Porous Organic Cage‑Based Quasi‑Solid‑State Electrolyte with Cavity‑Induced Anion‑Trapping Effect for Long‑Life Lithium Metal Batteries

Porous organic cages(POCs)with permanent porosity and excellent host–guest property hold great potentials in regulating ion transport behavior,yet their feasibility as solid-state electrolytes has never been testified in a practical battery.Herein,we design and fabricate a quasi-solid-state electrolyte(QSSE)based on a POC to enable the stable operation of Li-metal batteries(LMBs).Benefiting from the ordered channels and cavity-induced anion-trapping effect of POC,the resulting POC-based QSSE exhibits a high Li+transference number of 0.67 and a high ionic conductivity of 1.25×10^(−4) S cm^(−1) with a low activation energy of 0.17 eV.These allow for homogeneous Li deposition and highly reversible Li plating/stripping for over 2000 h.As a proof of concept,the LMB assembled with POC-based QSSE demonstrates extremely stable cycling performance with 85%capacity retention after 1000 cycles.Therefore,our work demonstrates the practical applicability of POC as SSEs for LMBs and could be extended to other energy-storage systems,such as Na and K batteries.

A Survey of Human Action Recognition and Posture Prediction

Human action recognition and posture prediction aim to recognize and predict respectively the action and postures of persons in videos.They are both active research topics in computer vision community,which have attracted considerable attention from academia and industry.They are also the precondition for intelligent interaction and human-computer cooperation,and they help the machine perceive the external environment.In the past decade,tremendous progress has been made in the field,especially after the emergence of deep learning technologies.Hence,it is necessary to make a comprehensive review of recent developments.In this paper,firstly,we attempt to present the background,and then discuss research progresses.Secondly,we introduce datasets,various typical feature representation methods,and explore advanced human action recognition and posture prediction algorithms.Finally,facing the challenges in the field,this paper puts forward the research focus,and introduces the importance of action recognition and posture prediction by taking interactive cognition in self-driving vehicle as an example.