phonon ; Battery (electricity) ; Raman spectroscopy ; Electrode ; Metastability ; Vibration ; Thermal conduction ; Electrical conductor ; Infrared Solid-state spectroscopy and crystallography ; Advancements in Battery Materials ; Advanced NMR Techniques and Applications
Authors: Chenyang Wang | Lexiang Yin | Peng Li | Qing Huang
Source: Preprints.org (preprint, 2025-05-29)
Field: Materials Science — Materials Chemistry

Preparation of a Nano-laminated Sc2SnC MAX Phase Coating on SiC Fibers via the Molten Salt Method The incorporation of MAX phase interface layers into silicon carbide (SiC) composites has been shown to significantly enhance mechanical properties, particularly under irradiation conditions. However, conventional Ti-based MAX phases suffer from thermal instability and tend to decompose at high temperatures. In this work, Sc2SnC coating was successfully synthesized on the surface of SiC fibers (SiCf) via an in-situ reaction between metals and pyrolytic carbon (PyC) in a molten salt environment. The PyC layer, pre-deposited by chemical vapor deposition (CVD), served as both a carbon source and a structural template. Characterization by SEM, XRD, and Raman spectroscopy confirmed the formation of Sc2SnC coatings with a distinctive hexagonal flake-like morphology, accompanied by an internal ScCx intermediate layer. By turning the Sc-to-Sn ratio in the molten salt, coatings with varied morphologies were achieved. ScCx was identified as a critical intermediate phase in the synthesis process. The formation of numerous defects during the reaction enhanced element diffusion, resulting in preferential growth orientations and diverse grain structures in the Sc2SnC coating. Molten salt ; Nano- ; Materials science ; Coating ; Salt (chemistry) ; Composite material ; Phase (matter) ; Chemical engineering ; Nanotechnology ; Metallurgy ; Chemistry ; Organic chemistry ; Engineering MXene and MAX Phase Materials ; Aluminum Alloys Composites Properties ; Advanced ceramic materials synthesis
Authors: Silviya Boycheva | Boian Mladenov | Ivan Dimitrov | Margarita Popova
Source: Journal of Composites Science (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Development of 3D-Printed Carbon Capture Adsorbents by Zeolites Derived from Coal Fly Ash The present study aims to develop 3D-structured adsorbents for carbon capture with the utilization of coal ash after its conversion into zeolites. For this purpose, printing paste mixtures with a viscosity of 800 Pa·s were developed based on an environmentally friendly and safe polymer binder filled with coal ash zeolite with the addition of bentonite as a filler. The optimal consistency of the printing mixtures for preserving the shape and dimensions of the 3D-printed structures was established. Various model configurations of the macrostructure of 3D adsorbents were developed, and the optimal settings of the extruding system for their printing were established. After calcination, the resulting 3D structures were studied using instrumental analysis techniques, investigating the influence of 3D structuring on the phase composition, surface characteristics, and adsorption capacity for CO2 capture in comparison with the initial powder coal ash zeolite adsorbents. The role of compensating cations in terms of the adsorption ability of powders in 3D-printed adsorbents was investigated. The current study offers an innovative and previously unexplored approach to a more expedient and practically significant utilization of aluminosilicate solid waste and, in particular, coal ash, through their 3D structuring and outlines a new research and technological direction in the development of economically advantageous, technologically feasible, and environmentally friendly 3D adsorbents. Fly ash ; Adsorption ; Aluminosilicate ; Zeolite ; Coal ; Environmentally friendly ; Anthracite ; Carbon fibers ; Bentonite Catalytic Processes in Materials Science ; Carbon Dioxide Capture Technologies ; Catalysts for Methane Reforming
Authors: Esha Eman Aslam | Waheed Zaman Khan | Mohammed Ateeq Mudassar | Usama Yousaf | Akhtar Hussain | Saqlain Aslam | Tariq Majeed
Source: Scholars Journal of Physics Mathematics and Statistics (article, 2025-12-27)
Field: Materials Science — Materials Chemistry

