jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Baoyu Wang (1395607) | Wenyu Chen (9135516) | Lingrui Zou (21592028) | Tao Wang (12008) | Zongwen Li (284112) | Zhongyi Wang (848368) | Haoran Xu (484184) | Lijun Xu (195394) | Shangui Lan (5571665) | Pu Feng (143588) | Yinchang Ma (12541239) | Dongxing Zheng (1397050) | Xin He (48101) | Yang Xu (178421) | Zhengdong Luo (2811640) | Zhenhua Wu (223924) | Yan Liu (25061) | Genquan Han (9197588) | Xixiang Zhang (2050636) | Bin Yu (14464) | Fei Xue (24567)
Source: Figshare (article, 2025-06-24)
Field: Materials Science — Materials Chemistry

A ProgrammableNonvolatile Schottky Diode Based onvan der Waals Ferroelectric Junction Programmable and nonvolatile Schottky junctions are highly desirable for next-generation electronic and neuromorphic systems. However, conventional metal–semiconductor and even van der Waals (vdW) Schottky diodes often suffer from fixed rectifying behaviors or limited tunability. Here, we report a programmable nonvolatile ferroelectric Schottky diode based on a vdW heterojunction between semimetallic 1T′-MoTe<sub>2</sub> and ferroelectric α-In<sub>2</sub>Se<sub>3</sub>. The diode exhibits near-ideal performance, including a rectification ratio exceeding 10<sup>4</sup>, a leakage current down to 1 pA, and an ideality factor as low as 1.38. By switching ferroelectric polarization, the Schottky barrier can be modulated in a programmable manner, enabling reversible, nonvolatile, and multilevel rectification states. The device demonstrates polarization-dependent photoresponse and transient integrate-and-leak dynamics, closely resembling biological spiking neurons. A spiking neural network is implemented based on this behavior, achieving image recognition accuracy up to 98.4%. This work establishes programmable ferroelectric Schottky diodes as promising candidates for low-power memory, reconfigurable logic, and neuromorphic vision. Schottky diode ; Neuromorphic engineering ; Ferroelectricity ; Schottky barrier ; Rectification ; Diode ; Metal–semiconductor junction 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices ; Advanced Memory and Neural Computing
Authors: Muhamad Akrom | Supriadi Rustad | Hermawan Kresno Dipojono | Ryo Maezono | Hideaki Kasai
Source: Expert Systems with Applications (article, 2025-12-13)
Field: Materials Science — Materials Chemistry

Enhanced quantum support vector regression with quantum kernels and virtual sampling for ABX3 perovskite formation energy Support vector machine ; Quantum ; Kernel (algebra) ; Mean squared error ; Quantum entanglement ; Representation (politics) ; Energy (signal processing) ; Sampling (signal processing) ; Feature (linguistics) ; Perovskite (structure) Machine Learning in Materials Science ; Perovskite Materials and Applications ; Heusler alloys: electronic and magnetic properties
Authors: Qihao Shi (19747001) | Ziyuan Li (1603000) | Yu Wang (12152) | Fufeng Liu (3308805) | Wenjie Jing (12897089)
Source: Figshare (article, 2025-08-10)
Field: Materials Science — Materials Chemistry

