jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ikechi Kalu Ndukwe | D. V. Yunovidov | Logic Yield | Mohammad Reza Bahrami | Manuel Mazzara | Temitope Olumide Olugbade
Source: Computer Optics (review, 2025-02-01)
Field: Chemistry — Analytical Chemistry

Quality inspection of fertilizer granules using computer vision – a review This research explores the fusion of computer vision and agricultural quality control. It investigates the efficacy of computer vision algorithms, particularly in image classification and object detection, for non-destructive assessment. These algorithms offer objective, rapid, and error-resistant analysis compared to human inspection. The study provides an extensive overview of using computer vision to evaluate grain and fertilizer granule quality, highlighting granule size's significance. It assesses prevailing object detection methods, outlining their advantages and drawbacks. The paper identifies the prevailing trend of framing quality inspection as an image classification challenge and suggests future research directions. These involve exploring object detection, image segmentation, or hybrid models to enhance fertilizer granule quality assessment. Fertilizer ; Computer vision ; Artificial intelligence ; Computer science ; Quality (philosophy) ; Agricultural engineering ; Environmental science ; Engineering ; Chemistry ; Physics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Roberto E. Blanco-Carapia | Ricardo Hernández-López | Sofía Lizeth Alcaráz‐Estrada | Rosa Elena Sarmiento‐Silva | Montserrat Elemi García-Hernández | Nancy Viridiana Estrada-Toledo | Gerardo Padilla-Bernal | Leonardo David Herrera-Zúñiga | Jorge Garza | Rubicelia Vargas | Eduardo González‐Zamora | Alejandro Islas‐Jácome
Source: International Journal of Molecular Sciences (article, 2025-08-07)
Field: Chemistry — Organic Chemistry

Multi-Component Synthesis of New Fluorinated-Pyrrolo[3,4-b]pyridin-5-ones Containing the 4-Amino-7-chloroquinoline Moiety and In Vitro–In Silico Studies Against Human SARS-CoV-2 A one-pot synthetic methodology that combines an Ugi-Zhu three-component reaction (UZ-3CR) with a cascade sequence (intermolecular <i>aza</i> Diels-Alder cycloaddition/intramolecular <i>N</i>-acylation/decarboxylation/dehydration) using microwave-heating conditions, ytterbium (III) triflate (Yb(OTf)<sub>3</sub>) as the catalyst, and chlorobenzene (for the first time in a multi-component reaction (MCR)) as the solvent, was developed to synthesize twelve new fluorinated-pyrrolo[3,4-<i>b</i>]pyridin-5-ones containing a 4-amino-7-chloroquinoline moiety, yielding 50-77% in 95 min per product, with associated atom economies around 88%, also per product. Additionally, by in vitro tests, compounds <b>19d</b> and <b>19i</b> were found to effectively stop early SARS-CoV-2 replication, IC<sub>50</sub> = 6.74 µM and 5.29 µM, at 0 h and 1 h respectively, while cell viability remained above 90% relative to the control vehicle at 10 µM. Additional computer-based studies revealed that the most active compounds formed strong favorable interactions with important viral proteins (M<sub>pro</sub>, NTDα and NTDo) of coronavirus, supporting a two-pronged approach that affects both how the virus infects the cells and how it replicates its genetic material. Finally, quantum chemistry analyses of non-covalent interactions were performed from Density-Functional Theory (DFT) to better understand how the active compounds hit the virus. Moiety ; Cycloaddition ; Chemistry ; Decarboxylation ; Trifluoromethanesulfonate ; Stereochemistry ; Atom economy ; Combinatorial chemistry ; Catalysis ; Organic chemistry Synthesis and Reactivity of Heterocycles ; Synthesis and biological activity ; Click Chemistry and Applications
Authors: Komal Zahra | Sumera Anwar | Fahad Shafiq | Shahbaz Khan | Kashaf Kahaf | Muhammad Ashraf
Source: Research Square (preprint, 2025-08-29)
Field: Materials Science — Materials Chemistry

