jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mahdi Hosseini | Milan Gaff | David Hui | Petr Konvalinka | Hynek Maňák | Haitao Li | Ahmad Hosseini | Pritam Ghosh
Source: REVIEWS ON ADVANCED MATERIALS SCIENCE (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Studying the effect of nanofillers in civil applications: A review Abstract The incorporation of nanofillers into structures has revolutionised the field of civil engineering. By enhancing mechanical, thermal, and functional properties, nanofiller-reinforced cemented structures offer solutions to challenges in durability, strength, and sustainability for modern construction. This article reviews the advances in nanofiller-reinforced fibre-reinforced polymer laminates, emphasising their role in civil applications, such as structural reinforcement, seismic retrofitting, and long-term durability improvements. The study also explores challenges in manufacturing, scalability, and environmental impact, providing directions for future research and large-scale adoption. Durability ; Sustainability ; Civil infrastructure ; Field (mathematics) ; Sustainable development Natural Fiber Reinforced Composites ; Carbon Nanotubes in Composites ; Smart Materials for Construction
Authors: Xiaorui Liu | Feng Zhao | Wei Guo | Tao Feng | Jialong Zhao
Source: Polymer Composites (article, 2025-04-18)
Field: Materials Science — Polymers and Plastics

Sustainable lightweight rice husk fiber‐reinforced polypropylene composites: Preparation, properties, and environmental benefits Abstract Rice husk is a byproduct of rice production, often discarded or burned, leading to environmental pollution. In this study, rice husk fiber‐reinforced polypropylene (RF/PP) composites were prepared using a physical foaming injection molding technique. The effects of fiber content on the physical, mechanical, and environmental properties of the RF/PP composite were analyzed. The results showed that the incorporation of RF reduced the melt flow rate, enhanced the crystallinity of PP, and increased both the density and moisture sensitivity. The RFs were uniformly dispersed within the PP matrix, improving the stability of the foaming process. The developed foamed RF/PP composite not only significantly reduced the material density (by 10%–14%) but also improved mechanical properties, including a 26.18% increase in flexural strength and a 58.85% increase in impact strength, while reducing carbon emissions by nearly 10%. This provides a sustainable solution for applications in automotive lightweighting, construction materials, and related fields. The research results promote the high‐value utilization of agricultural waste, support the development of a circular economy, and provide technological support for companies to fulfill their environmental responsibilities and enhance market competitiveness. Highlights Innovative use of rice husk fibers in sustainable RF/PP composites via foaming. RF improves composite molding and foaming quality. Eco‐friendly solution with reduced density for RF/PP composites. Comprehensive analysis reveals RF's potential in green, lightweight composites. Husk ; Materials science ; Composite material ; Polypropylene ; Fiber Natural Fiber Reinforced Composites ; Bamboo properties and applications ; Additive Manufacturing and 3D Printing Technologies
Authors: Dhanush Ghate D | H Saishma | M Adithya | Sudeep D. Ghate
Source: Research Square (preprint, 2025-01-06)
Field: Chemistry — Analytical Chemistry

Advancing Arecanut Quality Grading: A Comparative Analysis of YOLO Models with Hyperparameter Optimization Computer science ; Hyperparameter optimization ; Scalability ; Hyperparameter ; Artificial intelligence ; Machine learning ; Grading (engineering) ; Precision and recall ; Data mining ; Generalizability theory ; Database ; Statistics ; Mathematics ; Support vector machine ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Date Palm Research Studies
Authors: Or Oxenberg | Michael Fire | Galit Fuhrmann Alpert
Source: medRxiv (preprint, 2025-02-12)
Field: Chemistry — Analytical Chemistry

