jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Irina Manakova | М. Ф. Верещак | Gaukhar Yeshmanova | Zhandos Tleubergenov
Source: Materials (article, 2025-11-06)
Field: Materials Science — Materials Chemistry

Structural–Phase Transformations in Stainless Steel CF8 Under Ion Implantation and Thermal Treatment The γ ⟶ α'-transformation under implantation of austenitic-ferritic steel CF8 with <sup>57</sup>Fe ions to fluences of 1 × 10<sup>16</sup>, 5 × 10<sup>16</sup> and 1 × 10<sup>17</sup> ion/cm<sup>2</sup>, as well as the reverse α' ⟶ γ-transformation under thermal treatment, was studied using transmission Mössbauer spectroscopy (MS), conversion electron Mössbauer spectroscopy (CEMS), and X-ray diffraction (XRD) methods. It was found that implantation, which causes radiation damage with 36 dpa, results in the formation of α'-martensite. At higher fluences of implanted ions, the amount of α'-martensite increased, reaching 86 at.% within the irradiated layer. Annealing in the temperature range of 600-850 °C resulted in the observed reverse transformation of α'-martensite to γ-austenite. The dependence of the average effective magnetic field on the annealing temperature was established. Annealing (glass) ; Ion implantation ; Thermal treatment ; Transmission electron microscopy ; Irradiation ; Conversion electron mössbauer spectroscopy ; Atmospheric temperature range ; Radiation damage Fusion materials and technologies ; Metal and Thin Film Mechanics ; Ion-surface interactions and analysis
Authors: Xiaoming Zhang | Yue Huang | J. L. Li | Frank Chen | Jie Lian
Source: Crystals (article, 2025-01-12)
Field: Chemistry — Physical and Theoretical Chemistry

Dissolution and Pharmacokinetic Studies of Paracetamol-4,4′-Bipyridine Cocrystals Obtained Using Four Methods Paracetamol-4,4′-bipyridine cocrystals were synthesized using a solution method, reflux method, grinding method, and ultrasonic method. The structures and properties were characterized through the utilization of single-crystal X-ray diffraction (SXRD), powder X-ray diffraction (PXRD), polarized light microscopy (PLM), thermogravimetric analysis (TGA), elemental analysis (EA), and infrared spectroscopy (IR). The results show that the four methods synthesized different cocrystal morphologies, but the same structure and properties coupled with a notably high purity level. All featured strong hydrogen bonds formed between the paracetamol,4,4′-bipyridine and water molecules. An additional notable feature is the presence of π...π stacking interactions between the pyridine rings of adjacent 4,4′-bipyridine molecules. The solubility of paracetamol (active pharmaceutical ingredient, API) and the cocrystal was measured and discussed. In the dissolution experiment, the cocrystal showed a much faster dissolution rate than the API in simulated gastric fluid media (pH = 1.2). Furthermore, the pharmacokinetic (PK) behavior of the cocrystal and the API was investigated to evaluate the effectiveness of this strategy for enhancing the oral absorption of paracetamol. The in vitro and in vivo studies revealed that the paracetamol-4,4′-bipyridine cocrystal possessed an excellent dissolution behavior and an improved pharmacokinetic profile. Cocrystal ; Dissolution ; Powder diffraction ; Solubility ; Bipyridine ; Thermogravimetric analysis ; Chemistry ; Molecule ; Materials science ; Crystallography ; Nuclear chemistry ; Hydrogen bond ; Physical chemistry ; Organic chemistry ; Crystal structure Crystallography and molecular interactions ; Crystallization and Solubility Studies ; Phenothiazines and Benzothiazines Synthesis and Activities
Authors: Jiankun Li | Xiaowei Gu | Bohan Yang | Shenyu Wang | Zhihang Hu | Ziyang Hu | Xiaowei Ge
Source: Materials (article, 2025-09-09)
Field: Materials Science — Materials Chemistry

