jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chang Ming Li | Yong Tan | Chunyu Liu | Xun Gao | Zhong Lv | Hongchen Zhang | Yong Zhang
Source: Frontiers in Plant Science (article, 2025-11-18)
Field: Chemistry — Analytical Chemistry

Rice origin traceability using mid-infrared and fluorescence spectral data fusion This study overcomes the limitations of traditional single-spectroscopy techniques by constructing an intelligent discrimination system for rice geographic origin that integrates mid-infrared (MIR) and fluorescence (FLU) spectral feature fusion with machine learning. Using the "Zhongke Fa 5" rice variety from eight major production regions in Jilin Province, China, as the research object, spectral data were acquired using Fourier transform infrared (FTIR) and fluorescence spectrometers. A "Normalization-Smoothing-Multiplicative Scatter Correction" preprocessing framework was proposed, significantly enhancing the signal-to-noise ratio and separability of the spectral features. The complementary characteristics of the multispectral data were elucidated: MIR spectra (500-3750 cm<sup>-1</sup>) accurately represented molecular vibration features of key components such as starch, protein, and lipids, while FLU spectra (450-850 nm) effectively captured the fluorescence characteristics of phenolic compounds and protein-pigment complexes. The successive projections algorithm (SPA) was employed to extract 286-310 highly discriminative features from the original 7625-dimensional data, effectively mitigating the overfitting problem associated with high-dimensional data. The performance differences between data-level and feature-level fusion strategies were compared. The feature-level fusion model optimized by SPA demonstrated significant advantages, achieving a test set accuracy of 95.55%. Regarding algorithm performance, the logistic regression (LR) model combined with enhanced spectral features (LR-SPA) significantly outperformed support vector machine (SVM, 83.17%) and gradient boosting algorithms in terms of both precision (93.05%) and robustness. This study provides a revolutionary technical approach for agricultural product quality and safety supervision, holding substantial theoretical innovation and practical application value. As a primary goal, the abstract should render the general significance and conceptual advance of the work clearly accessible to a broad readership. References should not be cited in the abstract. Leave the Abstract empty if your article does not require one - please see the "Article types" on every Frontiers journal page for full details. Multispectral image ; Pattern recognition (psychology) ; Overfitting ; Support vector machine ; Discriminative model ; Sensor fusion ; Fusion ; Hyperspectral imaging ; Preprocessor ; Random forest Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Junlin Chen | J. Nie | Minqiang Guo | Hong Lin | Kaiqiang Wang
Source: Microchemical Journal (article, 2025-06-26)
Field: Chemistry — Analytical Chemistry

Near-infrared, Raman, and multi-modal fused molecular spectroscopy data for noninvasive assessment of large yellow croaker Raman spectroscopy ; Infrared ; Modal ; Infrared spectroscopy ; Analytical Chemistry (journal) ; Materials science ; Spectroscopy ; Near-infrared spectroscopy ; Remote sensing ; Environmental science ; Chemistry ; Environmental chemistry ; Optics ; Geology ; Physics ; Composite material ; Organic chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Ahmed Saleh Yaseen | Yosef Othman Homeda | Mohammad M. Al-Tufah | Mai M. Khalaf | Mohannd Faisal Shareef
Source: Obat (article, 2025-11-30)
Field: Materials Science — Polymers and Plastics

