jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuxin Zeng | Jieyu Yang | Yong Li
Source: Pharmaceutics (article, 2025-02-10)
Field: Materials Science — Biomaterials

Compartment Model and Neural Network-Based Analysis of Combination Medication Ratios <b>Background:</b> Combination medication strategies often involve complex interactions, making determining the appropriate pharmacodynamic component ratios challenging. <b>Methods:</b> This study established a time-dose relationship model through the compartment model, deriving the in vivo drug quantity ratios corresponding to the blood concentrations of the pharmacodynamic components. A neural network was then employed to establish a dose-effect relationship model between the blood concentrations of the pharmacodynamic components and the overall body response. Utilizing the feedback adjustment mechanism of neural networks continuously adjusts the network to achieve the desired drug efficacy, thereby deriving the corresponding dose ratio of the pharmacodynamic components. Empirical studies were conducted on combining <i>Cynanchum otophyllum</i> saponins <i>M</i><sub>1</sub> and <i>M</i><sub>2</sub> with phenobarbital for epilepsy treatment, as well as the anti-ischemic stroke activity of the prototype and metabolites of <i>Erigeron breviscapus</i>. <b>Results:</b> After adjusting the efficacy, the model recalculated the new ratio proportions for each combination, validated by the reduced Combination Index (<i>CI</i>). <b>Conclusions:</b> This model provides a new approach to combination medication strategies. Pharmacodynamics ; Phenobarbital ; Artificial neural network ; Pharmacology ; Compartment (ship) ; Computer science ; Component (thermodynamics) ; Ischemic stroke ; Medicine ; Artificial intelligence ; Pharmacokinetics ; Internal medicine ; Ischemia Phytochemistry and Bioactive Compounds ; Phytochemistry and Biological Activities ; Phytochemical Studies and Bioactivities
Authors: Ömer Şengül
Source: Journal of Scientific Reports-A (article, 2025-12-30)
Field: Materials Science — Polymers and Plastics

Mechanical properties of recycled polypropylene composites with walnut shell additives by injection moulding In this study, recycled polypropylene, which is one of the commonly used thermoplastics, and walnut shell powders were used as fillers. To increase the compatibility of the composite material, 3% SiO₂ particle size of 75 nm was added. In this research, a new composite material was produced by minimizing the harmful effects of the composite material on the environment. Some mechanical properties of this new material were investigated. Walnut shell powder was added to recycled Polypropylene (PP) material at the rates of 2, 5, 8, 10, 12.5, 15, 18, and 20%. The regenerated polypropylene matrix was modified with walnut shell–derived natural reinforcement, leading to a composite material exhibiting enhanced mechanical performance. Tensile test, density test, hardness test and Izod impact tests were applied to the new composite material. As a result of the tests, 15% Walnut Shell Powder (WSP) was found to be the most suitable filler. In the new composite material, 10% improvement in tensile strength, 11% improvement in yield strength and 106% improvement in modulus of elasticity occurred. As a result of the Izod impact test, 11% improvement occurred. The hardness and density value of the new composite material increased by 4 and 11%, respectively. The study verifies that the application of walnut shells as a natural filler considerably strengthens the environmental and performance-related benefits of recycled polypropylene composites. This study shows that when walnut shells are used as fillers, it enables recycled polypropylene composite to be much more advantageous as filler in terms of both environmental sustainability and performance. Polypropylene ; Composite number ; Ultimate tensile strength ; Izod impact strength test ; Young's modulus ; Filler (materials) ; Compatibility (geochemistry) Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Material Selection and Properties
Authors: Artem Sokolskyi | Nataliia Shyrokobokova | V. M. Pleskach | Oleksandr Petrashov
Source: New Materials and Technologies in Metallurgy and Mechanical Engineering (article, 2025-09-26)
Field: Materials Science — General Materials Science

