jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Junnosuke Takai | Yukimi Tanaka | Masahiro Yoshioka | Katsuhiro Shirono
Source:  (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Approximative Bayesian approach for uncertainty evaluation in machine learning-based hardness measurement Bayesian probability ; Measurement uncertainty ; Bayesian inference ; Pattern recognition (psychology) ; Calibration ; Bayes estimator Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Probabilistic and Robust Engineering Design
Authors: Xudong Wang | Guichen Teng | Xiangjian Meng | Zhenxiang Cheng | Tao Lin | Hao Shen | Xiaodan Wang | Jianlu Wang | Junhao Chu
Source: Advanced Electronic Materials (article, 2025-12-30)
Field: Materials Science — Materials Chemistry

Is There A Pure Electronic Ferroelectric? Abstract Different from traditional ferroelectrics whose polarization stems from ionic displacements mediated by phonons, electronic ferroelectrics exhibit spontaneous polarization originating from polar electronic ordering. Such electronic mechanisms promise devices with ultrafast switching speeds, lower energy consumption, and enhanced resilience to fatigue and depolarization fields inherent in conventional ferroelectrics. While early candidates are restricted to rare oxides and organic charge‐transfer salts, emerging systems—particularly 2D van der Waals moiré heterostructures—have significantly broadened this materials landscape. This review comprehensively examines ferroelectrics governed by electronic mechanisms, categorizing them according to microscopic origins, including spin correlations, charge ordering, orbital interactions, charge‐transfer instabilities, and excitonic phenomena. Representative materials span multiferroics, molecular crystals, and engineered van der Waals architectures. Crucially, we evaluate whether their ferroelectricity qualifies as purely electronic —defined by the absence of ionic displacements during polarization reversal—synthesizing recent theoretical and experimental advances to establish a unified framework for this evolving paradigm. van der Waals force ; Polarization (electrochemistry) ; Ionic bonding ; Ferroelectricity ; Electronic structure ; Ultrashort pulse ; Polar ; Exciton 2D Materials and Applications ; Chemical and Physical Properties of Materials ; Perovskite Materials and Applications
Authors: C. Förster | Carolin Müller
Source: Digital Discovery (article, 2025-11-06)
Field: Materials Science — Materials Chemistry

From handbooks to high-throughput: rule-based prediction of electronic absorption maxima from SMILES with ChromoPredict Automated Woodward–Fieser–Kuhn rule implementation bridges classical heuristics with modern predictive and interpretable machine learning tools. Maxima ; Maxima and minima ; Absorption (acoustics) ; Heuristics ; Field (mathematics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Xiaohui Du | Caixia Miao | Qi Sun | Haijiang Shi | Hongchen Han | Lili Chu | Guanghui Zhang | H Y Pang
Source: Polymers (article, 2025-12-30)
Field: Materials Science — Polymers and Plastics

Gamma Irradiation Resistance of Four Elastomers for Nuclear Sealing Applications The reliability of rubber materials in nuclear sealing applications depends on their resistance to ionizing radiation. To explicitly reveal the differences in radiation damage mechanisms among rubbers with varying molecular structures, this study investigated four typical elastomers-natural rubber (NR), butyl rubber (IIR), chloroprene rubber (CR), and nitrile rubber (NBR)-under <sup>60</sup>Co γ-irradiation at cumulative doses of 1, 10, and 100 kGy. By coupling macroscopic physical testing (mechanical, permeability) with microstructural characterization (FT-IR, DSC, crosslink density), a correlation between material structure and irradiation behavior was established. The results indicate that main-chain saturation dictates the dominant degradation mechanism: unsaturated rubbers (NR, CR, NBR) are dominated by cross-linking, macroscopically manifested as increased hardness and reduced ductility; conversely, saturated rubber (IIR) is dominated by main-chain scission, leading to a paste-like transition at 100 kGy and a complete loss of mechanical load-bearing and barrier functions. Comparatively, NR exhibited optimal overall stability due to "clean" cross-linking without significant oxidation. The overall radiation resistance ranking within the 0-100 kGy range is NR > CR > NBR > IIR. This study clarifies the "structure-mechanism-property" evolution law, providing a critical theoretical basis for lifetime prediction and rational material selection of rubber components in nuclear environments. Natural rubber ; Elastomer ; Chloroprene ; Nitrile rubber ; Irradiation ; Radiation resistance ; Compression set ; Saturation (graph theory) Polymer Nanocomposites and Properties ; Elasticity and Material Modeling ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Dmitri Lyalikov | Soorna Choheili | Ashley Zegler | Franco Victor Guillano | F. J. Fattoyev | Ehsan Atefi
Source: Engineering With Computers (article, 2025-08-29)
Field: Materials Science — Surfaces, Coatings and Films