Performance Optimization and Long-Term Strength of Basic Magnesium Sulfate Cement Prepared with Accelerated Carbonated Boron Mud Basic magnesium sulfate cement (BMSC) has attracted increasing attention as a low-carbon alternative to traditional Portland cement. Therefore, this study investigates the feasibility of using carbonated boron mud (CBM), an industrial solid waste, as a partial substitute for magnesium oxide (MgO) in BMSC. Prior to its incorporation into the cementitious matrix, boron mud (BM) underwent rapid carbonation treatment to improve its reactivity, microstructure compatibility, and CO<sub>2</sub> sequestration potential. Experimental results from macroscopic and microscopic analyses confirmed the effectiveness of the carbonation process, showing that the carbonate ions carried by the CBM were successfully incorporated into the cementitious system. These carbonate ions reacted with MgO to form stable magnesium carbonate phases, effectively suppressing the formation of magnesium hydroxide (Mg(OH)<sub>2</sub>), which typically detracts from strength and stability. Compared to BMSC specimens containing untreated BM, the CBM-modified BMSC exhibited significantly improved mechanical performance and excellent volume stability. Furthermore, the carbonation pre-treatment effectively mitigated volumetric instabilities associated with rapid MgO hydration, thereby promoting a more favorable environment for the formation of the crucial 5·1·7 phase (5Mg(OH)<sub>2</sub>·MgSO<sub>4</sub>·7H<sub>2</sub>O). Overall, this research presents a promising strategy for producing CBM-BMSC, offering a sustainable approach to CO<sub>2</sub> utilization and enhancing the volume stability of magnesium-based cements, providing a new direction for improving the sustainability of the concrete industry and advancing the development of magnesium cements. Carbonation ; Cementitious ; Magnesium ; Portland cement ; Microstructure ; Cement ; Boron ; Carbonate Magnesium Oxide Properties and Applications ; Layered Double Hydroxides Synthesis and Applications ; Concrete and Cement Materials Research
Authors: Pieter De Gauquier | Jordy Peeters | Fardine Ameli | Emmanuelle Lipka | Kenno Vanommeslaeghe | Yvan Vander Heyden | Debby Mangelings
Source: Journal of Chromatography Open (article, 2025-10-28)
Field: Chemistry — Spectroscopy

Predicting enantioseparations on polysaccharide-based stationary phases using achiral and chiral molecular descriptors • Forty-eight chiral molecules were analyzed on six polysaccharide chiral phases. • Models were built to predict the retention, separation and elution sequence. • Chiral descriptors were developed in-house to distinguish between enantiomers. • Predicted chromatograms were generated to visualize the expected separation. Achiral and chiral molecular descriptors were used to build models that predict the global retention of chiral molecules on the one hand, and their enantioselectivity, separation and elution sequence on the other. Chromatographic separations of 48 structurally diverse molecules were performed on six polysaccharide-based chiral stationary phases (CSPs) with two mobile phases (i.e. twelve chromatographic systems). Subsequently, “achiral” and “chiral” models were constructed. For the achiral models, either the mean retention time of the enantiomers ( t R ‾ ) or the logarithm of the mean retention factor (log k ¯ ) was modelled as a function of achiral descriptors, whereas for chiral models, the enantioselectivity was modelled using “Ensemble Steric and Electrostatic Chirality” (ESEC) descriptors. Initially, various types of ESEC descriptors were used to create models. A variable reduction technique, known as the Variable - Wootton, Sergent, Phan-Tan-Luu’s (V-WSP) algorithm, was applied to discard highly correlated descriptors. Stepwise multiple linear regression was applied to build the models. The achiral models for log k ¯ predicted the global retention rather well. For the enantioselectivity, separation and elution sequence, chiral models with good performance parameters and predictive abilities were obtained. Finally, the results from the achiral and chiral models were combined to predict the retention times of individual enantiomers. Predicted chromatograms were generated, to visualize both the expected and experimental separation. In most cases, the combined models yielded a moderate error (median relative error ranged from 5.8% to 22% across all systems) for the individual enantiomers. Elution ; Enantiomer ; Logarithm ; Molecule ; Molecular descriptor ; Steric effects ; Linear regression ; Chirality (physics) Analytical Chemistry and Chromatography ; Molecular spectroscopy and chirality ; Computational Drug Discovery Methods
Authors: Yingkai Xia | Wei Dong | Shaobin Yang | Shuang Wei | Jijiang Liu | Zheng Liang | Xuanyi Zhu | Jie Ding | Xu Zhang | Shuwei Tang | Ding Shen | Xiaoliang Wang | Weihua Xue | Haiyu Sun
Source: Small (article, 2025-11-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Decoding Potassium Ion Desolvation States for Enhanced Electric Double‐Layer Capacitance in Actual Porous Carbon Porous carbon (PC) electrodes store energy mainly via electric double-layer formation, governed by ion adsorption on pore surfaces and strongly influenced by ion desolvation states in PC, potassium ions can exist in five distinct desolvation states, denoted as [K(H<sub>2</sub>O)<sub>0-4</sub>]<sup>+</sup>. However, the mechanisms and thermodynamics and kinetics of individual desolvation states remain poorly understood. In this study, a methodology is developed to resolve and characterize the distinct desolvation states of ions within structurally realistic PCs. The corresponding desolvation energies and diffusion barriers of fully desolvated, partially desolvated, and fully solvated ion species are quantitatively determined. To enhance the electric double-layer capacitance(EDLC) of PC, a comprehensive design strategy is established based on a dual thermodynamic-kinetic optimization principle. This approach enables the identification of appropriate types and concentration windows of oxygen groups that synergize with specific ion desolvation states. The optimized PC achieved a specific capacitance of 273 F g<sup>-1</sup>, representing the highest value reported for coal-derived PC electrodes. The proposed methodology provides an integrated analytical and design framework for the development of aqueous supercapacitors and metal-ion battery carbon electrodes, and offers mechanistic insights into catalytic and adsorption-separation processes occurring in aqueous environments confined within carbon nanopores. Ion ; Aqueous solution ; Capacitance ; Carbon fibers ; Adsorption ; Supercapacitor ; Electrode ; Porosity Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Advanced battery technologies research
Authors: Devendra Sahu | Ritesh Kumar Dewangan | Surendra Pal Singh Matharu
Source: Tribology in Industry (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

