jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kok Suen Cheng | Maryam Jalali‐Mousavi | Jian Sheng
Source: Small (article, 2025-12-08)
Field: Materials Science — Surfaces, Coatings and Films

Soft Interferometric Nanostrain Sensor Reveals Solid‐Liquid Interfacial Tension Oscillation Amplified by Competitive Adsorption Understanding the adsorption of protein mixtures is significant for engineering biosurface functionalities that affect biomaterial hemocompatibility and infections over medical instruments. However, existing methods cannot detect real-time competitive adsorption events or require fluorophore labelling that may alter the adsorption characteristics. An interferometric nanostrain sensor has been developed to investigate real-time adsorption of unlabeled proteins. The sensor exploits the elastocapillary effect on the substrate nanometer deformation by a sessile protein drop to measure instantaneous interfacial tensions at its three-phase contact line. Using fetal bovine serum (FBS), it is shown that asymptotically, the solid-liquid interfacial tension (γ<sub>SL</sub>) decreases with increasing FBS concentrations, and the solid-vapor tension (γ<sub>SV</sub>) remains uncorrelated. Results link molecular adsorption events with macroscale surface energy. Dynamically, γ<sub>SL</sub> shows Langmuir adsorption characteristics on a coarse timescale. With the sensor's high spatiotemporal resolutions (2.38 nm or <0.25 mN m<sup>-1</sup>), it is reported for the first time a high-frequency, low-amplitude oscillatory modulation of γ<sub>SL</sub> at a fine timescale over the Langmuir adsorption curve. Compared with other colloids with fewer competing elements of adsorption, this oscillation is intrinsic to any colloidal system but strongly amplified by competitive adsorption. The sensor provides a novel ensemble-averaging technology to quantify competitive adsorption, thereby revealing new mechanisms for protein-surface interactions. Adsorption ; Surface tension ; Colloid ; Oscillation (cell signaling) ; Drop (telecommunication) ; Langmuir ; Interferometry ; Substrate (aquarium) ; Tension (geology) Polymer Surface Interaction Studies ; Advanced Biosensing Techniques and Applications ; Nanomaterials and Printing Technologies
Authors: Chao Ge
Source: Highlights in Science Engineering and Technology (article, 2025-11-17)
Field: Materials Science — Materials Chemistry

A Faults Recognition Method of Transmission Line Insulators Based on Generalized Neural Networks Regular recognition of transmission line insulators (TLI) and rapid diagnosis of their condition are crucial for the stable operation of power systems. To enhance the speed and accuracy of fault diagnosis for TLI, deep learning algorithms are applied to the recognition of TLI faults. However, traditional neural networks suffer from slow convergence speed and low accuracy in fault diagnosis. This paper proposes a recognition method for TLI based on generalized neural networks (GNN). A database for TLI is established by using aerial images, with image coordinates extracted as the sample input set. Then, by studying the characteristics and applications of GNN, a suitable network of GNN is developed to complete the selection and design of the recognition method for TLI. The simulation experiments show that GNN is able to accurately and quickly identify faulty TLI. Compared with the traditional method, the GNN converges faster, and the loss rate decreases by 27%. Artificial neural network ; Pattern recognition (psychology) ; Fault (geology) ; Transmission line ; Convergence (economics) ; Electric power transmission ; Transmission (telecommunications) ; Line (geometry) ; Power (physics) High voltage insulation and dielectric phenomena ; Power Line Inspection Robots ; Electrical Fault Detection and Protection
Authors: Liyu Zhong | Sheng Mao
Source: ArXiv.org (preprint, 2025-05-22)
Field: Materials Science — Materials Chemistry