D3SAI: a data-driven platform for measuring interfacial tension using machine learning and drop shape analysis Abstract Interfacial tension in biphasic systems plays a key role across many industrial processes. We present a Data-Driven Drop Shape Analysis method (D3SAI) that uses XGBoost to accurately estimate interfacial tension. D3SAI uses a pendant drop image of a biphasic liquid as an input and determines the interfacial tension through image processing, feature extraction, and machine learning. The accuracy of each step has been evaluated using both synthetic and experimental pendant drops. D3SAI implements a traditional drop shape analysis approach to generate a large library of synthetic pendant drops. This step is necessary to support reliable model training. Then, certain physical properties of the drop profile are extracted to represent the shape characteristics of the pendant drops. The extracted features are used to train the XGBoost model to predict interfacial tension. Using drop features rather than coordinates significantly reduces the input size and as a result the cost of computation in the training process. This makes D3SAI easier to retrain on large datasets for a variety of drop shapes and applications. D3SAI estimates the interfacial tension of well-deformed drops with less than 1.2% inaccuracy. Tests on experimental images confirm that D3SAI provides consistent and accurate results, making it suitable for large-scale measurements. Moreover, D3SAI predicts the surface tension of less-deformed (circular) drops with less than 8% inaccuracy. Although less-deformed drops are not ideal for surface tension measurements, they are sometimes necessary, for example, when working with ultra-low tension systems. Drop (telecommunication) ; Surface tension ; Drop out ; Mechanical engineering ; Tension (geology) ; Computer science ; Engineering drawing ; Mechanics ; Materials science ; Engineering ; Composite material ; Physics ; Thermodynamics Surface Modification and Superhydrophobicity ; Fluid Dynamics and Heat Transfer ; Advanced Data Storage Technologies
Authors: Aulal Muna | Rizky Aflaha | Syahla Salsabila | David Rusliman | Kuwat Trıyana | Aditya Rianjanu | Condro Wibowo | Hutomo Suryo Wasisto
Source: Sustainable Food Technology (article, 2025-12-10)
Field: Materials Science — Biomaterials

Polysaccharide-based layer-by-layer edible coatings for shelf-life extension of fresh tomatoes Comparative evaluation of various coating application methods of bi-layer chitosan-combined hydroxypropyl methylcellulose (chitosan/HPMC) in terms of coating performance, film homogeneity, and shelf-life extension on fresh tomatoes. Coating ; Extension (predicate logic) ; Raw material ; Life extension Nanocomposite Films for Food Packaging ; Polymer Science and PVC ; Postharvest Quality and Shelf Life Management
Authors: Chunyan Zhao | Zhong Ren | Yue Li | Jia Zhang | Weinan Shi
Source: Agriculture (article, 2025-07-15)
Field: Chemistry — Analytical Chemistry

Growth Stages Discrimination of Multi-Cultivar Navel Oranges Using the Fusion of Near-Infrared Hyperspectral Imaging and Machine Vision with Deep Learning To noninvasively and precisely discriminate among the growth stages of multiple cultivars of navel oranges simultaneously, the fusion of the technologies of near-infrared (NIR) hyperspectral imaging (HSI) combined with machine vision (MV) and deep learning is employed. NIR reflectance spectra and hyperspectral and RGB images for 740 Gannan navel oranges of five cultivars are collected. Based on preprocessed spectra, optimally selected hyperspectral images, and registered RGB images, a dual-branch multi-modal feature fusion convolutional neural network (CNN) model is established. In this model, a spectral branch is designed to extract spectral features reflecting internal compositional variations, while the image branch is utilized to extract external color and texture features from the integration of hyperspectral and RGB images. Finally, growth stages are determined via the fusion of features. To validate the availability of the proposed method, various machine-learning and deep-learning models are compared for single-modal and multi-modal data. The results demonstrate that multi-modal feature fusion of HSI and MV combined with the constructed dual-branch CNN deep-learning model yields excellent growth stage discrimination in navel oranges, achieving an accuracy, recall rate, precision, F1 score, and kappa coefficient on the testing set are 95.95%, 96.66%, 96.76%, 96.69%, and 0.9481, respectively, providing a prominent way to precisely monitor the growth stages of fruits. Hyperspectral imaging ; Artificial intelligence ; RGB color model ; Pattern recognition (psychology) ; Multispectral image ; Convolutional neural network ; Deep learning ; Computer science ; Fusion ; Feature (linguistics) ; Computer vision ; Mathematics ; Remote sensing ; Geography Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: Thị Cẩm Vân Võ | Minh Hoàng Phan | Ngoc Vu | Nguyễn Thùy Dương | Choowokomong Kiattawee | Nguyen Thai Huynh
Source: Y HOC TP HO CHI MINH (article, 2025-01-28)
Field: Chemistry — Organic Chemistry

