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Lithium alanates exhibit high theoretical specific capacities and appropriate lithiation/delithiation potentials, but suffer from poor reversibility, cycling stability, and rate capability due to their sluggish kinetics and extensive side reactions. Herein, a novel and facile solid‐state prelithiation approach is proposed to in situ prepare a Li3AlH6‐Al nanocomposite from a short‐circuited electrochemical reaction between LiAlH4 and Li with the help of fast electron and Li‐ion conductors (C and P63mc LiBH4). This nanocomposite consists of dispersive Al nanograins and an amorphous Li3AlH6 matrix, which enables superior electrochemical performance in solid‐state cells, as much higher specific capacity (2266 mAh g−1), Coulombic efficiency (88%), cycling stability (71% retention in the 100th cycle), and rate capability (1429 mAh g−1 at 1 A g−1) are achieved. In addition, this nanocomposite works well in the solid‐state full cell with LiCoO2 cathode, demonstrating its promising application prospects. Mechanism analysis reveals that the dispersive Al nanograins and amorphous Li3AlH6 matrix can dramatically enhance the lithiation and delithiation kinetics without side reactions, which is mainly responsible for the excellent overall performance. Moreover, this solid‐state prelithiation approach is general and can also be applied to other Li‐poor electrode materials for further modification of their electrochemical behavior.

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Background Attenuating inflammatory response and relieving pain are two therapeutic therapeutical goals for rheumatoid arthritis (RA). Anti-inflammatory and analgesic drugs are often associated with many adverse effects due to nonspecific distribution. New drug delivery systems with practical targeting ability and other complementary strategies urgently need to be explored. To achieve this goal, an acupoint drug delivery system that can target deliver anti-inflammatory drugs and simulate acupuncture in relieving pain was constructed, which can co-deliver triptolide (TP) and 2-chloro-N (6)-cyclopentyl adenosine (CCPA). Results We have successfully demonstrated that acupoint nanocomposite hydrogel composed of TP-Human serum album nanoparticles (TP@HSA NPs) and CCPA could effectively treat RA. The result shows that CCPA-Gel can enhance analgesic effects specifically at the acupoint, while the mechanical and thermal pain threshold was 4.9 and 1.6 times compared with non-acupoint, respectively, and the nanocomposite gel further enhanced. Otherwise, the combination of acupoint and nanocomposite hydrogel exerted synergetic improvement of inflammation, bone erosion, and reduction of systemic toxicity. Furthermore, it could regulate inflammatory factors and restore the balance of Th17/Treg cells, which provided a novel and effective treatment strategy for RA. Interestingly, acupoint administration could improve the accumulation of the designed nanomedicine in arthritic paws (13.5% higher than those in non-acupoint at 48 h), which may explain the better therapeutic efficiency and low toxicity. Conclusion This novel therapeutic approach-acupoint nanocomposite hydrogel, builds a bridge between acupuncture and drugs which sheds light on the combination of traditional and modern medicine. Graphical Abstract

Here, the challenges of sample efficiency and navigation performance in deep reinforcement learning for visual navigation are focused and a deep imitation reinforcement learning approach is proposed. Our contributions are mainly three folds: first, a framework combining imitation learning with deep reinforcement learning is presented, which enables a robot to learn a stable navigation policy faster in the target-driven navigation task. Second, the surrounding images is taken as the observation instead of sequential images, which can improve the navigation performance for more information. Moreover, a simple yet efficient template matching method is adopted to determine the stop action, making the system more practical. Simulation experiments in the AI-THOR environment show that the proposed approach outperforms previous end-to-end deep reinforcement learning approaches, which demonstrate the effectiveness and efficiency of our approach.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.