jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Leonid Legashev | Ivan Khokhlov | Irina Bolodurina | Alexander Shukhman | Svetlana Kolesnik
Source: Machine Learning and Knowledge Extraction (article, 2025-08-29)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction Models of Beneficial and Toxicological Effects of Zinc Oxide Nanoparticles in Rat Feed Nanoparticles have found widespread application across diverse fields, including agriculture and animal husbandry. However, a persistent challenge in laboratory-based studies involving nanoparticle exposure is the limited availability of experimental data, which constrains the robustness and generalizability of findings. This study presents a comprehensive analysis of the impact of zinc oxide nanoparticles (ZnO NPs) in feed on elemental homeostasis in male Wistar rats. Using correlation-based network analysis, a correlation graph weight value of 15.44 and a newly proposed weighted importance score of 1.319 were calculated, indicating that a dose of 3.1 mg/kg represents an optimal balance between efficacy and physiological stability. To address the issue of limited sample size, synthetic data generation was performed using generative adversarial networks, enabling data augmentation while preserving the statistical characteristics of the original dataset. Machine learning models based on fully connected neural networks and kernel ridge regression, enhanced with a custom loss function, were developed and evaluated. These models demonstrated strong predictive performance across a ZnO NP concentration range of 1–150 mg/kg, accurately capturing the dependencies of essential element, protein, and enzyme levels in blood on nanoparticle dosage. Notably, the presence of toxic elements and some other elements at ultra-low concentrations exhibited non-random patterns, suggesting potential systemic responses or early indicators of nanoparticle-induced perturbations and probable inability of synthetic data to capture the true dynamics. The integration of machine learning with synthetic data expansion provides a promising approach for analyzing complex biological responses in data-scarce experimental settings, contributing to the safer and more effective application of nanoparticles in animal nutrition. Zinc ; Nanoparticle ; Machine learning ; Computer science ; Artificial intelligence ; Materials science ; Nanotechnology ; Metallurgy Nanoparticles: synthesis and applications
Authors: М. А. Резвова | Т. В. Глушкова | Kirill Yu Klyshnikov | А.А. Пыкин | Tatiana B. Tkachenko | Tatiana N. Akentieva | A. E. Kostyunin | P. S. Onishchenko | N. N. Borisova | Marina P. Fokeeva | В. Г. Матвеева | Е. А. Сенокосова | E. O. Krivkina | Е. А. Овчаренко
Source: Frontiers in Bioengineering and Biotechnology (article, 2025-09-01)
Field: Materials Science — Biomaterials

Biocompatible nanocomposite cryogels with improved mechanical properties based on polyvinyl alcohol and carbon nanotubes for cardiovascular applications The unique properties of hydrogels have enabled their widespread use in various biomedical applications, including heart valve and blood vessel replacements. However, their current applications are limited by poor mechanical properties, including low strength, susceptibility to plastic deformation, and inadequate wear resistance, which are critical for load-bearing tissue replacements. Nanocomposite cryogels composed of polyvinyl alcohol and carbon nanotubes prepared using a DMSO/H<sub>2</sub>O solvent mixture, present a promising solution to address these limitations. Addition of nanoparticles up to 0.5% of the polymer mass showed a remarkable 59% increase in mechanical strength compared to single component cryogels. The reinforcement effect was also pronounced in strain hardening at large deformations. CNTs addition also enhanced the adhesion of Ea.hy 926 cells. However, the overall cell coverage on the cryogel surface was lower compared to control culture plastic, suggesting selective adhesion behavior. Comprehensive hemocompatibility testing showed minimal adsorption of protein molecules such as albumin and fibrinogen and no platelet adhesion when exposed to platelet rich plasma. Post contact platelet aggregation was same as untreated plasma. The studied materials also elicited minimal inflammatory response and no calcification unlike polytetrafluoroethylene which is clinically used, further proving biocompatibility. These findings suggest that PVA-based nanocomposite cryogels synthesized with dispersed CNTs hold significant promise for applications in cardiovascular surgery, particularly in the development of mechanically robust and biocompatible vascular grafts and heart valve prostheses. Polyvinyl alcohol ; Biocompatible material ; Carbon nanotube ; Nanocomposite ; Materials science ; Chemical engineering ; Nanotechnology ; Composite material ; Biomedical engineering ; Medicine Electrospun Nanofibers in Biomedical Applications ; Polymer Nanocomposite Synthesis and Irradiation ; Tissue Engineering and Regenerative Medicine
Authors: Prarthana Bora | Chinmoy Bhuyan | D. Shanthana Lakshmi | Swapnali Hazarika | M. Tańczyk | Srinivas T.G. Srimath
Source: Polymers (article, 2025-12-09)
Field: Materials Science — Biomaterials