Comparative Analysis of Machine Learning Techniques for Fault Diagnosis of Rolling Element Bearing with Wear Defects Rolling Element Bearings perform a vital function by ensuring the reliable and efficient operation of machinery in modern industries. Timely and accurate diagnosis of bearing faults is essential for preventing unexpected failures and minimizing downtime. This research addresses these challenges by employing advanced signal processing techniques and machine learning algorithms. The study investigates and optimizes fault diagnosis of rolling element bearings using various machine learning techniques, including Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP). The study utilizes naturally occurring defect vibrational data obtained from continuous running in the experimental test rig. Initially, a baseline for fault classification accuracy was established using raw vibration data. Then, Signal-to-Noise Ratio (SNR) was introduced to enhance data quality and alleviate the impact of noise. The model was further refined by extracting 14 types of features from the SNR-enhanced vibration data, presenting a comprehensive depiction of fault patterns and finally, machine learning techniques were applied to categorize faults using the aforementioned datasets, facilitating a comparative analysis of results. This optimization of the signal enhancement methodology significantly improved the fault diagnosis accuracy. As per the result obtained, Random Forest method consistently outperforms when applied to the feature-enhanced SNR dataset. The findings contribute to a more accurate and reliable identification of faults, offering significant advancements in the field of machinery health monitoring and predictive maintenance. Bearing (navigation) ; Fault (geology) ; Rolling-element bearing ; Element (criminal law) ; Engineering ; Structural engineering ; Computer science ; Forensic engineering ; Artificial intelligence ; Geology ; Seismology ; Acoustics ; Physics ; Vibration Metal Alloys Wear and Properties ; Gear and Bearing Dynamics Analysis ; Engineering Diagnostics and Reliability
Authors: Biao Chen | Xiaokun Huang | Shaokeng Tan | Guangjun Qiu | H. W. Lin | Xuejun Yue | Junzhi Chen | W. J. Zhong | Li Xiao | Le Zhang
Source: Horticulturae (article, 2025-07-12)
Field: Chemistry — Analytical Chemistry