Đánh giá hoạt tính ức chế Mpro – enzym protease của SARS-CoV-2 in vitro và in silico các dẫn chất 2-methylimidazol-4,5-dicarboxamid tổng hợp Đặt vấn đề: Nhiều nghiên cứu cho thấy các chất ức chế protease của HIV-1 có khả năng ức chế sự nhân lên của SARS-CoV và SARS-CoV-2 dẫn đến xu hướng sàng lọc lại khung cấu trúc các chất này với hoạt tính ức chế protease của SARS-CoV-2. Khung imidazol-4,5-dicarboxamid (I45DC) đã được công bố hoạt tính ức chế virus HIV-1 và các virus khác. Mục tiêu: Khám phá hoạt tính ức chế Mpro của các chất I45DC, cụ thể là các dẫn chất 2-methyl-imidazol-4,5-dicarboxamid tổng hợp bằng phương pháp in vitro và in silico. Đối tượng và phương pháp nghiên cứu: Các chất được khảo sát và đánh giá hoạt tính kháng Mpro của SARS-CoV-2 in vitro bằng phương pháp huỳnh quang sử dụng cơ chất FRET và in silico bằng mô hình docking trên Mpro (Mã PDB: 6LU7) với chất đồng kết tinh N3. Kết quả: Có 3 trong tổng số 5 chất có hoạt tính ức chế Mpro tương đương với lopinavir là chất 5b, 5c, 5d. Các dẫn chất này đều dock thành công vào khoang gắn kết với điểm số docking từ -6,9 đến -8,2 kcal/mol. Kết luận: Các dẫn chất tổng hợp được cho thấy tiềm năng gắn kết tốt trong khoang gắn kết của Mpro qua mô hình docking. Các chất thể hiện khả năng kháng Mpro của SARS-CoV-2 in vitro, trong đó, 3 chất có phần trăm ức chế tương đương chất đối chứng lopinavir (35-39%) ở 1 μM. Từ khoá: imidazol-4,5-dicarboxamid; Mpro; SARS-CoV-2 protease In vitro ; Chemistry ; In silico ; Protease ; Molecular biology ; Virology ; Microbiology ; Biology ; Enzyme ; Biochemistry ; Gene Synthesis and Characterization of Heterocyclic Compounds ; Computational Drug Discovery Methods ; Synthesis and biological activity
Authors: Mingda Li | Nguyễn Thanh Tùng | Chu-Liang Fu | Mouyang Cheng | Buxuan Li | Tyra E. Espedal | Zhantao Chen | Kuan Qiao | Kumar Neeraj | Abhijatmedhi Chotrattanapituk | Denisse Córdova Carrizales | Eunbi Rha | Tongtong Liu | Shivam N. Kajale | Deblina Sarkar | Donald A. Walko | Haidan Wen | Svetlana V. Boriskina | Gang Chen | Jeehwan Kim
Source: Research Square (preprint, 2025-08-27)
Field: Materials Science — Materials Chemistry

Spatiotemporal Mapping of Anisotropic Thermal Transport in GaN Thin Films via Ultrafast X-ray Diffraction Diffraction ; Ultrashort pulse ; Materials science ; Anisotropy ; X-ray ; Thermal ; Thin film ; X-ray crystallography ; Optoelectronics ; Optics ; Physics ; Nanotechnology ; Thermodynamics ; Laser Thermal properties of materials ; Thermography and Photoacoustic Techniques ; GaN-based semiconductor devices and materials
Authors: Ali Al-Zawqari | Gerd Vandersteen | Francesco Ferranti
Source: Optical and Quantum Electronics (article, 2025-11-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Clustering-enhanced supervised learning for nanophotonic design Categorical variable ; Cluster analysis ; Unsupervised learning ; Supervised learning ; Regularization (linguistics) ; Semi-supervised learning ; Field (mathematics) ; Deep learning ; Photonics Metamaterials and Metasurfaces Applications ; Photonic and Optical Devices ; Neural Networks and Reservoir Computing
Authors: Dulitha Prasanna Kulathunga | Davit A. Potoyan
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-03-06)
Field: Materials Science — Materials Chemistry

