jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xinpeng Bi (22351070) | Dezhi Cao (7490804) | Xinyue Wang (286629) | Dingkai Hu (16277547) | Qiang Wang (32383)
Source: Figshare (article, 2025-10-01)
Field: Chemistry — Spectroscopy

Correlation between σ‑Profile Characteristicsand Infinite Dilution Activity Coefficients of Choline Chloride-UreaDES: Experimental Determination and Machine Learning Interpretation This study integrates inverse gas chromatography (IGC) experiments with machine learning (ML) to systematically investigate the thermodynamic properties of choline chloride (ChCl)-urea (1:2) deep eutectic solvent (DES) and its interaction mechanisms with organic solvents. IGC measurements determined the infinite dilution activity coefficients (γ<sub>12</sub><sup>∞</sup>) and related thermodynamic parameters for 46 representative organic solvents within the temperature range of 303.15–343.15 K. Results revealed the hierarchy of solute–DES interaction strength: hydrocarbons (increasing with chain length) > alkenes > ethers > aromatics > ketones > esters > alcohols (weakest due to hydrogen bonding). To enhance γ<sub>12</sub><sup>∞</sup> prediction accuracy, a novel approach fused the quantized σ-profile partitioning descriptors of the DES with temperature as input features, constructing four ML models. Compared to the significant deviation of the COSMO-SAC model prediction (<i>R</i><sup>2</sup> = 0.8224), the Extreme Gradient Boosting (XGBoost) model demonstrated superior performance (test set <i>R</i><sup>2</sup> = 0.9979, average absolute relative deviation (AARD) < 20%). Feature importance analysis indicated that σ-profile regions corresponding to weak hydrogen bond acceptor (HBAs) character [S3: −0.0084 ≤ σ ≤ 0 e/Å<sup>2</sup>] and weak hydrogen bond donor character [S4, 0 ≤ σ ≤ 0.0084 e/Å<sup>2</sup>] contributed dominantly (42%) to the γ<sub>12</sub><sup>∞</sup> prediction. In contrast, the strongly polar region [S5, 0.0084 ≤ σ ≤ 0.02 e/Å<sup>2</sup>] reduced γ<sub>12</sub><sup>∞</sup> by enhancing interactions, confirming the “like dissolves like” principle. This framework enables high-precision γ<sub>12</sub><sup>∞</sup> prediction solely from molecular structures (Applicability Domain (AD) covers 93.85% of data), providing an efficient and reliable theoretical tool for DES-based green solvent design and optimization of industrial separation processes, such as benzene/methanol systems. Choline chloride ; Dilution ; Deep eutectic solvent ; Hydrogen bond ; Activity coefficient ; Solvent ; Eutectic system ; Absolute deviation ; Molecular descriptor ; Quantitative structure–activity relationship Adsorption, diffusion, and thermodynamic properties of materials ; Chemical Thermodynamics and Molecular Structure ; Ionic liquids properties and applications
Authors: Sørensen, Søren Strandskov; id_orcid 0000-0003-2230-7823 | Cao, Fengming; id_orcid 0000-0003-0761-5019 | Rasmussen, Philip | Finkler, Jonas Alexander | Henningsen, Mads Hyldgaard | Aouane, Mohamed | Bessas, Dimitrios | Bulled, Johnathan | Bossak, Alexei | Monaco, Giulio | Smedskjær, Morten Mattrup
Source: VBN Forskningsportal (Aalborg Universitet) (article, 2025-05-04)
Field: Chemistry — Inorganic Chemistry

Revealing the nature of the boson peak using metal-organic framework polymorphs Field (mathematics) ; Boson ; Sequence (biology) ; Spectrum (functional analysis) ; Context (archaeology) Metal-Organic Frameworks: Synthesis and Applications ; Advanced NMR Techniques and Applications ; Boron Compounds in Chemistry
Authors: Matthias Holzenkamp | Dongyu Lyu | Ulrich Kleinekathöfer | Peter Zaspel
Source: Machine Learning Science and Technology (article, 2025-09-22)
Field: Materials Science — Materials Chemistry

Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials Abstract Uncertainty estimations for machine learning interatomic potentials (MLIPs) are crucial for quantifying model error and identifying informative training samples in active learning (AL) strategies. In this study, we evaluate uncertainty estimations of Gaussian process regression (GPR)-based MLIPs, including the predictive GPR standard deviation and ensemble-based uncertainties. We do this in terms of calibration and in terms of impact on model performance in an AL scheme. We consider GPR models with Coulomb and smooth overlap of atomic positions representations as inputs to predict potential energy surfaces and excitation energies of molecules. Regarding calibration, we find that ensemble-based uncertainty estimations show already poor global calibration (e.g. averaged over the whole