Using Calibration Transfer Strategy to Update Hyperspectral Model for Quantitating Soluble Solid Content of Blueberry Across Different Batches Model updating is a challenging task with regard to maintaining the performance of non-destructive detection models while using hyperspectral imaging techniques for detecting the internal quality of fresh fruits like blueberries. Different sample batches and differences in hyperspectral image acquisition environments may lead to a significant decline in the performance of hyperspectral detection models. This study investigated the transferability of a hyperspectral model for the quantitating soluble solid content of blueberries across different batches for two harvest years. Hyperspectral images and SSC values of blueberries were collected from two batches, including 364 samples from 2024 and 175 samples from 2025. The differences between SSC measurements and spectral data across these two batches were analyzed. Based on the sample dataset of the year 2024, a high-performance quantitative model for detecting SSC values was established by combining it with partial least squares regression (PLSR) and competitive adaptive reweighted sampling (CARS). This high-performance model could achieve a high determination coefficient (RP2) of 0.8965 and a low root mean square error of prediction (RMSEP) of 0.3707 °Brix. Using the sample dataset for the year 2025, the hyperspectral model was updated by the semi-supervised parameter-free calibration enhancement (SS-PFCE) algorithm. The updated model performed better than those established using individual datasets from 2024 and 2025, and obtained an RP2 of 0.8347 and an RMSEP of 0.4930 °Brix. This indicates that the calibration transfer strategy is superior in improving hyperspectral model performance. This study demonstrated that the SS-PFCE algorithm, as a calibration transfer strategy, could effectively improve the transferability of the established model for detecting the SSC of blueberries across different sample batches. Hyperspectral imaging ; Calibration ; Chemistry ; Biological system ; Content (measure theory) ; Botany ; Horticulture ; Biology ; Environmental science ; Mathematics ; Computer science ; Artificial intelligence ; Statistics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Remote Sensing in Agriculture
Authors: Alexandre Segers | Cristian Castiglione | Christophe Vanderaa | Lennart Martens | Davide Risso | Lieven Clement
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-03-28)
Field: Chemistry — Spectroscopy

omicsGMF: a multi-tool for dimensionality reduction, batch correction and imputation applied to bulk- and single cell proteomics data The unprecedented speed and sensitivity of mass spectrometry (MS) unlocked large-scale applications of proteomics and even enabled proteome profiling of single cells. However, this fast-evolving field is hindered by a lack of scalable dimensionality reduction tools that can compensate for substantial batch effects and missingness across MS runs. Therefore, we present omicsGMF, a fast, scalable, and interpretable matrix factorization method, tailored for bulk and single-cell proteomics data. Unlike current workflows that sequentially apply imputation, batch correction, and principal component analysis, omicsGMF integrates these steps into a unified framework, dramatically enhancing data processing and dimensionality reduction. Additionally, omicsGMF provides robust imputation of missing values, outperforming bespoke state-of-the-art imputation tools. We further demonstrate how this integrated approach increases statistical power to detect differentially abundant proteins in the downstream data analysis. Hence, omicsGMF is a highly scalable approach to dimensionality reduction in proteomics, that dramatically improves many important steps in proteomics data analysis. Computer science ; Dimensionality reduction ; Imputation (statistics) ; Missing data ; Scalability ; Proteomics ; Data mining ; Principal component analysis ; Artificial intelligence ; Machine learning ; Chemistry ; Database Advanced Proteomics Techniques and Applications ; Gene expression and cancer classification ; Bioinformatics and Genomic Networks
Authors: Masoud Hasankhani | Hamid Vahed | Mohammad Bemani
Source: IET Optoelectronics (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dynamically Tunable Plasmonic Dual‐Band Metamaterial Absorber Utilising VO <sub>2</sub> /MoS <sub>2</sub> for Optical Communication Frequencies ABSTRACT This paper presents the design and analysis of a tunable metamaterial absorber based on the combination pattern of two‐dimensional molybdenum disulfide (MoS 2 ) and vanadium dioxide (VO 2 ) resonator. The design of the absorber structure is simple and includes an array of silicon on which the pattern of MoS 2 and vanadium is placed. By using the ability to adjust the MoS 2 carrier concentration and VO 2 phase transition properties, the absorber has dual control over its optical properties through temperature and voltage settings. This design provides the possibility of dynamic control of the resonance frequencies of the absorber. The absorber structure shows 99.3% absorption at 1375.8 nm and 94.3% at 1550.9 nm. By adjusting the parameters of the absorber structure, the absorption peak at wavelengths 1545.6 and 1743 nm reached 99.4% and 95.4%, respectively. The tunability of MoS 2 and vanadium allows precise tuning of absorption peak locations and makes the structure suitable for applications such as sensors, detectors, optical filters and telecommunication devices that require precise wavelength placement. Plasmon ; Metamaterial ; Optoelectronics ; Materials science ; Multi-band device ; Metamaterial absorber ; Dual (grammatical number) ; Tunable metamaterials ; Telecommunications ; Computer science Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Optical Wireless Communication Technologies
Authors: Mariusz Markowski | Przemysław Trzciński
Source: Scientific Reports (article, 2025-11-26)
Field: Materials Science — Biomaterials