Synthesis of Vanadium Pentoxide (V₂O₅) by Green Method Using Olive Oil as A Reducing Agent and Study of its Physical Properties This study reports the green synthesis of vanadium pentoxide (V₂O₅) using virgin olive oil as a natural and environmentally benign reducing agent. The approach aims to minimize the environmental impacts associated with conventional synthesis routes. Structural and physicochemical characterizations confirmed the successful formation of nanoscale V₂O₅. X‑ray diffraction (XRD) analysis indicated an average crystallite size of approximately 16.57 nm, evidencing high crystallinity. Fourier‑transform infrared spectroscopy (FTIR) revealed characteristic V=O and V–O–V vibrations with bands associated with physisorbed water, confirming the correct oxide framework. Field‑emission scanning electron microscopy (FE‑SEM) showed irregularly shaped nanoparticles with a representative particle diameter of ~32.62 nm. Brunauer–Emmett–Teller (BET) and Barrett–Joyner–Halenda (BJH) analyses yielded a specific surface area of 10.817 m²/g, a total pore volume of 0.024277 cm³/g, and a broad mesoporous distribution (20–90 nm). Energy‑dispersive X‑ray spectroscopy (EDX) confirmed the purity of V₂O₅ with weight fractions of V (69.40%) and O (30.60%), consistent with the stoichiometric composition. Overall, the results demonstrate the effectiveness of olive oil as a green reducing agent for preparing nanoscale V₂O₅, which is promising for catalysis, energy storage, sensors, and clean‑energy applications. Pentoxide ; Vanadium ; Crystallite ; Reducing agent ; Scanning electron microscope ; Nanoparticle ; Nanoscopic scale ; Mesoporous material ; Particle-size distribution Transition Metal Oxide Nanomaterials ; Vanadium and Halogenation Chemistry ; Catalysis and Oxidation Reactions
Authors: Zhiyuan Hua | Jie Yang | J Wang | Hao Cui | Zhilei Li | Wei Jin | Yunfeng Wang | Wei Feng | Wenkai Yang | Benshun Tian | Xiaohu Wang
Source: Microchemical Journal (article, 2025-12-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning-enhanced SERS detection of carbapenem-resistant enterobacteriaceae using PEI-magnetic enrichment and Au@Ag core-shell nanoprobe Enterobacteriaceae ; Escherichia coli ; Klebsiella pneumoniae ; Antibiotic resistance ; Pathogen ; Nanoprobe ; Workflow ; Immune recognition Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Bacterial Identification and Susceptibility Testing
Authors: Brzózka, Agnieszka | Domin, Kinga
Source: Jagiellonian University Repository (Jagiellonian University) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

5th International Workshop on Functional Nanostructured Materials (FuNaM-5) September 24th-26th, 2025, Krakow, Poland : book of abstracts Work (physics) ; Government (linguistics) Chemical and Physical Properties of Materials ; Aerogels and thermal insulation ; Covalent Organic Framework Applications
Authors: Yunpeng Gao | Xiangguo Zeng | Huayan Chen | Minghua Chi | Wei Li
Source: Ceramics International (article, 2025-12-05)
Field: Materials Science — Materials Chemistry

A multi-stage analysis of swelling-assisted fracture in lithium hydride ceramics under thermo-mechanical-hydrogen coupling: From sensitivity of hydrogen transport to phase-field prediction Hydrogen ; Fracture (geology) ; Cracking ; Hydride ; Hydrogen fuel ; Hydrogen embrittlement ; Ceramic ; Hydrogen production Hydrogen Storage and Materials ; Fusion materials and technologies ; Nuclear Materials and Properties
Authors: Shannon
M. Stephens (21170477) | Kyle M. Lambert (1617172)
Source: Figshare (article, 2025-04-24)
Field: Chemistry — Organic Chemistry