Recent Advances in Membrane-Based Air Filtration Technologies for Ambient Particulate Matter Separation Varied types of particulate matter (PM) persist in the environment and exert a harmful impact on public health. The aim of this review article is to explore the key role of membrane technology in the separation of PM from ambient air. Nanofibrous, microporous, Janus, photocatalytic and hollow fiber membranes have found significant utilization in the effective separation of PM. Recent advancements in membrane technology and their key properties such as antibacterial activity, flame retardancy, wettability, thermal stability and reusability have been underscored in this review article. Moreover, the principles of PM separation have been discussed in detail to understand the working pathway of a membrane air filter via physical, chemical or biological approaches. A brief comparison between the conventional air filters and membrane air filters is provided in terms of cost, separation principle and respective merits and demerits to understand the importance of membranes in the realm of PM separation. This study also highlighted the commercial status of PM air filters with respect to their cost and scalability. By focusing on the innovations in membrane filters, this review article has highlighted the futuristic approaches such as green fabrication techniques, highly efficient material incorporation, use of AI/ML, etc., to overcome the challenges associated with conventional air filters. Membrane ; Particulates ; Filtration (mathematics) ; Air filter ; Membrane technology ; Reusability ; Microfiltration ; Air filtration Electrospun Nanofibers in Biomedical Applications ; Membrane Separation Technologies ; Aerosol Filtration and Electrostatic Precipitation
Authors: Pushpanjali Pal | J.D. Stephen | Jinesh Mathew
Source: Microwave and Optical Technology Letters (article, 2025-04-29)
Field: Chemistry — Analytical Chemistry

Fiber Optic Device for the Detection of Adulteration of Olive Oil With Palm Oil ABSTRACT Adulteration of olive oil with palm oil is studied using an optical fiber probe‐based near‐infrared spectroscopy technique, from which four peaks were identified and explained. Then, the peak at around 1550 nm was selected to design a low‐cost fiber optic device to detect adulteration. The demonstrated device shows a power change of 0.09 dBm per 1% volume change of palm oil in olive oil. The total average power change observed in the study was 9 dBm for the adulteration. The experiment was repeated more than 100 times in each volumetric sample to estimate the measurement error, and the average error observed in power measurement was 1.7 ± 0.7 dBm. The device's measurement range was 0%–100% adulteration, and measurement accuracy was at least 20% adulteration without any data averaging; the latter can be improved by signal processing the data acquired by the sensor. The demonstrated low‐cost optical device can detect olive oil adulteration with palm oil non‐destructively, remotely, and instantaneously, showing high potential for commercialization. Palm oil ; Olive oil ; Fiber ; Materials science ; Pulp and paper industry ; Environmental science ; Engineering ; Food science ; Composite material ; Chemistry ; Agroforestry Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Advanced Chemical Sensor Technologies
Authors: Yaqian Ma | Yang Li | Nan Wang | Chenxiao Han | Qisheng Liu | Liqin Sun | Zhao Ma | Hailing Zhang
Source: Applied Microbiology and Biotechnology (article, 2025-09-03)
Field: Materials Science — Biomaterials

