jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiarui Cui | Shi Cao | Jie Hao | Yao Zhang | Shuang Gao | Jianshe Li | Fatimah Shuang | Longguo Wu
Source: Industrial Crops and Products (article, 2025-10-01)
Field: Chemistry — Analytical Chemistry

Construction of an industrial detection model for tomato organic acids based on low-rank adaptation and spatial feature fusion Tomato (Solanum lycopersicum L.) is not only a globally important crop for human consumption but also a potential source of industrially valuable organic acids. Organic acids such as citric and malic acid have widespread applications in bio-based chemical synthesis, fermentation, biodegradable plastics, and green solvents. In this study, we develop a novel non-destructive framework combining Low-Rank Adaptation (LoRA) with Adaptive Spatial Feature Fusion Network (ASFFN) to accurately predict organic acid content using hyperspectral imaging (HSI). The model integrates advanced feature fusion strategies and robust outlier detection to improve generalizability across tomato varieties. Comparative experiments demonstrate the superior performance of the proposed LoRA-ASFFN over conventional CNN and PLSR baselines. The model enables rapid and precise identification of tomatoes with high organic acid concentrations, providing an efficient pathway for industrial processing and extraction. This research contributes to advancing bio-based chemical supply chains and improving the economic value of tomato crops through data-driven, precision screening techniques. • A novel model predicts organic acid levels in multi-variety tomato samples. • Isolation Forest detects outliers using spectral and chemical deviations. • ASFF module enhances spectral feature fusion across multiple scales. • LoRA improves model adaptation while reducing parameter complexity. Hyperspectral imaging ; Malic acid ; Pattern recognition (psychology) ; Overfitting ; Feature (linguistics) ; Organic acid ; Adaptability ; Outlier Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Furong Fan | Zixiang Liao | Zhiguo Hé | Yaoyao Sun | Kuiguo Han | Yanqun Tong
Source: Photonics (article, 2025-11-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optical Sensor Technologies for Enhanced Food Safety Monitoring: Advances in Detection of Chemical and Biological Contaminants Optical sensing technologies are revolutionizing global food safety surveillance through exceptional sensitivity, rapid response, and high portability. This review systematically evaluates five major platforms, revealing unprecedented detection capabilities from sub-picomolar to single-cell resolution. Surface plasmon resonance achieves 0.021 ng/mL detection limits for veterinary drugs with superior molecular recognition. Quantum dot fluorescence sensors reach 0.17 nM sensitivity for pesticides, enabling rapid on-site screening. Surface-enhanced Raman scattering attains 0.2 pM sensitivity for heavy metals, ideal for trace contaminants. Laser-induced breakdown spectroscopy delivers multi-elemental analysis within seconds at 0.0011 mg/L detection limits. Colorimetric assays provide cost-effective preliminary screening in resource-limited settings. We propose a stratified detection framework that strategically allocates differentiated sensing technologies across food supply chain nodes, addressing heterogeneous demands while eliminating resource inefficiencies from deploying high-precision instruments for routine screening. Integration of microfluidics, artificial intelligence, and mobile platforms accelerates evolution toward multimodal fusion and decentralized deployment. Despite advances, critical challenges persist: matrix interference, environmental robustness, and standardized protocols. Future breakthroughs require interdisciplinary innovation in materials science, intelligent data processing, and system integration, transforming laboratory prototypes into intelligent early warning networks spanning the entire food supply chain. Food safety ; Sensor fusion ; Food supply ; Supply chain ; Surface plasmon resonance ; Emerging technologies ; Sensitivity (control systems) ; Wireless sensor network ; Plasmon Gold and Silver Nanoparticles Synthesis and Applications ; Melamine detection and toxicity ; Biosensors and Analytical Detection
Authors: Maninderjeet Singh (9128486) | Rajpiraveen Parthiban (22429002) | Erin M. Schroeder (22429005) | Saurabh Kumar Tiwary (22429008) | Maria Camila Belduque Correa (22429011) | Francisco C. Robles Hernandez (7026497) | Devin L. Shaffer (1890931) | Alamgir Karim (1501012)
Source: Figshare (article, 2025-10-14)
Field: Materials Science — Materials Chemistry