The Importance of AtomicCharges for Predicting Site-SelectiveIr‑, 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. Borylation ; Set (abstract data type) ; Atomic charge ; Molecule ; Feature (linguistics) ; Data set ; Training set ; Test set Catalytic C–H Functionalization Methods ; Organoboron and organosilicon chemistry ; Catalytic Cross-Coupling Reactions
Authors: Mohammad Mohammadi | Sima Najafzadehkhoei | George Vega Yon | Yunshan Wang
Source: ArXiv.org (preprint, 2025-07-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A novel approach for classifying Monoamine Neurotransmitters by applying Machine Learning on UV plasmonic-engineered Auto Fluorescence Time Decay Series (AFTDS) This study introduces a hybrid approach integrating advanced plasmonic nanomaterials and machine learning (ML) for high-precision biomolecule detection. We leverage aluminum concave nanocubes (AlCNCs) as an innovative plasmonic substrate to enhance the native fluorescence of neurotransmitters, including dopamine (DA), norepinephrine (NE), and 3,4-Dihydroxyphenylacetic acid (DOPAC). AlCNCs amplify weak fluorescence signals, enabling probe-free, label-free detection and differentiation of these molecules with great sensitivity and specificity. To further improve classification accuracy, we employ ML algorithms, with Long Short-Term Memory (LSTM) networks playing a central role in analyzing time-dependent fluorescence data. Comparative evaluations with k-Nearest Neighbors (KNN) and Random Forest (RF) demonstrate the superior performance of LSTM in distinguishing neurotransmitters. The results reveal that AlCNC substrates provide up to a 12-fold enhancement in fluorescence intensity for DA, 9-fold for NE, and 7-fold for DOPAC compared to silicon substrates. At the same time, ML algorithms achieve classification accuracy exceeding 89%. This interdisciplinary methodology bridges the gap between nanotechnology and ML, showcasing the synergistic potential of AlCNC-enhanced native fluorescence and ML in biosensing. The framework paves the way for probe-free, label-free biomolecule profiling, offering transformative implications for biomedical diagnostics and neuroscience research. Biomolecule ; Fluorescence ; Leverage (statistics) ; Monoamine neurotransmitter ; Nanomaterials ; Plasmon Gold and Silver Nanoparticles Synthesis and Applications ; Molecular Communication and Nanonetworks ; Nanocluster Synthesis and Applications
Authors: Naveen Kumari
Source: Bharati International Journal of Multidisciplinary Research and Development (BIJMRD). (review, 2025-01-12)
Field: Materials Science — Materials Chemistry

Advancements in Solid-State Chemistry: A Review of Structure-Property Relationships in Modern Materials Solid-state chemistry plays a pivotal role in understanding the intricate relationships between the structure and properties of materials, driving advancements in diverse technological applications.This review explores recent progress in structure-property correlations across emerging materials such as metal-organic frameworks (MOFs), perovskites, and two-dimensional (2D) materials.Special emphasis is placed on their applications in energy storage, catalysis, and optoelectronics.Methodological breakthroughs in computational modeling and advanced characterization techniques are highlighted, showcasing their transformative impact on materials discovery and development.The paper also discusses challenges in sustainable material synthesis and outlines future directions, including the integration of artificial intelligence for innovative solutions. Property (philosophy) ; Solid-state chemistry ; Solid-state ; Chemistry ; Nanotechnology ; Materials science ; Epistemology ; Physical chemistry ; Crystallography ; Philosophy X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials ; Thermal and Kinetic Analysis
Authors: Tianyi Shang | Siting Hou | Huixian Han | Changjian Xie
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Chemistry — Spectroscopy

Full-dimensional neural network potential energy surface for the multi-channel photodissociation of HNSiO <i>via</i> its S<sub>1</sub> band A full-dimensional potential energy surface (PES) for the first excited state S<sub>1</sub>(<sup>1</sup>A″) of HNSiO was built using the neural network method based on more than 91 000 <i>ab initio</i> points, covering six possible product channels for photodissociation reactions: HNSiO + <i>hv</i> → H(<sup>2</sup>S) + NSiO(<sup>2</sup>Π)/HN(<sup>1</sup>Δ) + SiO(<sup>1</sup>Σ<sup>+</sup>)/HNSi(<sup>3</sup>Π) + O(<sup>3</sup>P)/NSi(<sup>2</sup>Σ) + OH(<sup>2</sup>Π)/N(<sup>2</sup>D) + SiOH(<sup>2</sup>A')/HSi(<sup>2</sup>Σ) + NO(<sup>2</sup>Π). Seventeen stationary points consisting of five local minima and twelve transition states were found on the S<sub>1</sub> PES along the six dissociation pathways. Based on the newly constructed S<sub>1</sub> PES, the quasi-classical trajectory calculations were carried out to study the photodissociation dynamics of HNSiO(S<sub>1</sub>) in the total energy range of 5.5-7.7 eV. It was found that although six product channels are energetically available at high total energies, the dominant (>90%) products are HN + SiO and H + NSiO. At low total energies, the major products are HN + SiO, whereas for a total energy of >7.1 eV, the H + NSiO product channel becomes increasingly favored owing to the faster movement of the light H atom. This scenario is quite analogous to the photodissociation of HNCO in the S<sub>1</sub> band. In particular, a high proportion of trajectories undergo isomerization during dissociation because of the relatively low barriers separating the isomers. Owing to the much shallower HOSiN and NSiOH wells compared with the HNSiO wells on the S<sub>1</sub> PES, the transferred H atom after isomerization returns to the HNSiO wells or dissociates directly, which leads to quite small percentages of other products, namely, N + SiOH, NSi + OH, and HSi + NO. Photodissociation ; Surface (topology) ; Channel (broadcasting) ; Energy (signal processing) ; Potential energy surface ; Artificial neural network ; Atomic physics ; Materials science ; Molecular physics ; Physics ; Chemistry ; Photochemistry ; Computer science ; Telecommunications ; Quantum mechanics ; Molecule ; Artificial intelligence ; Mathematics ; Geometry Spectroscopy and Laser Applications ; Radiation Detection and Scintillator Technologies ; Advanced Semiconductor Detectors and Materials
Authors: David Kamson | Nirbhay N. Yadav | Lindsay Blair | Doris Lin | John Laterra | Karisa C. Schreck | Solmaz Sahebjam | Matthias Holdhoff | Peter C.M. van Zijl | Linda Knutsson
Source: Neuro-Oncology (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