test set). In contrast, the GPR standard deviation shows good global calibration, but when grouping predictions by their uncertainty, we observe a systematical bias for predictions with high uncertainty. Although an increasing uncertainty correlates with an increasing bias, the bias is not captured quantitatively by the uncertainty. Therefore, the GPR standard deviation can be useful to identify predictions with a high bias and error but, without further knowledge, should not be interpreted as a quantitative measure for a potential error range. Selecting the samples with the highest GPR standard deviation from a fixed configuration space leads to a model that overemphasizes the borders of the configuration space represented in the fixed dataset. This may result in worse performance in more densely sampled areas but better generalization for extrapolation tasks. Extrapolation ; Kriging ; Calibration ; Standard deviation ; Gaussian process ; Ground-penetrating radar ; Measure (data warehouse) ; Gaussian ; Generalization Machine Learning in Materials Science ; Fault Detection and Control Systems ; Gaussian Processes and Bayesian Inference
Authors: Xiaonan Wang | Jun Yin | Honghao Chen | Ju Qiu | Wentao Li | Peng He | Jiali Li | Iftekhar A. Karimi
Source: Research Square (preprint, 2025-01-07)
Field: Materials Science — Materials Chemistry

SurFF: Universal Model for Surface Exposure and Synthesizability Across Intermetallic Crystals Intermetallic ; Transferability ; Catalysis ; Density functional theory ; Scale (ratio) ; Materials science ; Surface (topology) ; Computer science ; Chemistry ; Computational chemistry ; Physics ; Mathematics ; Machine learning ; Metallurgy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electronic and Structural Properties of Oxides
Authors: Bilal Ibrahim | Anireju Dudun | Zainab Iyiola | Majid Hussain | Prince Obeng | Fathi Boukadi
Source:  (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

A Systematic Review of Underground Hydrogen Storage in the United States: Technical, Economic, and Future Insights. Abstract As the United States accelerates efforts toward a low-carbon economy, large-scale hydrogen deployment is expected to play a pivotal role in decarbonizing power generation, transportation, and hard-to-abate industrial sectors. Underground hydrogen storage (UHS) offers a viable pathway to address the intermittency of renewable energy sources and to meet long-duration storage requirements at scale. This study presents a systematic review of the technical, economic, and regional feasibility of UHS in the United States, focusing on key storage media including salt caverns, depleted gas reservoirs, and saline aquifers. The manuscript synthesizes current understanding of the geological, geochemical, microbial, and geo-mechanical factors that govern hydrogen storage performance in different formations. Salt caverns offer high deliverability and well-established operational history but are geographically limited. In contrast, porous media such as depleted gas fields and aquifers offer widespread availability but require more complex characterization and monitoring due to risks such as microbial hydrogen loss, mineral reactions, and permeability degradation. Through detailed analysis of experimental and modeling studies, we evaluate factors such as cushion gas requirements, pressure cycling effects, caprock integrity, and hydrogen embrittlement risks. The role of advanced tools, including machine learning and coupled reactive transport models, is also discussed in improving site characterization and performance prediction. Economically, the levelized cost of storage (LCOS) varies widely across storage types and regions, with salt caverns in the Gulf Coast offering the lowest LCOS under current market conditions. Forward-looking scenarios assess the impact of hydrogen demand growth, federal incentives (e.g., 45V tax credit), infrastructure needs, and hub-scale development strategies on future UHS deployment. Comparative evaluation of major U.S. projects in Utah, Colorado, and Texas reveals how regional geology, policy alignment, and infrastructure availability jointly influence UHS viability. This review identifies critical research gaps and development priorities including microbial loss mitigation, infrastructure repurposing, regulatory streamlining, and integrated techno-economic modeling. As hydrogen markets evolve, UHS will be essential to enabling grid stability, industrial decarbonization, and national energy security. The findings of this work provide a robust foundation for guiding UHS expansion in the United States, aligning with strategic climate and energy goals through 2050 and beyond. Hydrogen storage ; Computer science ; Environmental science ; Hydrogen ; Chemistry Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Chemical Looping and Thermochemical Processes
