jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zihui Cheng | Zhide Li | Denis Pustovoytov | Alexander Pesin | Zheng‐Yu Wang | Tongxin Zhao | Charlie Kong | Hailiang Yu
Source: Advanced Composites and Hybrid Materials (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Unveiling the mechanism for enhanced mechanical–electrical–tribological property integration in CuCrZr/Cu laminated metallic composites via cryorolling and short-term annealing CuCrZr/Cu/CuCrZr laminated metallic composites (LMCs) were fabricated by hot rolling (HR) followed by cryorolling (CR) and subsequent short-term annealing. The CuCrZr layers accounted for 20.0 vol% of the composite, while the core layer consisted of low-cost pure Cu. The resulting LMCs exhibited enhanced ultimate tensile strength (UTS) and wear resistance relative to the constituent materials. Furthermore, the electrical conductivity of the LMCs was significantly higher than that of the initial CuCrZr (38.3%IACS). After annealing at 673 K for 30 min, the LMCs reached a UTS of 292 MPa, an electrical conductivity of 96.7%IACS, and a wear rate of 2.38 × 10–4 mm3∙N–1∙m–1. These improvements were primarily attributed to optimized precipitation strengthening in the CuCrZr layers and the presence of a high density of annealing twins in the Cu layers. Microstructural characterization revealed uniformly distributed Cr-rich precipitates (≈ 4 nm) in the CuCrZr layer of the CR-A673K sample. Meanwhile, the Cu layer exhibited an annealing-twin area fraction of 43.2% and a 10% increase in the length fraction of Σ3 grain boundaries. This study demonstrates the feasibility of synchronizing CuCrZr aging with Cu recrystallization via CR and short-term annealing, thereby offering a practical design strategy for developing high-performance, low-cost alternatives to pure copper. Annealing (glass) ; Recrystallization (geology) ; Ultimate tensile strength ; Electrical resistivity and conductivity ; Microstructure ; Metal ; Grain size ; Grain boundary Microstructure and mechanical properties ; Aluminum Alloys Composites Properties ; Metallic Glasses and Amorphous Alloys
Authors: Tareq Nafea Alharby | Bader Huwaimel
Source: Scientific Reports (article, 2025-08-15)
Field: Materials Science — Materials Chemistry

Machine learning analysis of pharmaceutical cocrystals solubility parameters in enhancing the drug properties for advanced pharmaceutical manufacturing A new computational framework based on machine learning was developed for prediction of Hansen solubility parameters in preparation of pharmaceutical cocrystals with improved properties. The models of Kernel Ridge Regression (KRR), Multi-Linear Regression (MLR), and Orthogonal Matching Pursuit (OMP) were optimized in prediction of three Hansen solubility parameters. Each model's performance was assessed via measuring Root Mean Square Error (RMSE), R<sup>2</sup>, Mean Absolute Error (MAE), and Monte Carlo Cross-Validation (CV) scores using a Tabu Search method for optimization. The results demonstrated that KRR outperformed other models for predicting solubility parameters in the formulation. This comparative evaluation offers valuable perspectives on selecting models for similar regression assignments, stressing the significance of choosing the right algorithm according to particular output demands. The results are useful for design of medicines and screening coformers with solubility enhancement in pharmaceutical co-crystallization. Solubility ; Mean squared error ; Computer science ; Regression ; Linear regression ; Machine learning ; Mathematics ; Statistics ; Chemistry ; Organic chemistry Crystallization and Solubility Studies ; Crystallography and molecular interactions ; Drug Solubulity and Delivery Systems
Authors: Stefan Giron | Nikita Polin | Esmaeil Adabifiroozjaei | Yangyiwei Yang | Fernando Maccari | András Kovács | Trevor P. Almeida | Dominik Ohmer | Kaan Üstüner | Alaukik Saxena | M. Katter | Iliya Radulov | Christoph Freysoldt | Rafal E. Dunin–Borkowski | Michael Farle | Karsten Durst | Hongbin Zhang | Lambert Alff | Katharina Ollefs | Bai‐Xiang Xu | Oliver Gutfleisch | Leopoldo Molina‐Luna | Baptiste Gault | Konstantin Skokov
