jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wasit Chaikumming | Theeradech Songart | Puttipong Patumchat | Mohammed Aslam Khan | Keartisak Sriprateep
Source: Proceedings on Engineering Sciences (article, 2026-03-30)
Field: Materials Science — Polymers and Plastics

COMPUTER GEOMETRIC MODELING WITH FILAMENT ASSEMBLY MODEL AND CFD SIMULATION OF AIRPERMEABILITY IN KNITTED FABRICS This study presents a computational approach for predicting air permeability in knitted fabrics using advanced geometric modeling and computational fluid dynamics (CFD).Two fabric structures 11 rib and interlock were modeled using four established filament assembly-based geometric frameworks: Peirce, Leaf and Glaskin, Vassiliadis, and Kurbak.Three-dimensional CAD models of the knitted loops were developed using SolidWorks, incorporating filamentlevel details.These models were subjected to CFD analysis under standardized test conditions, with unit-cell domains and k- turbulence modeling used to simulate airflow.Air permeability results from simulations were compared with experimental data obtained under a 100 Pa pressure differential.Statistical metrics such as MAE, RMSE, and MAPE were applied to evaluate prediction accuracy.Among the models, Kurbak's framework demonstrated the highest correlation with experimental values, yielding the lowest error rates.Furthermore, two-way ANOVA results indicated that fabric type significantly affects prediction accuracy, while the geometric model type had no statistically significant effect.These findings underscore the potential of filament assembly models and CFD in accurately simulating fabric-level air permeability, especially when structural characteristics are properly defined. Computational fluid dynamics ; Geometric modeling ; Turbulence ; Permeability (electromagnetism) ; Protein filament ; Computational model ; Mathematical model ; Modeling and simulation Textile materials and evaluations ; Advanced Sensor and Energy Harvesting Materials ; Mechanical Behavior of Composites
Authors: Lei Yan | Taoying Luo | Chao Zhao | Honglin Ma | Yufei Wu | Chunqi Bai | Zibo Zhu
Source: Foods (article, 2026-02-05)
Field: Chemistry — Analytical Chemistry

Detection of Hidden Pest Rice Weevil (Sitophilus oryzae) in Wheat Kernels Using Hyperspectral Imaging The rice weevil (<i>Sitophilus oryzae</i>) is a major pest in stored wheat, and traditional detection methods face challenges in identifying its hidden life stages within kernels. This study develops a nondestructive method to detect <i>S. oryzae</i> (<i>Sitophilus oryzae</i>) infestation in wheat kernels using hyperspectral imaging, spectral preprocessing, feature extraction, and classification modeling. Hyperspectral data were collected from wheat kernels at different infestation stages (1, 11, 21, and 25 days (d)) and from healthy kernels. Spectral quality was optimized using SG smoothing, multiplicative scatter correction (MSC), and standard normal variate transformation (SNV). Feature extraction algorithms, including Competitive Adaptive Re-weighting Algorithm (CARS), Successive Projection Algorithm (SPA), and Iterative Retention of Information Variables (IRIV), were used to reduce data dimensionality, while classification models like Decision Tree (DT), K-nearest neighbors (KNN), and Support Vector Machine (SVM) were applied. The results show that MSC preprocessing provides the best performance among the models. After feature band selection, the MSC-CARS-SVM model achieved the highest accuracy for the 1 day and 25 d samples (95.48% and 96.61%, respectively). For the 11 d and 21 d samples, the MSC-IRIV-SPA-SVM model achieved the best performance with accuracies of 94.35% and 94.92%, respectively. This study demonstrates that MSC effectively reduces spectral noise and improves classification performance. After feature selection, the model shows significant improvements in both accuracy and stability. The study confirms the feasibility of using hyperspectral technology to identify healthy and <i>S. oryzae</i>-infested wheat kernels, providing theoretical support for early, nondestructive pest detection. Hyperspectral imaging ; Pattern recognition (psychology) ; Support vector machine ; Preprocessor ; Feature extraction ; Feature (linguistics) ; Random forest ; Projection (relational algebra) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Insect Pest Control Strategies
Authors: Alondra Jacqueline Esteban Gatica | Mariana Molina Morales | Aristeo Garrido Hernández | Giovanni García Domínguez | A. de Ita de la Torre | Joan Reyes-Miranda
Source: PÄDI Boletín Científico de Ciencias Básicas e Ingenierías del ICBI (article, 2026-04-06)
Field: Materials Science — Materials Chemistry

