jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hui Shan | Yuwen Su | Nuo Xu
Source: International Scientific Technical and Economic Research (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Construction of Self-Optimization and Quality Prediction Model for Intelligent Die Casting Unit Process Parameters With the deepening of intelligent transformation in the manufacturing industry, the die-casting industry faces an urgent need to improve product quality, production efficiency, and stability. Traditional trial-and-error process debugging methods relying on manual experience are no longer adequate to meet this challenge. Therefore, this research aims to construct an intelligent die-casting system integrating quality prediction and parameter self-optimization to achieve closed-loop optimization of the production process. First, the system collects multi-source process parameters such as injections and thermal parameters in the die-casting unit through an industrial Internet of Things (IoT) architecture. Based on this, machine learning algorithms such as XGBoost, random forest, and artificial neural networks are compared and screened, successfully constructing a high-precision quality prediction model with internal porosity as the core indicator. Then, using this prediction model as a surrogate model, a process parameter self-optimization strategy is developed, combining a multi-objective genetic algorithm and a Bayesian optimization strategy, to automatically seek multiple objectives such as quality, efficiency, and energy consumption while meeting equipment and process constraints. Experimental results show that this system can not only effectively predict key quality characteristics, but the process parameter combinations recommended by its self-optimization module significantly improve the product qualification rate and reduce the single-piece production cycle compared to traditional methods in actual production. This study successfully combines data-driven methods with the die-casting process mechanism, forming a complete technical solution from perception and prediction to decision-making, providing a theoretical model and system framework with practical value for the digital and intelligent upgrading of die-casting production. Process (computing) ; Artificial neural network ; Quality (philosophy) ; Production (economics) ; Product (mathematics) ; Expert system ; Energy consumption ; Soft sensor ; Bayesian network Machine Learning in Materials Science ; Digital Transformation in Industry ; Injection Molding Process and Properties
Authors: Osman Akmeşe | Cemalettin Baltacı | Enes Gültekin | Irmak İçen Taşkın | Meryem Rüveyda Sever | Dilek Çam Derin | Ömer Karpuz
Source: Turkish Journal of Analytical Chemistry (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

Phytogenic Silver and Copper Nanoparticles from Morus nigra L. Leaf as Multifunctional Agents: Antioxidant, Antidiabetic, Antimicrobial, and Anticancer Potency Driven by the increasing pursuit of eco-friendly and multifunctional nanomaterials, this study explores the eco-conscious production, detailed analysis, and cooperative antibacterial efficacy of silver (YAgNPs) and copper nanoparticles (YCuNPs) synthesized using Morus nigra leaf extract under alkaline aqueous conditions. The nanoparticles produced through biogenic methods showed unique physical and chemical features, as validated via FTIR, XRD, TEM, and EDX instrumentation, suggesting effective reduction and stabilization facilitated by plant-derived compounds. YAgNPs and YCuNPs synthesized in this study demonstrated markedly enhanced antioxidant capacities and significant α-amylase and α-glucosidase inhibition, highlighting their prospective role in diabetes management. Antimicrobial assays showed superior activity of YAgNPs in solution-phase assays, whereas YCuNPs showed stronger inhibition in solid diffusion-based setups. Notably, lower MIC and MBC concentrations were observed for YAgNPs across multiple Gram-positive and Gram-negative strains, including E. coli and S. aureus. The checkerboard assay demonstrated potent synergistic effects between both NPs and traditional antibiotics against clinically relevant pathogens. FICI values confirmed full synergy in several combinations, demonstrating their ability to potentiate antibiotic effects through membrane permeability enhancement and interference with bacterial resistance mechanisms. YAgNPs also showed notable cytotoxic effects against cancer cells against HeLa and HCT116 cell lines, with IC₅₀ values significantly lower, highlighting their oncological promise. Overall, the study emphasizes the diverse therapeutic applications of green-synthesized nanoparticles and suggests their potential use in cancer treatment and as enhancers for antimicrobial drugs. HeLa ; Antimicrobial ; Potency ; Antioxidant ; Cytotoxicity ; Membrane permeability ; Viability assay ; In vitro ; Bacteria ; Nanoparticle Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications
Authors: T.H. Tse
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-08-28)
Field: Materials Science — Materials Chemistry