Light–Matter Strategies for Next-Generation Solar Cells: Active-Medium Architectures, Ultrafast Carrier Dynamics, and Laser-Enabled Photonic Structuring This review reframes photovoltaic (PV) devices through a light–matter engineering lens, treating the absorber stack as an active medium whose optical, electronic, and thermal pathways must be co-optimized for both efficiency and operational reliability. It surveys how mature platforms (Si, CIGS, CdTe, III–V) and emerging absorbers (perovskites, organics, quantum dots, and 2D semiconductors) respond to optical-field design strategies, and why these strategies become more critical in tandem and multifunctional architectures where spectral and electrical matching govern performance. The methodology emphasizes linking ultrafast and spatially resolved spectroscopy (e.g., carrier relaxation, diffusion, and recombination signatures) to device-relevant loss channels, enabling diagnosis of interface- and grain-boundary-limited operation. Building on this foundation, the review compares plasmonic and dielectric nanostructures, metasurfaces, and photonic textures for light trapping while accounting for parasitic absorption and stability tradeoffs. It then consolidates laser-enabled photonic structuring (ns/ps/fs regimes, LIPSS, direct writing, local defect healing and contact formation) as a scalable route to pattern, repair, and tune thin-film PV stacks. Finally, it outlines fabrication and passivation options and discusses how laser steps can be integrated into manufacturing lines with inline metrology and yield control. Overall, the article provides a device-to-process roadmap for designing PV stacks where optical gains translate into durable, manufacturable performance. Photonics ; Photovoltaic system ; Ultrashort pulse ; Photovoltaics ; Plasmon ; Scalability ; Stack (abstract data type) ; Fabrication ; Metrology Chemical and Physical Properties of Materials ; Chalcogenide Semiconductor Thin Films ; Thin-Film Transistor Technologies
Authors: Safia Abdullah R Alharbi | Banat Gul | Muhammad Salman Khan | Siti Maisarah Aziz
Source: Luminescence (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

First‐Principles Exploration of the Mechanical Flexibility and Multifunctional Properties of Aluminum‐ and Zinc‐Based Chalcopyrites for Energy Applications The present study employs density functional theory to examine structural, optoelectronic, mechanical, and transport characteristics of ternary chalcopyrites semiconductors Al<sub>2</sub>ZnCh<sub>4</sub> (X = Se, Te) to investigate their multifunctional potential. Structural optimization supports the materials' thermodynamic stability in the tetragonal phase. The mechanical properties, determined through elastic constants and moduli, exhibit mechanical stability and ductility, with softness rising from S to Te, rendering them suited to flexible device applications. Optical features reveal high absorption in the visible to ultraviolet range, with peak absorption shifting towards lower energies as chalcogens change from Se to Te. This feature demonstrates its potential for solar energy accumulation and UV protection applications. Thermoelectric study displays strong Seebeck coefficients (> 300 μV/K), moderate electrical conductivity, and low electronic thermal conductivity. Al<sub>2</sub>ZnTe<sub>4</sub> exhibits the greatest power factor. The dimensionless figure of merit of Al<sub>2</sub>ZnSe<sub>4</sub> and Al<sub>2</sub>ZnTe<sub>4</sub> improves with temperature, attaining maximum values, demonstrating their potential for high-temperature thermoelectric applications. Thermoelectric effect ; Ternary operation ; Seebeck coefficient ; Dimensionless quantity ; Tetragonal crystal system ; Density functional theory ; Semiconductor ; Figure of merit ; Thermal stability Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Heusler alloys: electronic and magnetic properties
Authors: Zishuo Lan | Qionghuan Zeng | Weilong Ma | Xiaoqing Liang | Yanfen Li | Yu Chen | Yiming Chen | Xiaobing Hu | Junjie Li | Lei Wang | Jing Zhang | Zhijun Wang | Jincheng Wang
Source: ArXiv.org (preprint, 2025-11-13)
Field: Materials Science — Materials Chemistry