Machine Learning-EnhancedNanozyme Sensor Array forAccurate Multiple Quinolone Antibiotics Recognition The overuse of quinolone antibiotics (QNs) seriously endangers human health and the ecological environment. In this work, a copper dihydroxosulfate (Cu<sub>2</sub>(OH)<sub>2</sub>SO<sub>4</sub>) nanosheet exhibiting notable peroxidase-like (POD) and laccase-like (LAC) activities has been developed in basic deep eutectic solvents (DES). The unique physicochemical properties of QNs allow them to enhance the POD activity of Cu<sub>2</sub>(OH)<sub>2</sub>SO<sub>4</sub>, and with the extension of reaction time, this enhancement gradually intensifies. Conversely, when QNs are introduced into the LAC reaction system of Cu<sub>2</sub>(OH)<sub>2</sub>SO<sub>4</sub>, they significantly inhibit its LAC activity, with the degree of inhibition growing increasingly evident as the reaction time increases. A nanozyme sensing array has been developed via reaction dynamics to identify eight QNs. This method cleverly achieves self-calibration through two reverse signals, further improving the sensing performance of the sensor array. Moreover, through the optimization of various machine learning (ML), the precision of the concentration-independent recognition model built upon this array has been enhanced from 39.08% to 91.95%. This improvement is advantageous for the identification of unknown samples within actual samples. This work carries significant implications for enhancing the discrimination of QNs in complex samples. Eutectic system ; Quinolone ; Identification (biology) ; Photoacoustic imaging in biomedicine ; Antibiotics ; Work (physics) ; Pattern recognition (psychology) ; Support vector machine Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Advanced biosensing and bioanalysis techniques
Authors: Sara Gariglio | Cristina Malegori | Carolina Scagliarini | Paolo Oliveri | Monica Casale | Eugenio Alladio | Giorgia Sciutto | Alberto Mazzoleni | Emilio Catelli | Zelan Li | Sergey Kucheryavskiy
Source: VBN Forskningsportal (Aalborg Universitet) (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Dealing with non-linearity in the study of near infrared light penetration through matter The two-year research project INSIDE, funded by the Italian Ministry of Universities and Research – MUR, aims to understand and quantify the penetration of incident near-infrared (NIR) radiation coupled with hyperspectral imaging (HSI), depending on materials analysed and experimental settings, with the belief that a deeper knowledge on the topic would open up for new applications, with the help of advanced chemometrics.<br/><br/>To this goal, ad hoc multi-material samples were built employing a 3D printer, in the shape of 1×1×1 cm and 1×1×3 cm (base × height) parallelepipeds or cylinders. The resulting samples, respectively 37 and 8, were composed with strata of different thickness of polymers with distinct spectral signatures (PLA and PETG). The samples were then analysed via a NIR-HSI camera (Specim, Finland) operating in the reflectance mode within the 1000-2500 nm spectral range, and the obtained spectra were submitted to chemometric data analysis. Exploratory analysis through principal component analysis (PCA) was performed. Subsequently, the stratified components in terms of total height were modelled, and the quantification precision was investigated in terms of root mean square error in prediction.<br/><br/>Specifically, partial least squares (PLS) regression was initially applied on the parallelepipedal samples and tested on the cylindric ones. The obtained results pointed out the presence of two consequent linear trends with different slopes, which needed to be modelled separately to optimize performances. Non-linearity of light penetration was then confirmed by ordinary least squares (OLS) and multivariate curve resolution-alternating least squares (MCR-ALS), since both estimated the pixel-based composition of samples with limited accuracy. Subsequently, specific algorithms to deal with non-linearity were considered and applied. Then, regression by means of convolutional neural networks (CNN) was performed, with the aim of describing the whole range with a single model. Though CNN correctly dealt with the non-linear behaviour of the data, it confirmed that quantification becomes more challenging for deeper layers, as shown by a sudden increase of within-level standard deviation in the prediction results. Partial least squares regression ; Principal component analysis ; Hyperspectral imaging ; Near-infrared spectroscopy ; Ordinary least squares ; Multivariate statistics ; Penetration depth ; Penetration (warfare) ; Least-squares function approximation Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Optical Polarization and Ellipsometry
Authors: Qianyu Liu | Yichen Li | Dongxiao Chen | Philippe Sautet
Source: ACS Catalysis (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