A Compact Matrix Representation for Hydrocarbon and Its Applications on Monoterpene Carbocation Rearrangement Elementary Steps ABSTRACT This work proposes a compact matrix-based representation for hydrocarbon carbocations ([C x H y ] m + ). In addition, its application is explored for the elementary steps of non-stereoisomeric [C 10 H 17 ] + rearrangement. Carbocation ; Monoterpene ; Representation (politics) ; Hydrocarbon ; Matrix (chemical analysis) ; Chemistry ; Organic chemistry ; Computational chemistry ; Political science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Małgorzata Sztanke | Renata Łyszczek | A. Ostasz | Halina Głuchowska | Krzysztof Sztanke
Source: Molecules (article, 2025-01-23)
Field: Chemistry — Organic Chemistry

Thermal Characterisation and Toxicity Profile of Potential Drugs from a Class of Disubstituted Heterofused Triazinones The thermal characterisation and toxicity profile of a class of disubstituted heterofused triazinones were revealed in this article for the first time. The thermal behaviour of molecules <b>1</b>-<b>12</b> was investigated by means of TG and DSC analyses performed in an air atmosphere and by the coupled TG/FTIR technique in a nitrogen atmosphere. The heating atmosphere affects both the stability of compounds and the degradation mechanism. A two-step degradation occurs in air, while a one-step degradation takes place in nitrogen, both preceded by a melting process. Compound <b>3</b> shows the highest thermal stability, while molecule <b>10</b>-the lowest. The thermal decomposition of the studied heterocyclic molecules begins with the degradation of the bicyclic system, resulting in the formation of volatile gaseous products such as ammonia/hydrazine, hydrogen cyanide, carbon dioxide, and isocyanates. In the further stage, mainly aromatic compounds are released, and their chemical composition depends on the presence and type of substituents at the phenyl and benzyl moieties. In addition, the toxicity profiles of molecules were assessed in the animal (zebrafish) and cellular (erythrocytes) models, and the antihaemolytic activity was evaluated in the AAPH- and H<sub>2</sub>O<sub>2</sub>-induced haemolysis inhibition assays. It was found that all the tested compounds are safe for the developing zebrafish and red blood cells, and they are able to effectively protect erythrocytes from oxidative damage. These favourable properties make them promising drug candidates suitable for further in vivo studies. Chemistry ; Thermal stability ; Hydrazine (antidepressant) ; Molecule ; Cyanide ; Degradation (telecommunications) ; Thermal decomposition ; Haemolysis ; Hydrogen cyanide ; Acute toxicity ; Toxicity ; Organic chemistry ; Nuclear chemistry ; Biochemistry Synthesis and Characterization of Heterocyclic Compounds ; Chemical Reaction Mechanisms ; Energetic Materials and Combustion
Authors: Xiang Chen | Rongjian Gao
Source:  (article, 2025-05-16)
Field: Materials Science — Materials Chemistry

Finite element tensile fracture analysis of α-uranium based on crystal plastic model As the most stable structural form of uranium crystals, α-uranium exhibits a profound influence in multiple fields such as the nuclear industry, materials science, and medicine. its complex plastic deformation mechanism poses numerous challenges to the design and service in practical engineering. In order to deeply explore its mechanical properties and deformation mechanism, numerous researchers have conducted tensile tests on α-uranium. Nevertheless, its numerical model still needs further development. By combining the user defined subroutine Umat and the user defined element Uel, a tensile simulation of α-uranium is carried out based on the crystal plasticity model and the cohesive element, and the influence of the geometric morphology of defects on its mechanical properties is deeply discussed. Using the accumulated plastic strain contour, the accumulation process of plastic strain during the crack initiation and propagation is systematically analyzed. On this basis, defects with different geometric shapes are introduced. the defect morphology and angle are quantitatively characterized. The results show that the crystal plasticity model can effectively simulate the tensile and fracture behavior of α-uranium. When there are defects in the material, the cracks mainly concentrate on the local geometry perpendicular to the loading direction, and stress concentration occurs near the sharp corners, which in turn triggers the initiation and propagation of cracks, ultimately reducing the strength and toughness of the material. Plasticity ; Ultimate tensile strength ; Finite element method ; Toughness ; Deformation (meteorology) ; Fracture toughness ; Crystal plasticity ; Perpendicular ; Stress (linguistics) Nuclear Materials and Properties ; High-Velocity Impact and Material Behavior ; Microstructure and mechanical properties
Authors: Xiao Yang | Song Chen | Tan Lin | Yue Wang | Feng Gao | Zhimin Zhang | Xiao Zhou | Hongmei Lü
Source: npj Heritage Science (article, 2025-11-30)
Field: Chemistry — Organic Chemistry

