The realization of high-performance electromagnetic wave (EMW) absorbing materials is particularly dependent on the regulation of interface structures, and the construction of multi-component heterogeneous interfaces is an effective strategy to achieve high-performance EMW absorption in materials. In this study, a three-dimensional flower-like ZnS/Co1 -x S@C composite material was prepared by hydrothermal method, stepwise gas-phase selenization, and gas-phase sulfidation processes. The formation of multiple heterogeneous interfaces through stepwise gas-phase selenization and sulfidation enhanced the interface polarization ability of the material and significantly improved the electromagnetic wave loss efficiency. The results show that the ZnS/Co1 -x S@C composite material has excellent EMW absorption performance, with a minimum reflection loss (RLmin ) of -53.75 dB at 3.4 mm and a maximum effective absorption bandwidth (EABmax ) of 7.28 GHz at 3.2 mm. Density functional theory (DFT) calculations verified that the built-in electric field constructed at the heterogeneous interface strengthened the interface polarization loss. Meanwhile, the radar cross-section (RCS) simulation further demonstrated that the material has good electromagnetic stealth properties, providing a new idea for the research on the construction of multiple heterogeneous interface sulfide composites. (c) 2026 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.
Finite element (FE) methods are the benchmark for solid mechanics simulations, yet their computational cost becomes prohibitive in problems exhibiting localized nonlinearities, fine-scale features, or long-time dynamic evolution. In our earlier FE-neural operator (FE-NO) hybrid framework (Wang et al., 2025), physics-informed deep operator networks were coupled with FE solvers through overlapping domain decomposition with Dirichlet-Dirichlet interface exchange, accelerating computationally intensive subdomains employing NOs while preserving FE fidelity elsewhere. Two limitations remained: the overlapping formulation required redundant interface computations that increased inner Schwarz iteration counts, and the convolutional feature extractor restricted the NO subdomain to structured grids, precluding irregular geometries. This work addresses both limitations. A non-overlapping Schwarz alternating method with Neumann-Dirichlet interface exchange replaces the overlapping formulation, transmitting traction boundary conditions from the NO to FE rather than exchanging displacement. This eliminates the overlap layer and reduces the number of inner Schwarz iterations, while maintaining bounded error accumulation across all tested time horizons. To enable simulations on arbitrarily shaped subdomains, a Point-DeepONet is introduced, which directly operates on unstructured FE point clouds without interpolation, extending the framework to non-convex and irregular subdomain geometries. Furthermore, strain and stress operators are derived analytically from the displacement operators through the governing kinematic equations, rather than trained as independent networks, significantly reducing the number of trainable parameter sets, while enforcing mechanical consistency by construction. The framework is validated on three solid mechanics benchmarks: static linear elasticity, quasi-static hyperelasticity, and elastodynamics with both regular and irregular subdomain geometries. These results establish a non-overlapping FE-NO coupling paradigm that is geometry-flexible, parameter-efficient, and convergence-stable, providing a principled pathway for hybrid physics-based and operator-learning solvers in large-scale dynamic solid mechanics.
The extracellular matrix (ECM) undergoes extensive architectural remodeling during tumor progression, generating nanoscale alignment cues that profoundly influence behaviors of immune cells. However, whether ECM nanoarchitecture directly programs their immune secretory functions remains poorly defined. Here, we develop a magnetically switchable hydrogel that enables reversible, on-demand modulation of nanoscale ligand alignment without altering bulk stiffness or composition. Using this platform, we uncover a mechanotransduction pathway in tumor-associated macrophages (TAMs) whereby matrix nanoalignment enhances integrin β1-dependent mechanosensing and transient receptor potential cation channel subfamily V member 2 (TRPV2)-dependent Ca2+ signaling, thereby promoting phosphoinositide 3-kinase (PI3K)–Ras-related protein 11a (Rab11a)-associated small extracellular vesicle (sEV) release by approximately tenfold. Beyond increasing vesicle output, PI3K–mechanistic target of rapamycin complex 2-associated signaling altered the particle-normalized cargo profile of sEVs, with preferential enrichment of immunoregulatory molecules, including M2-promoting cytokines and miR-181a-5p. These sEVs reprogram macrophages toward an immunosuppressive M2-like phenotype, suppress cytotoxic CD8+ T-cell infiltration, and accelerate melanoma progression in vivo. Importantly, selective disruption of the TRPV2–Rab11a axis or pharmacological attenuation of ECM remodeling restores antitumor immunity and restricts tumor growth. Together, our findings demonstrate that ECM nanoarchitecture is sufficient to regulate macrophage sEV programming in a defined matrix environment and identify TRPV2-associated mechanotransduction as a potential therapeutic vulnerability in matrix-remodeled tumors.
Asphalt pavement segregation, characterized by non-uniform aggregate distribution, detrimentally impacts pavement performance and service life. Conventional two-dimensional (2D) image-based approaches are limited in quantifying three-dimensional (3D) texture geometry and multi-scale features, with environmental factors introducing evaluation uncertainties. This study proposes a framework to quantify segregation by decomposing it into two morphological types: surface aggregate clustering (assessed via 2D-entropy coefficient of variation for planar uniformity) and vertical stratification (evaluated using coarse-to-fine aggregate ratio derived from multi-scale relative energy for gradation deviation). By leveraging 3D laser scanning and 2D wavelet decomposition across nine scales, this research investigates five asphalt mixtures (AC-13, AC-16, AC-25, OGFC-13, SMA-13) under varying temperatures (95–165 °C) and compaction passes (12–24). Results reveal that temperature influences fine aggregate surface uniformity in dense-graded mixtures, while compaction enhances uniformity in SMA-13. Vertical segregation in dense-graded mixtures decreases with elevated temperatures, unaffected by compaction intensity. The study provides new perspectives and methods for assessing asphalt pavement segregation in pavement construction quality control scenarios.
Precise modeling of electric vehicle (EV) energy consumption is fundamental to the efficient design and management of modern transportation systems. While physics-based models offer superior interpretability, they often struggle with limited adaptability to dynamic driving conditions and heterogeneous vehicle platforms. To bridge this gap, achieving real-time and accurate calibration of physical model parameters becomes essential. This paper proposes a novel two-stage Bayesian optimization framework that integrates Contextual Bayesian Optimization (CBO) and Transfer Bayesian Optimization (TBO). In the first stage, the CBO module learns a context-aware mapping between operating conditions and physical parameters within a source domain. In the second stage, the TBO module leverages the learned prior knowledge to achieve rapid adaptation to a target vehicle domain with minimal data requirements. We evaluate the proposed framework using real-world datasets from BMW i3 and Tesla Model 3. Experimental results demonstrate that the proposed framework achieves a per-second WMAPE of 17.27% in cross-condition scenarios. For cross-vehicle transfer, the primary out-of-sample evaluation on the held-out 70% of the Tesla trips yields a WMAPE of 24.82% and a total energy error of 12.65%. The CBO results further demonstrate rapid convergence under a limited online evaluation budget. This research provides a scalable and sample-efficient solution for high-fidelity energy modeling across diverse driving conditions and vehicle platforms.