IMG-108. Dynamic Glucose Enhanced Imaging using Direct Water Saturation in Brain Tumors Abstract BACKGROUND Dynamic glucose-enhanced (DGE)-MRI utilizes D-glucose as a biodegradable contrast agent, offering tissue contrast uptake information similar to dynamic contrast-enhanced (DCE)- and dynamic susceptibility contrast (DSC)-MRI. DGE leverages chemical exchange saturation transfer (CEST), enhancing sensitivity by detecting water signal reductions from low-concentration solutes. To mitigate DGE’s proneness to motion artifacts and enhance signal-to-noise ratio, we proposed to measure the linewidth (LW) increase of the direct water saturation (DS) Z-spectrum consequent to the increase in transverse relaxation (DS-DGE). We present DS-DGE data from glioma patients at 3T and compare with DCE-/DSC-derived hemodynamic parameters. METHODS Six patients (4 glioblastomas, 2 low-grade astrocytomas) were scanned at 3T (Philips Elition RX). After a D-glucose infusion (D50, 0.5g/kg [max 35 g], 6.25 g/min), DS-DGE-MRI was performed acquiring 40 dynamics (each with a DS Z-spectrum with 32 offset-frequencies) yielding a resolution of 2.2x2.2x4.4 mm³. A deep learning-based Lorentzian fit generated LW. Pre-infusion baseline (LWbase) and post-infusion (LWavg) values were calculated along with DS-DGE AUC (LWavg-LWbase)/LWbase). DCE-/DSC-MRI data were analyzed using extended Toft and leakage-corrected tracer models, respectively. Mean±STDev were obtained for DS-DGE AUC, leakage-corrected CBV, Ktrans, and Ve from ROIs in tumor and normal white matter (WM). RESULTS DS-DGE AUC was over two-fold higher in contrast-enhanced tumor (CE) versus WM (2.7±1.15% vs 1.29±0.51%). Regions with Edema and Necrosis had ~50% higher DS-DGE AUC than in WM. In comparison, leakage-corrected CBV in both CE and Necrosis was ~25% lower than WM while Edema was similar to WM. Ktrans and Ve were higher in CE compared to both Edema and Necrosis (0.056±0.025 min-1 (CE), 0.027±0.017 min-1 (Edema), 0.031±0.028 min-1 (Necrosis) for Ktrans and 0.12±0.081 (CE), 0.053±0.058 (Edema), 0.075± 0.085 (Necrosis) for Ve. CONCLUSIONS DS-DGE MRI effectively highlighted glioma and produced contrast metrics similar to or complementary to gadolinium-based DCE/DSC-MRI supporting its potential for non-gadolinium-based imaging using D-glucose. Saturation (graph theory) ; White matter ; Glioma ; TRACER ; Edema ; Magnetic resonance imaging ; Laser linewidth ; Cerebral edema ; Necrosis Lanthanide and Transition Metal Complexes ; MRI in cancer diagnosis ; Advanced MRI Techniques and Applications
Authors: Yao‐Yu Wang | Xiaohua Wan | Cheng Chen | Fa Zhang | Xuefeng Cui
Source: Journal of Computer Science and Technology (article, 2025-05-01)
Field: Materials Science — Surfaces, Coatings and Films

