jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yulong Hu | Zhigang Zheng | He Ma | Shuguang Zhu | Yiming Yu | Jie Hong | Weiwei Tang | Jiale He | Libo Zhang | Changlong Liu | Guanhai Li | Xiaoshuang Chen
Source: Nanomaterials (article, 2025-02-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Plasmonic-Enhanced Infrared Absorption Platform for Broadband and Multiple Molecular Fingerprint Retrieval The mid-infrared (mid-IR) region, often referred to as the molecular fingerprint region, encompasses the distinctive absorption spectra characteristic of numerous important molecules. However, the intrinsically small molecular absorption cross-sections, combined with the size mismatch between nanoscale molecules and microscale mid-IR wavelengths, result in inherently weak light-molecule interactions. In this work, we propose a broadband, tunable platform based on plasmonic-enhanced infrared absorption for label-free retrieval of molecular fingerprints. By leveraging the strong near-field enhancement of the plasmonic structure, the platform significantly amplifies light-molecule interactions, enabling precise reconstruction of the fingerprint absorption spectra of target molecules. In addition, the proposed structure exhibits exceptional molecular detection capabilities across the wavelength range of 5-10 μm, with remarkable potential for distinguishing molecular mixture components. The results pave the way for the applications in chemical identification, biomedical diagnostics, environmental monitoring, and other interdisciplinary fields, which require miniaturized and high-precision sensing. Plasmon ; Broadband ; Infrared ; Fingerprint (computing) ; Materials science ; Absorption (acoustics) ; Optoelectronics ; Telecommunications ; Optics ; Computer science ; Physics ; Artificial intelligence Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Random lasers and scattering media
Authors: Matthew Leonard | Bratoljub H. Milosavljevic
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Spectroscopic and complementary thermodynamic study of liquid, supercooled, and glassy state of ethylene glycol Raman and IR spectroscopy were employed to uncover coalesced/unresolved peaks in the liquid, glassy, and crystalline phases of ethylene glycol (EG) in a temperature range of +24 to -160 °C. The temperature-resolved O-H stretching mode of supercooled EG exhibited continuous changes throughout the studied temperature range, including below the glass transition temperature (<i>T</i><sub>g</sub> = -121 °C). Conversely, the C-H stretching spectrum showed no significant variation within the same temperature range, indicating a complex synchronous change in the C-H⋯O and O-H⋯O bonds. Low-frequency (-200 to +300 cm<sup>-1</sup>) Raman spectra of glassy and crystalline EG were also reported and compared. The Raman spectrum of the C-C-O bending mode of EG was measured with a high signal-to-noise ratio (SNR > 1000), and the temperature-resolved intensity ratio of the <i>trans</i>/<i>gauche</i> bands was determined. This ratio decreased linearly down to the glass transition temperature, after which it remained constant. The lack of known polarizabilities for these conformers precludes direct thermodynamic determinations. Previous <i>ab initio</i> molecular dynamics simulations indicated room temperature <i>gauche</i> to <i>trans</i> conformer concentrations of 80% and 20%, respectively. Using this result as a benchmark, for the reaction <i>G</i> ⇄ <i>T</i>, the corresponding Δ<sub>r</sub><i>G</i> = -<i>RT</i> ln(<i>K</i><sub>eq</sub>) was calculated to be +3.44 kJ mol<sup>-1</sup>. Applying the van't Hoff equation in processing our temperature-resolved data yielded the following thermodynamic parameters for the same reaction: Δ<sub>r</sub><i>H</i> = (+3.06 ± 0.07) kJ mol<sup>-1</sup>, Δ<sub>r</sub><i>S</i> = (-1.14 ± 0.31) J mol<sup>-1</sup> K<sup>-1</sup>, and Δ<sub>r</sub><i>G</i> = (+3.40 ± 0.12) kJ mol<sup>-1</sup>. Another [T]/[G] ratio reported in an experimental NMR study was also used in the thermodynamic calculations; the results obtained were compared to the aforementioned data and discussed. Raman spectroscopy ; Supercooling ; Conformational isomerism ; Glass transition ; Atmospheric temperature range ; Ethylene glycol ; Analytical Chemistry (journal) ; Spectroscopy Material Dynamics and Properties ; Chemical Thermodynamics and Molecular Structure ; Adsorption, diffusion, and thermodynamic properties of materials
Authors: Hanting Zou | Ranyang Li | Xuan Xuan | Yongwen Jiang | Haibo Yuan | Ting An
Source: Foods (article, 2025-08-15)
Field: Chemistry — Analytical Chemistry

