jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Han-Yin Liu | Hanwei Chen | Chih-Chi Chang
Source: IEEE Sensors Journal (article, 2025-04-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Sn δ-Doped α-Ga<sub>2</sub>O<sub>3</sub>-Based MSM Photodetectors and Their Applications to Optoelectronic Artificial Synapse Metal-semiconductor-metal (MSM) photodetectors (PDs) based on Sn δ-doped α-Ga<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub>O<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> with δ-doping durations of 40 s, 60 s, and 80 s were fabricated and studied. Longer δ-doping duration enhanced responsivity, external quantum efficiency, and specific detectivity but increased dark current and slowed decay time. The MSM PD with an 80 s δ-doping duration achieved a responsivity of 145.23 A/W, an external quantum efficiency of 78297%, and a specific detectivity of 2.32 × 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> Jones. Beyond deep-UV sensing, these PDs demonstrated optoelectronic artificial synaptic behavior. The 80 s device exhibited 194% paired-pulse facilitation (PPF), 1377% spike-number-dependent plasticity (SNDP) with 64 pulses, and 344% spike-rate-dependent plasticity (SRDP) induced by 5 successive 10 Hz light pulses. Additionally, SNDP and SRDP enabled the transition from short-term to long-term plasticity, a behavior absent in undoped α-Ga<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub>O<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub>. These findings demonstrate the potential of Sn δ-doped α-Ga<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub>O<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> MSM PDs for artificial neural networks and multifunctional optoelectronic applications. Photodetector ; Optoelectronics ; Materials science ; Doping ; Gallium arsenide ; Synapse Ga2O3 and related materials ; Advanced Photocatalysis Techniques ; Electronic and Structural Properties of Oxides
Authors: Ishfaq Ahmed | Waseem Akhtar | Shanza Mubashir | Hafeez Anwar | Ao Chen | Jingtao Huang | Yong Liu | Qu Nan | Jingchuan Zhu
Source: Journal of Magnetism and Magnetic Materials (article, 2025-05-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning prediction of magnetic property of lanthanum substituted bismuth ferrite multiferroic with experimental validation Bismuth ferrite ; Multiferroics ; Materials science ; Bismuth ; Lanthanum ; Ferrite (magnet) ; Condensed matter physics ; Nuclear magnetic resonance ; Composite material ; Ferroelectricity ; Metallurgy ; Physics ; Nuclear physics ; Optoelectronics ; Dielectric Multiferroics and related materials ; Heusler alloys: electronic and magnetic properties ; Magnetic Properties and Synthesis of Ferrites
Authors: Zhengchun Du | Chaokun Song | Kang Guan | Pinggen Rao | Xiaohui Yang | Longteng Bai | Shengbo Shi | Jing Wang | Yongsheng Liu | Qingfeng Zeng
Source: Journal of the American Ceramic Society (article, 2025-09-01)
Field: Materials Science — Ceramics and Composites

Modified C/SiC composites with high ablation resistance: Data‐driven intelligent design and experimental validation Abstract Improving the ablation resistance of carbon fiber‐reinforced silicon carbide (C/SiC) composites is essential to meet the stringent demands of ultra‐high‐temperature applications, but traditional empirical design approaches are resource‐intensive and time‐consuming. This study presents a novel data‐driven methodology integrating machine learning (ML) with experimental validation to optimize C/SiC composite ablation resistance. Through systematic data preprocessing and feature analysis of 102 experimental samples, an XGBoost regression model was developed, achieving satisfactory prediction accuracy (mean absolute error &lt; ∼0.075, mean squared error &lt; ∼0.015, and coefficient of determination [R 2 ] &gt; ∼0.75) for ablation rate. SHapley Additive exPlanations (SHAP) analysis revealed that modifier properties, particularly standard enthalpy and melting point, predominantly influence ablation resistance. The ML‐guided design strategy, implemented through Bayesian optimization, led to the successful fabrication of ZrB 2 ‐modified C/SiC composites with exceptional ablation resistance. The optimized composite, containing approximately 20.0 vol.% ZrB 2 , achieved a linear ablation rate of 2.083 µm/s under oxyacetylene torch testing, representing a significant improvement over conventional compositions. Microstructural analysis confirmed the formation of a dense SiO 2 ‒B 2 O 3 protective layer, validating the predicted mechanism of enhanced ablation resistance. This work establishes a robust framework for accelerated development of ultra‐high‐temperature ceramics and demonstrates the efficacy of ML‐driven approaches in materials design optimization. An object‐oriented software with interactive graphical user interface has been developed. These methodologies have been integrated into an interactive software, Modified C/SiC Ablation Rate intelligent design Software (MARS), creating an efficient tool for the accelerated design of C/SiC composites with tailored ablation performance. Composite material ; Materials science ; Ablation ; Engineering ; Aerospace engineering Advanced ceramic materials synthesis ; Boron and Carbon Nanomaterials Research ; Aluminum Alloys Composites Properties
Authors: Jing Liu | Yuan Yin | Gang Liu
Source: International Journal of Electrochemical Science (review, 2025-10-15)
Field: Chemistry — Electrochemistry