Source: Nature Communications (article, 2025-12-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Identifying grain boundary and intragranular pinning centres in Sm2(Co,Fe,Cu,Zr)17 permanent magnets to guide performance optimisation Permanent magnets draw their properties from a complex interplay of chemical composition and phase, each with their associated intrinsic magnetic properties. Gaining an understanding of these interactions is the key to deciphering the origins of a permanent magnets' magnetic performance and facilitate the engineering of much improved-performing magnets. Here, we use advanced multiscale microscopy and microanalysis on a bulk Sm<sub>2</sub>(Co,Fe,Cu,Zr)<sub>17</sub> pinning-type high-performance magnet with outstanding thermal and chemical stability. Comparison of the microstructure in regions of different composition, we demonstrate that the pinning of magnetic domains, imaged by nanoscale magnetic induction mapping, is controlled by the composition and atomic arrangement of copper. This is confirmed by micromagnetic simulations. Contrary to the belief that grain boundaries are "weak links" in magnetic materials, we demonstrate grain boundaries undergo magnetization reversal at relatively low fields (0.1-0.3 T), but this remains confined to these regions and does not significantly impact the magnet's coercivity. Our results showcase that it is the optimal microstructure within the grain itself that is crucial for achieving the desired magnetic properties. Magnet ; Grain boundary ; Microstructure ; Micromagnetics ; Nanoscopic scale ; Magnetization ; Magnetic domain ; Grain size Magnetic Properties of Alloys ; Magnetic Properties and Applications ; Magnetic properties of thin films
Authors: Youssef Ehab Nagaty
Source: Alexandria Journal of Food Science and Technology (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Digital Image Analysis (DIA) in Food Technology Digital Image Analysis (DIA) has emerged as a transformative tool in food science, particularly in quality control, safety, and analysis. DIA techniques allow for non-destructive testing, rapid assessments, and remote accessibility, addressing the industry's growing demand for efficiency and accuracy. This technology converts visual data into computer-readable formats, enabling detailed analysis of food characteristics through pixel grids. Various DIA applications include detecting pesticide residues in grapes, acrylamide levels in Potato chips, and adulterants in food products such as milk and ethanol-based beverages. Additionally, DIA aids in grading dried figs, predicting moisture content of bell pepper in drying processes, estimating live animal weights without invasive methods and detecting nitrite in food samples. This paper explores the key components of DIA, such as image acquisition, feature extraction, and processing, alongside their practical applications in food quality control, illustrating the potential for DIA to enhance food safety and efficiency across the food industry. Digital image analysis ; Digital image ; Computer science ; Image (mathematics) ; Computer vision ; Digital imaging ; Artificial intelligence ; Image processing ; Food science ; Computer graphics (images) ; Biology Spectroscopy and Chemometric Analyses
Authors: Abdelmoneim H. Ali | Abdelaziz Elbarbary | Shao‐Quan Liu | Mayssa Hachem | Jahirul Ahmed Mazumder | Fawzi Banat | Xingguo Wang | Mutamed Ayyash
Source: npj Science of Food (article, 2025-12-15)
Field: Chemistry — Analytical Chemistry

Assessment of buffalo and bovine ghee authenticity using fatty acid profile analysis and FTIR spectroscopy Milk fat stands out for its superior quality, due to its bioactive lipids and rich flavor, unlike other plant and animal fats. The current study presents a novel approach to detect pork fat adulteration in buffalo and bovine ghee via fatty acid profiling, volatile compound analysis, and FTIR spectroscopy. Pure ghee samples were mixed with different levels (1, 2, 4, 8, 10, and 15%) of pork fat. By spiking ghee samples with pork fat at levels as low as 1%, we demonstrate that key indicators, such as butyric acid depletion, shifts in lipid quality indices, and distinctive FTIR spectral characteristics, can reliably signal adulteration. In addition, the principal component analysis revealed clear clustering patterns that differentiate pure and adulterated samples, highlighting the discriminatory power of the combined analytical methods. The findings of this study could offer a robust framework to ensure ghee authenticity and protect consumer trust in high-value dairy products. Fatty acid ; Bovine milk ; Fourier transform infrared spectroscopy ; Butyric acid ; Animal fat ; Milk fat Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Food Chemistry and Fat Analysis
Authors: Sudharsan Saravanan | Aditya Raman Hattimare | Atharva Bharat Mahadik | Arnav Singh | Uttamchand NarendraKumar | A. Raja Annamalai
Source: Journal of Manufacturing and Materials Processing (article, 2025-09-05)
Field: Materials Science — Biomaterials