RESEARCH ON THE INFLUENCE OF TECHNOLOGICAL FACTORS ON THE QUALITY OF COMPOSITE MATERIALS Purpose. The purpose of this study is to analyze the impact of the main stages of manufacturing composite products on the probability of various defects. Special attention is paid to finding and justifying practical ways to eliminate them at the early stages of the production process. The work is aimed at identifying the relationship between technological parameters, such as temperature, pressure and polymerization time, and the final quality, strength and reliability of the finished product. In addition, the goal is to develop methodological recommendations for manufacturers to minimize defects and increase production efficiency. Research methods. The work uses an analytical method and a detailed consideration of factors that directly affect the quality of composite materials. An analysis of scientific and technical literature was conducted, as well as systematization of production practice data. To detect hidden defects and assess their impact on the structure of the material, modern methods of non-destructive testing were used. This allowed obtaining objective data on the condition of the material without its destruction, which is critically important for preserving the integrity of products. Results. Based on literary data and production practice, attention is paid to the production factors that most affect the quality of manufacturing composite materials and parts made from them. Analysis and comparison of existing non-destructive testing methods to preserve the structure of products, as well as the use of various control methods to detect defects in the complex structure of composite materials, which can ensure high quality of the manufactured products. Practical value. The work examines the factors that affect the quality of the product during its operation, as well as technological factors and methods of their control. For the study, practical work was carried out to determine the influence of technological factors on the quality of the product, and methods for eliminating defects in the manufacturing of the part. Quality (philosophy) ; Production (economics) ; Reliability (semiconductor) ; Work (physics) ; Control (management) ; Production control Material Properties and Applications
Authors: A. Ousegui | Bernard Marcos
Source: Hydrogen (article, 2025-11-01)
Field: Chemistry — Inorganic Chemistry

Computational Fluid Dynamic Modeling and Parametric Optimization of Hydrogen Adsorption in Stationary Hydrogen Tanks This study investigates hydrogen storage enhancement through adsorption in porous materials by coupling the Dubinin–Astakhov (D-A) adsorption model with H2 conservation equations (mass, momentum, and energy). The resulting system of partial differential equations (PDEs) was solved numerically using the finite element method (FEM). Experimental work using activated carbon as an adsorbent was carried out to validate the model. The comparison showed good agreement in terms of temperature distribution, average pressure of the system, and the amount of adsorbed hydrogen (H2). Further simulations with different adsorbents indicated that compact metal–organic framework 5 (MOF-5) is the most effective material in terms of H2 adsorption. Additionally, the pair (273 K, 800 s) remains the optimal combination of injection temperature and time. The findings underscore the prospective advantages of optimized MOF-5-based systems for enhanced hydrogen storage. These systems offer increased capacity and safety compared to traditional adsorbents. Subsequent research should investigate multi-objective optimization of material properties and system geometry, along with evaluating dynamic cycling performance in practical operating conditions. Additionally, experimental validation on MOF-5-based storage prototypes would further reinforce the model’s predictive capabilities for industrial applications. Adsorption ; Hydrogen ; Hydrogen storage ; Parametric statistics ; Work (physics) ; Finite element method ; Coupling (piping) ; Porous medium ; Partial differential equation Metal-Organic Frameworks: Synthesis and Applications ; Hydrogen Storage and Materials ; Covalent Organic Framework Applications
Authors: Chang’an Zhou | Lingyan Zheng | Kaixin Meng | Wei Zheng | Kaixing Zhang | Qinghua Shi
Source: Vegetable Research (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Early detection of tomato leaf spot and wilt diseases based on hyperspectral imaging technology This study explores the application of hyperspectral imaging technology integrated with machine learning for the early detection of tomato leaf spot and blight diseases. Pathogens causing leaf spot and blight were introduced to young tomato seedlings through foliar sprays and root irrigation. A hyperspectral imaging system was established to capture detailed spectral images of tomato leaves during the initial stages of disease development. The collected hyperspectral data were preprocessed using smoothing algorithms (Savitzky-Golay, SG), multivariate scatter correction (MSC), standard normal variate transformation (SNV), and first derivative (1<sup>st</sup> Der). A support vector machine (SVM) detection model was trained to evaluate the effectiveness of these preprocessing methods. Through comprehensive modeling and comparative analysis, the 1<sup>st</sup> Der-SG preprocessing approach was identified as the most effective, achieving an overall detection accuracy of 79.3% on the test dataset. Furthermore, feature extraction was performed on the preprocessed data using competitive adaptive weighted sampling (CARS), successive projection algorithm (SPA), uninformative variable elimination (UVE), and principal component analysis (PCA). Subsequently, the dung beetle optimization (DBO) algorithm was employed to enhance the performance of support vector machines (SVM) and bi-directional long short-term memory networks (BiLSTM), resulting in the development of DBO-SVM and DBO-BiLSTM models. The most effective model combination, 1<sup>st</sup> Der-SG-UVE-DBO-BiLSTM, achieved an outstanding overall accuracy of 97.3% on the test set. This research highlights the significant potential of hyperspectral imaging technology for the early detection of tomato leaf spot and blight diseases. The findings provide valuable technical insights for tomato disease detection and establish a theoretical foundation for early disease identification in other crops. Hyperspectral imaging ; Leaf spot ; Remote sensing ; Horticulture ; Biology ; Computer science ; Artificial intelligence ; Geography Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use ; Water Quality Monitoring and Analysis
Authors: Neha Laxane | Khushwant S. Yadav
Source: Polymers for Advanced Technologies (article, 2025-11-01)
Field: Materials Science — Biomaterials

