Search Results (925)

This study introduces a composite method that integrates a diamond-coated tubular grinding electrode with short electric arc machining (SEAM) for GH4099. Mechanical micro-grinding and arc erosion act concurrently within the inter-electrode gap, enabling an in situ “erode–dress” coupling in which the grinding action levels nascent craters and promotes debris evacuation while SEAM supplies localized thermal–electrical energy for removal. A design-of-experiment scheme probes discharge and grinding factors, and performance is evaluated by material removal behavior, electrode wear, and surface integrity. Within a robust window (12–24 V; 500–2000 r/min), the composite process sustains stable discharges without catastrophic melting at 24 V and yields dense, uniform textures. Representative surfaces show controllable areal roughness (Sa ≈ 14–27 µm across 80#–600#), reflecting a practical finishing–efficiency trade-off. Multi-scale characterization (3D topography, cross-sectional metallography, SEM) evidences suppression of recast steps, macro-protrusions, and irregular pits, with more evenly distributed, shallower grinding traces compared to those with single-mode SEAM. The comparative analyses clarify discharge stabilization and recast-layer mitigation mechanisms, establishing a feasible pathway to high-quality, high-efficiency composite SEAM of GH4099 without resorting to overly aggressive electrical conditions. Full article

With the growing demand for sustainable and high-performance construction materials, alkali-activated materials (AAM) have attracted significant interest as eco-friendly al-ternatives to cement-based systems. Nevertheless, the tensile ductility and AAM–concrete interfacial bonding of polyethylene fiber-reinforced AAM remain insufficiently understood, and systematic knowledge on how activator modulus governs these multi-scale properties is still limited. This study aims to clarify how activator modulus (Ms = 0, 0.5, 0.8, 1.1, 1.4) influences the mechanical, interfacial, and microstructural behavior of an engineered AAM reinforced with polyethylene fibers. The effects are investigated through uniaxial tensile tests, single-fiber pull-out experiments, bond tests with concrete, and microstructural analyses (SEM, XRD, CT). Results show that an activator modulus of 1.1 yields the best overall performance, achieving a 28-day tensile strength of 3.77 MPa and ultimate tensile strain of 3.68%, representing increases of 231% and 64.6% compared with a modulus of 0. Microstructural observations confirmed that the optimized modulus promotes extensive gel formation, improves fiber–matrix interfacial bonding, and enhances strain-hardening with multiple microcracks. Interfacial tests further demonstrated that Ms strongly affects bond performance between AAM and concrete, with 1.0–1.1 providing balanced adhesion and matrix ductility, while excessive activation (Ms = 1.4) caused interfacial defects and bond deterioration. These findings deepen the understanding of the micromechanical role of activator modulus and provide guidance for the mix design of durable, high-ductility AAM suitable for sustainable infrastructure. Full article

Developing precise tumor-targeting delivery systems while minimizing off-target toxicity continues to pose significant challenges in medicine application. The integration of two different functional materials has emerged as a promising strategy in current biomedical research. Herein, a hybrid nanocomposite consisting of Fe₃O₄ and ZnO was synthesized via a simple approach and employed as a nanoscale drug delivery system to explore the loading capacity and stimuli-responsive release characteristics of the anticancer agent doxorubicin (DOX). Results show that the synthesized nanoparticles (NPs) exhibit a multi-scale nanostructure consisting of the spindle-like ZnO nanorods with a mean length of 280 nm, on which the Fe₃O₄ NPs with a diameter of around 16 nm are uniformly dispersed. The ZnO@Fe₃O₄ NPs possess superparamagnetic behavior and a fast response to the external magnet and demonstrate exceptional near-infrared (NIR) photothermal conversion efficiency. In drug release studies, the ZnO@Fe₃O₄ NPs achieve the controlled DOX release in the simulated acidic tumor microenvironment as well as NIR laser irradiation. Further, the ZnO@Fe₃O₄-DOX composites significantly suppress the viability of human cervical cancer cells (HeLa) upon laser activation. These findings suggest that ZnO@Fe₃O₄ NPs are promising candidates for combined photothermal therapy, magnetic-targeted drug delivery, and stimuli-responsive controlled release applications. Full article

