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Numerous endeavors have been dedicated to the development of composite polymer electrolyte (CPE) membranes for all-solid-state batteries (SSBs). However, insufficient ionic conductivity and mechanical properties still pose great challenges in practical applications. In this study, a flexible composite electrolyte membrane (FCPE) with fast ion transport channels was prepared using a phase conversion process combined with in situ polymerization. The polyvinylidene fluoride-hexafluoro propylene (PVDF-HFP) polymer matrix incorporated with lithium lanthanum zirconate (LLZTO) formed a 3D net-like structure, and the in situ polymerized polyvinyl ethylene carbonate (PVEC) enhanced the interface connection. This 3D network, with multiple rapid pathways for Li+ that effectively control Li+ flux, led to uniform lithium deposition. Moreover, the symmetrical lithium cells that used FCPE exhibited high stability after 1200 h of cycling at 0.1 mA cm−2. Specifically, all-solid-state lithium batteries coupled with LiFePO4 cathodes can stably cycle for over 100 cycles at room temperature with high Coulombic efficiencies. Furthermore, after 100 cycles, the infrared spectrum shows that the structure of FCPE remains stable. This work demonstrates a novel insight for designing a flexible composite electrolyte for highly safe SSBs. [ABSTRACT FROM AUTHOR]

Aiming at the problems of slow convergence speed and low tracking accuracy in attitude control and position tracking of quadrotor unmanned aerial vehicles (UAVs). This paper combines the fractional-order calculus theory with the backstepping sliding mode control algorithm, using the backstepping control to compensate for the nonlinearity of the system and the fractional-order theory to eliminate the jitter brought about by the sliding mode control, and proposes a new fractional-order backstepping sliding mode control strategy for the trajectory tracking control of the quadrotor UAV. The proposed fractional-order sliding mode surface increases the control flexibility and improves the robustness and anti-interference ability of the system to some extent. The stability analysis of the system is carried out using the Lyapunov stability theory, and the results prove the stability of the proposed controller. Finally, the effectiveness and feasibility of the proposed method are verified by comparing it with the traditional backstepping sliding mode controller. The simulation results show that the fractional-order reverse-step sliding mode control algorithm proposed in this paper is significantly better than other control algorithms in terms of convergence speed and also has a certain degree of superiority in terms of error elimination. [ABSTRACT FROM AUTHOR]

When facing motion and complex environmental conditions, infrared videos captured by thermal imaging devices often suffer from blurring, leading to unclear or missing details and positional information about the targets. To improve this problem, this work proposes an improved deblurring method suitable for infrared videos based on a deep learning-based deblurring network originally designed for visible light images. This method is built upon the D2Net network by introducing a spatial and channel reconstruction convolution for feature redundancy, enhancing the network's capability for image feature learning. In terms of the encoder-decoder module, a triple attention mechanism and fast Fourier transform are introduced to further improve the network's deblurring performance. Through ablative experiments on infrared datasets, the results demonstrate a significant improvement in deblurring performance compared to the original D2Net. Specifically, the improved network achieved a 1.42 dB increase in peak signal-to-noise ratio and a 0.02 dB increase in structural similarity compared to the original network. In summary, this paper achieves promising results in infrared video deblurring tasks, demonstrating the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]