Efficient secretory expression of type III recombinant human collagen with triple-helical structure in Komagataella phaffii Recombinant human collagen (rhCol) holds broad potential in biomedical and industrial applications due to its high purity and low immunogenicity. However, large-scale production of structurally stable and functionally active rhCol remains challenging. A novel strategy integrating collagen sequence optimization and microbial prolyl-4-hydroxylase (P4H) screening was developed to enable efficient production of triple-helical rhCol in Komagataella phaffii. Five Type III collagen variants (ColP1 ~ ColP5) were rationally designed based on interchain salt-bridge engineering to improve structural stability and biological activity, with ColP2 showing superior expression and functionality. A systematic evaluation of four microbial P4Hs identified Bacillus megaterium P4H (BmP4H) as the most effective catalyst for proline hydroxylation, enabling stable triple-helix formation. Combined with strain optimization, promoter and signal peptide screening, and 5-L scale fermentation, this approach achieved a high rhCol yield of 2.54 g/L with confirmed triple-helical structure. These results demonstrate an integrated and scalable platform for high-level production of functional recombinant collagen, providing a promising foundation for its industrial and clinical applications. Key Points • Co-expression of BmP4H enables stable triple-helical collagen in yeast. • Strain X-33, promoter P<sub>AOX1</sub>, and a-factor leader optimize collagen secretion. • Scale-up in 5L bioreactor achieves 2.54 g/L rhCol production. Recombinant DNA ; Triple helix ; Chemistry ; Biochemistry ; Cell biology ; Type (biology) ; Biology ; Stereochemistry ; Gene Collagen: Extraction and Characterization ; Enzyme Production and Characterization ; Silk-based biomaterials and applications
Authors: Song He | Chenxi Wang | Wenqing Shi | Chunyan Zhou | Chenxi Liu | Ying Xu | Yaxing Gui | Xinhui Jiang | Guorong Fan
Source: Chemistry & Biodiversity (article, 2025-01-31)
Field: Chemistry — Analytical Chemistry

Assessment of the Properties and Mechanism of Three Lotus ( <i>Nelumbo nucifera</i> Gaertn.) Parts for Sleep Improvement Lotus leaves (LL), petals (LP), and seeds (LS) are believed to have properties that can potentially improve sleep. However, their efficacy in improving sleep has not yet been fully validated. This study aimed to investigate the multitarget mechanisms of extracts from these lotus parts for sleep improvement using chemical analysis, bioactivity assessment, meta-analysis, network pharmacology evaluation, and molecular docking studies. The chemical components in LL, LP, and LS were qualitatively detected using ultra-high-performance liquid chromatography-ultraviolet-quadrupole time-of-flight tandem mass spectrometry, their total flavonoid and phenolic contents were determined, and their 2,2-diphenyl-1-picrylhydrazyl scavenging activity was estimated. Subsequently, relevant literature was collected for meta-analysis. Finally, potential targets were predicted using network pharmacology, and the results were validated by molecular docking studies. A total of 48 compounds, including 16 flavonoids, 26 alkaloids, 2 amino acids, and 4 other compounds, were identified from the lotus extracts. LL had the highest total flavonoid content, while LS had the highest total phenolic content. Extracts from both parts exerted significant antioxidant effects. A meta-analysis identified the potential of lotus to improve sleep. Armepavine, asimilobine, nuciferin, and luteolin were identified as key components, and AKT1, EGFR, DRD2, and PIK3R1 were revealed as key targets. PI3K-Akt was identified as a key signaling pathway. This study provides an important reference for studying the role of the lotus in improving sleep quality. Lotus ; Chemistry ; Luteolin ; Flavonoid ; Nelumbo nucifera ; Lotus effect ; Lotus japonicus ; Folium of Descartes ; Computational biology ; Traditional medicine ; Pharmacology ; Antioxidant ; Biochemistry ; High-performance liquid chromatography ; Chromatography ; Botany ; Organic chemistry ; Biology ; Medicine Chromatography in Natural Products ; Ginseng Biological Effects and Applications ; Traditional Chinese Medicine Analysis
Authors: Zihan Lin | Lijuan Niu | Hailong Zhang | Haitao Shen | Wenzhong Hu | Wenyuan Yin | Fei Guo
Source: Frontiers in Microbiology (article, 2025-09-15)
Field: Materials Science — Materials Chemistry