A lion-optimized efficientnet framework for non-destructive and rapid plant species identification in palm-leaf manuscripts Abstract Palm-leaf manuscripts (PLMs), essential Asian cultural artifacts, necessitate accurate species identification for their preservation. Traditional destructive analysis or manual inspection prone to errors hampers large-scale conservation efforts. To address this, we present PLNet, a lightweight framework integrating Lion-optimized EfficientNetB2 with domain-specific augmentation strategies. Lion optimizer enhances training stability, while task-driven augmentation simulates manuscript degradation. PLNet achieves exceptional performance on a diverse test set: 99.07% accuracy and 99.21% F1-score, outperforming ResNet50, DenseNet169, and YOLOv11-cls. Crucially, PLNet operates with high efficiency, utilizing only 7.7 million parameters and processing a folio in 0.31 s. To enable practical application, we developed PLNet-GUI for large-scale identification. Validated on 142,679 PLMs from 8 countries, the framework demonstrates robust real-world applicability, reveals regional species distribution patterns, and establishes a method shift in heritage conservation by replacing destructive practices with AI-driven preservation. Identification (biology) ; Species identification ; Plant species ; Cultural heritage ; Distribution (mathematics) ; Plant identification Wood and Agarwood Research ; Handwritten Text Recognition Techniques ; Species Distribution and Climate Change
Authors: Diego Medina-Castro | Héctor René Vega-Carrillo | Antonio Baltazar-Raigosa | Tzinnia Gabriela Soto-Bernal | R. López‐Callejas | Rodríguez Méndez
Source: Energies (article, 2025-06-19)
Field: Materials Science — Materials Chemistry

An Assessment of Replacing Aluminum Tubes Hosting Nuclear Fuels with Stainless Steel in a Subcritical Nuclear Reactor This computational study using MCNP5 evaluated the feasibility of replacing 6061-T6 aluminum with 316L stainless steel (SS-316L) for the tubes hosting the uranium slugs in the subcritical nuclear reactor Nuclear Chicago model 9000, thereby contributing to its preservation as a key resource for nuclear research and education in Mexico. Simulations and dosimetric analyses (ICRP/ICRU) confirmed subcriticality in both configurations. Notably, SS-316L demonstrated an effective attenuation of peripheral gamma radiation and a reduction in the ambient neutron dose, indicating a considerable improvement in radiological safety. Although a reduction in thermal and epithermal neutron fluence was observed, the similarity in the gamma spectrum suggests no significant alteration for gamma spectroscopic experiments. In conclusion, SS-316L presents a promising alternative that enhances radiological safety and reactor longevity, making it a worthy consideration as a replacement material. Further experimental investigation is recommended to assess material activation and the gamma dose in the vicinity of the fuel. Nuclear engineering ; Materials science ; Spent nuclear fuel ; Nuclear fuel ; Radiological weapon ; Neutron ; Nuclear reactor ; Radiochemistry ; Environmental science ; Nuclear physics ; Chemistry ; Engineering ; Physics Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear Physics and Applications
Authors: Kaustubh N. Kulkarni
Source: Preprints.org (preprint, 2025-05-14)
Field: Chemistry — Spectroscopy