Scalable data-driven modeling of microstructure evolution by learning local dependency and spatiotemporal translation invariance rules in phase field simulation Phase-field (PF) simulation provides a powerful framework for predicting microstructural evolution but suffers from prohibitive computational costs that severely limit accessible spatiotemporal scales in practical applications. While data-driven methods have emerged as promising approaches for accelerating PF simulations, existing methods require extensive training data from numerous evolution trajectories, and their inherent black-box nature raises concerns about long-term prediction reliability. This work demonstrates, through examples of grain growth and spinodal decomposition, that a minimalist Convolutional Neural Network (CNN) trained with a remarkably small dataset even from a single small-scale simulation can achieve seamless scalability to larger systems and reliable long-term predictions far beyond the temporal range of the training data. The key insight of this work lies in revealing that the success of CNN-based models stems from the alignment between their inductive biases and the physical priors of phase-field simulations specifically, locality and spatiotemporal translation invariance. Through effective receptive field analysis, we verify that the model captures these essential properties during training. Therefore, from a reductionist perspective, the surrogate model essentially establishes a spatiotemporally invariant regression mapping between a grid point's local environment and its subsequent state. Further analysis of the model's feature space demonstrates that microstructural evolution effectively represents a continuous redistribution of a finite set of local environments. When the model has already encountered nearly all possible local environments in the early-stage training data, it can reliably generalize to much longer evolution timescales, regardless of the dramatic changes in global microstructural morphology. Locality ; Scalability ; Prior probability ; Grid ; Invariant (physics) ; Artificial neural network ; Field (mathematics) ; Convolutional neural network ; Generalization Solidification and crystal growth phenomena ; Machine Learning in Materials Science ; Block Copolymer Self-Assembly
Authors: Alipour Bonab, Shahin | Waite, Thomas | Yazdani-Asrami, Mohammad
Source:  (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

Artificially intelligent framework for multi-output performance prediction in diverse solid oxide electrolyzer cells for green hydrogen production plants Hydrogen production ; Production (economics) ; Oxide ; Hydrogen ; Component (thermodynamics) Advancements in Solid Oxide Fuel Cells ; Chemical Looping and Thermochemical Processes ; Hybrid Renewable Energy Systems
Authors: Tariq Usman Saeed | Javed Iqbal | Aamir Ahmed
Source: Research Square (preprint, 2025-08-14)
Field: Materials Science — Materials Chemistry

Temperature-Tuned Performance of MXene-NiO Nanocomposites for High-Efficiency Supercapacitors Supercapacitor ; Nanocomposite ; Cyclic voltammetry ; Dielectric spectroscopy ; Capacitance ; Electrode ; Electrochemistry ; Current density MXene and MAX Phase Materials ; Supercapacitor Materials and Fabrication ; Advancements in Battery Materials
Authors: Sahel Fajal | Sujit K. Ghosh
Source: Chemical Communications (review, 2025-01-01)
Field: Chemistry — Organic Chemistry

Bromine sequestration by advanced functional porous materials The capture and storage of bromine have garnered significant attention in recent years due to its widespread industrial applications as well as its environmental impact as a pollutant. Advanced functional porous materials (AFPMs)-including metal-organic frameworks (MOFs), porous organic polymers (POPs), covalent organic frameworks (COFs), and porous organic cages (POCs)-have emerged as exceptional adsorbent materials in this field. Their high surface areas, tunable porosities, controllable structures, thermal/chemical stability, versatile molecular design, and capacity for functionalization (<i>via</i> pre- or post-synthetic modification) make them highly promising for bromine capture. This review highlights recent advancements in AFPMs for bromine capture from both gaseous and solution phases. We analyze key strategies to enhance bromine uptake, such as redox reactions, coordination interactions, bromination, functional group oxidation, heteroatom interactions, surface area/pore volume optimization, chemical functionalization, and post-synthetic modifications. Additionally, we discuss design strategies for developing next-generation AFPMs with superior bromine adsorption performance. As research progresses, we anticipate broader adoption of these materials in bromine capture applications. Bromine ; Surface modification ; Adsorption ; Porosity ; Heteroatom ; Polymer Nanomaterials for catalytic reactions ; Analytical chemistry methods development
Authors: Ryo Fujisawa | Mikito Fujinami | Hiromi Nakai
Source: Journal of Computational Chemistry (article, 2025-07-29)
Field: Materials Science — Materials Chemistry