Effect of Heat Treatment on High-Temperature Tribological Behavior of WE54 Alloy: An Experimental Study This study examines the high-temperature tribological behavior of WE54 Mg alloy under various conditions: as-cast, solution-treated (T4), age-hardened (T6), and secondary aged (S.A). Wear tests were performed using a pin-on-disc setup, applying a normal load of 10 N, with a sliding velocity of 1 m/s, a sliding distance of 1000 m, and temperatures from 25 °C to 150 °C. Responses such as the coefficient of friction and volumetric wear rate were recorded. The results indicate that heat treatment significantly influences the wear behavior of the WE54 alloy. The lowest volumetric wear rate (8.16 ± 1.47 mm3) and wear coefficient (0.112 ± 0.02) occurred in the as-cast sample at 100 °C, while the highest volumetric wear rate (14.68 ± 1.59 mm3) and wear coefficient (0.171 ± 0.02) were found in the S.A. sample at 150 °C. Surface characterization of worn samples was conducted using field emission scanning electron microscopy (FESEM) and X-ray diffraction (XRD). The wear mechanisms identified include abrasive wear, oxidative wear, and delamination across all conditions, regardless of temperature. The elevated volumetric wear rate at 150 °C, irrespective of the sample condition, is attributed to oxidation and thermal softening of the material. Tribology ; Alloy ; Materials science ; Metallurgy Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; MXene and MAX Phase Materials
Authors: Dwi Kusuma Wahyuni | Sumrit Wacharasindhu | Wichanee Bankeeree | Hunsa Punapayak | Sastia Prama Putri | Sehanat Prasongsuk
Source: Scientific Reports (article, 2025-07-01)
Field: Chemistry — Analytical Chemistry

Biotransformation of medicarpin from homopterocarpin by Aspergillus niger and its biological characterization The objective of this study was to convert homopterocarpin derived from Pterocarpus macrocarpus Kurz. heartwood to medicarpin using Aspergillus niger (strain UI X-172) and assess its antioxidant, antiplasmodial, and anticancer activities in silico and in vitro. This study highlighted biotransformation of homopterocarpin to medicarpin via demethylation. Medicarpin demonstrated antioxidant activity against 2,2-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS, IC<sub>50</sub> = 0.61 ± 0.05 µg/mL) and 1,1-diphenyl-2-picrylhydrazyl (DPPH, IC<sub>50</sub> = 7.50 ± 1.6 µg/mL), antiplasmodial activity against the Plasmodium falciparum strain 3D7 (IC<sub>50</sub> = 0.45 ± 0.35 µg/mL), and anticancer efficacy against a hepatocyte-derived carcinoma cell line (Huh7it-1 cells, IC<sub>50</sub> = 34.32 ± 5.56 µg/mL). Medicarpin also showed favorable antioxidant, antiplasmodial, and anticancer properties in silico with a binding affinity lower than commercial drugs. These results highlight the green synthesis of medicarpin by microbial transformation using A. niger, which demonstrates promising in vitro and computational activity, however, further studies are required for clinical development. Biotransformation ; Aspergillus niger ; Aspergillus ; Chemistry ; Microbiology ; Biotechnology ; Biology ; Biochemistry ; Enzyme Chromatography in Natural Products ; Bioactive natural compounds ; Natural product bioactivities and synthesis
Authors: Yuxiang Li | ThuyVy Duong | Mary R. Rooney | Christie M. Ballantyne | Josef Coresh | Jennifer A. Brody | Nona Sotoodehnia | Dan Arking | Joel S. Bader
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-11-21)
Field: Chemistry — Spectroscopy

cpiVAE: Robust and Interpretable Cross-Platform Proteomics Imputation Large-scale plasma proteomic studies often use different high-throughput affinity platforms, and measurements of the same protein across platforms are often discordant. Discordance hinders cross-study integration. Improving proteomics data integration would enable more powerful meta-analyses, improve statistical power for biomarker discovery, and provide a better understanding of proteome-phenotype relationships. Here we present a cross-platform proteomics imputation variational autoencoder (cpiVAE), a deep generative model for bidirectional imputation of protein abundances between two widely used platforms: Olink and SomaScan. Using a training cohort of paired measurements from the China Kadoorie Biobank (CKB), cpiVAE learns a joint latent representation that enables cross-platform imputation. The cpiVAE method improves benchmarks provided by established methods, k-nearest neighbors (KNN) Weighted Nearest Neighbors (WNN, from Seurat v4). The cpiVAE method achieves up to 30% higher correlation between imputed and true values than KNN and WNN. The cpiVAE method also generalizes well to an independent cohort from the Atherosclerosis Risk in Communities Study (ARIC). Without retraining, cpiVAE maintains high performance compared to benchmarks. Associations of imputed protein levels with clinical phenotypes closely mirror results using the actual measurements and increases power in a meta-analysis scenario. A