Bioaccumulation of Tire Wear Nanoparticles in Spinach (Spinacia oleracea L.) Leaves: Consequences for Metabolic Dysregulation and Growth Inhibition Spinacia ; Bioaccumulation ; Spinach ; Nanoparticle ; Chemistry ; Horticulture ; Botany ; Biology ; Nanotechnology ; Materials science ; Environmental chemistry ; Biochemistry Nanoparticles: synthesis and applications ; Microplastics and Plastic Pollution ; Seed Germination and Physiology
Authors: Marilyn Kaul | R. Vanselow | Ahmed Y. Sanin | Ulf D. Kahlert | Christoph Janiak
Source: Chemistry (article, 2025-10-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Solvent-Dependent Stabilization of Gold Nanoparticles: A Comparative Study on Polymers and the Influence of Their Molecular Weight in Water and Ethanol Gold nanoparticles (AuNPs) are attracting more and more attention in life sciences, especially due to their versatile physicochemical properties whereby their colloidal stability in water and organic solvents is crucial. In this study, a systematic comparison of different polymers, synthesis methods and solvents was carried out. The AuNPs were synthesized using the ligand exchange reaction/postsynthetic addition reaction (PAR) and the one-pot synthesis with the polymers poly(vinyl alcohol) (PVA), poly(ethylene glycol) (PEG), poly(vinylpyrrolidone) (PVP) and poly(acrylic acid) (PAA), each with different molar weight averages. Analysis of the AuNP@Polymer conjugates by transmission electron microscopy (TEM) finds essentially unchanged gold nanoparticle core sizes of 11–18 or 11–19 nm in water and ethanol, respectively. The hydrodynamic diameter from dynamic light scattering (DLS) lies largely in the range from 20 to 70 nm and ultraviolet-visible spectroscopy (UV-Vis) showed gold plasmon resonance band maxima between 517 and 531 nm over both synthesis methods and solvents for most samples. The polymer PVA showed the best colloidal stability in both synthesis methods, both in water and after transfer to ethanol. An increased instability in ethanol could only be noted for the PEG coated samples. For the polymers PVP and PAA, the stability depended more specifically on the combination of synthesis method, polymer molecular weight and solvent. Polymer ; Dynamic light scattering ; Colloid ; Nanoparticle ; Colloidal gold ; Transmission electron microscopy ; Surface plasmon resonance ; Ethanol Gold and Silver Nanoparticles Synthesis and Applications ; Nanomaterials for catalytic reactions ; Nanoparticles: synthesis and applications
Authors: Wookyoung Jang | Jangwon Byun | Jun Hee Choi | Bolam Kim | Ki Wan Bong
Source: Small (article, 2025-06-12)
Field: Materials Science — Biomaterials

Polyethylene Glycol Nanofiller for Robust Lyophilization of Graphically Encoded Hydrogel Microparticles Graphically encoded hydrogel microparticle-based biosensing is a promising suspension microarray platform by virtue of multiplexing capability, robust sensitivity, and facilitated downstream analysis. However, the absence of a long-term and stable storage protocol for the hydrogel microparticle has been a bottleneck for the sensing platform to be adapted to practical fields. In this study, the polyethylene glycol (PEG) nanofiller-mediated lyophilization strategy of the hydrogel microparticles is presented. To inhibit the lyophilization-induced deformation of the porous structure and geometries of the particles, PEG is utilized as the filler material occupying the porous region in the hydrogel particles to prevent the interaction between polymer chains and the collapse of the porous structure. Based on the filler effect, the high decoding accuracy (more than 95%) for the lyophilized microparticles after reconstitution can be achieved by outstanding preservation of the particle geometries. Furthermore, the immunoassay performance of the antibody-functionalized microparticles lyophilized with PEG nanofiller is comparable to that of the non-lyophilized particles. Finally, the possibility of long-term storage (more than 6 months) of the lyophilized microparticles is confirmed by thermal aging. This finding is expected to promote the hydrogel microparticle-based sensing platform to be extended to practical fields via the innovation of the storage protocol. Polyethylene glycol ; Materials science ; Microparticle ; Self-healing hydrogels ; Nanoparticle ; PEG ratio ; Chemical engineering ; Nanotechnology ; Polymer chemistry Electrospun Nanofibers in Biomedical Applications ; Hydrogels: synthesis, properties, applications ; Pickering emulsions and particle stabilization
Authors: Nikhilesh Nishikant Narkhede | Vijaya Chalivendra
Source: Journal of Composites Science (article, 2025-05-29)
Field: Materials Science — Polymers and Plastics