Extension of Composite Method and Machine‐Learned Electron Correlation Model to Fourth‐Period Elements Accurate and efficient correlation energy calculation is a major challenge in quantum chemistry. We propose an extended machine-learned electron correlation (ML-EC) model that estimates CCSD(T)/CBS correlation energy using descriptors from Hartree-Fock (HF) calculations with double-zeta basis sets. While the previous ML-EC model was limited to third-period elements, we extend it to fourth-period elements by modifying the composite method parameters. The optimized parameters accurately reproduce CCSD(T)/CBS correlation energies and correlation energy densities. Trained on G3/05 dataset molecules, the ML-EC model accurately predicts CCSD(T)/CBS correlation energies for test molecules. Reaction energies computed with the ML-EC model surpass DFT methods in accuracy. Additionally, the ML-EC model significantly reduces computational cost, achieving a speedup of over 50 times compared to conventional CCSD(T)/CBS calculations. These results demonstrate that the extended ML-EC model is a reliable and efficient method for correlation energy calculations, particularly for systems containing heavy elements. Electronic correlation ; Correlation ; Speedup ; Extension (predicate logic) ; Energy (signal processing) ; Chemistry ; Quantum ; Molecule ; Computational chemistry ; Atomic physics ; Statistical physics ; Physics ; Quantum mechanics ; Mathematics ; Computer science ; Geometry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; X-ray Diffraction in Crystallography
Authors: Kaan Mutluer | Ferhat Demiray
Source: Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi (article, 2025-11-28)
Field: Chemistry — Organic Chemistry

Theoretical Investigations of P and P2 Bridged Fullerene C20 The structural and electronic properties of C20 fullerenes linked by P and P2 were investigated via Density Functional Theory (DFT) in this study. The binding energies of the optimized (C20)2P and (C20P)2 structures were calculated as -41.06 eV and -21.78 eV, respectively. In the (C20)2P structure, it was observed that the inserted phosphorus atom altered the structure of one of the fullerenes, transforming the C20 framework, originally composed of twelve pentagons, into a structure consisting of two hexagons and ten pentagons. In the (C20P)2 structure, it was observed that the P – P bond between the phosphorus atoms ruptured, leading to the formation of a hexagon between the two fullerenes through bonding with carbon atoms. Under these conditions, the pentagonal structures of the fullerenes remained unchanged. The calculated GapHL values for the (C20)2P and (C20P)2 structures are 0.83 eV and 1.33 eV, respectively, indicating that they can be considered as semiconducting materials. Fullerene ; Density functional theory ; Carbon fibers ; Atom (system on chip) ; Electronic structure ; Binding energy ; Carbon atom ; Bond length Fullerene Chemistry and Applications ; Boron and Carbon Nanomaterials Research ; Graphene research and applications
Authors: Yunyue Su | Jiahui Chen | Zao Jiang | Z. Zhong | L. L. Wang | Qiang Liu | Zhang, Zhaoxiang
Source: ArXiv.org (preprint, 2025-08-04)
Field: Materials Science — Materials Chemistry