Authors: Zikang Su | Chen Zhang | Xianze Zhang | Shanshan Wang | Yaoxian Yang | Zhihua Wang | Wenxing Chen | Xue‐Qiang Zhang
Source: Nano Research (article, 2025-06-23)
Field: Materials Science — Surfaces, Coatings and Films

Probe of nanocatalysts in-action by ambient pressure photoelectron spectroscopy Elucidation of a physicochemical process on nanocatalysts, especially under continuously evolving conditions, is often heavily tool-driven because of technical challenges. Recently, ambient pressure X-ray photoelectron spectroscopy (APXPS) emerges as an emerging photon-in-electron-out technique in <em>in-situ/operando</em> analysis by bridging the pressure-gap between conventional ultra-high vacuum (UHV) and near ambient or even close to operating conditions, rendering the advancement of XPS from a UHV-based technique to a versatile and powerful tool that enables the specific probe of numerous events taking place at the gas/solid, liquid/solid and liquid/gas nanoscale interfaces which are critical to nanocatalysis research. For example, APXPS probes information on catalytically active phase and reaction kinetics in nanocatalytic processes; details inside the electric double-layer at an electrolyte/electrode interface can now be accessed; more efficient nanocatalyst design can be achieved and energy transfer venues can be optimized. Here, we aim to critically review the recent advances in instrumentation and the probe of the gas/solid, liquid/solid and gas/liquid nanoscale interfaces using APXPS-based methodologies, followed by putting forward an outlook of the development of APXPS as a rising <em>in-situ</em>/<em>operando</em> analytical means in surface science, nanocatalysis, nanoscience and materials science. Nanomaterial-based catalyst ; X-ray photoelectron spectroscopy ; Ambient pressure ; Nanotechnology ; Action (physics) ; Spectroscopy ; Materials science ; Chemistry ; Chemical engineering ; Physics ; Nanoparticle ; Engineering ; Thermodynamics Electron and X-Ray Spectroscopy Techniques ; Photocathodes and Microchannel Plates ; Catalytic Processes in Materials Science
Authors: Suna Ji | Chengda Liu | S. Ravi P. Silva | Meng Cai | Guosheng Shao | Peng Zhang
Source: Nano Research (article, 2025-07-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Transition-metal chalcogenophosphate: An emerging star in photoelectric conversion and storage materials The development of efficient and affordable electrode materials is key to the construction of clean energy storage systems. Transition-metal chalcogenophosphates (TMPX<sub>3</sub>) are a promising class of two-dimensional (2D) layered materials with great potential for energy storage, photoelectrocatalysis, and electronic devices due to their unique electronic structure and tunable bandgap. In this review, we systematically summarize the latest research progress on TMPX<sub>3</sub> materials, adopting a pioneering multidimensional analysis framework to overcome the limitations of a unified single perspective. Firstly, we reveal the mechanisms of P-S bond anisotropy and transition metal coordination on the energy band structure from the atomic scale; secondly, through the comparative analysis of the existing preparation methods and regulation strategies, the optimised pathways for precise control of the number of layers and large-scale production are proposed. On this basis, we focus on the applications of TMPX<sub>3</sub> in photoelectrocatalysis and metal batteries, and elucidate the cross-scale correlation mechanism between its electronic/interfacial properties and macroscopic performance. Finally, the challenges and future opportunities of the material are presented, with the aim of providing valuable insights into the multi-field, precisely coupled design and energy storage applications of TMPX<sub>3</sub> materials. Photoelectric effect ; Star (game theory) ; Materials science ; Metal ; Transition metal ; Nanotechnology ; Engineering physics ; Astrophysics ; Physics ; Metallurgy ; Chemistry ; Optoelectronics ; Catalysis ; Organic chemistry Crystal Structures and Properties ; Inorganic Chemistry and Materials ; Solid-state spectroscopy and crystallography
Authors: Samin Yeasar Zahan Sparsha | Usoshi Mohsin | Olakunle Ibrahim Oresegun | Reuben Zakari Kabantiyok | Sekete Maseala Camilla | Okeoma Kingsley Emeka