Prediction of Mechanical and Fracture Properties of Lightweight Polyurethane Composites Using Machine Learning Methods This study aims to investigate the effectiveness of two machine learning methods for the prediction of the mechanical and fracture properties of Cenosphere-reinforced lightweight thermoset polyurethane composites. To evaluate the effectiveness of the models, datasets from our experimental study of composites made of five different volume fractions (0% to 40%) of Cenospheres (hollow Aluminum Silicate particles) in increments of 10% are fabricated. Experiments are conducted to determine the effect of the volume fraction of Cenospheres on Young’s modulus (both in tension and compression), percentage elongation at break, tensile strength, specific tensile strength, and fracture toughness of the composites. Two machine learning models, shallow artificial neural network (ANN) and the non-linear deep neural network (DNN), are employed to predict the above properties. A parametric study was performed for each model and optimized parameters were identified and later used to predict the properties beyond 40% volume fraction of Cenospheres. The predictions of non-linear DNN demonstrated less slope than shallow ANN and, for mass density, the non-linear DNN had unexpected predictions of increasing mass density with the addition of lighter Cenospheres. Hence, a double-hidden-layer DNN is used to predict the mass density beyond 40%, which provides the expected behavior. Composite material ; Materials science ; Polyurethane ; Fracture (geology) Polymer Nanocomposites and Properties ; Asphalt Pavement Performance Evaluation ; Natural Fiber Reinforced Composites
Authors: Hangxiu Liu | Youyou Wang | Yiheng Wang | Jingyi Wang | Hanqing Hu | Xinyi Zhong | Qingjun Yuan | Jian Yang
Source: Foods (article, 2025-06-03)
Field: Chemistry — Analytical Chemistry

Effective Identification of Variety and Origin of Chenpi Using Hyperspectral Imaging Assisted with Chemometric Models Geographical origins and varietal characteristics can significantly affect the quality of Citri Reticulatae Pericarpium (Chenpi), making rapid and accurate identification essential for consumer protection. To overcome the inefficiency and high cost of conventional detection methods, this study proposed a nondestructive approach that integrates hyperspectral imaging (HSI) with deep learning to classify Chenpi varieties and their geographical origins. Hyperspectral data were collected from 15 Chenpi varieties (citrus peel) across 13 major production regions in China using three dataset configurations: exocarp-facing-upward (Z), endocarp-facing-upward (F), and a fused dataset combining random orientations (ZF). Convolutional neural networks (CNNs) were developed and compared with conventional machine learning models, including partial least-squares discriminant analysis (PLS-DA), support vector machines (SVMs), and a multilayer perceptron (MLP). The CNN model achieved 96.39% accuracy for varietal classification with the ZF dataset, while the Z-PLSDA model optimized with second derivative (D2) preprocessing and competitive adaptive reweighted sampling (CARS) feature selection attained 91.67% accuracy in geographical origin discrimination. Feature wavelength selection strategies, such as CARS, simplified the model complexity while maintaining a classification performance comparable to that of the full-spectrum models. These findings demonstrated that HSI combined with deep learning could provide a rapid, nondestructive, and cost-effective solution for Chenpi quality assessment and origin traceability. Pattern recognition (psychology) ; Artificial intelligence ; Hyperspectral imaging ; Computer science ; Feature selection ; Partial least squares regression ; Preprocessor ; Support vector machine ; Convolutional neural network ; Mathematics ; Machine learning Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Horticultural and Viticultural Research
Authors: T Vaitheeswaran | J. Jayakumar | Prakash Periasamy | Rudhra Anandan | R Ranjth | M. Nageswaran
Source: International Journal of Automotive Science And Technology (article, 2025-06-30)
Field: Materials Science — Polymers and Plastics

Nano Silicon Carbide Content on Functional Behaviour of Polypropylene Composites Featured With Basalt Fiber for Automotive Material Polypropylene (PP) is commonly used for lightweight automotive cabinetry and panel applications due to its unique properties, including low density, chemical resistance, improved fatigue resistance, and good thermal insulation. However, the PP matrix typically exhibits lower strength and impact toughness. This research aims to enhance the mechanical properties of PP composites by incorporating 20 weight percent (wt%) of basalt fiber (3-5 mm) along with varying wt% of silicon carbide (SiC) nanoparticles, using the hot compression molding technique. To improve adhesive strength, an epoxy coupling agent is used, and a compression load of 100 MPa is applied. The effectiveness of the composite processing, the fiber-ceramic combination in surface morphology, and the effects of SiC nanoparticles on stress-strain behavior, impact strength, and microhardness of both pure PP and the PP/20 wt% basalt fiber composites are evaluated. The results are compared with the properties of the unreinforced PP matrix. Transmission electron microscope (TEM) analysis revealed a uniform distribution of fibers and ceramics with minimal spacing between the fibers, contributing to the enhanced mechanical properties of the composite. Notably, the PP composite with 20 wt% basalt fiber and 5 wt% silicon carbide demonstrated a tensile strength of 48 MPa, with a slight decrease in strain percentage to 10.5% due to the increased SiC content. It also exhibited a high impact toughness of 24 kJ/m² and improved microhardness of 39 HV. This PP/20 wt% chopped basalt fiber/5 wt% silicon carbide nanoparticle hybrid nanocomposite shows great potential for automotive cabinetry and panel applications. Silicon carbide ; Composite material ; Materials science ; Polypropylene ; Nano- ; Basalt fiber ; Fiber Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Mechanical Behavior of Composites
Authors: Egor S. Rodionov | А. Н. Черепанов | A. G. Fazlitdinova | Timur T. Sultanov | Victor G. Lupanov | Polina N. Mayer | Alexander E. Mayer
Source: Dynamics (article, 2025-02-15)
Field: Materials Science — Materials Chemistry