Cryogenic Energy Storage (CES) has emerged as a promising solution for large-scale and long-duration energy storage, offering high energy density, zero local emissions, and compatibility with intermittent renewable energy sources. This systematic review critically examines recent advances in the numerical modeling of CES systems, with the objective of identifying prevailing methodologies, emerging trends, and existing research gaps. The studies analyzed are classified into three main categories: global thermodynamic modeling, simulation of specific components, and transient dynamic modeling. The findings highlight the continued use of thermodynamic models due to their simplicity and computational efficiency, alongside a growing reliance on high-fidelity CFD and transient models for more realistic operational analyses. A clear trend is also observed toward hybrid approaches, which integrate deterministic modeling with machine learning techniques and response surface methodologies to enhance predictive accuracy and computational performance. Nevertheless, significant challenges persist, including the absence of multiscale integrative models, the scarcity of high-resolution experimental data under transient conditions, and the limited consideration of operational uncertainties and material degradation. It is concluded that the development of integrated numerical frameworks will be critical to advancing the technological maturity of CES systems and ensuring their robust deployment in real-world energy transition scenarios. Additionally, the review also discusses local thermal non-equilibrium (LTNE) conditions, the influence of geometric and operational parameters, and the role of multidimensional and multi-region modeling in predicting thermal and exergy performance of packed-bed TES within LAES cycles. Full article

Reliable performance of TRISO (tristructural isotropic) nuclear fuel depends on the interplay between its multilayer architecture and the buffer-layer microstructure, which are difficult to isolate experimentally. We implement a multiscale, multiphysics model in the open-source MOOSE (Multiphysics Object-Oriented Simulation Environment) framework that couples particle-scale thermo-mechanical finite-element analysis with mesoscale phase-field fracture to link microstructure to effective stiffness and strength. The model resolves the combined influence of pore volume fraction, size, and aspect ratio and explicitly separates the effects of reduced load-bearing capacity from stress concentrations in the porous buffer. Simulations reveal substantial hoop stresses across coating layers under nominal thermal conditions due to material property mismatches and temperature gradients. In the buffer, stiffness and strength decrease with porosity; morphology is decisive: as aspect ratio decreases, strength degrades far more rapidly than stiffness, consistent with crack-like pores that amplify local stresses. The framework reproduces logarithmic trends with aspect ratio and explains the higher sensitivity of strength, providing parameters that can inform design and acceptance criteria (e.g., limits on pore elongation and porosity gradients). Implemented within MOOSE, the approach is readily extensible to irradiation-dependent kinetics, interface debonding, and uncertainty-quantified 3D analyses to support risk-informed TRISO fuel development. Full article

The growing pressure to achieve carbon neutrality has exposed major limitations in current industrial processes, which often operate in isolation, rely on simplified mass-balance assumptions, and struggle to manage increasingly complex material and energy flows. Traditional industrial symbiosis and circular economy strategies have improved resource efficiency, yet they rarely capture molecular-level interactions or enable coordinated optimization across multiple facilities, restricting their ability to support large-scale decarbonization. In this context, Carbon–Hydrogen–Oxygen Symbiosis Networks (CHOSYNs) have emerged as an advanced framework that integrates atomic-level targeting with multi-scale process systems engineering to identify synergies, valorization pathways, and cross-sector exchanges that conventional approaches overlook. This review consolidates the theoretical foundations, historical development, and recent applications of CHOSYNs, illustrating how it can enhance efficiency, reduce emissions, and strengthen resilience in energy systems, chemical industries, and circular resource management. Although the literature remains limited, existing studies demonstrate the promise of CHOSYNs as a unifying methodology for designing low-carbon industrial ecosystems. Key challenges related to scalability, validation, governance, and operational robustness are examined, and a roadmap is proposed to guide the evolution and practical deployment of CHOSYNs toward 2035. Full article