Size- and Facet-Dependent Behavior of Electron Storage at the Au/Anatase TiO <sub>2</sub> Interface under Ambient Conditions from Machine Learning The interfacial structure and electronic properties of gold (Au) nanoclusters supported on anatase TiO2 surfaces are key determinants of catalytic and photocatalytic activity for Au/TiO2 systems. Here, we present an atomistic study of realistic, ∼1.5 nm Au nanoclusters on reconstructed TiO2(001) and (101) facets under ambient air conditions, bridging the gap between small supported cluster models and experimental catalysts. We focus on the ability of Au/TiO2 to store photogenerated electrons that can be used later in dark conditions. Leveraging a high-dimensional Au–Ti–O neural-network potential coupled with stochastic surface walking and DFT refinement, we exhaustively sampled atomic configurations over 120,000 minima. Two distinct interfacial motifs emerge─Type (i), with oxygen adsorbed on Ti sites near the Au nanocluster, and Type (ii), with oxygen occupying Au3 hollow sites. While both motifs stabilize clusters on both facets, Type (ii) is notably more prevalent on (101), giving rise to deeper-lying O 2p states and a larger fraction of fully oxidized Auδ+ (δ ≥ 1) that can passivate the cluster. In contrast, Type (i) optimally balances neutral Au0 and partially oxidized Auδ+ (0 < δ < 1), enabling more than 0.90 vacant state per interfacial Au atom and enhancing electron-storage capability. Coupled with facet-dependent behavior and oxidation-state distributions, these findings deliver direct, experimentally relevant design principles for tailoring the electronic functionality of Au/TiO2 catalysts. Nanoclusters ; Passivation ; Cluster (spacecraft) ; Anatase ; Electron ; Catalysis ; Density functional theory ; Surface states ; Oxygen Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; TiO2 Photocatalysis and Solar Cells
Authors: Zhichao Yan | Xuechuan Wang | Zhen Wang | Yanfeng Zheng | Lingyi Zhang | WeiBing Zhang
Source: Analytical Chemistry (article, 2025-12-25)
Field: Chemistry — Spectroscopy

High Na-Adducted Ion Selectivity in Laser Desorption/Ionization Mass Spectrometry with a Steric-Tuned COF Laser desorption-ionization mass spectrometry (LDI-MS) is prized for its rapidity, simplicity, low sample consumption, and high throughput in metabolic analysis, with great potential as a diagnostic tool. Herein, an LDI-MS method with sulfone-containing covalent organic frameworks (S-COFs) as the substrate was developed. By engineering the electron cloud density and steric hindrance of the LDI substrate, selective binding sites with higher affinity for Na<sup>+</sup> than K<sup>+</sup> were obtained. DFT calculations confirmed that the sulfone group (O-O distance: 2.14 Å; O═S═O angle: 117.43°) favors Na<sup>+</sup> adsorption over K<sup>+</sup>. For glucose, the intensity of [M + Na]<sup>+</sup> was 75-fold higher than [M+K]<sup>+</sup>, enhancing detection sensitivity while simplifying serum metabolic fingerprints. S-COF-assisted LDI-MS exhibited robust responses to carbohydrates, amino acids, nucleosides, and other small-molecule metabolites. It achieved an excellent limit of detection (LOD) of 10 pg for glucose, 1,926 times more sensitive than the conventional organic matrix DHB. The method was successfully applied to detect serum metabolites in hepatitis C virus (HCV) patients and healthy controls. Support vector machine (SVM) and neural network (NNW) machine learning algorithms enabled high-precision HCV diagnosis, both yielding an AUC of 0.990. 36 metabolites were identified, and 5 of them were strongly enriched in the cysteine/methionine pathway (-log P = 5.70, Impact = 0.347), implicating perturbed redox homeostasis. This study not only offers novel insights into the modular design of functionalized COFs as LDI substrates but also provides a versatile tool for large-scale clinical diagnostics. Detection limit ; Steric effects ; Selectivity ; Substrate (aquarium) ; Covalent bond ; Matrix (chemical analysis) ; Adsorption ; Mass spectrometry Mass Spectrometry Techniques and Applications ; Forensic Fingerprint Detection Methods ; Analytical chemistry methods development
Authors: Yue Zhao | Zhiyu An | Jingjie Yeo | Shefford P. Baker
Source: Physical review. B./Physical review. B (article, 2025-10-17)
Field: Materials Science — Materials Chemistry