Efficient Co‐Loading of Paclitaxel and Gefitinib in <scp>PLGA</scp> Nanoparticles: Use of Design of Experiments ( <scp>DoE</scp> ) Approach for Enhanced Efficacy Against <scp>MCF</scp> ‐7/ <scp>MCF</scp> ‐ <scp>7ADR</scp> Cells ABSTRACT In this study co‐loading of paclitaxel and gefitinib in PLGA nanoparticles for breast cancer treatment has been explored as a strategy to overcome limitations of using the drug alone. Combination index studies using Chou–Talay method were used to identify the optimal ratio of both drugs that showed the maximum synergistic effect. Using the design of experiment ( DoE ) methodology, PLGA nanoparticles co‐loaded with paclitaxel and gefitinib were formulated and optimized. Further characterization and cell line evaluations were performed. The mean particle size obtained was 169.79 nm, and entrapment efficiencies of PTX and GEF were 89.17% and 76.89% (w/w), respectively. Formulated co‐loaded nanoparticles had enhanced cellular uptake and lower IC 50 in MCF ‐7 and drug‐resistant MCF ‐7/ ADR cells. Paclitaxel ; Gefitinib ; Drug ; Nanoparticle ; Particle size ; Cell culture ; Cancer cell lines ; Cancer treatment Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Advanced Drug Delivery Systems
Authors: Lukita Sari Ikhsan | Yu Masuda | Maciej Kretkowski | Wataru Inami | Yoshimasa Kawata
Source: Applied Sciences (article, 2025-12-18)
Field: Materials Science — Surfaces, Coatings and Films