Automatic Classification of EEG Signals, Based on Image Interpretation of Spatio-Temporal Information Abstract Brain-Computer Interface (BCI) applications provide a direct way to map human brain activity onto the control of external devices, without a need for physical movements. These systems, crucial for medical applications and also useful for non-medical applications, predominantly use EEG signals recorded non-invasively, for system control, and require algorithms to translate signals into commands. Traditional BCI applications heavily depend on algorithms tailored to specific behavioral paradigms and on data collection using EEG systems with multiple channels. This complicates usability, comfort, and affordability. Moreover, the limited availability of extensive training datasets limits the development of robust models for classifiying collected data into behavioral intents. To address these challenges, we introduce an end-to-end EEG classification framework that employs a pre-trained Convolutional Neural Network (CNN) and a Transformer, initially designed for image processing, applied here for spatiotemporal representation of EEG data, and combined with a custom developed automated EEG channel selection algorithm to identify the most informative electrodes for the process, thus reducing data dimensionality, and easing subject comfort, along with improved classification performance of EEG data onto subject’s intent. We evaluated our model using two benchmark datasets, the EEGmmidb and the OpenMIIR. We achieved superior performance compared to existing state-of-the-art EEG classification methods, including the commonly used EEGnet. Our results indicate a classification accuracy improvement of 7% on OpenMIIR and 1% on EEGmmidb, reaching averages of 81% and 75%, respectively. Importantly, these improvements were obtained with fewer recording channels and less training data, demonstrating a framework that can support a more efficient approach to BCI tasks in terms of the amount of training data and the simplicity of the required hardware system needed for brain signals. This study not only advances the field of BCI but also suggests a scalable and more affordable framework for BCI applications. Interpretation (philosophy) ; Electroencephalography ; Artificial intelligence ; Computer science ; Image (mathematics) ; Pattern recognition (psychology) ; Computer vision ; Natural language processing ; Psychology ; Neuroscience Spectroscopy and Chemometric Analyses ; Water Quality Monitoring Technologies
Authors: Xiaohui Zhao | Shixue Zhou | Magdy Abdelghany Elsayed | Zhongyuan Chen | Chunhui Jiang | Yongli Hu | Gumawa Windu Manggada
Source: Separations (article, 2025-06-01)
Field: Chemistry — Inorganic Chemistry

Insights into the Adsorptive Separation of Ethylene/Ethane in LTA-Type Zeolites Understanding the competitive adsorption mechanism is essential for the development of adsorptive separation of ethylene (C2H4) and ethane (C2H6). In this work, density functional theory calculations and molecular dynamics simulations were employed to investigate the adsorption of C2H4 and C2H6 in two LTA-type zeolites, ITQ-29 and 5A. The results show that the adsorption energies of the gas molecules in zeolite 5A are more negative than in ITQ-29, and the difference in adsorption energy between C2H4 and C2H6 in zeolite 5A is significantly larger than in ITQ-29, 13.3 versus 6.2 kJ/mol. Zeolite ITQ-29 demonstrates high C2H4/C2H6 ideal selectivity (43.5 at 5 ns) while exhibiting slow C2H4 uptake efficiency due to the small pore windows, hindering C2H4 diffusion (1.05 × 10−10 m2/s at 298 K). In contrast, zeolite 5A facilitates the faster diffusion of C2H4 molecules (3.25 × 10−9 m2/s at 298 K) and exhibits a modest C2H4/C2H6 selectivity of 1.11 at 5 ns in single-gas adsorption and 2.72 in equimolar binary mixture adsorption. To enhance C2H4/C2H6 selectivity, methyl phosphonic acid is introduced onto zeolite 5A to add a sieving layer that enables the C2H4 molecules to preferentially permeate, and the optimal coverage of methyl phosphonic acid is 50%, yielding a C2H4/C2H6 selectivity of 17.5 at 5 ns in mixture adsorption and preserving the C2H4 uptake efficiency. The insights into the competitive diffusion of molecules in the coating layer and inside the zeolites provide a theoretical basis for the rational design of high-performance adsorbents. Zeolite ; Ethylene ; Chemistry ; Adsorption ; Separation (statistics) ; Chemical engineering ; Organic chemistry ; Catalysis ; Computer science ; Engineering Zeolite Catalysis and Synthesis ; Catalytic Processes in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Han‐Bing Lan | Xueyuan LIN | Huanhuan Ma | Liuying Lu | Wenxia Liao | Yuting Wang | Yi Chen | Chang Mo Li
Source: Foods (article, 2025-12-27)
Field: Chemistry — Organic Chemistry