Coefficient of thermal expansion and elastic constants of metastable <i>β</i> -phase tungsten thin films as a function of nitrogen content The metastable A15 \ensuremath{\beta} phase of tungsten exhibits the largest spin Hall angle among all transition metals and is of interest for applications in spintronic devices. However, \ensuremath{\beta}-W is known to support very high stresses in such applications, which may affect properties and reliability. To understand these stresses, the thermomechanical properties must be known. The coefficient of thermal expansion (CTE) and elastic constants of \ensuremath{\beta}-W thin films containing 0--11.1 at. % nitrogen were determined within the ${24}^{\ensuremath{\circ}}\ensuremath{-}100{\phantom{\rule{0.16em}{0ex}}}^{\ensuremath{\circ}}\mathrm{C}$ temperature range using three methods: substrate curvature measurements of thermoelastic curves for films deposited on $\mathrm{Si}\phantom{\rule{0.16em}{0ex}}\ensuremath{\langle}100\ensuremath{\rangle}$ and fused silica substrates, ab initio molecular dynamics (AIMD) simulations, and classical molecular dynamics (MD) simulations using a Spectral Neighbor Analysis Potential fitted from AIMD results using machine learning. Single crystal elastic constants obtained from the simulations were used along with the measured textures of the films to calculate biaxial elastic moduli. Results from all three approaches were consistent. The results of the same methods applied to the well-known BCC \ensuremath{\alpha} phase of W were within 3% of reference values, and those for \ensuremath{\beta}-W are expected to have a similar accuracy. It was found that the CTE of \ensuremath{\beta}-W increases while the biaxial modulus decreases with increasing nitrogen content. The average CTE and biaxial modulus of pure \ensuremath{\beta}-W within ${24}^{\ensuremath{\circ}}\ensuremath{-}100{\phantom{\rule{0.16em}{0ex}}}^{\ensuremath{\circ}}\mathrm{C}$ were determined to be 4.931 $\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}6}{/}^{\ensuremath{\circ}}\mathrm{C}$ and 503 GPa, respectively, which are 10% higher and 11% lower than those of \ensuremath{\alpha}-W. Thermal expansion ; Thin film ; Tungsten ; Elastic modulus ; Metastability ; Atmospheric temperature range ; Phase (matter) ; Phase transition ; Thermoelastic damping ; Negative thermal expansion Fusion materials and technologies ; Copper Interconnects and Reliability ; Thermal properties of materials
Authors: Xi Wang | Amar Deep Pathak | David Thanoon
Source: Advanced Theory and Simulations (article, 2025-06-07)
Field: Materials Science — Metals and Alloys

Machine Learning for Sulfide Stress Cracking Prediction Abstract Stress Corrosion Cracking (SCC) poses a significant threat to production systems, arising from the interaction of tensile stresses and corrosive environments. Sulfide Stress Cracking (SSC), particularly associated with hydrogen sulfide (H 2 S) gas, is highly relevant in oil and gas production. Corrosion‐resistant alloys, such as Duplex Stainless Steel (DSS), help mitigate this issue. However, understanding the impact of environmental conditions and loads on SSC in DSS remains challenging. Existing standards lack insights into specific environmental factors. Modeling SSC using physics‐based approaches is computationally intensive. To address this, a novel machine learning (ML) framework utilizing decision tree‐based models and probabilistic graphical models (Bayesian network, BN) is developed. The dataset for DSS is curated from published literature, and data imbalance is addressed using advanced data curation methods. The framework aims to unravel the intricate factors driving SSC in DSS, providing an accurate predictive tool for the oil and gas industry. Cracking ; Sulfide ; Stress (linguistics) ; Computer science ; Materials science ; Composite material ; Metallurgy ; Philosophy Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Metallurgy and Material Forming
Authors: Yi‐Ting Tsai | Chi-Wei Wu | Ren‐Jei Chung | Gabriel Nicolo A. De Guzman | G. Li | Julius L. Leaño | Mu‐Huai Fang
Source: ACS Applied Materials & Interfaces (article, 2025-10-11)
Field: Chemistry — Organic Chemistry

Optical Labeling with Artificial Intelligence Using Infrared-Responsive Functional Textiles Functional textiles integrated with image recognition systems hold significant potential for advanced applications. However, conventional visible light dyes and colorants can be detected under normal lighting conditions and are difficult to conceal within materials. In this study, we introduce a sustainable and intelligent textile platform by integrating SiO<sub>2</sub>/Al<sub>2</sub>O<sub>3</sub>-coated hydrophilic Ag<sub>2</sub>S quantum dots into natural pineapple leaf fiber (PALF). This biogenic composite exhibits excellent photostability and water compatibility, with concealed optical functionality detectable only under short-wave infrared imaging. Unlike conventional visible dyes, the embedded emission remains invisible under ambient conditions, enabling covert information encoding. To achieve material-level recognition, a convolutional neural network (CNN) was trained on hyperspectral imaging data and successfully identified optically functionalized fibers with 82% accuracy. This was demonstrated even in millimeter-scale yarn samples (∼0.6 cm). This work exemplifies the synergy between renewable materials, nanophotonics, and artificial intelligence, offering practical utility in secure labeling, anticounterfeiting, and interactive textiles. Hyperspectral imaging ; Convolutional neural network ; Textile ; Optical fiber ; Composite number ; Infrared ; Fiber ; Multispectral image ; Visible spectrum Polydiacetylene-based materials and applications ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Longfei Ye | Yiwei Dai | Yifan Liu | Fengle Zhu | Jiandong Jiang | Fei Zhou | Xin Qiao | Fei Liu | Jiyu Peng
Source: Talanta (article, 2025-08-29)
Field: Chemistry — Analytical Chemistry

