Ultraviolet (UV) photodetectors (PDs) based on nanowire (NW) hold significant promise for applications in fire detection, optical communication, and environmental monitoring. As optoelectronic devices evolve towards lower dimensionality, multifunctionality, and integrability, multicolor PDs have become a research hotspot in optics and electronic information. This study investigates the enhancement of detection capability in a light-trapping ZnO NW array through modification with Pt nanoparticles (NPs) via magnetron sputtering and hydrothermal synthesis. The optimized PD exhibits superior performance, achieving a responsivity of 12.49 A/W, detectivity of 4.07 × 10¹² Jones, and external quantum efficiency (EQE) of 4.19 × 10³%, respectively. In addition, the Pt NPs/ZnO NW/ZnO PD maintains spectral selectivity in the UV region. These findings show the pivotal role of Pt NPs in enhancing photodetection performance through their strong light absorption and scattering properties. This improvement is associated with localized surface plasmon resonance induced by the Pt NPs, leading to enhanced incident light and interfacial charge separation for the specialized configurations of the nanodevice. Utilizing metal NPs for device modification represents a breakthrough that positively affects the preparation of high-performance ZnO-based UV PDs.

Ultraviolet (UV) photodetectors (PDs) are characterized by wide wavelength selectivity and strong anti-interference capability. The focus of research is not only limited to the adjustment of the structure composition, but it also delves deeper into its working mechanism and performance optimization. In this study, a heterojunction self-powered photodetector with a unique honeycomb structure was successfully constructed by combining the advantages of two semiconductor materials, zinc oxide (ZnO) and nickel oxide (NiO), using magnetron sputtering and hydrothermal synthesis. The detector has high responsivity, high detectivity and favorable spectral selectivity under UV irradiation. The nearly 10-fold increase in responsivity and detectivity of the detector with the introduction of the honeycomb structure under zero-bias conditions is attributed to the macroporous structure of the ZnO honeycomb nano-mesh, which increases the surface active sites and facilitates the enhancement of light trapping. This study provides significant value to the field of UV detection by improving detector performance through structural optimization.

With the widespread application of stainless steel rail vehicles, the resistance spot-welding process between stainless steel and low-carbon steel has become one of the key connection processes in vehicle body manufacturing. However, due to the differences in the material physical properties of these two types of steel, problems such as center offset often occur during the welding process. This study adopts the finite element analysis method to systematically analyze the changes in the force field and the temperature field during the welding process after adding a nickel intermediate layer between the two materials, as well as its impact on the physical properties of the joint. The results of the finite element analysis and the physical experiments show that adding a nickel intermediate layer can effectively suppress the center deviation of the weld nugget, optimize the microstructure of the nugget, improve the continuity of the microhardness distribution, and thereby enhance the joint strength of the spot welding.

Resistance spot welding of dissimilar steels is a key Linkage process in the manufacturing of rail passenger car bodies. However, there are problems such as core deviation caused by material physical property differences in the welding of dissimilar steels (stainless steel/low-carbon steel). This study improves the weldability of stainless steel and low-carbon steel by adding a nickel intermediate layer between them. The results show that adding a nickel intermediate layer can Valid compensate for heat Loss, suppress the deviation of the weld nucleus, optimize the size of the weld nucleus, and improve the Stability of the welding quality.

The reliability of traction systems in urban rail transit vehicles is critical to safe and efficient operations. However, existing reliability assessment methods face challenges such as state space explosion, computational complexity, and difficulties in modeling fault interdependencies and imperfect maintenance. This paper proposes an integrated framework that combines dynamic fault trees with fault dependency models and Monte Carlo simulation. The method captures fault dependencies through functional dependency gates incorporating fault impact factors, models component degradation using a two-parameter Weibull distribution, and accounts for imperfect maintenance through a age reduction factor. The simulation efficiency of three random number generators—LCG, MT, and PCG64—was compared. A Monte Carlo simulation of 100,000 trials under maintenance-free conditions yielded a system MTBF of 29.9 months. A mode significance analysis identified the pantograph control unit and the traction control unit as the most critical components. Under targeted maintenance based on component criticality, the MTBF increased to 39.77 months—a 33% improvement—while the peak failure rate was maintained at approximately 3%. The MT generator exhibited the fastest convergence, achieving stability within 1% after 3000 iterations. This framework provides a practical foundation for optimizing preventive maintenance strategies for urban rail transit systems, and sensitivity analysis confirmed the robustness of weak link identification to parameter variations. This method is applicable to other complex systems with interdependent failures and multiple maintenance schedules.