Formation of Aroma Characteristics in Roasted Camellia oleifera Seeds <i>Camellia oleifera</i> oil (CO) is an important edible oil with excellent nutritional value. Recently, there has been an increasing market demand for oils with distinct flavor profiles. However, the formation mechanisms of characteristic aromas in CO remain unclear. Therefore, this study investigated the effects of roasting (170 °C, 0-30 min) on free amino acids, soluble sugars, and volatile components in camellia seeds and the corresponding oils. To further elucidate the generation mechanisms of flavor compounds in CO, reaction systems simulating the Maillard reaction and lipid oxidation were constructed. The results show strong correlations between volatile compounds and both soluble sugars and free amino acids during roasting. The key flavor precursors identified included arginine, glutamic acid, glycine, histidine, leucine, phenylalanine, and lysine, as well as sucrose and glucose. The simulated systems indicated that the flavor compounds in CO were mainly derived from the Maillard reaction and lipid oxidation, with significant interactions enhancing its unique flavor. This study potentially provides scientific guidance for the production and flavor control of fragrant CO. Maillard reaction ; Flavor ; Aroma ; Roasting ; Lipid oxidation ; Sucrose ; Amino acid Edible Oils Quality and Analysis ; Food Chemistry and Fat Analysis ; Lipid metabolism and biosynthesis
Authors: Xiaoying Liang | Shuo Yang | Lin Mu | Huimin Shi | Zhifeng Yao | Chen Xu
Source: Agronomy (article, 2025-08-26)
Field: Chemistry — Analytical Chemistry

A Non-Destructive System Using UVE Feature Selection and Lightweight Deep Learning to Assess Wheat Fusarium Head Blight Severity Levels Fusarium head blight (FHB), a globally significant agricultural disaster, causes annual losses of dozens of millions of tons of wheat toxins produced by FHB, such as deoxyroscyliaceol, further pose serious threats to human and livestock health. Consequently, rapid and non-destructive determination of FHB severity is crucial for implementing timely and precise scientific control measures, thereby ensuring wheat supply security. Therefore, this study adopts hyperspectral imaging (HSI) combined with a lightweight deep learning model. Firstly, the wheat ears were inoculated with Fusarium fungi at the spike’s midpoint, and HSI data were acquired, yielding 1660 samples representing varying disease severities. Through the integration of multiplicative scatter correction (MSC) and uninformative variable elimination (UVE) methods, features are extracted from spectral data in a manner that optimizes the reduction of feature dimensionality while preserving elevated classification accuracy. Finally, a lightweight FHB severity discrimination model based on MobileNetV2 was developed and deployed as an easy-to-use analysis system. Analysis revealed that UVE-selected characteristic bands for FHB severity predominantly fell within 590–680 nm (chlorophyll degradation related), 930–1043 nm (water stress related) and 738 nm (cell wall polysaccharide decomposition related). This distribution aligns with the synergistic effect of rapid chlorophyll degradation and structural damage accompanying disease progression. The resulting MobileNetV2 model achieved a mean average precision (mAP) of 99.93% on the training set and 98.26% on the independent test set. Crucially, it maintains an 8.50 MB parameter size, it processes data 2.36 times faster, significantly enhancing its suitability for field-deployed equipment by optimally balancing accuracy and operational efficiency. This advancement empowers agricultural workers to implement timely control measures, dramatically improving precision alongside optimized field deployment. Fusarium ; Feature (linguistics) ; Feature selection ; Blight ; Head (geology) ; Selection (genetic algorithm) ; Computer science ; Biology ; Artificial intelligence ; Agronomy ; Horticulture Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Luana Centorame | Nicolò La Porta | Michela Papandrea | Adriano Mancini | Ester Foppa Pedretti
Source: Applied Sciences (article, 2025-05-31)
Field: Chemistry — Analytical Chemistry