Exact Expansion Formalism for Transport Properties of Heterogeneous Materials Characterized by Arbitrary Continuous Random Fields We derive an exact contrast-expansion formalism for the effective conductivity of heterogeneous materials (media) with local properties described by arbitrary continuous random fields, significantly generalizing the widely used binary-field models. The theory produces a rapidly convergent Neumann-series that, upon Gaussian closure via a Hermite expansion, yields closed-form first-, second- and third-order approximations, which achieve percent-level accuracy at first order for isotropic media. For anisotropic media, second-order approximations achieve sub-2% accuracy across a wide range of local property contrasts and correlations. Our formalism provides mathematically rigorous structure-property closures, with significant implications for the discovery and design of novel graded and architected materials with tailored transport properties. Formalism (music) ; Gaussian ; Anisotropy ; Isotropy ; Random field ; Gaussian random field ; Gaussian process ; Exact solutions in general relativity Thermal properties of materials ; Composite Material Mechanics ; Numerical methods in inverse problems
Authors: Yilong Zhou | Quan Jiang | Xiaoqin Wu | Chunyan Zhu | Zengming Shen | Y.Z. Miao | Liang He | Li‐Wen Xu | Yu‐Cheng Chen | Yipei Wang | Yi Xu
Source: Light Advanced Manufacturing (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Generating one-dimensional plasmonic arrays by laser-driven self-organization Assembling metal nanoparticles into a well-defined array and constructing strongly coupled hybrid systems enable high-quality resonances with narrow linewidths, which offer new opportunities to circumvent the hurdle of plasmonic losses.Herein, we propose a light-driven approach for generating plasmonic arrays by leveraging the selforganized patterns of tightly confined surface plasmon polaritons in single metal nanowires, which exhibit optimized unit structures, tunable interparticle spacings with supra-wavelength or sub-wavelength periods beyond the diffraction limit, and flexible alignment directions.We theoretically and experimentally show the mechanism of generating field patterns via the interplay of a standing wave and optical beating, followed by the formation of periodic geometries under a spatially modulated temperature distribution.We also fabricate plasmonic arrays on microfibres with diameters down to ~1.4 m and thereby construct a series of hybrid plasmonicphotonic resonators with narrow-band resonances (~3.9 nm linewidth) as well as a barcode system with high multiplexing capacity.Our results show the potential of simple, low-cost, and high-efficiency fabrication of plasmonic arrays and hybrids that may find applications in plasmonic array lasers, information encryption, and highresolution distributed sensing. Plasmon ; Materials science ; Nanotechnology ; Laser ; Optoelectronics ; Optics ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Photonic Crystals and Applications
Authors: J. Pablo Muñoz | Jinjie Yuan | Nilesh Jain
Source: ArXiv.org (preprint, 2025-01-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Mamba-Shedder: Post-Transformer Compression for Efficient Selective Structured State Space Models Large pre-trained models have achieved outstanding results in sequence modeling. The Transformer block and its attention mechanism have been the main drivers of the success of these models. Recently, alternative architectures, such as Selective Structured State Space Models (SSMs), have been proposed to address the inefficiencies of Transformers. This paper explores the compression of SSM-based models, particularly Mamba and its hybrids. We study the sensitivity of these models to the removal of selected components at different granularities to reduce the model size and computational overhead, thus improving their efficiency while maintaining accuracy. The proposed solutions, collectively referred to as Mamba-Shedder, achieve a speedup of up to 1.4x during inference, demonstrating that model efficiency can be improved by eliminating several redundancies with minimal impact on the overall model performance. The code is available at https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning. Transformer ; Compression (physics) ; Space (punctuation) ; Computer science ; Materials science ; Engineering ; Composite material ; Electrical engineering ; Voltage Copper Interconnects and Reliability ; Vibration and Dynamic Analysis
Authors: Elif Demirbas | Bruno Boucher | Matthew Baker | Joshua Andrews | William Amador Núñez de la Cruz | Sara Mueller | Samuel Serna-Otalvaro
Source: Sensors (article, 2025-12-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Review of Solid-State LiDAR Principles and Metasurface-Based LiDAR Sensors Light Detecting and Ranging (LiDAR) has been a promising solution for autonomous vehicles. For beam-steering mechanisms, solid-state LiDAR with microelectromechanical systems (MEMS) and optical phased arrays (OPAs) have demonstrated robust and compact alternatives to mechanical LiDAR with 360° rotating mirrors. Two-dimensional optical metasurfaces can be used for phase shift, deflecting the angle in LiDAR. If a LiDAR system only needs a fixed beam direction, then static metasurfaces can be used. If a LiDAR system requires beam scanning, dynamic (tunable) metasurfaces are necessary for efficient and adaptable operation. In this review article, we will discuss the principles of metasurface beam-steering mechanisms and discuss how metasurfaces can shift the incoming light's phase and deflect the angle. LiDAR based on metasurfaces provides promising solutions due to its flat optics feature, robust nature, and non-moving parts. Additionally, we will discuss and compare the field of view (FOV) of LiDAR based on metasurfaces. Tunable metasurfaces in LiDAR systems are crucial for real-time beam scanning, and they have advantages over traditional mechanical scanning mechanisms like faster scanning rates, increased reliability, more compact form factors, and larger fields of view. Lidar ; Ranging ; Phased-array optics ; Beam steering ; Microelectromechanical systems ; Field of view ; Laser beams Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Advanced Wireless Communication Technologies
Authors: Daniel Cristóbal Andrade-Girón | Juana Sandivar-Rosas | William Joel Marín Rodriguez | Marcelo Gumercindo Zúñiga-Rojas | Abrahán Cesar Neri Ayala | Ernesto Díaz-Ronceros
Source: Preprints.org (preprint, 2025-07-22)
Field: Materials Science — Materials Chemistry