Central Feature Network Enables Accurate Detection of Both Small and Large Particles in Cryo-Electron Tomography Computer science ; Feature (linguistics) ; Tomography ; Electron tomography ; Theory of computation ; Artificial intelligence ; Pattern recognition (psychology) ; Algorithm ; Physics ; Optics ; Scanning electron microscope Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Nuclear Physics and Applications
Authors: Adewale K. Ipadeola (12699590) | Mostafa H. Sliem (6316958) | Dana Abdeen (18147583) | Nicholas Laycock (17150956) | Ashwin RajKumar (22132576) | Phaneendra K. Yalavarthy (22132579) | Aboubakr M. Abdullah (1505017)
Source: Figshare (article, 2025-08-27)
Field: Materials Science — Materials Chemistry

Unmasking the Hidden Threat: Conductive Under-Depositsand Their Role in Preferential Weldment Corrosion of Carbon Steelunder Sour Conditions This work incorporates precorrosion conditioning and conductive deposits (FeS or FeS<sub>2</sub>) to explore corrosion mechanisms across base metal (BM), weld metal (WM), and heat-affected zone (HAZ) regions under simulated sour conditions (NaCl (3.5 wt %), Na<sub>2</sub>S<sub>2</sub>O<sub>3</sub> (1000 ppm), CH<sub>3</sub>COOH (100 ppm), CO<sub>2</sub>, 60 °C, pH ∼ 4, and 1000 rpm). Electrochemical studies demonstrated various corrosion rates (CR) hierarchies (CS-BM (0.34–0.42 mmpy) < CS-WM (0.38–0.73 mmpy) < CS-HAZ (0.48–1.75 mmpy)), which were exacerbated by FeS or FeS<sub>2</sub> deposition. This was ascribed to a variety of potential variations and localized acidification. Also, there were notable changes in the welded CS areas’ compositions and microstructures both before and after corrosion testing. By creating protective layers that reduce microgalvanic interactions, the commercial amine-based inhibitor CRW11 (200 ppm) showed remarkable potency by lowering the CRs (<0.1 mmpy) threshold with inhibition efficiency (IE = >80%). Corrosion products (α-Fe<sup><sub>2</sub></sup>O<sub>3</sub>, γ-FeOOH, and Fe<sub>3</sub>O<sub>4</sub>) were most significant, identified by Raman spectroscopy. 1D artificial pit tests showed varied pit propagation dynamics, especially under FeS-induced heterogeneity, with CS-BM-FeS having the highest pit depth (71.4 ± 11.8 μm), but increased pit density (568.0 mm<sup>2</sup>) was recorded for CS-HAZ-FeS<sub>2</sub>. This proves the impact of conductive deposits on the pitting of welded CS regions. These findings corroborated deposit-induced electrochemical heterogeneity with localized attack, which was most significant with FeS<sub>2</sub> deposit. Machine learning (ML) models, like random forest (RF), decision tree (DT), and extreme gradient boost (XGBoost), demonstrated excellent IE predictive ability (<i>R</i><sup>2</sup> = 0.99) for welded CS without/with conductive deposits. However, RF and XGBoost are ideal with the least RMSE/MAE (0.2/0.2 and 0.1/0.1) for welded CS without/with FeS and FeS<sub>2</sub>, respectively. These results provide a practical foundation for pitting and PWC in preconditioned welded CS with conductive deposits, allowing for the best material selection and corrosion control techniques in sour service applications. Corrosion ; Welding ; Base metal ; Electrical conductor ; Carbon steel ; Electrochemical noise ; Microstructure ; Metal Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Electrodeposition and Electroless Coatings