jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sophia Ann Suring | Bernard Maringgal | Surisa Phornvillay | Norhashila Hashim | Maimunah Mohd Ali
Source: Food and Bioprocess Technology (article, 2026-01-05)
Field: Chemistry — Analytical Chemistry

Non-destructive Classification of Harvested Terung Asam (Solanum lasiocarpum Dunal.) Utilising Thermal Imaging and Machine Learning Across Different Storage Days Thermal imaging has non-invasive qualities which have attracted the interest of researchers as it could improve agricultural methods, especially in the field of fruit classification based on their storage duration. This study explores the potential of thermal imaging and machine learning for the non-destructive classification of Terung Asam (Solanum lasiocarpum Dunal.) fruit at different storage durations. Nine thermal image parameters were analysed to monitor changes in fruit characteristics over day 0, day 7, day 14, day 21 and day 28. Key thermal image parameters, including major axis length (MajorAL), maximum intensity (MaxInt) and mean value within the region of interest (MeanROI), exhibited significant variations throughout the storage period, reflecting changes in fruit morphology and surface temperature associated with ripening and moisture loss. Correlation analysis revealed strong correlation between these parameters. The strongest correlation was found between MajorAL and MinorAL (r = 0.967) and between MaxInt and MajorAL (r = 0.962). Five machine learning models, i.e. Fine Decision Tree, Medium Decision Tree, RUSBoost Tree, Boosted Tree and Fine k-Nearest Neighbour (kNN), were evaluated for classification performance. Fine and Medium Decision tree achieved the highest classification accuracy at 86.7%, effectively distinguishing Terung Asam fruits based on storage duration. This study underlines thermal imaging as a reliable, non-invasive tool for post-harvest classification of Terung Asam fruit, improving storage monitoring and reducing waste. Future research should focus on deep learning integration to enhance classification performance over extended storage periods. Decision tree ; Contextual image classification ; Support vector machine ; Correlation ; Positive correlation ; Ripening ; Water content ; Thermal Spectroscopy and Chemometric Analyses ; Food Drying and Modeling ; Smart Agriculture and AI
Authors: Ali Khumaidi | Ridwan Raafi'udin | Nusa Setiani Triastuti | Linda Marlinda
Source: International Journal of Computing (article, 2026-03-31)
Field: Chemistry — Analytical Chemistry

Ensemble-Based Machine Learning with PSO and GA for Ship Coating Detection Using Portable Vis/NIR This study presents a method for detecting ship coating quality using a portable Vis/NIR spectroscopy system combined with machine learning. To improve accuracy, we integrated spectral transformation (Nippy), feature selection methods (PSO and GA), and ensemble learning models. The experiments involved four coating quality levels, producing 148 spectral samples. Results show that the proposed approach consistently outperforms single baseline models and traditional feature selection methods such as PCA and IFS. The best performance was achieved by combining Nippy with PSO, where the LDA algorithm reached 99.33% accuracy, while GA also showed strong results with both single and ensemble models. We also examined ensemble results at different stages of preprocessing and feature selection, showing that the ensemble maintained stable performance throughout the process. These findings demonstrate that the integration of spectral transformation and metaheuristic feature selection can enhance model robustness, providing more reliable and accurate coating quality detection for maritime applications. Feature selection ; Preprocessor ; Feature (linguistics) ; Pattern recognition (psychology) ; Ensemble learning ; Coating ; Transformation (genetics) Spectroscopy and Chemometric Analyses ; Machine Learning in Materials Science ; Corrosion Behavior and Inhibition
Authors: Sharly Claudia Alghai Sani | Dase Hunaefi | Faleh Setia Budi | Yessie Widya Sari | Ilham Akbar Ibrahim | Noviani Rustanto
Source: Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) (article, 2026-02-06)
Field: Chemistry — Analytical Chemistry