Comparison of Assembly Methods in Machine Learning for the Early Prediction of Acute Myocardial Infarction Cardiovascular disease (CVD) is one of the leading causes of mortality worldwide, which raises the fundamental need to apply efficient predictive tools to support clinical deci-sion-making. This study compares the predictive performance of Bagging, Random Forest, Extra Trees, Gradient Boosting, and AdaBoost ensemble learning algorithms applied to a clinical dataset of CVD patients. The methodology included data preprocessing and cross-validation to regulate generalization. Performance was evaluated using multiple metrics: accuracy, F1 score, precision, recall, Cohen&amp;#039;s Kappa, and area under the curve (AUC). Among the models evaluated, Bagging demonstrated the best overall performance (accuracy: 93.36 %± 0.22; F1 Score: 0.936; AUC: 0.9686), reaching also the lowest average rank (1.0) in Friedman&amp;#039;s test and placing, together with Extra Trees (90.76 %± 0.18; AUC: 0.9689), in the superior statistical group (group A) according to Nemenyi&amp;#039;s post-hoc test. Both models evidenced high agreement with the real labels (Kappa: 0.87 and 0.83, respec-tively), which reinforces their reliability in real clinical settings. The results validate the superiority of aggregation-based ensemble methods in terms of accuracy, stability, and concordance, highlighting Bagging and Extra Trees as preferred candidates for cardio-vascular diagnostic support systems, where reliability and generalization are essential. Myocardial infarction ; Cardiology ; Internal medicine ; Artificial intelligence ; Medicine ; Computer science ; Machine learning Machine Learning in Materials Science
Authors: Antonio Hidalgo-Toledo | Darío Bazco | Víctor Correa‐Pérez | Marta Martínez‐Júlvez | Javier Sancho
Source: International Journal of Biological Macromolecules (article, 2025-10-15)
Field: Materials Science — Materials Chemistry

Protein thermostabilization with Protposer: Pushing the stability limits and folding reversibility of a highly-stabilized apoflavodoxin Enhancing the stability of highly stable proteins represents an interesting challenge in protein design. We have used the computational tool Protposer to rapidly achieve large additional stabilization of apoflavodoxin, a protein strongly thermostabilized over the years through protein engineering based on educated guesses. By rationally combining top-ranked mutations onto a previously stabilized variant (6 M), we have generated a series of new mutants and characterized their stability by thermal and chemical denaturation. Relative to the starting variant, the T<sub>m</sub> of 10 M apoflavodoxin is nearly 9 °C higher, while the simplified 3 M and 4 M mutants, showing improved refolding properties, display increases of 6/7.5 °C, respectively. The thermostabilizing effects of individual mutations are close to additive and translate into a large increase in conformational stability at 25.0 °C. Comparison of the x-ray structures of progressively stabilized WT, 6 M and 10 M flavodoxins reveals a concomitant mild trend toward shorter hydrogen bonds, reduced internal cavity volumes and narrower tunnels. Overall, these conformational changes are minor, and a functional assay confirms the mutants also preserve their catalytic activity. These findings demonstrate that even highly stable proteins can be further stabilized through rational design using a simple computational tool that automatically analyses PDB files and identifies stabilizing mutations. Folding (DSP implementation) ; Protein folding ; Protein stability ; Protein Data Bank (RCSB PDB) ; Thermal stability ; Mutant ; Protein engineering ; Protein structure ; Rational design Enzyme Structure and Function ; Glycosylation and Glycoproteins Research ; Protein Structure and Dynamics
Authors: Ricardo Luiz Perez Teixeira | Albertino Damasceno | Haroldo Lhou Hasegawa
Source:  (article, 2025-01-01)
Field: Materials Science — Biomaterials