Two-DimensionalCovalent Organic Framework Films forHigh Dielectric Strength Electrically and Thermo-Mechanically StableLow Permittivity Dielectrics There is an urgent need to develop advanced dielectric materials with low permittivities (κ < 1.6) to address performance-limiting challenges for conventional and artificial intelligence (AI) microprocessors with miniaturized feature sizes, namely, delay in processing speeds, electronic crosstalk, high power consumption, and charge buildup. Known low-κ materials show poor dielectric strength and thermo-mechanical properties, rendering them unsuitable for next-generation electronic devices. Here, we demonstrate that the two-dimensional (2D) covalent organic frameworks (COF) films synthesized by the liquid–liquid interfacial reaction show very low permittivities (κ ≈ 1.17 at 100 kHz), ultrahigh dielectric strengths of ≈ 3908 MV/m at room temperature (≈2100 MV/m at 300 °C), low density of ≈1.1 g/cm<sup>3</sup>, and a high Young’s modulus of ≈3.4 GPa. These properties are enabled by the highly crystalline and tightly packed nanoporous nanosheets of 2D COFs arranged in periodic structures in 2D COF films and surpass the requirements for next-generation electronic devices. Dielectric ; Nanoporous ; Permittivity ; Dielectric strength ; Dielectric permittivity ; Electronic materials ; Modulus Covalent Organic Framework Applications ; Synthesis and properties of polymers ; Layered Double Hydroxides Synthesis and Applications
Authors: Ondřej Ambrož | Jan Čermák | Šárka Mikmeková
Source: Key engineering materials (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

The Influence of Sample Preparation on Correlative Microscopy with the Use of Artificial Intelligence Methods The preparation of metallographic samples remains a crucial aspect of microstructural analysis, especially with the continuous development of advanced materials and imaging techniques. Despite its significance, sample preparation is often underestimated, yet achieving a surface with minimal structural distortion is essential for accurate microstructure evaluation and data interpretation. This study aimed to optimize steel sample preparation methods to obtain surfaces suitable for correlative imaging using multiple microscopic techniques, including modern scanning electron microscopy (SEM) with sample bias and electron backscatter diffraction (EBSD). The results demonstrate that specific contrast features observed in SEM can, in some cases, be qualitatively verified using EBSD. Furthermore, variations in SEM settings, such as lower landing energy, influence information depth, which in turn affects the accuracy of phase quantification, particularly when utilizing artificial intelligence-based methods. Sample preparation ; Correlative ; Sample (material) ; Scanning electron microscope ; Distortion (music) ; Microstructure ; Microscopy Machine Learning in Materials Science ; Microstructure and mechanical properties ; Advanced Electron Microscopy Techniques and Applications
Authors: Qingkun Tian | Longgang Hou | Junmei Wang | Flemming J.H. Ehlers | Hui Su | Yawen Wang | Zhao Yuhong | Linzhong Zhuang
Source: Materials Research Letters (article, 2025-07-05)
Field: Materials Science — Materials Chemistry

Decoding solute-vacancy binding in transition metals via first-principles and machine learning Traditional solute-vacancy binding energy (SVBE) models based on atomic radii or electronegativity fail for transition metals. Through first-principles analysis, we show SVBE comprises solute-host bond energy and vacancy relaxation energy. For main-group solutes, both components align with parabolic trends, yielding predictable SVBE. In transition metals, solute-host bond energy remains parabolic, but vacancy relaxation energy shifts its peak due to d-orbital splitting-induced lattice distortion asymmetry, leading to non-parabolic SVBE. This coupling resolves historical discrepancies across alloy systems. A complementary data-driven approach identifies key electronic descriptors. The framework provides a universal principle for solute selection in diffusion-controlled alloy design. Vacancy defect ; Decoding methods ; Materials science ; Transition metal ; Chemical physics ; Transition (genetics) ; Crystallography ; Atomic physics ; Condensed matter physics ; Computer science ; Physics ; Chemistry ; Algorithm Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: Daimon Sakaguchi | Masaki Shimono | Hiroaki Gotoh
Source: The Journal of Physical Chemistry A (article, 2025-09-19)
Field: Chemistry — Spectroscopy