Evaluation of Feature Selection and Regression Models to Predict Biomass of Sweet Basil by Using Drone and Satellite Imagery The integration of precision agriculture technologies, such as remote sensing through drones and satellites, has significantly enhanced real-time crop monitoring. This study is among the first to combine multispectral data from both a drone equipped with Altum-PT camera and PlanetScope satellite imagery to predict fresh biomass in sweet basil grown in an open field, demonstrating the added value of integrating different spatial scales. A dataset of 40 sampling points was built by combining remote sensing data with field measurements, and seven vegetation indices were calculated for each point. Feature selection was performed using three different methods (F-score, Recursive Feature Elimination, and model-based selection), and the most informative features were then processed through Principal Component Analysis. Eight regression models were trained and evaluated using leave-one-out cross-validation. The best-performing models were Random Forest (R2 = 0.96 in training, R2 = 0.65 in testing) and k-Nearest Neighbours (R2 = 0.74 in training, R2 = 0.94 in testing), with kNN demonstrating superior generalization capability on unseen data. These findings highlight the potential of combining drone and satellite imagery for modelling basil agronomic traits, offering valuable insights for optimizing crop management strategies. Feature selection ; Selection (genetic algorithm) ; Satellite ; Regression ; Remote sensing ; Stepwise regression ; Environmental science ; Statistics ; Artificial intelligence ; Computer science ; Geography ; Mathematics ; Engineering Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: William E. Acree | Costas Panayiotou
Source: Liquids (article, 2025-09-25)
Field: Chemistry — Spectroscopy

Dispersion, Polar, and Hydrogen-Bonding Contributions to Solvation Free Energies A new method is presented for the estimation of contributions to solvation free energy from dispersion, polar, and hydrogen-bonding (HB) intermolecular interactions. COSMO-type quantum chemical solvation calculations are used for the development of four new molecular descriptors of solutes for their electrostatic interactions. The new model needs one to three solvent-specific parameters for the prediction of solvation free energies. The widely used Abraham’s LSER model is used for providing the reference solvation free energy data for the determination of the solvent-specific parameters. Extensive calculations in 80 solvent systems have verified the good performance of the model. The very same molecular descriptors are used for the calculation of solvation enthalpies. The advantages of the present model over Abraham’s LSER model are discussed along with the complementary character of the two models. Enthalpy and free-energy solvation information for pure solvents is translated into partial solvation parameters (PSP) analogous to the widely used Hansen solubility parameters and enlarge significantly their range of applications. The potential and the perspectives of the new approach for further molecular thermodynamic developments are discussed. Solvation ; Intermolecular force ; Enthalpy ; Implicit solvation ; Solubility ; Molecule Analytical Chemistry and Chromatography ; Various Chemistry Research Topics ; Phase Equilibria and Thermodynamics
Authors: Cyrille Santerre | David Touboul | Thierry Livache | Cyril Herrier
Source: Processes (article, 2025-05-07)
Field: Chemistry — Spectroscopy

Proof-of-Concept Study on Supercritical Fluid Chromatography Hyphenated with a Fast Optoelectronic Nose for Online Monitoring of Odorant Molecules In this study, we explored the use of surface plasmon resonance (SPR) and Mach–Zehnder interferometry for detecting compounds in complex mixtures separated by supercritical fluid chromatography. Each molecule was individually injected and analyzed by supercritical fluid chromatography (SFC) in a 10% alcoholic solution. The fingerprints obtained via the sensors were then compared to the fingerprints of the same molecules present in a lemon essential oil (EO) at the same dilution. The results show a remarkable correlation between UV sensors and electronic noses (e-nose), enabling compound detection. The obtained signals are normalized and presented as radar charts to visualize the specific olfactory signatures of each molecule. The olfactory profiles of monoterpenes C10H16 such as α-pinene and limonene show notable differences, as do the C10H16O isomers (citral, geranial, and neral). Mach–Zehnder interferometry also allows for the discrimination of limonene enantiomers, a challenging task for current chromatography techniques. Statistical analysis confirms the ability of these technologies to differentiate compounds, including isomers. Even if UV detection is more sensitive than SPR, e-noses (SPR and Mach–Zehnder interferometers) offer the unique advantage of providing specific signatures for each compound, facilitating real-time identification. This study demonstrates the effectiveness of combining e-noses with SFC for rapid, non-destructive detection of volatile compounds. This concept can be extended to other terpenoids and volatile compounds, and hybridization with gas chromatography could be a future potential development. Supercritical fluid ; Supercritical fluid chromatography ; Chromatography ; Proof of concept ; Electronic nose ; Chemistry ; Computer science ; Gas chromatography ; Artificial intelligence ; Organic chemistry Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Chromatography in Natural Products
Authors: Cláudia Elisabeth Munte | Raphael Kreitner | Reinhard Rachel | Karl O. Stetter | Werner Kremer | Hans Robert Kalbitzer
Source: Scientific Reports (article, 2025-08-05)
Field: Materials Science — Biomaterials