SpectraLLM: Uncovering the Ability of LLMs for Molecule Structure Elucidation from Multi-Spectral Automated molecular structure elucidation remains challenging, as existing approaches often depend on pre-compiled databases or restrict themselves to single spectroscopic modalities. Here we introduce \textbf{SpectraLLM}, a large language model that performs end-to-end structure prediction by reasoning over one or multiple spectra. Unlike conventional spectrum-to-structure pipelines, SpectraLLM represents both continuous (IR, Raman, UV-Vis, NMR) and discrete (MS) modalities in a shared language space, enabling it to capture substructural patterns that are complementary across different spectral types. We pretrain and fine-tune the model on small-molecule domains and evaluate it on four public benchmark datasets. SpectraLLM achieves state-of-the-art performance, substantially surpassing single-modality baselines. Moreover, it demonstrates strong robustness in unimodal settings and further improves prediction accuracy when jointly reasoning over diverse spectra, establishing a scalable paradigm for language-based spectroscopic analysis. Code is available at https://github.com/OPilgrim/SpectraLLM. Robustness (evolution) ; Generalization ; Process (computing) ; Domain (mathematical analysis) ; Natural language ; Joint (building) ; Data modeling Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Monika Tyagi | Monika Dubey
Source:  (preprint, 2025-04-15)
Field: Chemistry — Analytical Chemistry

Analysis and ANFIS Modeling with Advancement of Schiff base Metal Complexes for Diabetes Mellitus Treatment Diabetes mellitus, a metabolic disease marked by persistently high blood sugar, is a major global health concern. Side effects, resistance, and the incapacity to adequately address oxidative stress and related consequences are frequently the limitatio Diabetes mellitus ; Adaptive neuro fuzzy inference system ; Metal ; Internal medicine ; Medicine ; Base (topology) ; Materials science ; Metallurgy ; Endocrinology ; Computer science ; Artificial intelligence ; Mathematics ; Mathematical analysis Spectroscopy and Chemometric Analyses
Authors: Nahed Hazim Al-Haidery | Hanan A. Al‐Hazam | Nuha Hussain Al-Saadawy
Source: Trends in Sciences (article, 2025-07-05)
Field: Chemistry — Organic Chemistry

Synthesis, Structural Analysis, and Theoretical Study of Amino Acid-Copper (II) Complexes via XRD and DFT New amino acid derivatives were synthesized to create copper (II) complexes, with the binding site influenced by pH levels. These complexes, derived from amino acids like alanine, histidine, tryptophan, and lysine, were characterized using various spectroscopic techniques, including IR, UV-Vis, and X-ray diffraction, which confirmed their expected crystal forms. Density functional theory (DFT) calculations were employed to analyze the electronic structures and stability of the complexes, revealing a correlation between the highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital (LUMO), energy gaps and their reactivity, thus aiding in the rational design of these compounds for applications in catalysis and biomimetic chemistry. HIGHLIGHTS New complexes were successfully prepared by reacting amino acids with terephthaloyl isothiocyanate (TPI). Study the reactivity of the isothiocyanate group (–N=C=S) of TPI with the amino (–NH₂) and carboxyl (–COOH) functional groups in amino acids. FT-IR: Confirm bond formation (e.g., C=O, C=S, N–H stretching vibrations) and peak shifts due to complexation. NMR (1H): Identify chemical shift changes indicative of coordination or hydrogen bonding. X-ray diffraction (XRD): Determine crystallinity or amorphous nature. Optimized geometry, HOMO-LUMO energies, and global reactivity descriptors (e.g., hardness, electrophilicity). GRAPHICAL ABSTRACT Copper ; Chemistry ; Crystallography ; Density functional theory ; Computational chemistry ; Organic chemistry Inorganic and Organometallic Chemistry
Authors: Uzma Zahoor | Panpan Wang | Faiyaz Shakeel | Abdul Munam Khan | Waqar Ahmed | Chong Li | Yigang Cao | Chun-Yao Niu
Source: Research Square (preprint, 2025-05-13)
Field: Materials Science — Materials Chemistry

Size and doping effects on the HER performance in Ag and Cu clusters Doping ; Materials science ; Optoelectronics Catalytic Processes in Materials Science ; ZnO doping and properties ; Copper-based nanomaterials and applications