Dynamic Plasticity and Fracture of Al 7075 and V95T1 Alloys: High-Velocity Impact Experiments A novel method to measure dynamic flow stress and corresponding strain rates obtained from Taylor tests using profiled samples with a reduced cylindrical head part was applied to study the dynamic characteristics of similar commercial 7075 and V95T1 aluminum alloys. The measured dynamic flow stress is verified using a classical Taylor’s approach with uniform cylinders and compared with the literature data. Our study shows that the dynamic flow stress of 7075 alloy, which is 786 MPa at strain rates of (4–8) × 103 s−1, exceeds the value of 624 MPa for V95T1 alloy at strain rates of (2–6) × 103 s−1 by 25%. The threshold impact velocity resulting in fracture of the 4 mm head part of the profiled samples is 116–130 m/s for 7075 alloy and only 108 m/s for V95T1 alloy. The fracture pattern is also different between the alloys with characteristic shear-induced cracks oriented at 45° to the impact direction in the case of V95T1 alloy and perpendicular to the breaking off head part in the case of 7075 alloy. On the other hand, the compressive fracture strain of V95T1 alloy, which is 0.29–0.36, exceeds that of 7075 alloy, which is 0.27–0.33, by approximately 8%. Thus, V95T1 aluminum alloy exhibits less strength but is more ductile, while 7075 aluminum alloy exhibits more strength but is simultaneously more brittle. Plasticity ; Materials science ; Fracture (geology) ; Mechanics ; Composite material ; Physics High-Velocity Impact and Material Behavior ; Microstructure and mechanical properties ; Metal Forming Simulation Techniques
Authors: Supanut Phattarateera | Pongsakorn Pandee | Thitipa Thitisarot | Teerapat Rodcharoen | Poonsub Threepopnatkul
Source: Polymer Engineering and Science (article, 2025-05-14)
Field: Materials Science — Biomaterials