This review surveys the recent literature on the fire resistance of reinforced concrete (RC) columns based on a bibliometric analysis of publications to reveal research trends and focus areas. The collected studies are synthesized from the perspectives of materials, structural behaviors, parameter influences, and predictive modeling. From the material aspect, the review summarizes the degradation mechanisms of conventional concrete at elevated temperatures and highlights the improved performance of ultra-high-performance concrete (UHPC) and reactive powder concrete (RPC), where dense microstructures and fiber bridging effectively suppress spalling and help maintain residual capacity. In terms of structural behavior, experimental and numerical studies on RC columns under fire are reviewed to clarify the deformation, failure modes, and effects of axial load ratio, slenderness, cover thickness, reinforcement ratio, boundary restraint, and load eccentricity on fire endurance. Parametric analyses addressing the influence of these factors, as well as the heating–cooling history, on overall stability and post-fire performance is discussed. Recent advances in thermomechanical finite element analysis and the integration of data-driven approaches such as machine learning have been summarized for evaluating and predicting fire performance. Future directions are outlined, emphasizing the need for standardized parameters for fiber-reinforced systems, a combination of multi-scale numerical and machine-learning models, and further exploration of multi-hazard coupling, durability, and digital-twin-based monitoring to support next-generation performance-based fire design. Full article

The mechanical and durability performance of cementitious materials is fundamentally governed by the symmetry, anisotropy, and hierarchical organization of their microstructures. Conventional experimental characterization—based on imaging, spectroscopy, and physical testing—often struggles to capture these multiscale spatial patterns and their nonlinear correlations with macroscopic performance. Recent advances in machine learning (ML) provide unprecedented opportunities to interpret structural symmetry and anisotropy through data-driven analytics, computer vision, and physics-informed models. Furthermore, we summarize cases where symmetry-informed descriptors improve performance prediction accuracy in fiber- and nano-modified composites, demonstrating that ML-based symmetry analysis can substantially complement the limitations of conventional experimental-based characterization. We confirm that image-based models such as CNN and U-Net quantify the directionality and connectivity of pores and cracks, and that physically informative neural networks (PINNs) and heterogeneous data-based models enhance physical consistency and computational efficiency compared to conventional FEM and CFD. Finally, we present the conceptual and methodological foundation for developing AI-based microstructural symmetry analysis, aiming to go beyond simple prediction and establish a conceptual foundation for AI-driven cement design based on microstructure–performance causality. Full article

Steel, a fundamental material in modern industry, is widely used across manufacturing, construction, and energy sectors. Steel surface defects exhibit characteristics such as multiple classes, multi-scale features, small detection targets, and low-contrast backgrounds, making detection difficult. We propose RAC-RTDETR, a lightweight real-time detection algorithm designed for accurately identifying small surface defects on steel. Key improvements include: (1) The ARNet network, combining the ADown module and the RepNCSPELAN4-CAA module with a CAA-based attention mechanism, results in a lighter backbone network with better feature extraction and enhanced small-object detection by integrating contextual information; (2) The novel AIFI-ASMD module, composed of Adaptive Sparse Self-Attention (ASSA), Spatially Enhanced Feedforward Network (SEFN), Multi-Cognitive Visual Adapter (Mona), and Dynamic Tanh (DyT), optimizes feature interactions at different scales, reduces noise interference, and improves spatial awareness and long-range dependency modeling for better detection of multi-scale objects; (3) The Converse2D upsampling module replaces traditional upsampling methods, preserving details and enhancing small-object recognition in low-contrast, sparse feature scenarios. Experimental results on the NEU-DET and GC10-DET datasets show that RAC-RTDETR outperforms baseline models with MAP improvements of 3.56% and 3.47%, a 36.18% reduction in Parameters, a 40.70% decrease in GFLOPs, and a 7.96% increase in FPS. Full article