Analysis of Asymmetric Reduction of Ketones Using Three-Dimensional Electronic States In this study, we introduced an approach combining transition state analysis and data-driven modeling that leverages the electronic states of ketone substrates to systematically analyze the selectivity of asymmetric reduction. Transition state analysis of the reduction of acetophenone and trifluoroacetophenone using the Corey-Bakshi-Shibata catalyst, <i>B</i>-chlorodiisopinocamphenylborane, and alpine borane successfully explained the reaction selectivity. Analysis was extended to a broader set of 315 asymmetric reduction reactions by incorporating the grid-based electronic and electrostatic features of the ketone substrates. The high predictive performance of the model (<i>r</i><sub>test</sub><sup>2</sup> = 0.82) confirms its generalizability and demonstrates its potential application in rational reaction design and catalyst development. Acetophenone ; Ketone ; Reduction (mathematics) ; Borane ; Generalizability theory ; Selectivity ; Catalysis ; State (computer science) Analytical Chemistry and Chromatography ; Surface Chemistry and Catalysis ; Asymmetric Hydrogenation and Catalysis
Authors: Song Jin (642035) | Xinyu Li (568185) | Guangze Yang (9225487) | Zhen Zhang (86004) | Javen Qinfeng Shi (17297022) | Yun Liu (84346) | Chun-Xia Zhao (1654945)
Source: Figshare (article, 2025-04-30)
Field: Materials Science — Materials Chemistry

Active Learning-BasedPrediction of Drug CombinationEfficacy Combination therapy, which involves the use of multiple drugs, has emerged as a promising approach to cancer treatment. However, traditional combination therapy development is constrained by the vast experimental design space, requiring exhaustive testing of drug ratios, concentrations, and encapsulation strategies. In this study, we present a computational intelligence method combining active learning and fine-grid optimization to predict the efficacy of drug combinations, focusing on dual-drug-loaded polymeric nanoparticles for cancer therapy. Our approach harnesses Gaussian Process Regression to predict both drug efficacy and associated uncertainty, enabling rapid identification of optimal conditions with only 25% of the experimental effort. This method was successfully applied to optimize dual-drug systems, including doxorubicin and docetaxel, demonstrating significant reductions in experimental workload without compromising precision. Our study has demonstrated the potential of AI-driven methodologies in overcoming the challenges posed by traditional experimental designs in the drug delivery field. Drug ; Cancer drugs ; Drug delivery ; Cancer treatment ; Efficacy ; Identification (biology) ; Gaussian process Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Machine Learning and Algorithms
Authors: Daimon Sakaguchi (18332040) | Masaki Shimono (121314) | Hiroaki Gotoh (1771738)
Source: Figshare (article, 2025-09-19)
Field: Chemistry — Inorganic Chemistry

Analysis of AsymmetricReduction of Ketones UsingThree-Dimensional Electronic States In this study, we introduced an approach combining transition state analysis and data-driven modeling that leverages the electronic states of ketone substrates to systematically analyze the selectivity of asymmetric reduction. Transition state analysis of the reduction of acetophenone and trifluoroacetophenone using the Corey–Bakshi–Shibata catalyst, <i>B</i>-chlorodiisopinocamphenylborane, and alpine borane successfully explained the reaction selectivity. Analysis was extended to a broader set of 315 asymmetric reduction reactions by incorporating the grid-based electronic and electrostatic features of the ketone substrates. The high predictive performance of the model (<i>r</i><sub>test</sub><sup>2</sup> = 0.82) confirms its generalizability and demonstrates its potential application in rational reaction design and catalyst development. Acetophenone ; Ketone ; Reduction (mathematics) ; Borane ; Generalizability theory ; Selectivity ; Catalysis ; State (computer science) Asymmetric Hydrogenation and Catalysis ; Machine Learning in Materials Science ; Radical Photochemical Reactions
Authors: Marianne Al Hayek | Nadine Abdallah Saab | Olga Assainova | Mohammed El Amine Bechar | Marwa Elbouz | Klervi Crenn | Claudie Monot | C. Julien | William Mauguen | Ayman Alfalou
Source:  (article, 2025-08-07)
Field: Chemistry — Analytical Chemistry

