The service reliability of critical catenary components is strongly influenced by damage evolution at dynamic contact interfaces. In this study, a numerical framework is developed to simulate the dynamic contact behavior and wear progression of catenary droppers by coupling Archard’s wear law with an adaptive remeshing strategy. Surface degradation is explicitly incorporated into the contact formulation through an improved boundary representation, enabling a quantitative linkage between interface damage and the corresponding mechanical responses. The simulations indicate that, after geometric reconstruction of the worn surface, the contact interface exhibits a pronounced stress-gradient evolution. The most severe damage is predicted at the contact region between the central strand and one outer strand, and the spatial damage pattern is primarily governed by discontinuous contact. Moreover, thermally induced material softening has a limited effect on the peak contact stress, which is dominated instead by the applied load and local contact geometry. The proposed framework provides a computational basis for assessing dropper wear and estimating catenary lifetime, thereby supporting reliability-oriented maintenance and safer rail operations.

Efforts to improve C2+ selectivity in CO2 electroreduction have increasingly focused on strategies that deliberately induce catalyst surface reconstruction to create and maintain active sites. Among these, approaches using anodic pulses have gained particular attention for their ability to modulate the copper catalyst surface in situ. However, the underlying Cu surface reconstruction mechanisms triggered by anodic polarization still remain unclear. Here, we show that applying anodic potentials to copper can lead to two distinct surface reconstructions: surface oxide formation or metal dissolution, each defining a different reconstruction pathway with contrasting impacts on product selectivity. Oxide-derived reconstruction transiently enhances C2 over C1 selectivity but gradually loses effectiveness during operation, while dissolution-redeposition reconstruction continuously forms C2-selective sites, resulting in a progressive increase in C2 selectivity over time. Leveraging this mechanistic understanding, we implement electrolyte engineering by introducing trace Cu2+ ions under cathodic conditions to directly activate the dissolution-redeposition pathway without anodic bias. This strategy drives a dynamic electrochemical interface that sustains active-site regeneration and enables controllable selectivity, offering an energy-efficient alternative.

Electrochemical water splitting for hydrogen production is limited by the slow kinetics of the oxygen evolution reaction (OER). The tunable structure and anion-exchange capability of layered double hydroxides (LDHs) underpin their promise as OER catalysts. Consequently, the strategic incorporation of foreign anions is viewed as a powerful approach to engineer their active sites and boost catalytic activity. This review summarizes how doping with anions such as NO3−, PO43−, Cl−, F−, and Sq2− modifies the electronic structure of LDHs. These anions regulate the local coordination environment, induce oxygen vacancies, and alter metal oxidation states, thereby synergistically optimizing both the adsorption–evolution mechanism (AEM) and the lattice oxygen oxidation mechanism (LOM). For instance, NO3− promotes surface reconstruction, F− activates lattice oxygen, PO43− stabilizes the interface, Cl− reshapes reaction pathways, and Sq2− maintains interfacial alkalinity. Collectively, rational anion engineering lowers the overpotential, increases current density, and improves stability, establishing an effective design framework for advanced LDH-based OER electrocatalysts.

Realizing cascade regulation to photocarrier dynamics via heterophase homojunction construction and surface reconstruction for enhanced photoelectrochemical performance.

A hybrid Zernike–Lyapunov framework for aberration-based Statistical Wavefront Reconstruction of chaotic optical surfaces

Photography techniques for 3D surface reconstruction during autopsy.

Fixed-mesh isogeometric analysis for phreatic surface reconstruction in 2D steady-state seepage flow

AQUDF: Adaptive frequency annealing with quadratic-enhanced implicit surface reconstruction

Three-dimensional pavement surface reconstruction and roughness assessment using RGB-D cameras

Disclosing surface reconstruction and intermediate pathways in oxygen evolution reaction on 3D-printed Ni–Fe–Cr-based integrated electrodes by in-situ Raman and infrared spectroscopy

3-dimensional surface geometry, optical properties dataset of Scots pine and Norway spruce shoots.

Dynamic surface reconstruction of pentlandite catalyst for enhanced water oxidation reaction.

Dynamic anion leaching-readsorption in Ni3S2@MoO3 heterostructures boosts the electrocatalytic activity of anion exchange membrane water electrolyzers.

ABSTRACT Electrochemical reduction of carbon dioxide (CO 2 ) to value‐added chemicals, particularly ethylene, offers a promising approach to mitigating carbon emissions while enabling renewable electricity storage in chemical form. Among various catalysts, Cu‐based materials are uniquely capable of converting CO 2 into multi‐carbon (C 2+ ) products through complex multi‐electron and multi‐proton transfer pathways. However, achieving high selectivity and efficiency remains challenging due to the intricate interplay between catalyst structure, oxidation state, local reaction microenvironment, and intermediate coverage. This review systematically discusses the reaction mechanisms of CO 2 reduction on Cu electrodes, highlighting key active sites, surface reconstructions, and the influence of morphology and oxidation states on C─C coupling pathways. Recent progress in the rational design of Cu‐based catalysts, through morphology control, interfacial engineering, alloying, and microenvironment modulation, is summarized and critically analyzed. Moreover, special emphasis is placed on synchrotron radiation‐based characterization techniques that provide element‐specific, time‐resolved in situ insights into the dynamic evolution of Cu catalysts and intermediates. These advanced methods bridge experimental and theoretical understanding, offering fundamental guidance for developing efficient, durable, and selective CO 2 ‐to‐ethylene electrocatalysts.