New Analysis for Interdiffusion Fluxes Determined From Experimental Concentration Profiles in a Multicomponent Diffusion Couple with Composition Dependent Partial Molar Volumes New equations are derived for determination of interdiffusion fluxes from experimental concentration profiles of multicomponent diffusion couples with composition dependent partial molar volumes of the diffusing components. The newly derived equations are applied to determine interdiffusion fluxes in experimental diffusion couples of FCC Ni-Cr-Al system. It is shown that for the case of varying partial molar volumes, the expressions discerned from the popular works of Sauer- Freise and Wagner are valid only for binary systems. Their use in determination of interdiffusion fluxes in ternary and higher order systems may lead to large errors; in the Ni-Cr-Al couple studied here, a mere 3% variation in deviation from ideality of molar volume is seen to give as high as -40% errors in interdiffusion fluxes, which may propagate to greater than 100% errors in interdiffusion coefficients determined from such fluxes. The newly derived expressions for fluxes in number fixed and volume fixed frames of reference are independent of the reference coordinate and they are also valid for multicomponent multiphase diffusion couples. In the absence of data on composition dependent molar volumes in ternary and higher order systems, the interdiffusion analysis so far has been carried out with assumption of constant molar volume. However, the newly developed equations in the present study and the observed large effect of molar volume on the flux analysis are expected to correct this trend. Diffusion ; Molar ; Composition (language) ; Materials science ; Molar ratio ; Chemistry ; Thermodynamics ; Analytical Chemistry (journal) ; Chromatography ; Geology ; Organic chemistry ; Physics ; Catalysis Diffusion Coefficients in Liquids ; Adsorption, diffusion, and thermodynamic properties of materials ; Advanced Mathematical Modeling in Engineering
Authors: Zahra Rahimian Omam | Hamidreza Taghvaee | Ali Araghi | María García-Fernández | Guillermo Álvarez-Narciandi | George C. Alexandropoulos | Okan Yurduseven | Mohsen Khalily
Source: arXiv (Cornell University) (preprint, 2025-01-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metasurfaces-Enabled Wave Computing for Future Wireless Systems: Opportunities and Challenges The next generations of wireless networks are envisioned to integrate communications, sensing, and computing into a unified platform, demanding ultra-high data rates, submillisecond latency, and unprecedented energy efficiency. However, conventional digital processors face limitations in scalability, cost, and power consumption that hinder this vision. Wave computing, enabled by programmable metasurfaces, offers an alternative paradigm according to which signal processing operations are implemented in the domain of the propagation of electromagnetic waves. This approach transforms metasurfaces from passive wavefront shapers into functional analog processors capable of executing tasks such as beamforming, sensing, imaging, and machine learning at the speed of light with minimal power consumption. This article provides an overview of metasurface-enabled wave computing, highlighting its fundamental principles and key application scenarios for future wireless systems, including integrated sensing and communications, artificial intelligence acceleration, over-the-air channel estimation, and computational electromagnetic imaging. Future research directions are outlined in response to the major open challenges of the technology, aiming to enable large-scale deployment of wave computing in practical wireless networks. Holography ; Data science ; Computer science ; Optics ; Physics Metamaterials and Metasurfaces Applications ; Advanced Wireless Communication Technologies ; Advanced Antenna and Metasurface Technologies
Authors: Aleksander M. Haack | Konstantinos Kalogeropoulos
Source:  (preprint, 2025-06-02)
Field: Chemistry — Spectroscopy

Data processing and analysis in positional proteomics Proteolytic cleavage is an irreversible post translational modification (PTM), and dysregulation of protease activity is often a hallmark in disease. Aberrant proteolysis can alter protein abundance or function, disturbing cellular state and resulting Proteomics ; Computer science ; Data science ; Chemistry Advanced Proteomics Techniques and Applications
Authors: Mengjie Li | Lifu Zhang | Deshuai Yuan | Xuejian Sun | Qingxi Tong
Source: Lubricants (article, 2025-09-04)
Field: Chemistry — Analytical Chemistry

A Hyperspectral Analysis-Based Approach for Estimation of Wear Metal Content in Lubricating Oil Lubricating oil reflects mechanical component aging and wear. Accurate quantification of its wear metals is essential for equipment safety and intelligent maintenance. This study introduces a rapid, non-destructive method for detecting wear metal content in lubricating oil using hyperspectral technology to overcome limitations such as bulky, expensive instruments and destructive testing in current spectroscopic techniques. Absorption spectra of 98 marine gearbox oil samples were acquired using Hach UV-Vis and GLT optical fiber spectrometers. We propose a multi-head attention mechanism enhanced genetic algorithm (MHA-GA) for deep feature extraction, integrating attention weights into band selection and fitness evaluation to identify key features under multi-element interference. Wear metal prediction models were constructed using random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGBoost). Results demonstrate MHA-GA outperformed traditional genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) in feature selection. The MHA-GA-XGBoost model performed best. Fe prediction R2 reached 0.96 (Hach) and 0.93 (GLT), with RPDs of 5.33 and 3.90. For Cu, R2 reached 0.91 and 0.83, with RPDs of 3.35 and 2.42. The results indicate that hyperspectral technology combined with machine learning enables effective non-destructive wear metal quantification, offering a promising strategy for intelligent maintenance and condition monitoring of lubricating oil. Hyperspectral imaging ; Materials science ; Petroleum engineering ; Environmental science ; Computer science ; Artificial intelligence ; Geology Spectroscopy and Chemometric Analyses ; Cultural Heritage Materials Analysis ; Thermography and Photoacoustic Techniques
Authors: Antonio J. Oliveros-Ortiz | Jessica M. Lorenzo-García | Gabriela Rodríguez‐García | Rosa E. del Río | Armando Talavera‐Alemán | Mario A. Gómez-Hurtado
Source: Journal of Natural Products and Resources (article, 2025-10-17)
Field: Chemistry — Spectroscopy