Source: Open Access Research Journal of Engineering and Technology (article, 2025-11-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advancements in metal organic frameworks for energy storage applications: A review of current research and future directions The increasing energy needs globally with pressing environmental requirements necessitate the discovery of new energy storage technologies. Metal-Organic Frameworks (MOFs) have emerged as a revolutionary class of porous materials with superior structural tunability, high surface area, and porosity and are leading candidates for next-generation energy applications. In this review, an overview of the recent advances in MOF-based materials as utilized in different energy storage technologies is presented. We review the multifunctional applications of MOFs and their derivatives as electrode materials in various batteries, including lithium-ion, sodium-ion, lithium-sulfur and zinc batteries, and high-performance supercapacitors. Their crucial application for hydrogen storage, as well as their computational and AI-driven approach, is also discussed. A critical analysis of the current challenges, including low electronic conductivity and structural instability, is presented alongside a description of promising future directions, such as the use of MOF-derived composites, hybrid architectures, and the integration of computational design methods. Overall, this review highlights the significant promise of MOFs for transforming the landscape of sustainable energy technology. Energy storage ; Metal-organic framework ; Hydrogen storage ; Current (fluid) ; Sustainable energy ; Energy (signal processing) Supercapacitor Materials and Fabrication ; Metal-Organic Frameworks: Synthesis and Applications ; Thermal Expansion and Ionic Conductivity
Authors: Mohammadrahim Kazemzadeh | B. Zhang | He, Tao | Haoran Liu | Zihe Jiang | Zhiwei Hu | Xiaohui Dong | Chaowei Sun | Wei Jiang | Xiaobo He | Shuyan Li | Gonzalo Alvarez-Perez | Ferruccio Pisanello | Huatian Hu | Wen Chen | Hongxing Xu
Source: ArXiv.org (preprint, 2025-04-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Seeing Beyond Dark-Field RGB Capabilities: Deep Spectral Extrapolation of Ultrasmall Plasmonic Nanogaps Localized surface plasmons can confine light within a deep-subwavelength volume comparable to the scale of atoms and molecules, enabling ultrasensitive responses to near-field variations. On the other hand, this extreme localization also inevitably amplifies the unwanted noise from the response of local morphological imperfections, leading to complex spectral variations and reduced consistency across the plasmonic nanostructures. Seeking uniform optical responses has therefore long been a sought-after goal in nanoplasmonics. However, conventional probing techniques by dark-field (DF) confocal microscopy, such as image analysis or spectral measurements, can be inaccurate and time-consuming, respectively. Here, we introduce SPARX, a deep-learning-powered paradigm that surpasses conventional imaging and spectroscopic capabilities. In particular, SPARX can batch-predict broadband DF spectra (e.g., 500-1000 nm) of numerous nanoparticles simultaneously from an information-limited RGB image (i.e., below 700 nm). It achieves this extrapolative inference beyond the camera's capture capabilities by learning the underlying physical relationships among multiple orders of optical resonances. The spectral predictions only take milliseconds, achieving a speedup of three to four orders of magnitude compared to traditional spectral acquisition, which may take from hours to days. As a proof-of-principle demonstration for screening identical resonances, the selection accuracy achieved by SPARX is comparable to that of conventional spectroscopy techniques. This breakthrough paves the way for consistent plasmonic applications and next-generation microscopies. Plasmon ; Extrapolation ; RGB color model ; Broadband ; Noise (video) ; Spectral imaging ; Spectroscopy ; Surface plasmon Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Metamaterials and Metasurfaces Applications
Authors: Juan Liu | Liuyang He | Jianrong Zhang | Junjie Zhu
Source: MetalMat (article, 2025-06-01)
Field: Materials Science — Materials Chemistry

