The aim of this study was to develop of high safety, high performance sulfide-based all-solid-state Li-ion battery (ASSLIB). The ASSLIB composes of a flexible, high ionic conductive,moisture insensitive sulfide-based solid-state electrolyte (SSE) film, Si/C composite anode material with self-healing polymer binder, high capacity LiNi0.8Co0.1Mn0.1O2 (NMC811) composite cathode,garnet-in-polymer solid composite interlayer to prevent decomposition of sulfide-based SSE with electrode materials.

We develop an asynchronous framework across userkernel spaces for deep learning applications on the improvement of Wi-Fi performanceimplement it in the driver of commodity Intel Wi-Fi cards. Under this framework, we apply deep reinforcement learning to developing an intelligent rate adaptation (RA) algorithm (DRL-RA), which can achieve the highest throughput in varying channel conditions given many rate options of current Wi-Fi technologies. Its on-learning capability can learn how to efficiently approach the best rate from the experiences of its common usage patternenvironment.

Using PVDF-HFP, lithium salt, LLZOplasticizer to prepare HSE membrane (ơi=4.6×10-4 S/cm, 25oC). The Free-standingcathode-supported membranes in the pouch-type NCM811HSELi ASSLBs were designed to enhance the mechanical strength (Li-Nafion-coated cellulose support, the total membrane thickness is 40-60 µm), improve Li-ion conductivity (Doped LLZO/Li salt), reduce interfacial impedance (PVDF-HFP/Li salt/plasticizer),inhibit Li dendritic deposition. The energy density of the ASSLBs has reached 300 Wh/kg, which exceeds the current best LIBs by 50.