Thermal Adaptation of Extremozymes: Temperature-Sensitive Contact Analysis of Serine Proteases Enzyme thermal adaptation reflects a delicate interplay between sequence, structure, and dynamics of proteins, fine-tuning the catalytic activity to environmental demands. Understanding these evolutionary relationships can drive bioengineering advances, including industrial enzyme design, biocatalysts for extreme conditions, and novel therapeutics. This work explores sequence-dynamics connections in subtilisin-like serine protease homologs using a recently developed computational methodology that uses expanded ensemble simulations and temperature-sensitive contact analysis. We reveal that thermophilic enzymes achieve thermal stability through extensive salt bridges and hydrophobic networks, while psychrophilic enzymes rely on targeted interaction stability for cold adaptation. An unsupervised cluster analysis of residue conservation, flexibility, and hydrophobic interactions provides a comprehensive view of residue-specific contributions to thermal adaptation. These findings underscore the coordinated roles of conserved and variable regions in enzyme stability and offer a framework for tailoring enzymes to specific thermal properties for biotechnological applications. Proteases ; Serine ; Adaptation (eye) ; Chemistry ; Materials science ; Biochemistry ; Biology ; Enzyme ; Neuroscience Enzyme Structure and Function ; Protein Structure and Dynamics ; Enzyme Production and Characterization
Authors: Qinlin Xiao | RA Gu | Li Li | Jing Wen | Xun Zhang | Yifeng Shen | Yang Liu | Lan Xiao | Qinqin Tang | Jun Yang | Yong He | Juan Yang
Source: Frontiers in Plant Science (article, 2025-08-07)
Field: Chemistry — Analytical Chemistry

Quantitative determination of blended proportions in tobacco formulations using near-infrared spectroscopy and transfer learning Accurate detection of blending proportions in tobacco formulations is crucial for ensuring the quality consistency and flavor stability of cigarette products. In recent years, modeling approaches based on near-infrared spectroscopy (NIRS) have attracted significant attention for the quantitative analysis of tobacco blending. However, due to variations in tobacco composition and spectral characteristics across different cigarette brands, the generalization ability of NIRS-based models often declines when applied to cross-brand prediction tasks. To address this issue, this study takes the detection of blending proportions of tobacco silk in tobacco formulations as the research focus, and investigates transfer learning strategies aimed at enhancing the cross-brand adaptability of NIRS-based models. A partial least squares regression (PLSR) model was first developed using NIRS data from four different tobacco brands, achieving high prediction accuracy on the combined dataset (RMSEP = 1.20%). However, when the model trained on a single brand was applied to predict other brands, the prediction performance decreased notably. To improve model adaptability, three approaches were explored: Transfer Component Analysis (TCA), Correlation Alignment (Coral), and model updating. The results show that TCA-PLSR achieved substantial reductions in prediction error in most transfer tasks involving large discrepancies in feature distributions. Coral-PLSR demonstrated superior performance in transfer tasks involving similar spectral feature distributions. Additionally, in transfer tasks characterized by substantial distribution differences, the Updated-TCA-PLSR model, which incorporates a small proportion of target domain samples into the source domain before domain adaptation, yielded accurate predictions of tobacco silk blending proportions. These findings demonstrate that transfer learning and model updating offer practical, flexible, and robust approaches for enhancing the performance of NIRS-based models, supporting more accurate and consistent quality control in industrial-scale formulated tobacco production. Partial least squares regression ; Adaptability ; Computer science ; Generalization ; Feature selection ; Transfer of learning ; Feature (linguistics) ; Biological system ; Mathematics ; Artificial intelligence ; Machine learning ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Paulo Henrique Silva Lopes | Joyce Maria Gomes da Costa | Adriana Duarte Dalla Costa
Source: OBSERVATÓRIO DE LA ECONOMÍA LATINOAMERICANA (article, 2025-10-31)
Field: Materials Science — Biomaterials