Propiedades de recubrimientos de fosfato de manganeso modificados con nanopartículas de Al2O3 Este estudio presenta los resultados de la síntesis y caracterización de recubrimientos de fosfato de manganeso (Mn3(PO4)2) modificados con partículas de Al2O3 depositados sobre sustratos de acero AISI 1020. El objetivo fue evaluar la influencia de la incorporación de alúmina en las propiedades estructurales, morfológicas y de adherencia de los recubrimientos. Se incorporaron tres concentraciones distintas de Al2O3 (0.2, 0.4 y 0.6 g/l), con un tamaño de partícula de 0.05 µm, en el baño de fosfatado. Las condiciones de dispersión de las partículas de alúmina se evaluaron utilizando dos tipos de ácidos (clorhídrico y nítrico) y dos concentraciones diferentes de relación ácido/partículas, con el objetivo de optimizar su integración en la capa de fosfato y minimizar la aglomeración. La caracterización se llevó a cabo mediante microscopía electrónica de barrido (MEB) para evaluar la morfología superficial y características de los granos de fosfato, difracción de rayos X (DRX) para la identificación de fases cristalinas, pruebas de adherencia y calidad de los recubrimientos, de acuerdo con la norma VDI 3198 que consiste en realizar una indentación al recubrimiento con un durómetro Rockwell, escala C (150 kg) y analizar la zona deformada alrededor del recubrimiento, y finalmente se determinaron los espesores de los recubrimientos con el equipo PosiTector-6000. El análisis por MEB reveló que a bajas concentraciones de Al2O3 se tiene la morfología característica de los granos de fosfato de manganeso (granos hexagonales), pero a concentraciones altas de Al2O3 se pierde la morfología y se observan granos irregulares. Para el caso de las pruebas de adherencia se determinaron fallas aceptables ya que no hubo delaminación del recubrimiento. Statistical analysis ; Particle size ; First order Corrosion Behavior and Inhibition ; Chemical and Environmental Engineering Research ; Concrete and Cement Materials Research
Authors: Paul August Rentz | Alīna Mihailova | Horácio Heinzen | Martine Bergaentzlé | Elisa Ruhland | Marivil Islam | Islam Hamed | Christina Vlachou | Simon Kelly | Saïd Ennahar | Dalal Werner
Source: Foods (article, 2026-02-19)
Field: Chemistry — Analytical Chemistry

Rapid Detection of Black Pepper Adulteration with Endogenous and Exogenous Materials: Assessment of Benchtop and Handheld Infrared Spectrometers Black pepper is the most widely used spice crop globally and has significant economic value, making it a target for economically motivated adulteration. A wide range of organic and inorganic bulking materials has been used as adulterants in black pepper. Development of rapid non-targeted screening methods for use at different stages of the black pepper supply chain is extremely important for the identification and prevention of evolving fraudulent practices. This study has assessed the potential of benchtop Fourier Transform infrared with attenuated total reflectance (FTIR-ATR), benchtop Fourier Transform near-infrared (FT-NIR), and two handheld NIR spectrometers, coupled with chemometrics, for the discrimination of black pepper (<i>Piper nigrum</i>), pepper from other species and genera (non-<i>Piper nigrum</i>) and a broad range (<i>n</i> = 27) of endogenous and exogenous adulterants. Spiked samples were prepared to imitate pepper adulteration with seven different adulterants at five levels of adulteration (5%, 25%, 50%, 75%, 95% <i>w</i>/<i>w</i>). Orthogonal partial least squares discriminant analysis (OPLS-DA) achieved 100% total prediction accuracy for both FTIR-ATR and FT-NIR in differentiating authentic <i>Piper nigrum</i> and adulterant samples. The handheld microNIR 1700ES resulted in a 91.30% correct classification rate, while the SCiO model achieved 86.96% prediction accuracy. Detection of black pepper adulteration with multiple adulterants was performed using data-driven soft independent modelling of class analogy (DD-SIMCA). The highest performance of the DD-SIMCA model was achieved by FTIR-ATR (100% sensitivity and 100% specificity) followed by FT-NIR (98% sensitivity and 99% specificity). The handheld microNIR 1700ES resulted in 95% sensitivity and 90% specificity. This study demonstrated that FTIR-ATR and FT-NIR, coupled with DD-SIMCA, can effectively detect black pepper adulteration with multiple endogenous and exogenous adulterants. The handheld NIR (microNIR1700ES) clearly demonstrated the potential for rapid and effective verification of <i>Piper nigrum</i> authenticity outside the laboratory. Pepper ; Adulterant ; Linear discriminant analysis ; Near-infrared spectroscopy ; Partial least squares regression ; Sensitivity (control systems) Spectroscopy and Chemometric Analyses ; Piperaceae Chemical and Biological Studies ; Food Drying and Modeling
Authors: Peiyao Zhao | Ying Jiang | Hongxian Wang | Limin Guo | Xiaohui Wang
Source: Journal of Advanced Ceramics (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