Rapid and Quantitative Prediction of Tea Pigments Content During the Rolling of Black Tea by Multi-Source Information Fusion and System Analysis Methods Efficient and convenient intelligent online detection methods can provide important technical support for the standardization of processing flow in the tea industry. Hence, this study focuses on the key chemical indicators-tea pigments in the rolling process of black tea as the research object, and uses multi-source information fusion methods to predict the changes of tea pigments content. Firstly, the tea pigments content of the samples under different rolling time series of black tea is determined by system analysis methods. Secondly, the spectra and images of the corresponding samples under different rolling time series are simultaneously obtained through the portable near-infrared spectrometer and the machine vision system. Then, by extracting the principal components of the image feature information and screening characteristic wavelengths from the spectral information, low-level and middle-level data fusion strategies are chosen to effectively integrate sensor data from different sources. At last, the linear (PLSR) and nonlinear (SVR and LSSVR) models are established respectively based on the different characteristic data information. The research results show that the LSSVR based on middle-level data fusion strategy have the best effect. In the prediction results of theaflavins, thearubigins, and theabrownins, the correlation coefficients of the testing sets are all greater than 0.98, and the relative percentage deviations are all greater than 5. The complementary fusion of the spectrum and image information effectively compensates for the problems of information redundancy and feature missing in the quantitative analysis of tea pigments content using the single-modal data information. Black tea ; Fusion ; Pigment ; Food science ; Chemistry ; Biological system ; Computer science ; Biology Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis ; Tea Polyphenols and Effects
Authors: Ian H. Williams
Source: Pure and Applied Chemistry (article, 2025-05-23)
Field: Chemistry — Organic Chemistry

Virtual transition states Abstract Many organic reaction mechanisms are complex and may involve both multiple steps in series and multiple pathways in parallel. Consequently, for many reactions occurring in condensed media (including enzyme-catalyzed reactions) there is no single rate-determining step associated with a unique transition state (TS): in general, any ‘transition-state structure’ derived from experimental kinetics investigations of a complex mechanism is an average corresponding to a virtual TS. Computational simulation is now capable of yielding valuable insight, complementary to experiment, for minima and saddle points on potential-energy surfaces, corresponding to intermediates and TSs on Gibbs-energy surfaces for complex reactions with multiple TSs in parallel or in series. For a reaction with multiple steps in series, the apparent Gibbs energy of activation (corresponding with a virtual TS) is a sum of terms, one for each contributing real TS j ; the kinetic significance w j of each is given by exp(Δ ‡ G j /RT)/exp(Δ ‡ G app /RT). An analogous expression applies to the kinetics of reaction steps in parallel, except that each Gibbs energy is preceded by a minus sign, and the contribution w i of each real TS to Δ ‡ G app is its Boltzmann weighting, and the mole fraction of the lowest-energy reactant conformer must be factored in. Examples of both types of reaction are discussed to illustrate the concept of the virtual TS. Chemistry ; Transition (genetics) ; Transition state ; Computational chemistry ; Nanotechnology ; Organic chemistry ; Catalysis ; Biochemistry Organometallic Complex Synthesis and Catalysis ; Cyclopropane Reaction Mechanisms ; Advanced Chemical Physics Studies
Authors: Laith Ghanem | Alper Taner | Hüseyin Sauk
Source: Journal of Food Process Engineering (article, 2025-07-01)
Field: Chemistry — Analytical Chemistry