Dual-modal fusion of hierarchical image features and spectral data for efficient quantitative analysis of mineral elements in rice (Oryza sativa L.) leaves Chemistry ; Oryza sativa ; Dual (grammatical number) ; Biological system ; Pattern recognition (psychology) ; Artificial intelligence ; Biochemistry ; Linguistics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; GABA and Rice Research
Authors: Emad Al Ibrahim (7822334) | Nathan Morgan (2449687) | Simon Müller (7433873) | Saikiran Motati (22661079) | William H. Green (1318902)
Source: Figshare (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Accurately PredictingSolubility Curves via a ThermodynamicCycle, Machine Learning, and Solvent Ensembles Determining solubilities of organic molecules is critical in various fields such as pharmaceuticals, agrochemicals, and environmental science. Knowing how a solute will dissolve in different solvents and at different temperatures is essential for drug formulation, synthesis, purification, and crystallization. Hard-to-estimate solubility limits currently hinder the design of new processes, making innovation more expensive. We propose a fast and general method for predicting the solubilities of neutral organic molecules in a wide range of solvents and temperatures. Our method uses a thermodynamic fusion cycle to combine machine learning predictions of the activity coefficient, fusion enthalpy, and melting point temperature. This method was tested on a combined data set with more than 100,000 experimental solubility values, showing better or comparable performance to competing methods on many solubility benchmarks even at elevated temperatures. We also introduce reference ensembling to leverage all available experimental solubilities for a given solute in estimating its solubility in a different solvent. Reference ensembling is also shown to enhance the robustness of models trained directly on solubility data. Solubility ; Fusion ; Leverage (statistics) ; Melting point ; Solvent ; Molecule ; Hildebrand solubility parameter ; Activity coefficient Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Yi-Ting Tsai (309320) | Chi-Wei Wu (22416657) | Ren-Jei Chung (1925308) | Gabriel Nicolo
A. De Guzman (22416660) | Guan-Jie Li (22416663) | Julius L. Leaño (22416666) | Mu-Huai Fang (1594912)
Source: Figshare (article, 2025-10-12)
Field: Materials Science — Materials Chemistry

Optical Labelingwith Artificial Intelligence UsingInfrared-Responsive Functional Textiles Functional textiles integrated with image recognition systems hold significant potential for advanced applications. However, conventional visible light dyes and colorants can be detected under normal lighting conditions and are difficult to conceal within materials. In this study, we introduce a sustainable and intelligent textile platform by integrating SiO<sub>2</sub>/Al<sub>2</sub>O<sub>3</sub>-coated hydrophilic Ag<sub>2</sub>S quantum dots into natural pineapple leaf fiber (PALF). This biogenic composite exhibits excellent photostability and water compatibility, with concealed optical functionality detectable only under short-wave infrared imaging. Unlike conventional visible dyes, the embedded emission remains invisible under ambient conditions, enabling covert information encoding. To achieve material-level recognition, a convolutional neural network (CNN) was trained on hyperspectral imaging data and successfully identified optically functionalized fibers with 82% accuracy. This was demonstrated even in millimeter-scale yarn samples (∼0.6 cm). This work exemplifies the synergy between renewable materials, nanophotonics, and artificial intelligence, offering practical utility in secure labeling, anticounterfeiting, and interactive textiles. Hyperspectral imaging ; Convolutional neural network ; Optical fiber ; Composite number ; Textile ; Multispectral image ; Infrared ; Fiber Carbon and Quantum Dots Applications ; Advanced Sensor and Energy Harvesting Materials ; Advanced Cellulose Research Studies