Recent progress of electrochemical sensors for accurate detection of heavy metal ions in water: A comprehensive review Water pollution represents a critical global environmental challenge, with heavy metal ions constituting a major class of contaminants. These toxic ions enter the human body through multiple pathways, including the consumption of contaminated drinking water, dermal contact, and bioaccumulation in the food chain. Once introduced, they pose severe health risks due to their high toxicity, strong bioaccumulation potential, and slow excretion rate. Such characteristics enable them to accumulate over time, creating long-term threats to both ecosystems and public health. Addressing these risks requires the development of highly sensitive, accurate, and reliable detection methods to monitor and identify heavy metal pollutants effectively, thereby ensuring environmental monitoring and public health protection. The growing prominence of electrochemical sensing devices in the identification and quantification of toxic metal ions is attributed to their low cost, high selectivity, exceptional sensitivity, and ability to achieve very low detection limits. This review provides a comprehensive overview of recent developments in electrochemical sensing platforms for monitoring heavy metals in aqueous environments and identifies emerging directions for future research. The review is structured to first examine electrode fabrication strategies, detailing the properties of electrodes and the characteristics of electrodes produced through various methods. Next, it reviews research progress concerning the modification of electrodes, emphasizing a variety of modifying materials, including inorganic compounds, organic frameworks, and biomaterials. Subsequently, progress in the supporting analytical frameworks is discussed, including signal recognition, data processing, and predictive modeling algorithms that enable intelligent analysis. Finally, current trends in electrochemical sensing are summarized, and perspectives are offered for the development of novel electrochemical sensor technologies for reliable and precise detection of heavy metal ions. • Electrochemical sensors modified with inorganic components and organic frameworks (particularly MOFs and COFs) show evolving capabilities in the detection of heavy metal ions. • Biomaterials exhibit increasing potential in electrochemical sensors for trace-level heavy metal analysis. • Novel insights into machine learning models used in detecting heavy metal contaminants. Bioaccumulation ; Metal ions in aqueous solution ; Environmental monitoring ; Heavy metals ; Electrochemistry ; Human health ; Environmental pollution Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Analytical Chemistry and Sensors
Authors: Ning Wang | Qingwen Su | Hongjun Xiang | Biyao Zhou | Jiaxin Huang | Fangyuan Zheng | Ruijie Zhu | Yoshitaka Aoki | Ling Meng | Feng Jiao | Baoyin Yuan | Chunmei Tang | Siyu Ye
Source: eScience (article, 2025-10-25)
Field: Materials Science — Materials Chemistry

Machine learning-driven discovery of high-performance Co/Sr-free air electrodes for protonic ceramic electrolysis cells Protonic ceramic electrolysis cells (PCECs) have emerged as a promising solid-state ion device, attracting considerable attention for efficient hydrogen generation. However, PCECs face multiple constraints, most notably the lack of high-performance Co/Sr-free air electrodes. Because the current widely used air electrodes generally contain Co and Sr elements, they encounter serious problems of thermochemical expansion and Sr segregation. Traditional air electrode development primarily relies on experience-guided experiments and trial-and-error methods, which is time-consuming and inefficient in exploring vast material compositional spaces. The introduction of machine learning (ML)-driven discovery of air electrodes provides a transformative new approach to the fast development of PCECs. Here, based on the well-constructed ML models, high-performance novel Co/Sr-free Ca 0.5 La 0.5 Fe 1- x Sc x O 3- δ oxides are successfully designed as the air electrodes of PCECs. Among them, Ca 0.5 La 0.5 Fe 0.8 Sc 0.2 O 3- δ (CLFS0.2) oxide demonstrates exceptional properties, including the high hydrated proton defects amount (0.143 mol unit −1 at 550 °C), high catalytic activity, and low thermal expansion coefficient. As a result, PCEC with CLFS0.2 air electrode achieves a current density of 1.58 A cm −2 at 1.3 V at 650 °C, which is higher than the most of Co-free air electrodes and rivals mainstream Co/Sr-containing air electrodes. • A robust ML framework was employed to discover high-performance Co/Sr-free air electrodes. • CLFS0.2 demonstrated exceptional hydration ability with an HPC of 0.173 mol unit −1 at 500 °C. • Remarkable operational stability (200 h) and TEC (13.4 × 10 −6 K −1 ) confirmed the excellent robustness of CLFS0.2. • A record current density of 1.15 A·cm −2 at 1.3 V and 600 °C was achieved in PCEC with CLFS0.2 air electrode. Electrode ; Electrolysis ; Ceramic ; Oxide ; Hydrogen ; High-temperature electrolysis ; Polymer electrolyte membrane electrolysis ; Electrochemistry Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Gas Sensing Nanomaterials and Sensors
Authors: Chenbo He | Rui Yang | Z.H. Wang | G.H. Tang | Cheng Bi | Jingjing Sun | Xiaoyan Wang | Chencheng Sun | LI Jun-ning
Source: Composites Part B Engineering (article, 2025-11-13)
Field: Chemistry — Spectroscopy