Application of Elliptical Fourier Analysis and Color Properties in Hazelnut Classification Using Machine Learning Algorithms ABSTRACT The accurate classification of hazelnut cultivars is critical for ensuring product consistency, quality control, and market competitiveness in the food industry. Conventional identification methods remain manual, time‐consuming, and error‐prone, highlighting the need for automated alternatives. This study presents a novel, real‐time machine vision system for classifying 11 hazelnut cultivars using a single side‐view image. The proposed approach integrates three complementary feature extraction techniques: Elliptical Fourier Analysis (EFA) for contour and shape decomposition, circular masking for curvature quantification, and brown color gradient analysis for surface tone assessment. The extracted features—fully normalized and dimensionless to account for variations in imaging angle, distance, and nut positioning—were classified using three machine learning algorithms: Support Vector Machine with Radial Basis Function (SVM‐RBF), Multilayer Perceptron (MLP), and Extreme Learning Machine (ELM‐RBF). Among the classifiers, SVM‐RBF achieved the highest performance with an F1‐score of 0.92 for multi‐view images and 0.89 for side‐view only. MLP and ELM‐RBF followed with competitive yet slightly lower scores. The system demonstrated high robustness, computational efficiency, and interpretability. Overall, the proposed method offers a lightweight, scalable, and non‐destructive solution for hazelnut cultivar classification and demonstrates strong potential for real‐time deployment in industrial sorting lines and embedded systems in precision agriculture. Algorithm ; Computer science ; Artificial intelligence ; Fourier transform ; Pattern recognition (psychology) ; Mathematics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Nuts composition and effects
Authors: Ramon Torres Ortega | Mario Velasco | Jair de Jesús Arrieta Baldovino
Source: Preprints.org (preprint, 2025-02-04)
Field: Materials Science — Polymers and Plastics

Characterization of the Pozzolanic Potential of Oil Palm Kernel Shell Ash Obtained Through Optimization of Physicochemical Processes Oil palm kernel shell ash (POFA), a byproduct of the highly cultivated agro-industrial sector in Colombia, has been widely used for its pozzolanic properties, which enhance the mechanical and durability characteristics of concrete. Six POFA samples were analyzed after undergoing drying, cutting, grinding, crushing, and calcination at temperatures ranging from 500&amp;deg;C to 1000&amp;deg;C. SEM-EDS/EDX analysis, X-ray fluorescence (XRF), and Loss on Ignition (LOI) tests were conducted to characterize its pozzolanic potential. The results revealed that the SiO₂ content increased with the calcination temperature, reaching a peak of 76.8% at 1000&amp;deg;C. However, calcination at 600&amp;deg;C was identified as the optimal temperature, as it balances impurity removal without inducing the formation of crystalline silica, which would negatively affect the material's reactivity. Considering the optimal calcination temperature and the high initial LOI values, which exceeded 70% in the first calcination stage, a second calcination was performed on the 500&amp;deg;C sample by increasing the temperature to 600&amp;deg;C. This resulted in an LOI of 3.33%, according to ASTM C311 standards for natural pozzolans used in Portland cement concretes. Pozzolan ; Palm kernel ; Characterization (materials science) ; Palm kernel oil ; Palm oil ; Pozzolanic activity ; Shell (structure) ; Kernel (algebra) ; Materials science ; Pulp and paper industry ; Chemical engineering ; Mathematics ; Composite material ; Chemistry ; Engineering ; Nanotechnology ; Cement ; Food science ; Pure mathematics Polymer Nanocomposites and Properties ; Spectroscopy and Chemometric Analyses ; Mineral Processing and Grinding
Authors: Paolo D’Elia | G Pappalardo | Aldo R. Boccaccini | Liliana Liverani
Source: Advanced Intelligent Discovery (article, 2025-09-24)
Field: Materials Science — Biomaterials

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers The electrospinning process allows the fabrication of nano‐ and microfibers, which are suitable for a variety of applications. These fibers are particularly valuable in tissue engineering for scaffold fabrication, as they mimic the morphology of the native extracellular matrix. The process involves several critical parameters that are essential for producing bead‐free fibers. Traditionally, optimizing the electrospinning process has relied on “trial‐and‐error” approaches and recent advancements have introduced artificial intelligence (AI)‐based tools to predict fiber properties by selecting appropriate input features for the models. In this work, machine learning models are utilized to classify and predict the fabrication of cotton‐wool‐like electrospun fibers. The dataset comprises data from both literature and experimental sources. Among the supervised models tested, the k‐Nearest Neighbors (KNN) algorithm demonstrates superior performance in terms of F1‐score, accuracy, precision, and recall. The results from the model inference validate this approach as valuable support in the experimental design for producing three‐dimensional cotton‐wool‐like electrospun fibers. To further integrate AI models into researchers' experimental routine activities, a toolbox for model training and inference is successfully developed and presented. Process (computing) ; Toolbox ; Inference ; Electrospinning ; Variety (cybernetics) ; Process modeling Electrospun Nanofibers in Biomedical Applications ; Silk-based biomaterials and applications ; Textile materials and evaluations
Authors: Hikmat Ayoola Sodiq | Temitayo Oluwaseyi Ogunwoye | Nicodemus Chidera Omekawum | Oluranti Agboola | Patrick Ebelechukwu Akuagwu | Miracle Chiemerie Umeh | M. A. Umar
Source: Global Journal of Engineering and Technology Advances (article, 2025-07-12)
Field: Materials Science — Materials Chemistry