Biosynthesis, optimization, and multifunctional biomedical applications of gold nanoparticles mediated by Streptomyces sp. YJD18 This study employed extracellular secretions from <i>Streptomyces</i> sp. YJD18, isolated from saline-alkali soil, to synthesize gold nanoparticles (AuNPs) using optimized conditions: 60 min boiling, 48 h mycelial suspension, pH 6.25, 1.0 mM chloroauric acid, 1:1 supernatant-to-water ratio, and 9.0 g mycelial wet weight. AuNP formation was confirmed by a yellow-to-ruby red color shift and a 525 nm UV-Vis absorption peak. TEM and SAED revealed irregular, polycrystalline spheres with good dispersity. Zeta potential (-11.6 mV) and DLS (20-30 nm) confirmed uniform surface properties and narrow size distribution. FTIR showed C-H and amide groups did not contribute to AuNP synthesis. The AuNPs exhibited dose-dependent cytotoxicity against cancer (cervical, lung, liver, breast) and normal (kidney, liver) cells, optimal wound healing at 30 μg/mL (58.07% at 48 h), and concentration-dependent DPPH scavenging (IC<sub>50</sub>: 12,419.00 μg/mL), suggesting biomedical potential. Chloroauric acid ; Zeta potential ; Colloidal gold ; Cytotoxicity ; Nanoparticle ; Extracellular polymeric substance ; Fourier transform infrared spectroscopy ; Mycelium ; Biocompatibility Nanoparticles: synthesis and applications ; Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications
Authors: Ji Won Choi | Soo Been Cho | Mohamad Soleh Hidayat | Woon-Ha Hwang | Young-Son Cho | Hoonsoo Lee | Byoung–Kwan Cho | Geonwoo Kim
Source: Frontiers in Plant Science (article, 2025-10-07)
Field: Chemistry — Analytical Chemistry

Application of multimodal data fusion and explainable AI for classifying water stress in sweet potatoes Sweet potato (Ipomoea batatas L.) exhibits strong resilience in nutrient-poor soils and contains high levels of dietary fiber and antioxidant compounds. It also is highly tolerant to water stress, which has also contributed to its global distribution, particularly in regions prone to climatic variability. However, frequent abnormal climatic events have recently caused declines in both the quality and yield of sweet potatoes. To address this, machine learning (ML) and deep learning (DL) models based on a Vision Transformer-Convolutional Neural Network (ViT-CNN) were developed to classify water stress levels in sweet potato. RGB-thermal imagery captured from low-altitude platforms and various growth indicators were used to develop the classifier. The K-Nearest Neighbors (KNN) model outperformed other ML models in classifying water stress levels at all growth stages. The DL model simplified the original five-level water stress classification into three levels. This enhanced its sensitivity to extreme stress conditions, improve model performance, and increased its applicability to practical agricultural management strategies. To enhance practical applicability under open-field conditions, several environmental variables were newly defined to calculate the crop water stress index (CWSI). Furthermore, an integrated system was developed using gradient-weighted class activation mapping (Grad-CAM), explainable artificial intelligence (XAI), and a graphical user interface (GUI) to support intuitive interpretation and actionable decision-making. The system will be expanded into an online and fixed-camera platform to enhance its applicability to smart farming in diverse field crops. Artificial neural network ; Field (mathematics) ; Resilience (materials science) ; Sensor fusion ; Deep learning ; Agriculture ; Graphical user interface ; Precision agriculture ; Water stress Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Water Quality Monitoring and Analysis
Authors: A.Raghuvardhan Babu
Source: Journal of Information Systems Engineering & Management (article, 2025-02-05)
Field: Materials Science — Polymers and Plastics

Analysing the Impact of Machine Learning on Textile Quality Enhancement and Defect Detection One of the mainstays of global manufacturing is the textile sector, where quality control is essential to guaranteeing both customer happiness and product dependability. Conventional techniques for identifying flaws in fabric textures and improving quality frequently depend on manual examination, which is laborious, arbitrary, and prone to mistakes. This study examines the revolutionary effects of machine learning (ML) in improving textile quality and identifying flaws. Machine learning (ML) provides accurate, automated, and scalable methods for detecting anomalies in fabric textures and enhancing manufacturing efficiency by utilizing sophisticated algorithms, such as supervised learning, unsupervised learning, and deep learning approaches. This paper highlights the effective use of ML models in the textile industry by reviewing current practices and investigating ML applications in pattern recognition, anomaly detection, and predictive maintenance. There is also discussion of difficulties including integration into conventional procedures, computing complexity, and data restrictions. The results highlight how machine learning (ML) has the potential to transform the textile industry by lowering faults, streamlining procedures, and spurring innovation in quality control systems. This study ends with suggestions for future developments, such as new technologies and cooperative strategies to strengthen machine learning's position in the textile sector. Textile ; Quality (philosophy) ; Computer science ; Manufacturing engineering ; Engineering ; Artificial intelligence ; Machine learning ; Materials science ; Composite material ; Physics Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Color Science and Applications
Authors: Marta A. Teixeira | Inês Leite | Raquel Gonçalves | Helena Vilaça | Catarina Guise | Carla Silva
Source: Preprints.org (preprint, 2025-07-02)
Field: Materials Science — Biomaterials