BIOMATERIAIS METÁLICOS E TECNOLOGIAS DE MANUFATURA ADITIVA: AVANÇOS E DESAFIOS NA ENGENHARIA DE PRODUÇÃO DE IMPLANTES BIOMÉDICOS Physics ; Humanities ; Art Electrospun Nanofibers in Biomedical Applications
Authors: Pulia Venkatachalapathy Keerthivasan | S.M. Sivagami | Tanvi Vijay | N.K. Annamalai
Source: Materiali in tehnologije (article, 2025-02-05)
Field: Materials Science — Materials Chemistry

INVESTIGATION ON MICROSTRUCTURAL AND GEOMETRICAL CHARACTERIZATION OF COLD METAL TRANSFER HARDFACED INCONEL 718 ON MEDIUM CARBON STEEL This study presents a compelling exploration of the microstructural and mechanical enhancements achieved with Inconel 718 hardfaced layers on AISI 1045 medium carbon steel through the advanced cold metal transfer (CMT) process. By precisely adjusting process parameters such as travel speed and wire feed rate, this research examines their effects on bead geometry and dilution for both stringer- and oscillation-type depositions. Microstructural analysis unveiled defect-free hardfaced layers, achieving seamless metallurgical bonding between the substrate and hardfacing material. Notably, oscillation-type deposition with optimized parameters (6 m/min wire feed rate and 20 cm/min travel speed) achieved an impressively low dilution of 3.38 %, surpassing stringer-type deposition in quality. Furthermore, hardness testing highlighted a significant improvement in the surface durability as oscillation-type deposition reached 254 HV, while the value of the substrate was 172 HV. SEM-EDX analysis confirmed the inclusion of critical alloying elements such as nickel, niobium, and molybdenum, reinforcing the hardfaced layer’s robust composition. These findings underscore the CMT process’s capacity to fabricate high-quality, low-dilution Inconel 718 hardfaced layers, providing substantial resistance to corrosion and wear. The finely tuned parameters identified here offer valuable guidance for the industries seeking enhanced performance in demanding environments. Materials science ; Inconel ; Metallurgy ; Characterization (materials science) ; Carbon steel ; Metal ; Carbon fibers ; Composite material ; Nanotechnology ; Corrosion ; Composite number ; Alloy Metal Alloys Wear and Properties ; Advanced materials and composites ; Metal and Thin Film Mechanics
Authors: О.V. Yefimov | Т.V. Potanina | М.М. Pylypenko | A. Baliński | Тetiana Harkusha
Source:  (article, 2025-10-13)
Field: Materials Science — Materials Chemistry

METHODS AND APPROACHES FOR IMPROVING THE FUEL ROADS AND ABSORBING ROADS NPP REACTOR CORE PROPERTIES BASED ON THE INTERVAL AND REGRESSION ANALYSIS MODELS AS COMPONENTS ARTIFICIAL INTELLIGENCE We will consider the prospects of using new methods and approaches for evaluating and improving the physicomechanical and physicochemical properties (indicators of microhardness, corrosion resistance, heat resistance, radiation resistance, etc.) of fuel rods zirconium cladding, component heat-releasing assemblies, hafnium absorbing roads and their clusters of NPP reactors core for Ukrainian nuclear technologies. We think such methods are more effective and correct than the well-known ones. We propose to apply them to solve the urgent problem of increasing the nuclear and radiation safety of the operation of NPP power units. These methods and approaches are based on the mathematical tools application of interval and regression analysis as components of artificial intelligence in simulation. The experiment data for evaluating the construction materials properties of the fuel pins and absorbing rods are characterized by uncertainty and ambiguity. Modelling using the methods of interval and polynomial regression analysis allows for increasing by 4…6% the accuracy of the physicomechanical and physicochemical properties estimation of fuel rods zirconium cladding and hafnium absorbing rods of a nuclear reactor core. The formalization of such methods and approaches creates a new methodology for evaluating and optimizing the properties of fuel pins and absorbing rods, their components, assemblies and clusters for the Ukrainian nuclear fuel cycle to increase the nuclear and radiation safety of NPP reactors. Cladding (metalworking) ; Nuclear fuel ; Rod ; Boiling water reactor ; Nuclear reactor core ; Nuclear power plant ; Light-water reactor ; Spent nuclear fuel ; Interval (graph theory) ; Nuclear power Nuclear Materials and Properties ; Nuclear and radioactivity studies ; Nuclear Engineering Thermal-Hydraulics
Authors: Rashidah Ghazali | Herlina Abdul Rahim | ⁠Syahidah Nurani Zulkifli
Source: IAES International Journal of Artificial Intelligence (article, 2025-08-01)
Field: Chemistry — Analytical Chemistry