BaTiO <sub>3</sub> -based multilayer ceramic capacitors with superior stability in energy-storage and pulse properties Dielectric capacitors exhibiting high energy-storage properties and excellent pulse performance are critical components in advanced pulsed power systems. The multilayer ceramic capacitors (MLCCs) are the most widely used capacitors, due to the high integration and excellent high-temperature endurance. Nevertheless, achieving high energy density and reliable performance at wide temperature range and prolonged cycling remains the major challenge for existing commercial MLCCs based on linear dielectrics or Pb-based antiferroelectrics. Herein, we developed BaTiO<sub>3</sub>-based relaxor ferroelectric MLCCs with X9S characteristic for high-voltage applications. The BaTiO<sub>3</sub>-based MLCCs exhibiting a good energy density of 6.09 J cm<sup>-3</sup> and an ultrahigh efficiency of 98.2%, with a superior stability in temperature (&lt;10%, 24~250 ℃) and fatigue (after 10<sup>6</sup> cycles, &lt;4% at 200 ℃). The pulse properties of MLCCs show a minimal variation (&lt;4.5%, 22~200 ℃). Moreover, 6878-size MLCCs with 300 nF can release a peak current of 3600 A at rated voltage of 3000 V, which exhibit higher peak currents than linear MLCCs and better stability than Pb-based MLCCs. The excellent performance and superior stability demonstrate that the relaxor ferroelectrics MLCCs have great potential for advanced energy-storage and pulsed power applications. Ceramic capacitor ; Capacitor ; Dielectric ; Ceramic ; Power density ; Ferroelectricity ; Pulse (music) ; Voltage Ferroelectric and Piezoelectric Materials ; Dielectric materials and actuators ; High voltage insulation and dielectric phenomena
Authors: Wilaiwan Nuchthongmuang | Sahanat Petchsri | Thaweesakdi Boonkerd | Siriporn Sripinyowanich | Touchkanin Jongjitvimol
Source: HAYATI Journal of Biosciences (article, 2026-01-13)
Field: Chemistry — Organic Chemistry

Phenetic Study of &lt;i&gt;Tabernaemontana divaricata&lt;/i&gt; (L.) R.Br. ex Roem. &amp;amp; Schult. in Thailand Pinwheel Jasmine (Tabernaemontana divaricata L.; Apocynaceae) is widely distributed across South Asia, Thailand, and southern China and exhibits substantial morphological variation. This variability has resulted in persistent taxonomic confusion, particularly in Thailand, where distinguishing among phenotypic forms and related taxa remains challenging. Accurate taxonomic delimitation is essential for botanical classification, horticultural applications, conservation, and the reliable identification of plant resources. This study examined the taxonomic status of Pinwheel Jasmine in Thailand using numerical taxonomic approaches, including cluster and discriminant analyses. A total of 40 morphological characters (20 quantitative and 20 qualitative) were assessed across 210 operational taxonomic units (OTUs) representing seven phenotypes. Both analyses consistently resolved the OTUs into two distinct groups: (1) T. divaricata ‘Ceylon Jasmine’ (Phud Supachok) and (2) other Pinwheel Jasmine forms. Five quantitative characters were identified as the most diagnostic: lamina length (LML), lamina width (LMW), corolla lobe length (CLL), distance from the corolla tube base to stamen attachment (DCS), and style length (STL). These traits exhibited exceptionally high F-values in K-means cluster analysis (404.11, 432.90, 327.54, 530.04, and 181.02, respectively; p&lt;0.01), indicating strong discriminatory power. The results demonstrate that Phud Supachok is consistently and clearly morphologically distinct from other Pinwheel Jasmine forms. However, because this study is based solely on phenetic data, additional molecular evidence is required before definitive taxonomic conclusions can be made. Integrative studies combining molecular and morphological data are recommended to refine species delimitation within Tabernaemontana. Taxon ; Taxonomy (biology) ; Morphometrics ; Lamina ; Numerical taxonomy ; Taxonomic rank ; Identification (biology) ; Morphological analysis Chemical synthesis and alkaloids ; Flowering Plant Growth and Cultivation ; Genetic diversity and population structure
Authors: Ruilong Hao | Zhe Wang | Jianhua Zhang | Xiwen Kang | Huiqin Ma | Guoping Yu
Source: Frontiers in Plant Science (article, 2026-03-20)
Field: Chemistry — Analytical Chemistry

