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Abstract The utilization of hot carriers as a means to surpass the Shockley‐Queasier limit represents a promising strategy for advancing highly efficient photovoltaic devices. Quantum dots, owing to their discrete energy states and limited multi‐phonon cooling process, are regarded as one of the most promising materials. However, in practical implementations, the presence of numerous defects and discontinuities in colloidal quantum dot (CQD) films significantly curtails the transport distance of hot carriers. In this study, the harnessing of excess energies from hot‐carriers is successfully demonstrated and a world‐record carrier diffusion length of 15 µm is observed for the first time in colloidal systems, surpassing existing hot‐carrier materials by more than tenfold. The observed phenomenon is attributed to the specifically designed honeycomb‐like topological structures in a HgTe CQD superlattice, with its long‐range periodicity confirmed by High‐Resolution Transmission Electron Microscopy(HR‐TEM), Selected Area Electron Diffraction(SAED) patterns, and low‐angle X‐ray diffraction (XRD). In such a superlattice, nonlocal hot carrier transport is supported by three unique physical properties: the wavelength‐independent responsivity, linear output characteristics and microsecond fast photoresponse. These findings underscore the potential of HgTe CQD superlattices as a feasible approach for efficient hot carrier collection, thereby paving the way for practical applications in highly sensitive photodetection and solar energy harvesting.

Aiming at the problems of low stability of path planning, inability to avoid dynamic obstacles, and long path planning for multi unmanned aerial vehicles (UAV) in mountainous environment, a path planning method for UAV was proposed based on the fusion of Sparrow Search Algorithm (SSA) and Bioinspired Neural Network (BINN). The method first scans the flight environment and smoothes the surface, then raises it to obtain the safe surface, and uses SSA to find a series of nodes with the lowest comprehensive cost on the safe surface. Then, B-spline curves are used to fit these nodes, so that the planned path is smooth to meet the flight requirements of the UAV. When the dynamic obstacle is detected in the predetermined trajectory, the improved BINN method is used to carry out local path replanning to achieve the purpose of dynamic obstacle avoidance. Computer simulation results demonstrate that the fusion algorithm can plan a collision-free path in a mountainous environment, and the planned path is smooth and short. Compared with the Artificial Bee Colony Algorithm (ABC) and Dragonfly Algorithm (DA), the fusion algorithm has obvious advantages in the stability of path planning and planned path length, and has the ability of dynamic obstacle avoidance.