NIR-Based Predictive Modelling for the Quantification of Sucrose, Glucose, and Fructose in Brown Sugar from Oil Palm Trunk Sap Using oil palm trunk sap as a raw material for brown sugar is an innovative alternative for local product diversification. However, craftsmen's limited access to laboratory analysis methods is challenging to maintain product quality consistency. This study aims to evaluate the feasibility of using near-infrared spectroscopy (NIRS) combined with chemometric modelling for the estimation of sucrose, glucose, and fructose content in brown sugar derived from oil palm trunk sap. This method combines destructive analysis using high-performance liquid chromatography (HPLC) as a reference with non-destructive NIRS analysis and partial least squares regression (PLSR) modelling. The prediction model performed very well for glucose with an R² of 0.991, while for sucrose it was 0.850 and fructose 0.860. However, the relatively high values of SEC and SEP and the low prediction consistency (&lt;20%) indicate that the current chemometric strategy is not yet fully adequate, suggesting the need for a larger and more process-representative sample set, more rigorous consideration of sample representativeness and laboratory reference uncertainty (SEL), and the inclusion of laboratory reference error (SEL) from HPLC data to enable more robust and reliable model development. These findings indicate that NIRS has potential as a fast and non-destructive method for brown sugar quality control, but further development is needed to make the model more reliable under various production conditions. Sugar ; Fructose ; Partial least squares regression ; Sucrose ; Palm oil ; Raw material ; Chemometrics ; Predictive modelling Spectroscopy and Chemometric Analyses ; Sugarcane Cultivation and Processing ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Daniel Magalhães da Cruz | Ignacio Melito | Tales Luiz Popiolek Júnior | Marcelo de Ávila Barreto | Fernanda Mazuco Clain | Carlos Eduardo Marcos Guilherme
Source: Acta Polytechnica (article, 2026-01-15)
Field: Materials Science — Polymers and Plastics

Experimental investigation of the effect of test speed on rupture performance of synthetic yarns This study experimentally investigated the effect of testing speed on the mechanical behaviour of five types of synthetic fibres – aramid, high-modulus polyethylene (HMPE), liquid crystal polymer (LCP), polyamide, and polyester – through yarn break load (YBL) tests, ISO 2062. Twelve different speeds were evaluated, ranging from 50 to 1 000 mm min−1. The discrete rupture force and rupture strain results were stable and showed no statistically significant variations as a function of speed, which was reflected in the fitted mathematical models exhibiting stabilised curves across the studied range. The stress-strain curves also showed a high degree of similarity, with a particular emphasis on LCP, which demonstrated extremely consistent behaviour across all tested speeds. For HMPE, a slight trend of increased strain at lower speeds was observed, attributed to its viscoelastic nature. The tangent modulus curves revealed more noticeable variations in the initial tangent modulus response for each fibre, where lower speeds resulted in steeper slopes, indicating a greater instantaneous stiffness. It is concluded that, although the testing speed does not affect the overall rupture parameters (rupture force and strain), it can influence the behaviour in the initial regions, particularly regarding the determination of modulus and stiffness. Viscoelasticity ; Tangent modulus ; Tangent ; Modulus ; Dissipation factor ; Polyester ; Polymer ; Polyethylene Textile materials and evaluations ; Mechanical Behavior of Composites ; Structural Analysis and Optimization
Authors: Jidong Li | Kai Deng
Source: Journal of Materials Processing and Design (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Plasma Modification of the Surface of Magnesium-nickel Alloy: Enhancement of Oxidation Resistance and Optimization of Hydrogen Storage Kinetics To address the application bottleneck of magnesium-nickel alloys as hydrogen storage materials, which involves their susceptibility to oxidation and slow hydrogen storage kinetics, this study employed plasma immersion ion implantation technology to modify the surface of Mg₂Ni alloys. The research systematically explored the regulatory laws of process parameters on the surface structure, oxidation resistance, and hydrogen storage kinetics of the alloys. The microstructure of the modified layer was characterized using SEM, XRD, and XPS methods. The performance optimization effect was evaluated through static oxidation experiments, thermogravimetric analysis, and hydrogen absorption/desorption kinetics tests. The results showed that when the plasma treatment power was 400 W, the treatment time was 30 min, and the working pressure was 0.5 Pa, a dense modified layer approximately 2.5 μm thick was formed on the alloy surface, mainly consisting of the composite phase of Ni₃P and MgO. Compared with the unmodified sample, the oxidation rate of the modified alloy in a static oxidation experiment at 600 ℃ for 10 h decreased by 68.3%, and the activation energy of oxidation increased to 186.5 kJ/mol. The hydrogen storage kinetics performance was significantly optimized. At 200 ℃ and 3 MPa hydrogen pressure, the saturated hydrogen absorption time was shortened from 1200 s to 380 s, the hydrogen absorption rate constant increased by 2.1 times, and the storage capacity retention rate after 50 hydrogen absorption/desorption cycles remained at 92.7%, which was 23.5 percentage points higher than that of the unmodified sample. Mechanism analysis indicated that the dense structure of the modified layer could effectively prevent oxygen diffusion to enhance oxidation resistance, while the formation of the Ni₃P catalytic phase and increased surface defects could accelerate the hydrogen adsorption-diffusion process. The proposed plasma modification process provides technical support for the preparation of high-performance magnesium-nickel hydrogen storage alloys and is of great significance for promoting the industrial application of hydrogen storage technology. Hydrogen storage ; Hydrogen ; Kinetics ; Alloy ; Microstructure ; Redox ; Chemical kinetics ; X-ray photoelectron spectroscopy ; Hydrogen production Hydrogen Storage and Materials ; Magnesium Alloys: Properties and Applications ; Hydrogen's biological and therapeutic effects
Authors: Beatrice Ceccanti | Mattia Galanti | Ivo Roghair | Martin van Sint Annaland
Source: ArXiv.org (article, 2026-01-14)
Field: Materials Science — Materials Chemistry