Towards Early Detection: Physics-based Hyperspectral Models for the Detection of Tomato Plant Diseases Smart agriculture is essential for achieving a successful ecological transition, but its progress is limited by unresolved technological challenges. This study addresses these challenges by integrating Visible and Near-InfraRed (VNIR) hyperspectral imaging with physics-based modeling to develop tools for the early and accurate detection of plant diseases. Unlike conventional RGB imaging, hyperspectral imaging offers rapid, non-invasive monitoring capable of identifying plant diseases before visible symptoms emerge. Relying on physics-based inversion models—particularly the PROSPECT model—this work focuses on retrieving detailed physico-chemical and biophysical parameters from tomato leaves affected by powdery mildew. The results demonstrate the feasibility of using PROSPECT modeling in conjunction with VNIR hyperspectral data to effectively detect and characterize disease at early stages. This methodology shows strong potential for advancing precision agriculture, with artificial intelligence integration proposed as a future enhancement. Hyperspectral imaging ; VNIR ; Plant disease ; Precision agriculture ; Inversion (geology) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Abirami S., Ahamed mubeen M., Jainaf Nachiya R. A. M.*
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-22)
Field: Chemistry — Organic Chemistry

Cubosomes-Enabled Theranostics: A Futuristic Approach for Simultaneous Diagnosis and Therapy in Precision Medicine Over more than two decades, the field of theranostics has evolved as a game-changing strategy in precision medicine, merging diagnostics and therapeutics into one integrated system. Cubosomes, a kind of lipid-based nanoparticle with bicontinuous cubic phases, have drawn a lot of interest for its capacity to encapsulate both hydrophilic and hydrophobic compounds, give prolonged release, and allow for targeted delivery. Their modular structure enables surface functionalisation with ligands or polymers, which improves biocompatibility and site-specific activity. Cubosome-enabled theranostics have shown promise in oncology, cardiovascular disease, and neurological disorders, with improved bioavailability, lower toxicity, and simultaneous monitoring of therapeutic response using multimodal imaging modalities such as MRI, fluorescence imaging, and photothermal/photodynamic techniques. Despite obstacles in large-scale development, stability, and regulatory approval, innovations in nanotechnology, artificial intelligence, and personalised medicine are accelerating their adoption. This review focusses on cubosomes structural properties, functional adaptability, and biological applications, emphasising their potential as smart, patient-specific theranostic platforms. Precision medicine ; Modalities ; Biocompatibility ; Modular design ; Medical imaging ; Therapeutic modalities Polydiacetylene-based materials and applications ; Lipid Membrane Structure and Behavior ; Nanoplatforms for cancer theranostics
Authors: Bingbing Chen | Dongfeng Li | Liyuan Wang | Xiangyun Ge | Chenfeng Li
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

A Novel Data-Driven Digital Reconstruction Method for Polycrystalline Microstructures Crystallite ; Computer science ; Microstructure ; Computer vision ; Algorithm ; Materials science ; Metallurgy Machine Learning in Materials Science ; Image Processing and 3D Reconstruction
Authors: Hecheng Chen | Shaojie Jiang
Source: Talanta (article, 2025-12-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Intelligent theranostics all-in-one: Synergistic application of stimuli-responsive SERS nanoprobes for disease diagnosis and targeted therapy Precision medicine ; Targeted drug delivery ; Targeted therapy ; Transformative learning ; Bench to bedside ; Nanomedicine ; Drug delivery ; Disease Gold and Silver Nanoparticles Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Ali Mehmet | Mubashir Mansoor | Mert Saraçoğlu | Servet Tımur | Onur Ergen
Source: Computer Physics Communications (article, 2025-11-27)
Field: Materials Science — Materials Chemistry