Advancements in Direct Air Capture Technologies: Engineering Solutions for Scalable CO₂ Removal Direct Air Capture (DAC) is an innovative technology that extracts carbon dioxide (CO₂) directly from the atmosphere, offering a vital tool to combat climate change by reducing greenhouse gas levels. This review explores advancements in DAC systems to support large-scale CO₂ removal, targeting 1–10 billion tons annually by 2050 to achieve global net-zero emissions. Recent progress includes improved methods using solid materials and liquid solutions, which have increased efficiency by 15–20% and lowered costs to $200–600 per ton of CO₂ removed. Operational facilities now capture up to 1 million tons of CO₂ yearly, demonstrating practical success. Engineering solutions, such as modular designs and renewable energy use, reduce costs by 15% and energy needs by 10–20%, enabling expansion to larger scales. DAC can achieve net CO₂ reductions of up to 0.9 tons per ton captured, but its environmental benefits depend on clean energy sources to minimize emissions during operation. Challenges include high costs, significant energy requirements, and limited global infrastructure for CO₂ storage and transport. Future efforts should focus on developing durable materials, building 10–20 DAC hubs worldwide by 2035, and introducing stronger financial incentives to cut costs to $100 per ton. This study highlights DAC’s potential to significantly contribute to climate goals, provided technological and policy barriers are overcome. By advancing engineering and fostering global cooperation, DAC can complement emissions reduction efforts, ensuring a sustainable path to net-zero. Scalability ; Process engineering ; Environmental science ; Systems engineering ; Waste management ; Computer science ; Engineering ; Operating system Catalytic Processes in Materials Science ; Carbon Dioxide Capture Technologies ; Industrial Gas Emission Control
Authors: Xuanyu Wu | Xuanguang Wu | Xiaofeng Xu | Weiqi Chen | Bingyan Wei | Sheng Liu | Xuetao Gan | Jianlin Zhao | Peng Li
Source: Chinese Optics Letters (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dual-mode liquid-crystal element for polarization imaging with an extended depth of field Extended depth-of-field (DoF) imaging enhances longitudinal clarity but traditionally fails to record polarization information.Here, we propose a liquid-crystal Pancharatnam-Berry (PB) phase element that extends DoF while sensing polarization ellipticity.Leveraging cubic-phase-induced defocus-insensitive point spread functions, a 10 cm DoF is achieved in a 10-cmfocal-length 4f system.Linear PB phases enable spin-orbit interactions to detect polarization states, overcoming irreversible polarization loss in conventional methods.This dual-functionality system integrates DoF extension with polarization sensing, enhancing multidimensional imaging capabilities and holding promise for advancing machine vision, microscopic imaging, and optical detection by merging depth clarity with polarization-based object differentiation. Polarization (electrochemistry) ; CLARITY ; Point spread function ; Optical imaging ; Field of view ; Depth of field Liquid Crystal Research Advancements ; Optical Polarization and Ellipsometry ; Advanced Optical Imaging Technologies
Authors: Vitória Carolina Dantas Alves | Sebastião Ferreira de Lima | Dthenifer Cordeiro Santana | Rafael Ferreira Barreto | Rebeka de Correia de Souza Cunha | Ana Carina da Silva Cândido Seron | Larissa Pereira Ribeiro Teodoro | Paulo Eduardo Teodoro | Rita de Cássia Félix Alvarez | Cid Naudi Silva Campos | Carlos Antônio da Silva | Fábio Luíz Checchio Mingotte
Source: AgriEngineering (article, 2025-06-03)
Field: Chemistry — Analytical Chemistry