post-hoc feature importance matrix enables interpretation of this AI model. Protein pair features extracted from cpiVAE have significant overlap with known associations in the Search Tool for the Retrieval of Interacting Genes (STRING) database. In summary, cpiVAE offers an accurate, generalizable, and interpretable solution for cross-platform proteomic imputation, enabling integrated analyses across studies with proteomics measured on different platforms. This user-friendly framework and pre-trained model weights are available under a BSD2 open source license at https://github.com/joelbaderlab/cpiVAE_v1. Imputation (statistics) ; Autoencoder ; Biobank ; Proteogenomics ; Proteomics ; Quantitative proteomics ; Pattern recognition (psychology) ; Statistical power Advanced Proteomics Techniques and Applications ; Bioinformatics and Genomic Networks ; Single-cell and spatial transcriptomics
Authors: Mahmoud El‐Sharkawy | Modhi O. Alotaibi | Esawy Mahmoud | Kholoud Ahmed El-Naqma | R. Kanany | Medhat G. Zoghdan | M. Shabana
Source: PeerJ (article, 2025-06-18)
Field: Materials Science — Biomaterials

Novel glauconite compounds improve soil properties and sugar beet (<i>Beta vulgaris</i> L.) yields in saline soils Sugar beet is essential for sugar production, supporting food industries and renewable energy resources. A two-season field experiment (2021/2022 and 2022/2023) evaluated the effects of different potassium (K) sources, including traditional potassium sulfate (K), glauconite powder (G), and foliar glauconite extracts (20- and 40-mL L<sup>-1</sup>) extracted with sulfuric acid (GS), humic acid (GH), or hot water (GW), on soil properties, sugar beet yield, and sugar quality in saline soils. The results showed that GH and G treatments significantly improved soil properties by reducing electrical conductivity (EC), exchangeable sodium percentage (ESP), and bulk density (BD), while increasing organic matter (OM) and infiltration rate (IR). The application of glauconite extracted by humic acid in 40 mL (GH2) treatment improved soil nutrient availability, notably increasing nitrogen (by 73.4%), potassium (by 137.2%), cupper (by 219.7%), and manganese (by 316.7%) compared to control, while GS2 enhanced ferrous (by 213.7%) and zinc (by 363.7%). GH2 application led to remarkable improvements in sugar beet yield, with average increases in root yield (94.84%), shoot yield (100.45%), total sugar yield (137.22%), and sucrose (20.5%) compared to the control, whereas GW treatments showed the least improvements. Despite GW recording the lowest sugar impurities, GH2 recorded the lowest alkalinity level. Advanced analytical techniques such as heatmaps, self-organizing maps (SOM), and while non-metric multidimensional scaling (NMDS) analysis revealed strong positive correlations between soil properties, sugar beet responses and sugar quality attributes. These findings underscore the potential incorporation of new innovative cost-effective alternative foliar amendment derived from glauconite rock-waste extraction with humic acid highlighting a sustainable agricultural strategy for managing soil health and crop productivity contributing to food security and environmental sustainability. Sugar beet ; Glauconite ; Agronomy ; Sugar ; Soil water ; Chemistry ; BETA (programming language) ; Environmental science ; Biology ; Mineralogy ; Soil science ; Food science Clay minerals and soil interactions ; Magnetic and Electromagnetic Effects ; Silicon Effects in Agriculture
Authors: Saud Alqahtani | Ali Alqahtani | Ahmed A. Almrasy
Source: Luminescence (article, 2025-08-01)
Field: Chemistry — Analytical Chemistry

Firefly Algorithm–PLS for Synchronous Fluorescence Analysis of Olmesartan and Rosuvastatin in Pharmaceutical and Plasma Samples This study presents a validated analytical method for the simultaneous determination of olmesartan and rosuvastatin using synchronous spectrofluorimetry integrated with advanced chemometric modeling. The method addresses the limitations of conventional approaches by combining the firefly algorithm (FA) with partial least squares (PLS) regression to overcome spectral overlap challenges. Experimental conditions were optimized, with ethanol as solvent and 0.05% w/v SDS as fluorescence enhancer, achieving a 1.6- and 1.4-fold increase in sensitivity for olmesartan and rosuvastatin, respectively. The FA-PLS approach demonstrated superior performance over conventional PLS, requiring fewer latent variables (2 vs. 4) while achieving lower detection limits in the ng/mL range, markedly tighter residual distributions (±2%), and improved prediction accuracy (RRMSEP: 1.34 and 1.40). The method was successfully validated according to ICH guidelines and applied to pharmaceutical