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Proton exchange membrane (PEM) fuel cell faces the inevitable challenge of the cold start at a sub‐freezing temperature. Understanding the underlying degradation mechanisms in the cold start and developing a better starting strategy to achieve a quick startup with no degradation are essential for the wide application of PEM fuel cells. In this study, the comprehensive in situ non‐accelerated segmented techniques are developed to analyze the icing processes and obtain the degradation mechanisms under the conditions of freeze–thaw cycle, voltage reversal, and ice formation in different components of PEM fuel cells for different freezing time. A detailed degradation mechanism map in the cold start of PEM fuel cells is proposed to demonstrate how much degradation occurs under different conditions, whether the ice formation is acceptable under the actual operating conditions, and how to suppress the ice formation. Moreover, an ideal starting strategy is developed to achieve the cold start of PEM fuel cells without degradation. This map is highly valuable and useful for researchers to understand the underlying degradation mechanisms and develop the cold start strategy, thereby promoting the commercialization of PEM fuel cells. The degradation mechanisms under the conditions of freeze–thaw cycle, voltage reversal, and ice formation in different components of PEM fuel cells for different freezing time are revealed. The different degradation degrees and mechanisms under conditions are summarized into a degradation mechanism map, which can help researchers understand the underlying degradation mechanisms in various processes and develop the cold start strategy.

Understanding the ice nucleation mechanism in the catalyst layers (CLs) of proton exchange membrane (PEM) fuel cells and inhibiting icing by designing the CLs can optimize the cold start strategies, which can enhance the performance of PEM fuel cells. Herein, mitigating the structural matching and templating effects by adjusting the surface morphology and wettability can inhibit icing on the platinum (Pt) catalyst surface effectively. The Pt(211) surface can inhibit icing because the atomic spacing of (211) crystalline surface is much larger than the characteristic distance of ice crystal, thereby mitigating the structural matching effects. A water overlayer on the Pt surface induced by the strong attraction of Pt can act as a template for ice layers and plays an important role in the icing process. Buckling of water overlayer due to the larger atomic spacing of (211) crystalline surface mitigates the templating effect and inhibits icing. Moreover, the water overlayer on the hydrophobic Pt(211) surface with fewer water molecules also mitigates the templating effect, which makes ice nucleation more difficult than homogeneous nucleation. These findings reveal the ice nucleation mechanisms on the Pt catalyst surface from the molecular level and are valuable for catalyst designs to inhibit icing in CL. A water overlayer on the Pt surface induced by the strong attraction of Pt atoms can act as a template for the ice layers and plays an important role in the ice nucleation process. Mitigating the structural matching and templating effects by adjusting the surface morphology and wettability can inhibit icing on the Pt catalyst surface during the cold start.

The composition design of asphalt mixtures plays a pivotal role in determining pavement performance and durability. To improve skeleton stability, paving uniformity, and mechanical strength, this research proposes a three-dimensional optimization framework for asphalt mixture design, focusing on aggregate gradation and optimum asphalt content. A skeleton-dense and anti-segregation gradation optimization method was developed by integrating a previously established skeleton-dense model with a segregation tendency prediction approach. In parallel, a mechanically driven method for determining optimum asphalt content was proposed by introducing the maximum migration shear stress as a performance-based alternative to the conventional Marshall stability parameter. Research results show that asphalt mixtures designed and compacted with the optimized gradation exhibit significantly enhanced high-temperature stability, while maintaining satisfactory low-temperature cracking resistance and moisture susceptibility. Field validation was conducted through the construction of a trial pavement section using the optimized gradation under recommended mixing and compaction temperatures. The resulting pavement demonstrated excellent compaction, strong resistance to segregation, and a highly stable spatial structure. These findings confirm the effectiveness of the proposed methodology in enhancing the high-temperature deformation resistance and overall structural integrity of asphalt mixtures.

Platinum (Pt) catalyst performance loss caused by a high local oxygen transport resistance is an urgent problem to be solved for proton exchange membrane fuel cells (PEMFCs). Rationally arranging Pt particles on carbon support is the primary approach for reducing mass transport resistance. Herein, using a unique method coupling Hybrid Reverse Monte Carlo, molecular dynamics simulations, and experimental measurements, a Pt particle arrangement strategy is proposed to reduce local oxygen transport resistance, based on a molecular‐level understanding of its impact. The densely arranged Pt particles with a small interparticle distance lead to the denser ionomer layer due to the co‐attraction effect, leading to a high local oxygen transport resistance. The nonuniformly arranged Pt particles with various interparticle distances cause the heterogeneous ionomer density, inducing the heterogeneous oxygen transport. Increasing the Pt‐Pt interparticle distance from 2 to 5 nm substantially reduces the local oxygen transport resistance by over 50%. The uniform arrangement of Pt particles makes the ionomer layer density more homogeneous, resulting in more uniform oxygen transport. Therefore, uniformly arranging Pt particles with an interparticle distance of >5 nm on carbon support is preferred for reducing local oxygen transport resistance and improving the homogeneity of oxygen transport. This paper elucidates the influence of the Pt particle arrangement on carbon support on oxygen transport, identifying that a close arrangement of Pt particles results in a co‐attraction effect that hinders oxygen transport. It is therefore recommended that uniformly arranging Pt particles on carbon support with a >5 nm interparticle spacing can avoid the co‐attraction effect, thus improving oxygen transport.