Predicting Pineapple Quality from Hyperspectral Data of Plant Parts Applied to Machine Learning Food quality detection by machine learning (ML) is more practical and sustainable as it does not require sample preparation and reagents. However, the prediction of pineapple quality by hyperspectral data applied to ML is not known. The aim of this study was to verify accurate ML models for predicting pineapple fruit quality and the best inputs for algorithms: Artificial Neural Networks (ANNs), M5P (model tree), REPTree decision trees, Random Forest (RF), Support Vector Machine (SMV) and Zero R. Three inputs were used for each model: leaf reflectance, peel reflectance, and fruit reflectance. The machine learning model SVM, stood out for its best results, demonstrating good generalization capacity and effectiveness in predicting these attributes, reaching accuracy values above 0.7 for Brix and ratio, using fruit reflectance. In terms of the overall efficiency of the input variables, peel and fruit were the most informative, with peel standing out for the estimation of secondary metabolism compounds, while the fruit showed excellent performance in predicting flavor-related attributes, such as acidity, °Brix and ratio, as mentioned previously, above 0.7. These results highlight the potential of using spectral data and machine learning in the non-destructive assessment of pineapple quality, enabling advances in monitoring and selecting fruits with better sensors. Hyperspectral imaging ; Quality (philosophy) ; Computer science ; Machine learning ; Artificial intelligence ; Agricultural engineering ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Date Palm Research Studies
Authors: Évelyne Martin | Carlo Massobrio
Source: Journal of Non-Crystalline Solids (article, 2025-02-01)
Field: Materials Science — Materials Chemistry

Amorphous structures and statistical sampling in first-principles molecular dynamics: The prototypical case of glassy GeSe<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si114.svg" display="inline" id="d1e867"><mml:msub><mml:mrow/><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msub></mml:math> The atomic structure of glassy GeSe 3 is obtained via first-principles molecular dynamics (FPMD) calculations by employing a simulation cell of 480 atoms. We complement and improve results previously published (Ref. Micoulaut et al., (2013)) on a smaller system (120 atoms) characterized by a marked disagreement with neutron scattering experiments on the occurrence of Ge − Ge homonuclear bonds. The present calculations show that a finite number of Ge − Ge bonds may occur on specific trajectories depending on the initial conditions selected for the quench from the liquid state. This conclusion is substantiated by the observation that the first trajectory we produced resulted in a negligible number of such homonuclear bonds, while the second did feature some of them. In terms of tetrahedral connections (either edge-sharing or corner-sharing) FPMD results appear to overestimate the edge-sharing ones, the extent of the disagreement depending on the reference experimental probe employed for the comparison (neutron scattering or NMR). • The occurrence of homonuclear Ge–Ge bonds is a contentious feature of glassy GeSe 3 . • In regard to this feature, new molecular dynamics data agree with experiments. • A system of unprecedented size (480 atoms) and two distinct trajectories are employed. • The impact of the initial configuration in the liquid state is found to be crucial. Sampling (signal processing) ; Amorphous solid ; Scalable Vector Graphics ; Dynamics (music) ; Molecular dynamics ; Computer science ; Combinatorics ; Mathematics ; Physics ; Crystallography ; Chemistry ; Computational chemistry ; World Wide Web ; Optics Phase-change materials and chalcogenides ; Glass properties and applications ; Nonlinear Optical Materials Studies
Authors: Kanglai He | Jinghan Yang | Fei Yu | Nuo Wei | Qian-Wei Liang | Jia‐Wei Feng | Tian‐Ci Yi | Xiang-Sheng Chen | Guy Smagghe | Shun‐Hua Gui | Tong‐Xian Liu
Source: Journal of Nanobiotechnology (article, 2025-05-19)
Field: Materials Science — Materials Chemistry

Silicon-coated carbon quantum dots composite nanomaterials mediate pest resistance activation in tobacco (Nicotiana tabacum) This study demonstrates that SiO<sub>2</sub>@CDs exhibit excellent performance as a multifunctional insecticide in managing aphid-induced plant pest infestations, highlighting their promising and environmentally friendly potential in pest control and agroecosystem optimization. Nicotiana tabacum ; PEST analysis ; Aphid ; Malondialdehyde ; Chemistry ; Materials science ; Nanomaterials ; Nanotechnology ; Oxidative stress ; Horticulture ; Biology ; Biochemistry Carbon and Quantum Dots Applications ; Nematode management and characterization studies ; Nanoparticles: synthesis and applications