The mango core quality non-destructive testing and comprehensive harvesting decision-making model based on portable near-infrared spectroscopy technology It is of great significance to detect the degree of mango maturity accurately and quickly in terms of timely harvesting according to market needs and building a differentiated post-harvest field sorting system. In this study, three main mango varieties—’Tainong’, ‘Guifei’, and ‘Jinhuang’—that originated in Hainan, China, were used as subjects. An H-100F portable near-infrared spectrometer was used to collect the absorption spectra between 650- and 950-nm wavelengths at 20 days before as well as 20 days after the commercial harvesting time. Four types of quality indicators, including fruit firmness, pH value, soluble solid content (SSC), and dry matter content (DMC), were measured in the laboratory to finally establish the non-destructive detection (NDT) and comprehensive harvesting decision-making model. As the results indicated, in the range of 650–950 nm, the average value of the spectrum absorbance for the three mango varieties decreased as the grade of maturity increased. The value change correlated significantly with fruit firmness, pH, SSC, and DMC (%). The Kennard–Stone algorithm method was used to divide the four quality indicators into correction sets and prediction sets. The original spectrum and the spectrum preprocessed using six methods, including multiplicative scatter correction, standard normal variate transform, Savitzky–Golay convolution smoothing, Savitzky–Golay convolution derivative, vector normalization, and maximum–minimum normalization, were then preferably selected to build a processing model for the four quality indicators of the three mango varieties. The test results showed that for all three mango varieties, both the revised standard error of mean squared root and the indicated standard error of mean squared root of the four quality indicators were less than 1.42, 0.62, 0.78, and 1.4, and 1.52, 0.38, 0.94, and 1.5, respectively. The accuracy rates of the model to test the grade of maturity for ‘Tainong’, ‘Guifei’, and ‘Jinhuang’ were 83%, 90%, and 81%, respectively. The above results indicated that the mango NDT model that was established on the portable near-infrared spectroscopy technology can reliably detect the fruit firmness, pH, SSC, and DMC of mangoes and can distinguish the grade of maturity for different mango varieties. The research results are of great significance to the decision-making of mango harvesting and the differentiated field sorting thereafter. Mean squared error ; Sorting ; Absorbance ; Quality (philosophy) ; Near-infrared spectroscopy ; Range (aeronautics) ; Standard deviation ; Dry matter Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Zhongkuo Zhang | Jie Ren
Source: Tehnicki vjesnik - Technical Gazette (article, 2026-03-01)
Field: Chemistry — Spectroscopy

Research on the Heat Transfer Characteristics and Energy-Saving Performance Prediction Model of Vacuum Insulation Board Composite Wall This study conducts a quantitative analysis of the thermal bridge effect caused by the joints during the construction of vacuum insulation panels, revealing a significant positive correlation between the width of the joints and the increase in the heat transfer coefficient of the wall.Calculations show that when the width of the board joint increases to the upper limit of 20 mm, the deterioration rate of the heat transfer coefficient of the wall can reach 40% (i.e., the insulation performance will decline by approximately 33%).Based on this, a dynamic correction model for thermal conductivity is proposed.This model achieves the engineering quantitative correction of the thermal bridge effect by introducing the panel gap attenuation factor, providing a key parameter basis for the thermal design of the insulation system.At the same time, energy consumption calculations are conducted for passive buildings using composite insulation boards.The impact of vacuum insulation board failure on building energy consumption before and after is analyzed, as well as the methods for ensuring the operation of the building in the later stage, providing a reference for the subsequent engineering design of the built-in insulation system using composite insulation boards. Thermal bridge ; Vacuum insulated panel ; Thermal insulation ; Thermal conductivity ; Dynamic insulation ; Heat transfer ; Heat transfer coefficient ; Joint (building) ; Thermal Aerogels and thermal insulation ; Building Energy and Comfort Optimization ; Hygrothermal properties of building materials
Authors: Chi Yang | Yung-Chin Hsiao | Chi‐Ching Lee | Lichieh Julie Chu | Ta-Sen Yeh | Ping-Chang Cheng | Ping Tang | Jau-Song Yu
Source: Molecular & Cellular Proteomics (article, 2026-02-03)
Field: Chemistry — Spectroscopy