Broiler meats tenderness prediction using near infrared spectroscopy against non-linear predictive modelling Near infrared (NIR) spectroscopy is a non-invasive analytical technique known for its ability to assess the quality attributes of meat products. However, the linear models utilized, partial least square (PLS) and principal component regression (PCR) achieved unsatisfactory performances of meat physical attributes prediction. Hence, in this research, for its inherent advantages in modelling nonlinear system, artificial neural network (ANN) is augmented to the components of PCR and PLS. Through the augmentation, the principal component neural network (PCNN) and latent variable neural network (LVNN) models are developed. From the results obtained, it shows that PCNN and LVNN successfully surpassed their respective linear versions of PCR and PLS by 70% higher shear force prediction performances. The LVNN proved to achieve the best prediction in breast meat with root mean square error of prediction (RMSEP) of 0.0769 kg and coefficient of determination (RP2) of 0.8201 whilst for drumsticks, RMSEP=0.1494 kg and RP2=0.8606. NIR spectroscopy technology integrated with machine learning yields a promising non-invasive technique in predicting the shear force of intact raw broiler meat. Tenderness ; Broiler ; Computer science ; Biological system ; Materials science ; Food science ; Chemistry ; Biology Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality
Authors: Benjun Cheng | Ming Huang | Guoqi Liu | Feng Wu | Xiaocheng Liang
Source: Materials (article, 2025-07-31)
Field: Materials Science — Ceramics and Composites

Wettability-Enhanced SiC–Graphite Synergy in Al2O3-SiC-C Castables: Carbon Resource Comparation, Sintering Response, and Latent Rheology Effects Research on raw materials for Al<sub>2</sub>O<sub>3</sub>-SiC-C refractory castables used in blast furnace troughs is relatively well established. However, gaps remain in both laboratory and industrial trials concerning the performance of castables incorporating SiC-modified flake graphite and alternative carbon sources. This study investigated the sintering behavior, mechanical properties, and service performance of Al<sub>2</sub>O<sub>3</sub>-SiC-C castables utilizing varying contents of modified flake graphite, pitch, and carbon black as carbon sources. Samples were characterized using SEM, XRD, and EDS for phase composition and microstructural morphology analysis. Key findings revealed that the thermal expansion mismatch between the SiC coating and flake graphite in SiC-modified graphite generated a microcrack-toughening effect. This effect, combined with the synergistic reinforcement from both components, enhanced the mechanical properties. The SiC modification layer improved the wettability and oxidation resistance of the flake graphite. This modified graphite further contributed to enhanced erosion resistance through mechanisms of matrix pinning and crack deflection within the microstructure. However, the microcracks induced by thermal mismatch concurrently reduced erosion resistance, resulting in an overall limited net improvement in erosion resistance attributable to the modified graphite. Specimens containing 1 wt.% modified flake graphite exhibited the optimal overall performance. During industrial trials, this formulation unexpectedly demonstrated a water reduction mechanism requiring further investigation. Materials science ; Graphite ; Composite material ; Flake ; Sintering ; Wetting ; Microstructure ; Thermal shock ; Coating Advanced ceramic materials synthesis ; Aluminum Alloys Composites Properties ; Advanced materials and composites
Authors: Sudad H Al-Obaidi | Falah H. Khalaf
Source: Journal of Waste Management & Recycling Technology (article, 2025-08-31)
Field: Chemistry — Analytical Chemistry

Displacement of Reservoir Oil by Nanoflooding: Physicochemical Mechanisms This study explores the physicochemical mechanisms involved in oil displacement by aqueous solutions containing nanoagents. It delves into these critical issues and reveals the primary pathways for innovative solutions. The use of nanoparticles in oil recovery processes can significantly enhance the characteristics of the injected fluid. These enhancements include improvements in viscosity and density and reductions in surface tension. Additionally, nanoparticles can help optimise emulsification, leading to more effective oil recovery overall. During nanoflooding, the properties of the rock matrix change, particularly its wettability and heat transfer coefficient, both of which improve. The physicochemical mechanisms of oil displacement by nanoagents are discussed, along with the influence of the adsorption process on displacement efficiency Displacement (psychology) ; Enhanced oil recovery ; Wetting ; Viscosity ; Adsorption ; Matrix (chemical analysis) ; Process (computing) ; Oil production Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Lubricants and Their Additives