Deep Operator Networks for Surrogate Modeling of Cyclic Adsorption Processes with Varying Initial Conditions Deep Operator Networks are emerging as fundamental tools among various neural network types to learn mappings between function spaces, and have recently gained attention due to their ability to approximate nonlinear operators. In particular, DeepONets offer a natural formulation for PDE solving, since the solution of a partial differential equation can be interpreted as an operator mapping an initial condition to its corresponding solution field. In this work, we applied DeepONets in the context of process modeling for adsorption technologies, to assess their feasibility as surrogates for cyclic adsorption process simulation and optimization. The goal is to accelerate convergence of cyclic processes such as Temperature-Vacuum Swing Adsorption (TVSA), which require repeated solution of transient PDEs, which are computationally expensive. Since each step of a cyclic adsorption process starts from the final state of the preceding step, effective surrogate modeling requires generalization across a wide range of initial conditions. The governing equations exhibit steep traveling fronts, providing a demanding benchmark for operator learning. To evaluate functional generalization under these conditions, we construct a mixed training dataset composed of heterogeneous initial conditions and train DeepONets to approximate the corresponding solution operators. The trained models are then tested on initial conditions outside the parameter ranges used during training, as well as on completely unseen functional forms. The results demonstrate accurate predictions both within and beyond the training distribution, highlighting DeepONets as potential efficient surrogates for accelerating cyclic adsorption simulations and optimization workflows. Generalization ; Operator (biology) ; Benchmark (surveying) ; Context (archaeology) ; Nonlinear system ; Convergence (economics) ; Artificial neural network ; Partial differential equation ; Process (computing) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Stochastic Gradient Optimization Techniques
Authors: Lucas Svensson | Babak Sadigh | Christine Wu | Paul Erhart
Source: arXiv (Cornell University) (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