Sonar-neus:voxel-based efficient neural implicit surface reconstruction for forward-looking sonar.

Abstract When reconstructing the micro-topography of metallic workpieces with structured light, highly reflective and low-reflective regions in high-dynamic-range scenes often cause information loss or measurement errors, thereby degrading reconstruction accuracy. Conventional HDR approaches rely on capturing and fusing multiple sets of differently exposed images; although these methods extend the imaging dynamic range, they markedly reduce capture and reconstruction efficiency. To address this issue, this study focuses on milled surfaces with micron-scale roughness and high dynamic range and proposes a structured-light 3D reconstruction method that incorporates complementary exposures. The method segments captured surface images into high-reflectance and low-reflectance regions based on grayscale values and, by maximizing regional information entropy, determines optimal complementary exposure times for each region type. A lightweight dual-exposure fusion model, PSA-CurveNet, performs feature-level complementary fusion of exposure pairs guided by fusion curves to generate high-quality fringe patterns for 3D reconstruction.Experimental results demonstrate that the proposed complementary-exposure approach can fully reconstruct reflective milled workpiece surfaces. Compared with other methods, it significantly reduces the number of captured images and improves reconstruction efficiency while maintaining comparable accuracy.

Neural shape representation generally refers to representing 3D geometry using neural networks, e.g., computing a signed distance or occupancy value at a specific spatial position. In this paper we present a neural-network architecture suitable for accurate encoding of 3D shapes in a single forward pass. Our architecture is based on a multi-scale hybrid system incorporating graph-based and voxel-based components, as well as a continuously differentiable decoder. The hybrid system includes a novel way of voxelizing point-based features in neural networks by projecting the point "feature-field" onto a grid. This projection is insensitive to local point density, and we show that it can be used to obtain smoother and more detailed reconstructions, in particular when combined with oriented point clouds as input. Our architecture also requires only a single forward pass, instead of the latent-code optimization used in auto-decoder methods. Furthermore, our network is trained to solve the well-established eikonal equation and only requires knowledge of the zero-level set for training and inference. We additionally propose a modification to the aforementioned loss function for the case that surface normals are not well defined, e.g., in the context of non-watertight surfaces and non-manifold geometry. Overall, our method consistently outperforms other baselines on the surface reconstruction task across a wide variety of datasets, while being more computationally efficient and requiring fewer parameters.

Cr-Doping Promoted Surface Reconstruction of Ni(OH) ₂ Electrocatalysts toward Efficient Adipic Acid Electrosynthesis

In this study, we use low-energy electron diffraction, X-ray photoelectron spectroscopy, temperature-programmed desorption, and density functional theory calculations to unveil that low-energy electron irradiation (140.8 eV) induces local reconstruction of the irradiated area on the anatase TiO2(001)-(1 × 4) surface to the (1 × 1) domain, accompanied by the formation of Ti3+ species in the subsurface region and subsurface oxygen vacancies/surface hydroxyl groups, whereas the unirradiated regions retain the (1 × 4) periodicity. The low-energy electrons locally stimulate desorption of oxygen from the anatase TiO2(001)-(1 × 4) surface probably via interatomic Auger processes, creating surface oxygen vacancies with associated Ti3+ species that consequently trigger the local (1 × 4) → (1 × 1) surface reconstruction. Such a local surface reconstruction process is facilitated by surface hydroxyl groups. The created surface oxygen vacancies and Ti3+ species tend to migrate into the bulk, leading to a recovery of the (1 × 4) surface structure. The anatase TiO2(001)-(1 × 1) domain provides reactive Ti5c surface sites with a much larger density than the reactive Ti4c surface sites on the corresponding anatase TiO2(001)-(1 × 4) surface. These results demonstrate the sensitivity of TiO2 surface structures to the surface reduction, which commonly occurs for TiO2-involved (photo/electro)catalysts.

Rare-earth tritellurides (RTe3, R = lanthanide) represent an ideal platform for investigating charge density wave (CDW) order and associated Fermi surface reconstructions. While quantum oscillations have been observed in several RTe3 members, a detailed exploration of the low-temperature electronic transport properties of high-quality SmTe3 single crystals remains limited. In this study, we synthesized SmTe3 single crystals with a high residual resistivity ratio of 411. Transport measurements reveal pronounced Shubnikov–de Haas quantum oscillations below 30 K. The carriers in the observed Fermi pockets exhibit an extremely small effective mass of 0.119 m0 and a high mobility reaching 3.78×104 cm2 V−1 s−1. Furthermore, the material demonstrates remarkable anisotropic magnetoresistance, indicative of a highly anisotropic Fermi surface. Our work establishes high-quality SmTe3 as a compelling system for studying high-mobility transport in a CDW background, providing crucial insights for potential applications in advanced electronic devices.