formulations and plasma samples, yielding recoveries of 99.87 ± 1.02% and 99.68 ± 0.56% for olmesartan and rosuvastatin, respectively. Statistical comparison with the reported HPLC method confirmed method equivalence through t-tests, F-tests, and interval hypothesis testing. Environmental impact assessment using AGREE (0.75) and BAGI (75.0) indices demonstrated superior greenness compared to the conventional HPLC method while maintaining excellent analytical practicality, establishing this method as a sustainable alternative for routine pharmaceutical analysis and biomedical applications. Rosuvastatin ; Olmesartan ; Chromatography ; Rosuvastatin Calcium ; Chemistry ; Computer science ; Algorithm ; Mathematics ; Pharmacology ; Medicine ; Internal medicine Analytical Methods in Pharmaceuticals ; Pesticide Residue Analysis and Safety ; Spectroscopy and Chemometric Analyses
Authors: Arved Wintzer | Bilen Emek Abali | Kaspars Dadzis
Source: Computer Methods in Applied Mechanics and Engineering (article, 2025-01-30)
Field: Materials Science — Materials Chemistry

Multiphysics simulation of crystal growth with moving boundaries in FEniCS Crystal growth processes and the Czochralski process in particular involves various physical phenomena such as heat transfer, phase change or liquid flows and requires a coupled multiphysical model for realistic numerical simulations. In this work, a new and extendable model is developed using the open-source software FEniCS. Basic equations for electromagnetic induction, heat conduction and radiation as well as phase change are thoroughly derived up to a finite element form and discussed together with the assumed boundary conditions and approximations. Verification of the FEniCS model with an analytical case demonstrated an accuracy with an error below 1%. A comparison with experimental results and numerical data from a similar model achieved a good agreement and showed opportunities for further improvement of melt flow modeling in particular. Multiphysics ; Finite element method ; Mechanics ; Mechanical engineering ; Materials science ; Computer science ; Physics ; Structural engineering ; Engineering Solidification and crystal growth phenomena ; Metallurgical Processes and Thermodynamics ; Advanced Numerical Methods in Computational Mathematics
Authors: Wei-Ling Hsu | Xinyu Lin | Chia-Min Ho | Cheng‐Fu Yang | Kuei‐Kuei Lai
Source: Photonics (article, 2025-09-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ultra-Broadband Solar Absorber Design Covering UV to NIR Range Based on Cr–SiO2 Metamaterial Planar Stacked Structures This paper presents the design of an ultrabroadband solar absorber, developed using a metamaterial stack composed of only two materials, consisting of alternating layers of Cr and SiO2. Starting with a Cr layer as the substrate, multiple pairs of Cr and SiO2 were stacked sequentially, where one Cr layer and one SiO2 layer constitute a single pair. To further enhance performance, a cylindrical Cr structure was added to the top. A key innovation of this work lay in its material simplicity and cost efficiency, relying solely on two inexpensive materials, Cr and SiO2. Additionally, the inclusion of the top Cr cylinder was found to significantly enhance absorptivity. Simulations demonstrate that removing this feature led to a noticeable reduction in absorptivity of approximately 10% across the 500–2000 nm wavelength range. Another important finding is the effect of the number of Cr–SiO2 pairs on absorption behavior. When the number of pairs increases from four to five, the average absorptivity decreases slightly, but the absorption bandwidth is notably broadened. Further increasing six pairs resulted in a marginal increase in bandwidth, while maintaining the average absorptivity. Moreover, a low-absorptivity dip at 360 nm was slightly mitigated, rising to approximately 0.900. Based on these insights, a six-pair metamaterial structure was chosen for further optimization. Utilizing COMSOL Multiphysics® simulation software (version 6.0), the absorber was successfully engineered to achieve high performance across an exceptionally broad spectral range, from 200 nm to 2160 nm. Under optimal design parameters, it exhibited an average absorptivity of 0.950, with absorptivity consistently exceeding 0.900 throughout this range. This demonstrates the absorber’s strong potential for efficient solar energy harvesting using a structurally simple and cost-effective design. Molar absorptivity ; Planar ; Metamaterial absorber ; Metamaterial ; Absorption (acoustics) ; Wavelength ; Bandwidth (computing) ; Stack (abstract data type) ; Layer (electronics) Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Thermal Radiation and Cooling Technologies