Complete ¹H NMR Assignment of 3-Oxo-γ-Costic Acid from Ageratina glabrata Nuclear Magnetic Resonance (NMR) is a powerful technique for elucidating the structure of organic molecules, and it is especially useful in determining the structure of natural products, including terpenes. 3-Oxo-γ-costic acid is an eudesmene-type sesquiterpene that has been isolated from different vegetable species of the Asteraceae and Lauraceae families. Its ¹H NMR has been reported with some discrepancies in the chemical shift assignments as well as in J values. In the present work, 3-oxo-γ-costic acid was isolated from the dichloromethane extract of the aerial parts of Ageratina glabrata by chromatographic means and the complete assignment of the ¹H NMR spectrum of 3-oxo-γ-costic acid is reported using a hybrid protocol that involves a DFT-calculated spectrum and the experimental ¹H spectrum. The calculated spectrum was constructed based on the DFT-calculated J values for each hydrogen, and the respective chemical shifts are those recorded by experimental means, which are supported by 2D NMR. Then, the completely calculated ¹H spectrum was simulated in the Mnova software and compared with the experimental spectrum. This methodology is especially useful for the detection of low magnitude NMR couplings (&lt;2 Hz), which are complicated to measure experimentally; then, this hybrid protocol, experimental-DFT, could be applied to NMR determination of more complex sesquiterpenes. Lauraceae ; Chemical shift ; Dichloromethane ; Two-dimensional nuclear magnetic resonance spectroscopy ; Sesquiterpene ; Carbon-13 NMR ; Proton NMR ; Nuclear magnetic resonance spectroscopy Analytical Chemistry and Chromatography ; Sesquiterpenes and Asteraceae Studies ; Plant-Derived Bioactive Compounds
Authors: Naiara Brandão | Priscilla Alvarenga Rocha | Victor Miranda Amazonas | Wilson da Silva Rocha | Nicole Medeiros Nascimento | M. D. B. Santos | Allison Gonçalves Silva | Erik Galvão Paranhos da Silva | Marcos Almeida Bezerra | Cléber Galvão Novaes
Source: Brazilian Journal of Analytical Chemistry (article, 2025-08-29)
Field: Chemistry — Analytical Chemistry

Multivariate Assessment of Ground Coffee Samples from Bahia, Brazil on the Basis of Mineral Composition This study determined metals in ground coffee samples from different producing regions in Bahia and used Principal Component Analysis (PCA) and Kohonen neural networks as multivariate techniques to assess sample similarity based on their mineral composition. The samples were prepared using a decomposition method with dilute acid and hydrogen peroxide in a closed system under high pressure. Microwave Induced Plasma Optical Emission Spectrometry (MIP OES) was used to quantify the elements Al, Co, Cr, Cu, Fe, Mn, Ni, V and Zn. For all samples, the elements with the highest concentrations were Mn, Fe, Al, Zn and Cu; and the lowest concentrations were obtained for the elements Cr, Ni, Co and V. The results of this study provide and aggregate relevant nutritional information about coffee produced in the Southwest and Chapada Diamantina regions of Bahia. Multivariate statistics ; Composition (language) ; Multivariate analysis ; Mineral ; Mathematics ; Geography ; Mineralogy ; Statistics ; Environmental science ; Geology ; Metallurgy ; Materials science ; Linguistics ; Philosophy Heavy Metals in Plants ; Coffee research and impacts ; Crystallization and Solubility Studies