Biophysical characterization and solution structure of the cannulae-forming protein CanA from the hyperthermophilic archaeon Pyrodictium abyssi CanA from Pyrodictium abyssi, the main constituent of the extracellular protein network of this archaeon, forms a hollow-fiber network in the presence of divalent ions. The polymerization of CanA induced by divalent ions is characterized by (at least) two processes with rate constants of 0.19 and 0.03 ms<sup>-1</sup> at 298 K with a critical monomer concentration of 2.48 µM. A non-polymerizing mutant, K<sub>1</sub>-CanA, was created, and the NMR solution structure could be determined by multidimensional NMR spectroscopy. It mainly consists of β-pleated sheets and 2 small α-helices, arranged as β<sub>1</sub>β<sub>2</sub>β<sub>3</sub>β<sub>4</sub>α<sub>1</sub>β<sub>5</sub>β<sub>6</sub>α<sub>2</sub>β<sub>7</sub>β<sub>8</sub>β<sub>9</sub>β<sub>10</sub>β<sub>11</sub>β<sub>12</sub>β<sub>13</sub>. Of the 13 β-strands, 8 form a non-canonical jellyroll class I fold. Several interaction sites for divalent ions could be identified by [<sup>1</sup>H, <sup>15</sup>N]-SOFAST-HMQC spectroscopy in two main surface areas called BA1 and BA2, located at both ends of the jellyroll. The binding of divalent ions to the monomer induces significant local structural changes in these areas. In general, the affinities for Mg<sup>2+</sup>-ions to the sites in BA1 are smaller than those for Ca<sup>2+</sup>-ions. In contrast, in binding area BA2, Mg<sup>2+</sup>- and Ca<sup>2+</sup>-affinities are similar. The data suggest a conformational selection mechanism induced by ion binding as a first step in the polymerization process of CanA. Divalent ; Chemistry ; Crystallography ; Monomer ; Nuclear magnetic resonance spectroscopy ; Polymerization ; Affinities ; Ion ; Binding site ; Lysine ; Stereochemistry ; Polymer ; Biochemistry ; Amino acid ; Organic chemistry Calcium Carbonate Crystallization and Inhibition ; Enzyme Structure and Function ; Force Microscopy Techniques and Applications
Authors: Gustavo Vasconcelos | André Roberto Ortoncelli | Fabiana Martins Costa | Marlon Marcon
Source: Revista de Informática Teórica e Aplicada (article, 2025-02-20)
Field: Chemistry — Analytical Chemistry

Deep Semantic Segmentation Applied to Honey Bee Comb Cells Machine learning is increasingly prevalent in various fields, including beekeeping, where it has been applied to honeycomb analysis through Semantic Segmentation of images. This study evaluates different neural network architectures and encoders using the Intersection over Union (IoU) method to segment Apis mellifera honeycombs. A comparative analysis of semantic segmentation techniques was conducted, revealing advancements in the field with the SegNet architecture combined with the MobileNet encoder for feature extraction. We present a new dataset of 10 comb cell images, which we combine with the images from the DeepBee dataset in our experiments. Our results surpass the state-of-the-art results for comb cell segmentation in the DeepBee dataset. Artificial intelligence ; Honey bee ; Segmentation ; Computer science ; Biology ; Natural language processing ; Zoology Spectroscopy and Chemometric Analyses