The potential of graphene for electric field sensing is limited by its zero bandgap. This study employs a combined first-principles and experimental approach to enhance its response via heterojunctions with ZnO, SnO₂, and Al₂O₃. Calculations reveal spontaneous formation and interfacial charge transfer in all systems, with SnO₂/graphene exhibiting the most significant charge transfer (0.3636 e) and inducing a finite bandgap (0.017–0.064 eV). Experimentally, SnO₂-graphene/PDMS composites demonstrated the highest relative permittivity (3.19) and a 7.76% increase in normalized induced voltage over pure PDMS within 50 Hz–50 kHz. This work establishes a direct correlation between interfacial charge transfer, bandgap opening, and macroscopic dielectric enhancement, identifying SnO₂/graphene as the optimal heterojunction. The integrated multi-scale methodology provides a clear design principle for high-performance, graphene-based field-sensitive materials. Full article

Lithium minerals are essential raw materials for new energy storage systems, and accurate instance segmentation of their microscopic images is crucial for efficient resource exploration and utilization. However, existing segmentation methods face challenges when processing lithium mineral images, including complex texture overlaps, missed detection of small particles, and deployment difficulties on edge devices, making it hard to balance segmentation accuracy with inference speed. To address these challenges, this paper proposes an efficient instance segmentation method based on YOLO11-seg, named EMS-YOLO-seg. First, we designed Multi-Scale Partial Convolution (MSPConv) and integrated it into the C3k2 module. The modified C3k2-MSP module optimizes the model’s receptive field and enhances its multi-scale feature extraction capability. We replaced the PSABlock module with the CBAM attention mechanism, introducing the C2PSA-CBAM module, which strengthens the model’s channel focus and feature extraction abilities. The redesigned Segment-LSCDMSP segmentation head reduces computational complexity and improves detection efficiency. Experimental results on our custom-built lithium mineral microscopic image dataset show that compared to the baseline YOLO11n-seg model, the EMS-YOLO-seg model achieved a 0.8% and 0.8% improvement in $\mathrm{m}\mathrm{A}{\mathrm{P}}_{50}^{\mathrm{b}\mathrm{o}\mathrm{x}}$ and $\mathrm{m}\mathrm{A}{\mathrm{P}}_{50:95}^{\mathrm{b}\mathrm{o}\mathrm{x}}$, respectively, and a 1% and 0.7% improvement in $\mathrm{m}\mathrm{A}{\mathrm{P}}_{50}^{\mathrm{m}\mathrm{a}\mathrm{s}\mathrm{k}}$ and $\mathrm{m}\mathrm{A}{\mathrm{P}}_{50:95}^{\mathrm{m}\mathrm{a}\mathrm{s}\mathrm{k}}$. Additionally, the model reduced the number of parameters by 52.1%, FLOPs by 18.6%, model size by 49.4%, and increased FPS by 12.7%. This study provides reliable technical support for accurate instance segmentation of lithium mineral microscopic images and demonstrates strong scene adaptability and promising potential for real-time deployment under industrial environments and resource-constrained scenarios. Full article

This paper provides a comprehensive review of the dynamic tensile behavior of concrete, focusing on its implications for seismic-resistant and impact-prone structures such as dams. The present work distinguishes itself in the following ways: providing the first comprehensive synthesis explicitly focused on large-aggregate dam concrete behavior across the seismic strain rate range (${10}^{-4}$ to ${10}^{-2}$ s⁻¹), which is critical yet underrepresented in the existing literature; integrating recent experimental and numerical advances regarding moisture effects, load history, and cyclic loading—factors that are essential for dam safety assessments; and critically evaluating current design guidelines for concrete dams against state-of-the-art research to identify gaps between engineering practice and scientific evidence. Through the extensive synthesis of experimental data, numerical simulations, and existing guidelines, the study examines key factors influencing dynamic tensile strength, including strain rate effects, crack evolution, testing techniques, and material variables such as moisture content, load history, and aggregate size. Experimental results from spall tests, split Hopkinson pressure bar configurations, and cyclic loading protocols are analyzed, revealing dynamic increase factors ranging from 1.1 to over 12, depending on the strain rates, saturation levels, and preloading conditions. The roles of inertial effects, free water (via the Stefan effect), and microstructural heterogeneity in enhancing or diminishing tensile performance are critically evaluated. Numerical models, including finite element, discrete element, and peridynamic approaches, are discussed for their ability to simulate crack propagation, inertia-dominated responses, and moisture interactions. The review identifies and analyzes current design guidelines. Key conclusions emphasize the necessity of integrating moisture content, load history, and mesoscale heterogeneity into dynamic constitutive models, alongside standardized testing protocols to bridge gaps between laboratory data and real-world applications. The findings advocate for updated engineering guidelines that reflect recent advances in rate-dependent fracture mechanics and multi-scale modeling, ensuring safer and more resilient concrete infrastructure under extreme dynamic loads. Full article