Mapping strain at the atomic scale with PyNanospacing: An AI-assisted approach to TEM image processing and visualization Visualization ; Atomic units ; Python (programming language) ; Lattice (music) ; Transmission electron microscopy ; Image processing ; High-resolution transmission electron microscopy ; Contour line Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electronic and Structural Properties of Oxides
Authors: Youssef Ismail | Jayadev Seelam
Source: Journal of Pharma Insights and Research. (article, 2025-04-05)
Field: Chemistry — Analytical Chemistry

Ultra-Performance Liquid Chromatography (UPLC) in Analytical Method Development and Validation Following In-vitro Studies Ultra-performance liquid chromatography (UPLC) has emerged as a powerful analytical tool, offering superior resolution, sensitivity, and rapid analysis compared to conventional high-performance liquid chromatography. The integration of UPLC with advanced detection systems, particularly mass spectrometry, has transformed pharmaceutical research, biomarker discovery, and metabolomics studies. Recent innovations in column technology, including sub-2-micron particles and core-shell materials, have significantly enhanced separation efficiency and detection limits. UPLC plays a crucial role in drug metabolism studies, stability assessments, and bioactive compound quantification following in vitro experiments. The technique has proven invaluable in toxicology studies, enabling precise identification and quantification of metabolites in biological matrices. Method validation parameters for UPLC ensure reliable and reproducible results, encompassing specificity, linearity, accuracy, precision, and stability studies. Notable applications include rapid metabolite profiling, protein characterization in biopharmaceuticals, and biomarker identification in disease diagnosis. Despite challenges such as operational costs and complex sample preparation requirements, ongoing technological advancements in microfluidics, artificial intelligence integration, and green chromatography techniques continue to expand UPLC capabilities. Chromatography ; High-performance liquid chromatography ; In vitro ; Chemistry ; Biochemistry Analytical Methods in Pharmaceuticals ; Analytical Chemistry and Chromatography
Authors: Wanwen Li | Ying Chen | Junfei Liu | Junru Zhang | Liyong Niu | Dandan Zhu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

ParaSpectra: A Generative Digital Twin Framework for the OPA-based Fluorescence Determination of Ammonium Reagent ; Generative grammar ; Fluorescence ; Inefficiency ; Ammonium ; Data modeling Spectroscopy and Chemometric Analyses ; Machine Learning in Materials Science ; Water Quality Monitoring and Analysis
Authors: Haosheng Chen | Ayat Hussein Adhab | Bhavesh Kanabar | Anupam Yadav | M K Ranganathaswamy | Rishabh Thakur | Parveen Kumar | Braj Krishna | Morug Salih Mahdi | Aseel Salah Mansoor | Usama Kadem Radi | Nasr Saadoun | Samim Sherzod | Aseel Smerat
Source: Sadhana (article, 2025-11-04)
Field: Materials Science — Polymers and Plastics

Modeling viscosity of carbon dioxide-saturated polyethylene glycol polymer Robustness (evolution) ; Viscosity ; Polyethylene glycol ; Mean squared error ; Bayesian optimization ; Mean absolute percentage error ; Mean squared prediction error ; Polymer ; Bayesian probability Polymer Foaming and Composites ; Phase Equilibria and Thermodynamics ; Carbon dioxide utilization in catalysis
Authors: S Y Li | Jiaqi Liu | Yufei Bi | Guomin Fu | Haobo Chai | Xiaohan Wang | Yuchen Yue | Haoran An | Wei Feng
Source: Current Opinion in Solid State and Materials Science (article, 2025-12-29)
Field: Materials Science — Materials Chemistry

Graphene-based sensors for multiscenario applications: from functional interface engineering to artificial intelligence-driven optimization Flexibility (engineering) ; Adaptability ; Automation ; Field (mathematics) ; Intelligent sensor ; Industry 4.0 ; Interface (matter) Graphene research and applications ; Advanced Sensor and Energy Harvesting Materials ; Graphene and Nanomaterials Applications