Contrast Enhancement of SEM Image Using Photoelectric Effect Under UV LED Irradiation Scanning electron microscopy (SEM) is widely used for nanoscale imaging and the study of surface fine structure. However, its image quality is often limited by low secondary electron (SE) yield and surface charging, especially on insulating or micro-structured materials. In this study, we introduce a non-destructive technique that significantly improves SEM image acquisition by irradiating the specimen surface with ultraviolet (UV) light during observation. This approach leverages the photoelectric effect to enhance SE emission, resulting in a higher signal-to-noise ratio and improved image contrast in SEM imaging. Experiments were conducted using an in situ UV irradiation module on three representative samples: a 1 µm thick gold film (Au) deposited on a 525 µm thick silicon (Si) substrate, a black silicon (b-Si) sample, and a GaN substrate. The results demonstrate clear SE signal enhancement and effective charge mitigation under UV illumination. This UV-assisted SEM technique offers a simple and practical approach to improving electron-beam-based imaging and is expected to expand capabilities for high-contrast observation of nanoscale materials. Photoelectric effect ; Nanoscopic scale ; Ultraviolet ; Irradiation ; Secondary electrons ; Scanning electron microscope ; Silicon ; Image contrast Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Photocathodes and Microchannel Plates
Authors: Xinlong Yang | Ziyu Hu | Xiaohong Shao | Fawei Zheng
Source: Communications Materials (article, 2025-07-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multiferroicity in two dimensional Co2Cl2 monolayer Multiferroicity has been studied for about 70 years and resurging recently due to rapid experimental and theoretical advances. However, two-dimensional ferromagnetic-ferroelastic multiferroicity, which is crucial to magneto-elastic nanodevices, is still very rare. Here, we propose that the Co2Cl2 monolayer exhibits ferromagnetic-ferroelastic multiferroicity. The system has two asymmetric phases: FE-I, a metallic ferromagnetic phase, and FE-II, a semiconducting antiferromagnetic phase, which can be switched by strain. We show that Co2Cl2 monolayers undergo both metal-insulator and ferromagnetism-antiferromagnetism transitions due to the different crystal fields surrounding Co2+ ions. Interestingly, both the ferromagnetic and antiferromagnetic phases exhibit high critical temperatures, making this material promising for device applications. Despite decades of research, two-dimensional ferromagnetic-ferroelastic multiferroicity remains elusive, hindering advancements in magneto-elastic nanodevices. Here, the authors reveal that Co2Cl2 monolayers exhibit multiferroicity with strain-switchable phases and high critical temperatures, offering significant potential for future device applications. Monolayer ; Materials science ; Geology ; Nanotechnology Multiferroics and related materials ; 2D Materials and Applications ; Perovskite Materials and Applications
Authors: Ilia Khomchenko | Andrei Fionov | Alexey Alekseev | D.V. Volkov | I. A. Semerikov | N. Kolachevsky | Aleksey K. Fedorov
Source: Research Square (preprint, 2025-06-26)
Field: Chemistry — Spectroscopy

Detection states of ions in a Paul trap via conventional and quantum machine learning algorithms Trap (plumbing) ; Ion trap ; Algorithm ; Ion ; Quantum ; Computer science ; Artificial intelligence ; Physics ; Quantum mechanics Mass Spectrometry Techniques and Applications ; Cold Atom Physics and Bose-Einstein Condensates ; Quantum Information and Cryptography
Authors: Komal Mishra
Source: Journal of Information Systems Engineering & Management (article, 2025-01-24)
Field: Chemistry — Analytical Chemistry

Feature-Augmented Convolutional Neural Network Optimisation for Wheat Yellow Rust Identification Nowadays, yellow rust is a type of condition having a huge domain triggering excessive adverse effects on wheat. Manually addressing wheat yellow rust using the standard approach is not highly effective. In order to enhance this condition, a Deep Learning (DL)-based method was applied in this analysis to identify wheat yellow rust from the Yellow Rust-19 dataset leaf images. The solution offered was constructed using the Optimized Feature-Augmented Convolutional Neural Network (OFAC-Net) model to classify R (Minor infection), MR (Small and Medium Infection), MR-MS (Moderate Resistance and Susceptible), MS (Medium Infection), and S (Major signs of infection), aiming to improve the efficiency of detecting and classifying yellow rust in wheat leaf images. The implemented OFAC-Net models integrate Principal Component Analysis (PCA) used for feature extraction and reduce the dimensionality, alongside hybrid feature selection techniques utilizing the Whale Optimization Algorithm (WOA) and Firefly Algorithm (FFA) to refine the extracted features and boost accuracy. This combination addresses existing challenges and improves classification performance, with the results being classified through a Convolutional Neural Network (CNN). The implemented model attained a classification Accuracy of 98%, Precision of 98.76%, Recall of 99%, and Mean Square Error (MSE) of 1.98%. The dataset, curated from the several severity levels of yellow rust disease, in wheat, consists of a total of 15,000 wheat leaf images. It was designed to support the classification tasks, with leaf images evenly divided across severity levels of yellow rust disease in wheat. These results highlight the techniques` superiority over traditional machine learning (ML) techniques and other advanced models. The proposed OFAC-Net model is a promising solution for real-time agricultural applications, offering both high performance and computational efficiency suitable for mobile and edge devices. Convolutional neural network ; Rust (programming language) ; Identification (biology) ; Feature (linguistics) ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Botany ; Biology ; Linguistics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Food Supply Chain Traceability
Authors: U. Janiszewska | Łukasz Marcjan | Sandra Gajoch | Krzysztof Jaśkowiec | Adam Bitka | Marcin Małysza | D. Wilk-Kołodziejczyk
Source: Archives of Foundry Engineering (article, 2025-06-27)
Field: Materials Science — Materials Chemistry