X-ray absorption near-edge structure (XANES) spectra are employed to characterise the coordination numbers of metallic elements within materials. However, conventional XANES analysis methods frequently rely on preconceived assumptions regarding the analysed samples, which may not fully satisfy the requirements of scientific research and industrial applications. To mitigate such reliance, a novel approach based on the Gated Adaptive Network for Deep Automated Learning of Features (GANDALF) is proposed. To effectively extract multi-scale information from the XANES spectrum, the spectrum was segmented into multiple scales. Each segment was fitted using a pseudo-Voigt function, with the absorption edge position. The GANDALF algorithm, a table-based deep learning approach, was employed to model the coordination environment of absorbing elements. The proposed method was validated using a previously published open-access dataset. For vanadium-containing samples, the model achieved R² values of 0.7837 on test sets with non-integer coordination numbers, whereas the random forest model only achieved 0.6328. Furthermore, our results highlight the significant importance of the post-edge peak when predicting coordination numbers using the full spectrum. Full article

Thermal runaway (TR) in lithium-ion batteries presents a significant safety hazard for electric vehicles (EVs), often resulting in fire or explosion. Mitigating TR requires thermal-protection strategies capable of delaying or suppressing heat propagation within battery pack cases (BPCs). This study proposes a flame-retardant BPC design and evaluates its effectiveness through a combined approach using CFD-based thermal analysis and multiscale experimental validation. In the CFD model, a heat-source temperature of 1107 °C was applied to simulate the thermal load during TR, together with a coolant flow rate of 17 L/min. Material-level verification was conducted through high temperature specimen tests, in which flame-retardant pads were heated to a target of 1100 °C with an allowable tolerance of ±10% for 5 min; the unheated (backside) temperature remained below 160 °C. Full-scale assessment involved heating the BPC upper case at temperatures exceeding 500 °C for 10 min, where the backside temperature remained below 150 °C. Module-level TR experiments further confirmed that the flame-retardant layer reduced the external temperature from 240–260 °C to below 150 °C. The results demonstrate that the proposed design effectively delays thermal penetration and maintains external safety thresholds, offering practical guidelines for developing safer EV battery systems. Full article

Piezoelectric composites have found a wide range of applications in smart structures and devices and effective numerical methods should be developed to simulate not only the macroscopic coupled piezoelectric performances, but also the details of the local distributions of the stress and electric field. In this paper, we proposed a multi-scale asymptotic algorithm based on the Second-Order Two-Scale (SOTS) analysis method for the piezoelectric eigenvalue problem in perforated domain with periodic micro-configurations. The eigenfunctions and eigenvalues are expanded to the second-order terms and the homogenized eigensolutions; the expressions of the first- and second-order correctors are derived successively. The first- and second-order correctors of the eigenvalues are determined according to the integration forms of the correctors of the corresponding eigenfunctions. Explicit expressions of the homogenized material coefficients are derived for the laminated structures and the finite element procedures are proposed to compute the homogenized solutions and the correctors numerically. The error estimations for the approximations of eigenvalues are proved under some regularity assumptions and a typical numerical experiment is carried out for the two-dimensional perforated domain. The computed results show that the SOTS analysis method is efficient in identifying the piezoelectric eigenvalues accurately and reproducing the original eigenfunctions effectively. This approach also provides an efficient computational tool for piezoelectric eigenvalue analysis and can extend to other multi-physics problems with complex microstructures. Full article