Polyvinyl alcohol and tapioca starch bioplastic films enriched with lavender essential oil and β‐cyclodextrin for enhanced food packaging applications Abstract This study was primarily aimed to develop films for food packaging by utilizing thermoplastic starch (TPS) and polyvinyl alcohol (PVA). The films were purposefully modified with lavender essential oil (LVO) and β‐cyclodextrin (β‐CD). The impact of TPS content on PVA film properties and the influence of LVO/β‐CD content on the properties of PVA/TPS/G/LVO/β‐CD films were investigated in this research. Blends of PVA, TPS, and (LVO/β‐CD) were processed by using twin‐screw and blown‐film extrusion. Different TPS levels (0%, 20%, 30%, and 40% by weight), LVO/β‐CD contents (0%, 0.5%, 1.0%, and 1.5% by weight), and LVO concentrations (0.1, 0.2, and 0.3 ml/g of β‐CD) were varied in these films with fixed glycerol (G) at 20 parts per hundred resin. As a consequence, the integration of TPS into PVA/G films would increase thermal stability but decrease glass transition temperature, melting temperature, and crystallinity. However, it would decrease tensile strength and modulus. Furthermore, the addition of LVO/β‐CD into PVA/TPS blends led to the enhancement of thermal stability and crystallinity, although it would decrease tensile strength and modulus. In terms of antibacterial activities, PVA60/TPS40/G/LVO/β‐CD film exhibited better resistance against Staphylococcus aureus (78%) compared to Escherichia coli (64%). However, it was noteworthy that thermal stability and mechanical properties would be decreased with the increment of LVO content. Highlights Films TPS/PVA with lavender oil and β‐cyclodextrin were successfully prepared. Adding TPS to the film increased thermal stability but decreased crystallinity. The addition of TPS and LVO/β‐CD reduced tensile strength and modulus. Film/LVO/β‐CD film showed better resistance to Staphylococcus aureus (78%) than to Escherichia coli (64%). Increasing LVO improved crystallinity but decreased mechanical properties. Materials science ; Crystallinity ; Polyvinyl alcohol ; Ultimate tensile strength ; Thermoplastic ; Thermal stability ; Starch ; Composite material ; Glycerol ; Chemical engineering ; Food science ; Chemistry ; Organic chemistry Nanocomposite Films for Food Packaging ; biodegradable polymer synthesis and properties ; Natural Fiber Reinforced Composites
Authors: Cagri Yardimci | Mevlüt Ersoy
Source: Research on Engineering Structures and Materials (article, 2025-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Optimizing deep learning architectures for SEM image classification using advanced dimensionality reduction techniques Non-negative Matrix Factorization (NMF) and Singular Value Decomposition (SVD) are widely recognized as pivotal dimensionality reduction techniques in the literature, particularly for deep learning applications involving large and high-dimensional datasets like SEM images. This study systematically evaluates the impact of SVD and NMF on the performance, efficiency, and energy consumption of four deep learning architectures: GoogleNet, AlexNet, ResNet, and SqueezeNet. By applying these techniques to reduce dataset dimensions, we observed that SVD excelled in computational efficiency, achieving up to 35% faster processing times compared to raw datasets. NMF, on the other hand, provided superior feature interpretability, which proved beneficial for tasks requiring meaningful pattern extraction. Energy consumption analysis revealed that SVD led to a 28% reduction in computational energy cost on average, making it a practical choice for resource-constrained environments. Among the evaluated models, ResNet consistently delivered the highest classification accuracy after dimensionality reduction, showing an improvement of 4-6% over models trained on non-reduced data. These findings underscore the critical role of dimensionality reduction in enhancing the scalability, energy efficiency, and classification accuracy of deep learning models, offering valuable insights for optimizing high-dimensional data applications in both academic and industrial contexts. Dimensionality reduction ; Artificial intelligence ; Reduction (mathematics) ; Computer science ; Deep learning ; Pattern recognition (psychology) ; Curse of dimensionality ; Machine learning ; Mathematics Electron and X-Ray Spectroscopy Techniques ; Advancements in Photolithography Techniques ; Machine Learning in Materials Science
Authors: Manima Mishra | Amit Jaiswal | Arun Kumar | Pramod Kumar | Murli Dhar Mitra | Ranjeet Kumar
Source: Journal of Chemical Crystallography (article, 2025-12-16)
Field: Chemistry — Organic Chemistry

Experimental and Computational Studies of Intermolecular Interactions Analysis of Cyanopyridine Derivative and its Application in Anti-inflammatory Activities Intermolecular force ; Non-covalent interactions ; Intermolecular interaction ; Organometallic chemistry ; Derivative (finance) ; Hydrogen bond ; Docking (animal) Synthesis and biological activity ; Computational Drug Discovery Methods ; Synthesis and Reactivity of Heterocycles
Authors: Yuta Otsuka | Tadahiro Higashinakao | Hiroshi Kōno | Masafumi Kikuchi
Source: Journal of Nondestructive Evaluation (article, 2025-04-25)
Field: Chemistry — Analytical Chemistry

Monitoring Dental Zinc Oxide Polyacrylate Cement Using Near-Infrared Spectroscopy Abstract This study aims to investigate the reaction kinetics of polycarboxylate cements using NIR spectroscopy with chemometrics. To achieve 100% inspection of medical devices and drugs, it is desirable to develop non-destructive, non-contact quality control methods. One effective method for this is chemometrics, which uses near-infrared spectroscopy and chemometrics. A dental cement, polycarboxylate cement, was prepared, and the spectral changes from 1 min to 15 min were observed using NIR spectroscopy. The obtained spectra were evaluated by two-dimensional correlation spectroscopy. Carboxylate changes in the cement were analyzed using principal component analysis, and a score plot against loadings and time was shown. It was suggested that the hardening mechanism of cement can be plotted as a pseudo-first-order reaction. Chemometrics ; Cement ; Spectroscopy ; Materials science ; Infrared spectroscopy ; Hardening (computing) ; Near-infrared spectroscopy ; Analytical Chemistry (journal) ; Principal component analysis ; Mineralogy ; Composite material ; Chemistry ; Optics ; Chromatography ; Organic chemistry ; Mathematics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Thermography and Photoacoustic Techniques