Electron beam welding (EBW) process is employed to weld Ta-10W and GH3128 plates of 2 mm thick, which is very beneficial to the development of aerospace engine combustor. The microstructure of the as-welded (AW) and solution-treated (ST) samples are characterized. The phase transformation program of the fusion zone (FZ) of GH3128 and Ta-10W joints is L → L + M 6 C → L + γ + M 6 C → L + γ + M 6 C + M 23 C 6 → γ + M 6 C + M 23 C 6 → γ + M 23 C 6 + P . Whether in the AW or ST state, the reaction layers formed by hard intermediate phases are found between the FZ and Ta-10W. Comparing the two states, voids zone is formed at the interface of the reaction layer near the FZ in the ST state. Additionally, it is also found that the growth of the reaction layer was promoted at the solution temperature. It is certain that the solution treatment keeps many positive significances. First of all, the banded P phase worsening the properties of materials has not been formed. Secondly, the performance of the FZ is strengthened by the dispersed granular M 6 C. Thirdly, the distribution of coincident-site lattice (CSL) grain boundaries (GBs) in the FZ is more uniform than that in the AW state.

This research aims to study the dissimilar welded joint of rhenium and GH3128 made by electron beam welding. The microstructure, solidification behavior, mechanical properties, and Re distribution behavior of the weld were systematically researched. The results showed that an excellently joint is obtained, and no defects such as cracks and pores were found in the weld. But thermal cracks occur on the Re base material under the influence of heat input. The fusion zone was mainly composed of columnar dendrites, while the plane grains grow near the Re-side fusion line (reaction layer), which was always enriched in the Re element. Re is evenly distributed in the Ni matrix as a solid solution element so that the hardness of the joint is always higher than that of the GH3128 base material. The tensile strength of the joint reached 498 Mpa and the elongation was about 3%. The tensile fracture occurred at the position of the reaction layer. The high hardness and brittleness of the reaction layer are attributed to the high content of Re and a lot of low-angle grain boundaries.

In this paper, electron beam welding of tantalum and Inconel 718 superalloy was performed. The formability, microstructure, defect characteristics and mechanical properties of joints were investigated by controlling the position of the electron beam. The weld zone of tantalum and Inconel 718 joints was mainly composed of columnar crystals and dendrites during the welding of non-beam offset and 0.5 mm beam offset to tantalum. The reaction layer composed of a large number of intermetallic compounds was found on the tantalum side, and it was the place where the fracture occurred. Tensile strength of the joints was 313 MPa and 138 MPa, respectively, and the joints exhibited brittle fracture mode due to the formation of voids and cracks in the reaction layer. The microhardness of the weld zone was higher than that of the base metal due to the strengthening effect of tantalum. Fortunately, when the beam deviated by 0.5 mm to the Inconel 718 side, equiaxed grains formed in the weld zone, and the morphology of the reaction layer changed, which improved the toughness of the joint. The tensile strength of the joint reached 480 MPa under the condition of 0.5 mm beam deviated to the Inconel 718 side.

With the rapid growth and development of proton-exchange membrane fuel cell (PEMFC) technology, there has been increasing demand for clean and sustainable global energy applications. Of the many device-level and infrastructure challenges that need to be overcome before wide commercialization can be realized, one of the most critical ones is increasing the PEMFC power density, and ambitious goals have been proposed globally. For example, the short- and long-term power density goals of Japan’s New Energy and Industrial Technology Development Organization are 6 kilowatts per litre by 2030 and 9 kilowatts per litre by 2040, respectively. To this end, here we propose technical development directions for next-generation high-power-density PEMFCs. We present the latest ideas for improvements in the membrane electrode assembly and its components with regard to water and thermal management and materials. These concepts are expected to be implemented in next-generation PEMFCs to achieve high power density. This Perspective reviews the recent technical developments in the components of the fuel cell stack in proton-exchange membrane fuel cell vehicles and outlines the road towards large-scale commercialization of such vehicles.

This present work utilizes an interpretability model to understand and explain the decisions of deep learning models. The use of DeepLIFT is proposed and attributions of a study case are obtained. Benchmarking against two other interpretability models, namely Grad-CAM and dCAM, is conducted. Results show that DeepLIFT can provide precise attributions to the model inputs in both temporal and spatial directions. A parametric study is also carried out to understand the effects of deep learning model structure on the attributions obtained from the interpretability model. Ten different convolutional neural network model structures are considered. Three important observations are made: 1) changes in the model structure have minor effects on the attributions in the temporal direction, but 2) they have negligible effects on attributions in the spatial direction, and 3) convolutional layers need to be fixed to avoid attribution discrepancies. By understanding the model decision and the resulting effects of the model structure, it is hoped that this work can contribute to the development of trustworthy deep learning models for the fire research community.

High-quality reconstruction of Aerosol Optical Depth (AOD) fields is critical for Atmosphere monitoring, yet current models remain constrained by the scarcity of complete training data and a lack of uncertainty quantification.To address these limitations, we propose AODDiff, a probabilistic reconstruction framework based on diffusion-based Bayesian inference. By leveraging the learned spatiotemporal probability distribution of the AOD field as a generative prior, this framework can be flexibly adapted to various reconstruction tasks without requiring task-specific retraining. We first introduce a corruption-aware training strategy to learns a spatiotemporal AOD prior solely from naturally incomplete data. Subsequently, we employ a decoupled annealing posterior sampling strategy that enables the more effective and integration of heterogeneous observations as constraints to guide the generation process. We validate the proposed framework through extensive experiments on Reanalysis data. Results across downscaling and inpainting tasks confirm the efficacy and robustness of AODDiff, specifically demonstrating its advantage in maintaining high spatial spectral fidelity. Furthermore, as a generative model, AODDiff inherently enables uncertainty quantification via multiple sampling, offering critical confidence metrics for downstream applications.