Biopolymer-based coatings and immobilization matrices for environmental sustainability: a systematic review of patent trends (1992–2025) The growing interest in sustainable materials has driven the development of immobilization matrices and functional coatings based on biopolymers, which can replace synthetic products and foster the circular economy. Given the scarcity of studies addressing technological innovation through the lens of patent analysis, this study aimed to map and analyze technological trends in biopolymer-based systems contributing to environmental sustainability, circular economy, and public policy advancement. A systematic review of patents was conducted and registered on the Open Science Framework platform, following the PRISMA 2020 guidelines. The review covered five international databases and documents published between 1992 and 2025. A total of 21 eligible patents were identified, revealing a marked increase in the use of biopolymers such as cellulose, alginate, pectin, and industrial residues in applications including water treatment, biodegradable packaging, bioactive systems, and environmental remediation. The findings also highlight the rise of startups and universities as key drivers of sustainable innovation, although significant gaps remain regarding large-scale validation and life cycle assessment. The study advances theoretical understanding of biopolymers within the bioeconomy framework by introducing patent analysis as a methodological tool for innovation and sustainability research. Moreover, it provides valuable insights for developing environmental and sustainability policies, emphasizing practical strategies to reduce environmental impacts, valorize industrial residues, and promote green technologies in sanitation, agriculture, packaging, and healthcare. Sustainability ; Patent analysis ; Scarcity ; Life-cycle assessment ; Circular economy ; Sustainable development ; Industrial ecology Nanocomposite Films for Food Packaging ; biodegradable polymer synthesis and properties ; Natural Fiber Reinforced Composites
Authors: Esmée Versteegen | Mahsa Akbari Lakeh | Anastasia Swanson | Gerjen H. Tinnevelt | Aoife Gowen | Jeroen J. Jansen | Mahdiyeh Ghaffari
Source: Journal of Chemometrics (article, 2025-07-23)
Field: Chemistry — Analytical Chemistry

Enhanced Discrimination of Thawed and Nonfrozen Chicken Thighs Using Convex Hull Peeling in Visible Spectral Imaging ABSTRACT Hyperspectral imaging (HSI) combines spectral and spatial data, producing complex 3D datasets that require efficient data reduction methods for improved computational efficiency and prediction accuracy. This study introduces convex hull peeling to enhance the discrimination of thawed and nonfrozen chicken thighs. By removing pixels with noise‐dominated spectra and targeting deeper data layers, this method improved model robustness and reduced training time from 426 to 5 s. Essential spectral pixels (ESPs), located on the convex hull in principal component space, effectively preserved critical data, achieving 81% classification accuracy, comparable with using the full dataset. Sensitivity and specificity were 74% and 89%, respectively, demonstrating improved specificity with a slight trade‐off in sensitivity. Piece‐based accuracy reached 100%, highlighting the potential of this approach for noninvasive food quality assessment. This study underscores the efficiency and adaptability of ESPs and convex hull peeling for complex datasets. Convex hull ; Hull ; Regular polygon ; Mineralogy ; Mathematics ; Chemistry ; Geology ; Geometry ; Oceanography Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Identification and Quantification in Food
Authors: Maddalena Bracchi | Francesco Nicotra | Laura Russo
Source: RSC Chemical Biology (article, 2025-01-01)
Field: Materials Science — Biomaterials

Glyco-functionalization of ECM mimics, influence in morphology and cell behaviour Current tissue engineering strategies primarily focus on replicating mechanical properties of the extracellular matrix (ECM); however, several studies have underscored the critical role of the ECM biochemical cues in developing functional tissue substitutes. Among these, glycans are known to play a key role in regulating cell fate. In this study we developed ECM mimics glyco-conjugated with two different glycans, α-d-glucopyranose (αGlc) and β-d-galactopyranose (βGal), to investigate their influence on morphology and cell behaviour. The ECM mimics were generated crosslinking glycosylated gelatin samples, functionalised with the glycans by reductive amination, and hyaluronic acid. The crosslinking was performed by previous functionalization of gelatin and hyaluronic acid with tyramine, to enable enzymatic phenol-phenol coupling <i>via</i> horseradish peroxidase (HRP) and hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>). The glyco-conjugated hydrogels exhibited markedly different morphologies, characterized by increased fibrous content, smaller pore sizes, and more wrinkled surfaces. Bone marrow-derived mesenchymal stem cells (BM-MSCs) seeded in hydrogels functionalized αGlc and βGal exhibited a more elongated morphology and differential glycosignature compared to controls. Gelatin ; Hyaluronic acid ; Self-healing hydrogels ; Extracellular matrix ; Horseradish peroxidase ; Morphology (biology) ; Tissue engineering ; Mesenchymal stem cell ; Hydrogen peroxide Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine ; 3D Printing in Biomedical Research