Efficient method for calculation of low-temperature phase boundaries Understanding phase stability and phase transformations is central to predicting material behavior under varying thermodynamic conditions. One of the earliest and most influential applications of density functional theory in materials science has been the prediction of pressure-induced phase transitions at 0 K. Extending these calculations to finite temperatures, however, requires accounting for thermal, quantum, and anharmonic contributions to the free energy, often at significant computational cost. In this work, we present a general and efficient framework for calculating low-temperature phase boundaries by combining the Clausius-Clapeyron equation with the quasi-harmonic approximation. This methodology requires a minimal number of calculations, while naturally incorporating internal degrees of freedom and allowing for the inclusion of quantum and low-order anharmonic effects. We illustrate the accuracy and efficiency of the approach by constructing the phase diagram of silica in the pressure range from -2 to 12 GPa and temperatures up to 1750 K. To this end, we employ both density functional theory and a machine-learned interatomic potential, enabling well-converged free energy estimates and a rigorous comparison between first-principles and data-driven models. Anharmonicity ; Degrees of freedom (physics and chemistry) ; Stability (learning theory) ; Density functional theory ; Phase diagram ; Range (aeronautics) ; Phase (matter) ; Thermodynamic integration Machine Learning in Materials Science ; High-pressure geophysics and materials ; Phase Equilibria and Thermodynamics
Authors: Stefan Tenzer | David Teschner | Zixuan Xiao | Tim Maier | David Gómez-Zepeda | Mateusz Krzysztof Łącki | Michal Startek | Ute Distler | Tanja Ziesmann | Mathias Wilhelm | Andreas Hildebrand
Source: Research Square (preprint, 2026-03-12)
Field: Chemistry — Spectroscopy

Complete Simulation of timsTOF PASEF Raw Datasets with Timsim Enables Precise Evaluation of False Discovery and Phosphosite Localization Error Rates False discovery rate ; False positive paradox ; Calibration ; Sensitivity (control systems) ; Scalability ; Ground truth ; Benchmarking ; Workflow Advanced Proteomics Techniques and Applications ; vaccines and immunoinformatics approaches ; Machine Learning in Bioinformatics
Authors: Zhanzhao Yin | Ding Zhang | Longyu Li | Yuping Gao | Yongsheng Liu | Xiangjian Wan | Rujun Ma | Yongsheng Chen
Source: Nature Communications (article, 2026-04-03)
Field: Materials Science — Materials Chemistry

Solution-processed photovoltaic and thermoelectric hybrid systems with efficiency exceeding 50% Photovoltaic-thermoelectric (PV-TE) hybrid systems offer a platform for enhancing the energy conversion efficiency of photovoltaic devices. However, they still suffer from energy losses and limited efficiency improvements owing to underutilized parasitic thermal energy and electrical parameters mismatches between PV and TE components. Here, we presented a comprehensive theoretical analysis and simulation based on a PV-TE Thermo-Electrical Coupling Model, predicting that the maximum efficiency of the system could reach 60.34% with state-of-the-art PV and commercial TE technologies. Following this model, we fabricated hybrid systems with organic and perovskite solar cells coupled with thermoelectric cells, achieving record-high efficiencies of 34.85% and 42.03% at 298 K, and 43.16% and 50.28% at 313 K, respectively, under AM 1.5 G illumination, with optimal thermal utilization and current matching between series-connected PV and TE modules. This work highlights the potential of PV-TE hybrid systems and could offer guidance for designing higher-efficiency systems, driving future advancements in photovoltaics. Photovoltaic system ; Hybrid system ; Thermoelectric effect ; Photovoltaics ; Thermoelectric generator ; Energy conversion efficiency Advanced Thermoelectric Materials and Devices ; Advanced Materials and Semiconductor Technologies ; Chalcogenide Semiconductor Thin Films
Authors: Jacek Fal | David Cabaleiro
Source: Processes (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

Editorial for the Special Issue “New Trends and Processes in Nanofluids and Carbon-Based Nanoparticles” The accelerating advancements in energy technologies, thermal management systems, and electrochemical processes have intensified efforts to develop high-performance materials that outperform conventional heat transfer media and electrolyte solutions [...] Nanofluid ; Thermal management of electronic devices and systems ; Heat transfer ; Energy transfer ; Heat transfer fluid Chemical and Physical Properties of Materials ; Aerogels and thermal insulation ; Electrophoretic Deposition in Materials Science
Authors: Pınar Acar Bozkurt | Sibel Erol Dağ | Meltem ÇELİK | Süheyla Pınar Çelik | Leyla Açık
Source: Polymer Bulletin (article, 2026-01-19)
Field: Materials Science — Polymers and Plastics

