Hydrogen production via alkaline water/seawater electrolysis is pivotal for sustainable energy conversion, yet remains impeded by the sluggish kinetics of the oxygen evolution reaction (OER) and severe anodic corrosion induced by chloride ions (Cl-). Herein, we rationally design a NiCr-LDH/CeO2 heterostructure synthesized via a one-step electrodeposition strategy. CeO2 effectively modulates electrochemical reconstruction triggered by Cr leaching and re-adsorption, as it serves as an "oxygen and electron buffer". This modulation strengthens the metal-oxygen covalent bond and structural stability, establishing the rapid and sustainable OER process via the lattice oxygen oxidation mechanism. Furthermore, the Lewis acid property of CeO2 enhances OH- adsorption capacity, while synergizing with the adsorbed CrO4 2- layer to electrostatically repel corrosive Cl- within the Helmholtz layer, endowing NiCr-LDH/CeO2 with robust corrosion resistance. Consequently, NiCr-LDH/CeO2 achieves ultralow overpotentials of 211 and 217 mV at 10 mA cm-2 in alkaline water and simulated seawater, respectively, with robust long-term stability at industrially relevant current density. This work elucidates the structure-activity relationships in heterostructured catalysts, providing a viable strategy to unlock superior intrinsic activity and corrosion resistance in water/seawater oxidation electrocatalysts. (c) 2026 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.
Short carbon fiber-reinforced polymer (SCFRP) composites exploit the intrinsic conductivity of the carbon fiber network for self-sensing, yet no predictive model couples their anisotropic, rate-dependent fracture to piezoresistive damage identification. This work presents a finite deformation multiphysics phase-field framework coupling a viscoelastic-viscoplastic constitutive model, an anisotropic crack resistance formulation, and a piezoresistive conductivity model. The three sub-problems are unified through the second-order fiber orientation tensor, which simultaneously defines fiber family directions, crack resistance anisotropy, and principal conduction paths of the carbon fiber network. A damage-coupled conductivity tensor captures both strain-driven geometric-kinematic resistance changes and irreversible network severance driven by the phase-field variable. The framework is coupled to an eight-electrode electrical impedance tomography configuration, and the normalized inter-electrode conductance ratios serve as inputs to a feedforward artificial neural network that infers normalized crack length and mechanical compliance without mechanical sensing. The network achieves R2 = 0.99 on held-out configurations, confirming generalization across the microstructure space. The framework establishes a physics-based, computationally efficient route for real-time structural health monitoring and inverse damage assessment in SCFRP composites.
In this paper, we investigate cyclic vectors of S⁎ on the full Fock space. First, we extend the Douglas-Shapiro-Shields factorization to the full Fock space and apply it to characterize the symbol φ for which Hφ is injective. Second, we show that the Champernowne sequence is a necessary condition for cyclic vectors of S⁎, and we completely characterize cyclic vectors by Douglas-Shapiro-Shields factorization. Finally, we examine cyclic sets on model spaces.
Automatic segmentation of breast tumors in ultrasound images plays a critical role in breast cancer diagnosis. However, the task remains challenging due to the high morphological variability of tumors, their indistinct boundaries with surrounding tissues, and the inherent presence of speckle noise in ultrasound imaging. To address these challenges, we propose a Spatial-Frequency Collaborative Transformer network, which integrates collaborative modeling in both spatial and frequency domains and explicitly suppresses feature redundancy. Specifically, our framework includes three key components: a superpixel feature transformation module to enhance structural consistency in the spatial domain, a wavelet boundary enhancement module to strengthen boundary perception using frequency-domain decomposition, and a sparse spatial reduction attention mechanism to effectively suppress noise and irrelevant background features. We evaluate our method on two public datasets and one private dataset, demonstrating superior performance over several deep-learning-based segmentation methods.
The lattice Boltzmann method (LBM) has evolved over the past three decades into a powerful and widely used computational framework for simulating multiphase and multicomponent flows. This unique hybrid review combines a large-scale bibliometric analysis with an in-depth thematic review, providing a comprehensive perspective on current developments and future trends in multiphase LBM. Bibliometric results of 5657 publications (1991–2025) reveal exponential growth in publications, strong international collaboration networks, and a clear shift from early interface-capturing schemes toward more stable, physically consistent, and high-performance formulations. The thematic review highlights core families of multiphase LBM models, including color-gradient, pseudopotential, free-energy, and phase-field approaches, emphasizing their theoretical foundations, strengths, limitations, and representative applications. Emerging trends such as high-density-ratio stabilization, wetting and contact-line modeling, hybrid LBM-continuum solvers, machine-learning-accelerated LBM, and GPU-optimized implementations are also discussed. The review further provides a comparative discussion of leading multiphase models alongside the core challenges in the field and the strategies proposed to address them. Finally, the key future directions are outlined, including exascale-ready algorithms, data-driven closure models, quantum-inspired LBM formulations, and thermodynamic consistency for extreme regimes. Together, this work provides a concise and integrated overview of the intellectual and technical advances in multiphase LBM, serving as an essential reference for researchers seeking to understand its evolution, current status, and future directions.