MsTargetPeaker: A Quality-Aware Deep Reinforcement Learning Approach for Peak Identification in Targeted Proteomics Targeted mass spectrometry enables precise peptide quantification by identifying high-quality chromatographic peaks for area integration. Automated peak identification remains challenging, particularly for low-abundance targets, because of interference and noise. Existing approaches typically rely on two supervised learning models, one for selecting peak regions and the other for performing downstream quality control in a separate postprocessing step. However, deferring quality assessment to a separate stage may limit the ability to refine peak boundaries in pursuit of improved quality, as the initial selection is performed without explicit awareness of quality-related criteria. In this study, we present MsTargetPeaker, a quality-aware search procedure for identifying peak regions in targeted proteomics data. The method employs a reinforcement learning agent to guide Monte Carlo tree search to efficiently explore chromatograms and localize target peaks while minimizing interference. Peak quality is dynamically assessed during the search via a custom-designed reward function, which prioritizes regions with desirable peak characteristics and enables accurate and robust boundary determination. The reward function further incorporates cross-sample consensus profiles of candidate boundaries to improve the identification of low-quality or ambiguous signals. These innovations support fine-grained peak identification, enhancing both peak quality and quantification precision. In addition, the transparent reward calculation allows MsTargetPeaker to generate interpretable diagnostic quality reports, providing comprehensive metrics across transitions, peak groups, and sample replicates. This facilitates efficient detection of problematic cases for manual curation. Collectively, MsTargetPeaker offers a practical advancement toward robust and automated peak identification in targeted proteomics. Identification (biology) ; Reinforcement learning ; Quality (philosophy) ; Limit (mathematics) ; Function (biology) ; Pattern recognition (psychology) ; Selection (genetic algorithm) Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Protein purification and stability
Authors: Umrbek Mavlanov | Sardorbek Atajanov | Sharofiddin Nuriddinov | Sardorjon Shukurov Salimovich | Soyibjon Bozorov | Tomasz Pawel Czaja | Bekzod Khakimov
Source: Journal of Food Composition and Analysis (article, 2026-01-13)
Field: Chemistry — Organic Chemistry

Validation of benchtop NMR-based quantification of saturated, monounsaturated, and polyunsaturated fatty acids in edible oils Proton nuclear magnetic resonance (1H NMR) spectroscopy offers rapid quantification of saturated (SFA), monounsaturated (MUFA), and polyunsaturated (PUFA) fatty acids in oils. While high-field NMR has been widely applied, its high operational costs limit accessibility. In contrast, benchtop NMR provides an affordable and userfriendly option for oil analysis. However, its lower sensitivity and resolution have led methods to rely on chemometric modeling, requiring spectra acquired under identical conditions as the reference model, thereby restricting applicability. In this study, we validated for the first time a benchtop (80 MHz) 1H NMR method for direct quantification of SFA, MUFA, and PUFA in oils using integration of characteristic resonances, enabling a model-free and reproducible approach. To our knowledge, this is the first benchtop NMR validation of SFA, MUFA, and PUFA resonances using authentic triglyceride standards, glyceryl tridecanoate, glyceryl trioleate, and glyceryl trilinoleate, respectively. Binary and ternary mixtures, natural oil blends, and spiked sunflower oils prepared from these standards enabled the direct comparison of fatty acid quantification between benchtop and high-field (500 MHz) 1H NMR. Our optimized workflow enabled both platforms to deliver comparable accuracy and reproducibility. This validated, model-free approach provides reliable fatty acid profiling, accelerating oil analysis in research and quality control laboratories. Polyunsaturated fatty acid ; Sunflower oil ; Fatty acid ; Detection limit ; Vegetable oil ; Food chemistry ; Resolution (logic) ; Nuclear magnetic resonance spectroscopy Edible Oils Quality and Analysis ; NMR spectroscopy and applications ; Food Chemistry and Fat Analysis