Analysis of the Possibility of Using Selected Artificial Intelligence Algorithms for the Assessment of the Microstructure of Vermicular Cast Iron This paper presents an analysis of artificial intelligence algorithms in the context of their applicability to the automatic analysis of microstructure images. In the example presented, reference is made to exemplary images of the microstructure of vermicular cast iron. A characteristic feature of this alloy is the shape of the graphite separations. The microstructure consists of elements that humans can learn to recognise quite simply. Developing an application that recognises ‘dark colour’ and ‘worm shape’ is no longer so straightforward. The determination of the ‘dark’ colour in the algorithm becomes problematic, because depending on the conditions under which the photo was taken (e.g. time: day/night), the actual intensity values are altered. A similar situation occurs in the determination of shape, which varies from case to case. Such a classification is very general and results in large differences between instances of the class. Even a term like ‘relatively large’ can change depending on the size of the graphite separation itself. A dark colour can be represented as a sudden change in image intensity, i.e. large values of the gradient modulus. The question arises: what happens if ‘dark’ can be more than one microcomponent, for example graphite and perlite. A good solution would be to define an associated set of features that would more precisely define just this component of the microstructure - that is, its shape, colour and surroundings. The paper uses the local feature paradigm to do this. Referring to the literature, it can be pointed out that [1] local features are referred to as non-small and specific parts of an image. Distinctive image features need to be distinguished in order to detect these places of interest. In this case, they are: edges, spots and ridges. Microstructure ; Materials science ; Metallurgy ; Cast iron Metal Alloys Wear and Properties ; Metallurgical Processes and Thermodynamics ; Surface Treatment and Coatings
Authors: Aqsa Shaheen | Anam Tariq | Fouzia Ismat | Aamir Shehzad | Moazur Rahman
Source: Proceedings of the Pakistan Academy of Sciences B Life and Environmental Sciences (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Preliminary X-ray Diffraction Analysis of the Membrane Protein styMdtM from Salmonella Typhi Structural studies of proteins provide a comprehensive understanding of their functions, and form the basis for designing drugs that can modulate their activity. However, the structural characterization of membrane proteins is often challenging due to their hydrophobic nature. This article highlights key structural and functional aspects of a representative membrane protein, STY4874 – also known as styMdtM, which is an efflux transporter encoded by the genome of Salmonella Typhi, the causative agent of typhoid fever in humans. Our structural studies have, so far, been focused on obtaining diffraction-quality crystals of the protein. For this purpose, crystallization trials have been performed using both the wild-type and mutant forms of the protein, in the presence or absence of ligands (substrates or inhibitors). Crystals of the wild-type styMdtM and its thermally stable mutants diffracted anisotropically to resolutions between 4.5 and 8 Å, rendering the collected data not useful for structure solution. On the other hand, crystals of a 13-amino acid deletion mutant isotropically diffracted to 10 Å, fueling our interest in engineering truncated styMdtM mutants for obtaining crystals suitable for diffraction to high resolutions for structure determination. Here, we discuss preliminary findings and key lessons learned from our studies, spanning over a decade. Mutant ; Salmonella typhi ; Membrane protein ; Diffraction ; Membrane ; Crystallization ; Efflux Enzyme Structure and Function ; Bacterial Genetics and Biotechnology ; Porphyrin Metabolism and Disorders