A data-driven synergistic optimization framework for thermo-mechanical properties of oriented fiber-reinforced aerogel composites Aerogel ; Thermal conductivity ; Thermal insulation ; Thermal expansion ; Volume fraction ; Thermal ; Microstructure ; Elastic modulus Aerogels and thermal insulation ; Thermal properties of materials ; Composite Material Mechanics
Authors: Li J | Yu Wu | Wenxuan Jiang | Weiqing Xu | Ying Zhou | Yu Xin | Yiwei Qiu | Yifei Chen | Liuyong Hu | Lirong Zheng | Wenling Gu | Chengzhou Zhu
Source: Analytical Chemistry (article, 2025-06-10)
Field: Materials Science — Materials Chemistry

Atomic Ga Site Enables Photonanozymes with Specific Inhibition Modes for Primary Drug Screening Enzyme inhibition plays a crucial role in drug discovery by governing interactions between molecules and distinct enzymatic sites, facilitating the identification of early drug candidates. However, most nanozymes have been limited to single active site inhibition models, leaving gaps in understanding inhibitor interactions and their dynamic modulation by external stimuli. Hence, an in-depth understanding of nanozyme inhibition across different functional domains, particularly under external stimuli like light irradiation, remains challenging. Herein, we report a carbon nitride photonanozyme with atomically dispersed Ga-N<sub>3</sub>-coupled cyano sites (Ga-CN) for analyzing and screening antithyroid drugs via photoregulated inhibition modes. Ga sites modulate the electronic structure of cyano sites, enhancing photoinduced charge separation and synergistically boosting peroxidase-like activity. Inhibition kinetics reveal that thiol-containing drugs exhibit distinct mixed inhibition modes by targeting different active sites, with inhibition markedly enhanced under light. Leveraging these differential inhibition patterns, we developed a Ga-CN-based sensor array, integrating machine learning for precise antithyroid drug screening, facilitating early drug discovery. Chemistry ; Primary (astronomy) ; Drug ; Nanotechnology ; Computational chemistry ; Pharmacology Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Nanoplatforms for cancer theranostics
Authors: Zhi-Yu Xiao | Bowen Kan | Huan Ma | Bowen Zhao | Honghui Shang
Source: Journal of Chemical Theory and Computation (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

NNQS-AFQMC: Neural Network Quantum States Enhanced Fermionic Quantum Monte Carlo We introduce an efficient approach to implement neural network quantum states (NNQS) as trial wave functions in auxiliary-field quantum Monte Carlo (AFQMC). NNQS are a recently developed class of variational ansätze capable of flexibly representing many-body wave functions, though they often incur a high computational cost during optimization. AFQMC, on the other hand, is a powerful stochastic projector approach for ground-state calculations, but it normally requires an approximate constraint via a trial wave function or trial density matrix, whose quality affects the accuracy. Recently, it has been shown (Xiao et al., arXiv2505.18519) that a broad class of highly correlated wave functions can be integrated into AFQMC through stochastic sampling techniques. In this work, we apply this approach and present a direct integration of NNQS with AFQMC, allowing NNQS to serve as high-quality trial wave functions for AFQMC with manageable computational cost. We test the NNQS-AFQMC method on the challenging nitrogen molecule (N<sub>2</sub>) at stretched geometries. Our results demonstrate that AFQMC with an NNQS trial wave function can attain near-exact total energies, highlighting the potential of AFQMC with NNQS to overcome longstanding challenges in strongly correlated electronic structure calculations. We also outline future research directions for improving this promising methodology. Wave function ; Quantum Monte Carlo ; Monte Carlo method ; Quantum ; Artificial neural network ; Variational Monte Carlo ; Function (biology) ; Projector Machine Learning in Materials Science ; Quantum many-body systems ; Quantum and electron transport phenomena
Authors: Shannon M. Stephens | Kyle M. Lambert
Source: The Journal of Organic Chemistry (article, 2025-04-23)
Field: Chemistry — Organic Chemistry