Comprehensive Evaluation Of Wet Spun Polyhydroxyalkanoate Fibres: Morphology, Crystallinity, And Thermal Properties In response to the increasing environmental concerns, significant efforts have been made to reduce the reliance on fossil fuel-based plastics, driving the development of sustainable alternatives such as polyhydroxyalkanoates (PHAs). This study investigates the processing of various PHAs into fibres, focusing on their morphological, thermal, and mechanical properties. Different PHAs were spun into fibres at a 15% (w/v) con-centration using wet spinning techniques. Among the PHAs studied, commercially available PHBHHx, used as a reference, exhibited a spongy morphology in the fibres and demonstrated thermal vulnerability due to its rapid degradation. Blended fibres showed enhanced morphological and mechanical properties compared to neat fibres. In Fouri-er-transform infrared spectroscopy (FTIR), no differences were observed between the unprocessed polymers and the wet spun polymeric fibres, indicating that the wet spinning process did not affect the molecular structure of the polymers. Thermal and mechanical evaluations confirmed the miscibility between the polymers in the blends. Overall, these results highlight the potential of polymer blending to enhance fibre functionality and broaden their application potential. However, the study also empha-sizes the need to explore optimized feed rates to improve fibre production efficiency and increase their resistance, to enlarge their suitability for various applications. Crystallinity ; Polyhydroxyalkanoates ; Morphology (biology) ; Materials science ; Polymer science ; Thermal ; Composite material ; Chemical engineering ; Geology ; Engineering ; Physics ; Thermodynamics ; Paleontology biodegradable polymer synthesis and properties ; Additive Manufacturing and 3D Printing Technologies ; Electrospun Nanofibers in Biomedical Applications
Authors: Bo Dong | Yun-Fei Jia | Wei Wang
Source: Chinese Journal of Mechanical Engineering (article, 2025-01-31)
Field: Materials Science — Biomaterials

Designing Load-Bearing Bio-Inspired Materials for Simultaneous Static Properties and Dynamic Damping: Multi-Objective Optimization for Micro-Structure Abstract Biological load-bearing materials, like the nacre in shells, have a unique staggered structure that supports their superior mechanical properties. Engineers have been encouraged to imitate it to create load-bearing bio-inspired materials which have excellent properties not present in conventional composites. To create such materials with desirable mechanical properties, the optimum structural parameters combination must be selected. Moreover, the optimal design of bio-inspired composites needs to take into account the trade-offs between various mechanical properties. In this paper, multi-objective optimization models were developed using structural parameters as design variables and mechanical properties as optimization objectives, including stiffness, strength, toughness, and dynamic damping. Using the NSGA-II optimization algorithm, a set of optimal solutions were solved. Additionally, three different structures in natural nacre were introduced in order to utilize the better structure when design bio-inspired materials. The range of optimal solutions that obtained using results from previous research were examined and explained why this collection of optimal solution ranges is better. Also, optimal solutions were compared with the structural features and mechanical properties of real nacre and artificial biomimetic composites to validate our models. Finally, the optimum design strategies can be obtained for nacre-like composites. Our research methodically proposes an optimization method for achieving load-bearing bio-inspired materials with excellent properties and creates a set of optimal solutions from which designers can select the one that best suits their preferences, allowing the fabricated materials to demonstrate preferred performance. Load bearing ; Bearing (navigation) ; Structural engineering ; Materials science ; Dynamic load testing ; Computer science ; Composite material ; Engineering ; Artificial intelligence Calcium Carbonate Crystallization and Inhibition ; Bone Tissue Engineering Materials ; Electrophoretic Deposition in Materials Science