Comparison of the analytical model with neural network model on the case of heat exchanger behavior under fouling The article presents mathematical modeling of the impact of fouling on heat transfer in shell-and-tube exchangers using analytical methods and an artificial neural network (ANN). The article shows the limitations of using ANN diagnosis of fouling on the behavior of industrial shell and tube heat exchangers. The analytical heat exchanger model is a reference model used for the comparative assessment of the ANN model. The analytical model is treated by the authors as a model providing accurate numerical calculations, aiming at diagnosis of fouling on the heat exchanger behavior during the operation of an industrial installation. The proposed model of the exchanger is characterized by sufficient accuracy from the point of view of the exchanger diagnosis under fouling, and the calculation error does not exceed 1%. The article compares the numerical results of the heat exchanger duty calculated by the use of the proposed analytical method with the heat exchanger duty obtained by ANN. Comparing the analytical model and the ANN model, the coefficient of determination R<sup>2</sup> is about 7%, which indicates a significant discrepancy in results between the analytical model and the ANN model. The presented article states that the ANN fouling model operates as an extrapolator. Thus, the application of ANN is limited to the case of small variations in ​​measured inlet process parameters. Meanwhile, the analytical model is more universal because it is less sensitive to the variations in ​​measured process parameters. Heat exchanger ; Fouling ; Artificial neural network ; Shell and tube heat exchanger ; Process (computing) ; Heat transfer ; Heat transfer coefficient ; Plate heat exchanger Calcium Carbonate Crystallization and Inhibition ; Materials Engineering and Processing ; Erosion and Abrasive Machining
Authors: Farid Khoriyanto | Eko Hidayanto | Ngurah Ayu Ketut Umiati | Fajar Arianto | Nawang Wulan
Source: International Journal of Advanced Multidisciplinary Research and Studies (article, 2025-05-02)
Field: Materials Science — Materials Chemistry

Attenuation Performance of Heavy Metal-Based Concrete for Neutron Radiation Shielding Materials in Proton Therapy Facilities using Monte Carlo Simulation Proton therapy is a cancer treatment method that utilizes high-energy protons to deliver radiation doses precisely to tumors by using the Bragg Peak phenomenon. However, the interaction of protons with body tissues can produce secondary neutron radiation, which has the potential to increase the risk of secondary cancers and endanger medical personnel. Therefore, effective shielding materials are needed to reduce neutron radiation exposure. This study aims to evaluate the effectiveness of several shielding materials, including Portland concrete (M0), M1 (zinc oxide and silica nano concrete, 3.02 g/cm³), M2 (concrete with 30% PbO addition, 2.60228 g/cm³), M3 (concrete with Tin and Bismuth alloy addition, 6.238 g/cm³), M4 (Chrome Ore and Titanium Dioxide concrete, 5.1 g/cm³), and M5 (Ferrochrome Slag Aggregate concrete, 2.581 g/cm³), in absorbing proton and neutron radiation. The Monte Carlo simulation method (MCNP) was used to analyze the attenuation parameters, such as linear attenuation coefficient, mass attenuation coefficient and Half Value Layer (HVL). The results showed that M4 material has the best value and attenuation coefficient compared to other materials, making it an optimal choice for shielding with a smaller thickness. Thus, this study provides insight into developing safer and more effective shielding materials for proton therapy facilities. Electromagnetic shielding ; Monte Carlo method ; Attenuation ; Radiation shielding ; Proton therapy ; Neutron ; Nuclear engineering ; Proton ; Materials science ; Nuclear physics ; Physics ; Engineering ; Optics ; Composite material ; Mathematics Radiation Shielding Materials Analysis ; Graphite, nuclear technology, radiation studies ; Boron Compounds in Chemistry