The Importance of Atomic Charges for Predicting Site-Selective Ir-, Ru-, and Rh-Catalyzed C–H Borylations A supervised machine learning model has been developed that allows for the prediction of site selectivity in late-stage C-H borylations. Model development was accomplished using literature data for the site-selective (≥95%) C-H borylation of 189 unique arene, heteroarene, and aliphatic substrates that feature a total of 971 possible sp<sup>2</sup> or sp<sup>3</sup> C-H borylation sites. The reported experimental data was supplemented with additional chemoinformatic descriptors, computed atomic charges at the C-H borylation sites, and data from parameterization of catalytically active tris-boryl complexes resulting from the combination of seven different Ir-, Ru-, and Rh-based precatalysts with eight different ligands. Of the over 1600 parameters investigated, the computed atomic charges (e.g., Hirshfeld, ChelpG, and Mulliken charges) on the hydrogen and carbon atoms at the site of borylation were identified as the most important features that allow for the successful prediction of whether a particular C-H bond will undergo a site-selective borylation. The overall accuracy of the developed model was 88.9% ± 2.5% with precision, recall, and F1 scores of 92-95% for the nonborylating sites and 65-75% for the sites of borylation. The model was demonstrated to be generalizable to molecules outside of the training/test sets with an additional validation set of 12 electronically and structurally diverse systems. Catalysis ; Chemistry ; Computational chemistry ; Organic chemistry Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis ; Organoboron and organosilicon chemistry
Authors: Jiawei Song | Juxiang Chen | Wenhai Chu | Yu-qiong Gao | Naiyun Gao
Source: Journal of Environmental Sciences (article, 2025-06-18)
Field: Chemistry — Organic Chemistry

PSO-SVM optimized dielectric barrier discharge plasma for energy-efficient degradation of sulfamethoxazole in wastewater Wastewater ; Degradation (telecommunications) ; Plasma ; Sulfamethoxazole ; Environmental science ; Chemistry ; Pulp and paper industry ; Waste management ; Computer science ; Environmental engineering ; Engineering ; Physics Nanomaterials for catalytic reactions ; Advanced Photocatalysis Techniques ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Ran Yang | Wei Wan | Yiming Feng
Source: Food Bioscience (article, 2025-07-24)
Field: Chemistry — Analytical Chemistry

Hyperspectral imaging for non-destructive origin authentication of peeled garlic products Hyperspectral imaging ; Authentication (law) ; GARLIC POWDER ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Chemistry ; Computer security Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Xiaoyang Bi | Dezhi Cao | Xinyue Wang | Dingkai Hu | Qiang Wang
Source: The Journal of Physical Chemistry B (article, 2025-10-01)
Field: Chemistry — Organic Chemistry

Correlation between σ-Profile Characteristics and Infinite Dilution Activity Coefficients of Choline Chloride-Urea DES: 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 Free Radicals and Antioxidants ; Chemical and Physical Properties in Aqueous Solutions ; Analytical Chemistry and Chromatography
Authors: Zhang Zhexuan | Yang Zhou | Xue Li | Wu Yi Chong | Zihua Zhang | Shibo Li | Zhenying Huang
Source: Materials Characterization (article, 2025-03-04)
Field: Materials Science — Materials Chemistry

Fabrication mechanism and high-temperature properties of bicontinuous Ti2AlN/TiAl composites: Experimental, DFT, and DL investigations Materials science ; Fabrication ; Composite material ; Mechanism (biology) MXene and MAX Phase Materials ; Intermetallics and Advanced Alloy Properties ; Metal and Thin Film Mechanics
Authors: Jingyu Yang | Jinbo Pan | Shixuan Du
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2025-05-01)
Field: Materials Science — Materials Chemistry

Understanding Surface/Interface‐Induced Chemical and Physical Properties at Atomic Level by First Principles Investigations ABSTRACT The scientific trajectory in contemporary materials research has transitioned toward surface and interface engineering as critical determinants of functional performance, facilitating atomic‐level precision in modulating physical and chemical properties for advanced applications spanning functional device architectures, catalytic systems, and electrochemical technologies. However, persistent challenges in atomic‐scale characterization and the resource‐intensive nature of empirical optimization necessitate systematic implementation of first‐principles calculations to elucidate fundamental mechanisms underlying experimental observations and enable rational design of surface/interface modifications. This review examines three advancements in ab initio calculations for interfacial engineering: (1) revealing the mechanism of selective assembly and activation phenomena on surfaces, (2) theoretical predictions of interface engineering strategies, and (3) developing material databases with ionic/van der Waals components. We further address computational challenges while proposing quantum‐mechanical methods to design next‐gen materials with customized interfacial properties. Interface (matter) ; Surface (topology) ; Chemical physics ; Materials science ; Nanotechnology ; Engineering physics ; Chemistry ; Physics ; Physical chemistry ; Geometry ; Mathematics ; Adsorption Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Semiconductor materials and devices