Metal‐Based Materials for CO<sub>2</sub> Conversion in MES and PBS Systems ABSTRACT Metal‐based materials—including pure metals, alloys, compounds, and MOFs—play pivotal roles in microbial electrosynthesis (MES) and photocatalytic biohybrid systems (PBS), offering promising routes for sustainable CO 2 conversion. This review outlines the distinctive electronic, optical, and catalytic properties of these materials that enhance extracellular electron transfer (EET), interfacial coupling, and catalytic activity. Advantages, such as enhanced light harvesting, quantum efficiency, and interface integration, are discussed, alongside persistent challenges in material stability, microbial compatibility, and system scalability. Emerging approaches—including single‐atom catalysts (SACs), pathway engineering, and data driven material exploration—show promise in addressing these limitations. This review aims to guide the rational development of MES and PBS systems, thereby fostering progress in sustainable CO 2 conversion. Metal ; Materials science ; Nanotechnology ; Chemical engineering ; Metallurgy ; Engineering Catalytic Processes in Materials Science ; CO2 Reduction Techniques and Catalysts ; Advanced battery technologies research
Authors: Pornchanan Chanchot (22312754) | Siwaporn Khemphet (19134785) | Sayan Pudwat (19134788) | Nattasamon Petchsang (1286463) | Tanakorn Osotchan (1717927) | Yong-Hoon Kim (238256) | Rawat Jaisutti (2891576)
Source: Figshare (article, 2025-09-25)
Field: Chemistry — Organic Chemistry

Colorimetric Sensorfor Low-Level Ammonia DetectionUsing Ag-Nanoparticle-Decorated Zinc Oxide Nanopellet/PolydiacetyleneComposites with Machine-Learning Assistance Colorimetric sensors have attracted considerable attention as simple and rapid tools for on-site testing, enabling the detection of specific analytes through visible color changes. In this work, ultrasensitive point-of-care colorimetric sensors integrated with machine learning (ML)-assisted analysis have been developed for the high-accuracy detection of low-level ammonia. The sensor was fabricated by combining the color-responsive properties of polydiacetylene (PDA) with the plasmonic enhancement of silver nanoparticles (Ag NPs) decorated on zinc oxide (ZnO) nanopellets. As a result, the sensor exhibits an ultrasensitive performance, capable of detecting ammonia concentrations as low as 200 ppb. The as-prepared AgZnO/PDA sensors exhibit a distinct blue color that visibly changes to red upon exposure to ammonia. Compared with pristine PDA and ZnO/PDA sensors, they demonstrate enhanced sensitivity and a faster response time. This improvement is attributed to the presence of Ag NPs, which promote stronger interaction with ammonia molecules and facilitate relaxation of the PDA backbone. Quantitative evaluation of the color response can be performed by using optical absorption spectroscopy. In addition, highly accurate qualitative and quantitative identification of ammonia was achieved through ML-assisted analysis of the absorption spectra. For on-site ammonia testing, smartphone-captured color images analyzed using an ML model have been evaluated. The model effectively accounts for variations in camera optics and ambient lighting conditions. Moreover, the practical application of the sensor demonstrated a good predictive performance in detecting ammonia content in aquarium wastewater samples. These results indicate that the AgZnO/PDA composites represent a promising platform for low-level ammonia detection, enabling high-accuracy on-site prediction through ML assistance. Ammonia ; Zinc ; Analyte ; Absorption (acoustics) ; Colorimetry ; Absorption spectroscopy ; Analytical Chemistry (journal) Polydiacetylene-based materials and applications ; Gas Sensing Nanomaterials and Sensors ; Covalent Organic Framework Applications
Authors: Xijun Wu | Xuan Zhao | Jiangtao Wang | Shixin Wang | Yungang Zhang
Source: Journal of Food Composition and Analysis (article, 2025-11-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine-learning-assisted surface-enhanced Raman spectroscopy for detecting phenolic antioxidants in oils using Ag@ZnO/g-C₃N₄ nanocomposites Raman spectroscopy ; Detection limit ; Substrate (aquarium) ; Nanocomposite ; Antioxidant ; Density functional theory ; Relative standard deviation ; Chemometrics Gold and Silver Nanoparticles Synthesis and Applications ; Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses
Authors: Keshab Kumar | A. Rajakumar | С. БАЙБАКОВ Г | Lavanya Ravi
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Experimental and Theoretical Study of Hybrid Solar-Thermoelectric Heating Systems Thermoelectric effect ; Engineering physics ; Materials science ; Environmental science ; Nuclear engineering ; Physics ; Engineering ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Solar Thermal and Photovoltaic Systems