In situ synthesis and functional properties of poly(butyl methacrylate)/Na-montmorillonite composites Abstract Poly(butyl methacrylate) (PBMA)/sodium montmorillonite (Na-MMT) composites were successfully synthesized via in situ free-radical polymerization using benzoyl peroxide as an initiator to examine the influence of unmodified clay layers on the structural, thermal, and functional properties within the composites. The resulting composites were comprehensively characterized by Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), scanning and transmission electron microscopy (SEM, TEM), and Brunauer–Emmett–Teller (BET) surface area analysis. TGA results indicated that the incorporation of Na-MMT promoted an intercalated morphology and the formation of a compact, thermally stable hybrid network. XRD and SEM analyses confirmed an increase in interlayer spacing and significant changes in surface morphology, evidencing effective monomer intercalation into the clay galleries. The addition of Na-MMT notably reduced water uptake and moisture retention efficiency, generated a tunable micro-/mesoporous structure, reflecting the strong reinforcing effect of the unmodified clay. Furthermore, the PBMA/Na-MMT composites exhibited superior antibacterial activity compared to pure PBMA, demonstrating synergistic polymer–clay interactions. Overall, this study establishes in situ organo–inorganic hybridization as an efficient strategy for developing multifunctional PBMA–clay materials with enhanced structural, thermal, and antibacterial properties, suitable for potential applications in packaging, coatings, and antimicrobial surfaces. Thermogravimetric analysis ; In situ polymerization ; Scanning electron microscope ; Intercalation (chemistry) ; Montmorillonite ; Benzoyl peroxide ; Monomer ; Polymerization Polymer Nanocomposites and Properties ; Hydrogels: synthesis, properties, applications ; Clay minerals and soil interactions
Authors: N. M Mrs. Orjioke | Orjiocha Samuel Ibezim | Paul M. Ejikeme
Source: Discover Chemistry. (article, 2026-04-10)
Field: Materials Science — Biomaterials

Green synthesis of cellulose nanocomposites from Pentaclethra macrophylla Benth pod as efficient adsorbent for the removal of lead ion in aqueous solution Progressive industrialization and increasing world population have led to severe water pollution. In this study, Penthaclethra macrophylla benth pod was suggested as possible inexpensive adsorbent for the synthesis of cellulose nanocrystals, and the removal of lead at low concentrations. FTIR peaks observed at 3204 and 3414 cm− 1 represented the stretching and bending vibrations of O-H, while the peak at 664.2 cm− 1 represented the β-glycosidic linkage of the cellulose. XRD peaks at 2θ = 12.4o, 20.3o and 26.4o represented the typical cellulose-I structure. Surface morphology revealed a stacked flake-like structure, while transmission electron micrographs indicated clusters of spherical-shaped nanoparticles, with particle sizes between 1 and 10 nm. EDX spectra showed the presence of carbon, oxygen, and other trace elements. Maximum adsorption percentage was noted for CNC-GO-PLA-MWCNT (99.73), CNC-GO (99.77), and CNC (99.75), at the pH of 8. Isotherm plots revealed that the adsorption behaviour of Pb(II) ion was best described by the Freundlich model (R2 > 0.99). Kinetics study showed that the PSO model (R2 > 0.99) has a better fit for all nanocomposites. Thermodynamic investigation showed negative values for all ΔG° across samples and temperatures. Adsorption is exothermic for all composites, except for CNC-GO-PLA-MWCNT. Positive values of ΔS° indicated increased disorder. Adsorption ; Aqueous solution ; Cellulose ; Freundlich equation ; Population ; Fourier transform infrared spectroscopy ; Nanocomposite ; Point of delivery Advanced Cellulose Research Studies ; Adsorption and biosorption for pollutant removal ; Nanocomposite Films for Food Packaging