Lithium-metal anodes show significant promise for the construction of high-energy rechargeable batteries due to their high theoretical capacity (3860 mAh g -1 ) and low redox potential (-3.04 V vs. a standard hydrogen electrode). When Li metal is used with conventional liquid and solid electrolytes, the poor lithiophilicity of the electrolyte results in an unfavorable parasitic reaction and uneven distribution of Li + flux at the electrode/electrolyte interface. These issues result in limited cycle life and dendrite problems associated with the Li-metal anode that can lead to rapid performance fade, failure and even safety risks of the battery. The lithiophilicity at the anode/electrolyte interface is important for the stable and safe operation of rechargeable Li-metal batteries. In this review, several factors that affect the lithiophilicity of electrolytes are discussed, including surface energy, roughness and chemical interactions. The existing problems and the strategies for improving the lithiophilicity of different electrolytes are also discussed. This review helps to shed light on the understanding of interfacial chemistry vs. Li metal of various electrolytes and guide interfacial engineering towards the practical realization of high-energy

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In recent years, with the rapid development of camera technology and portable devices, we have witnessed a flourish of user generated videos, which are gradually reshaping the traditional professional video oriented media market. The volume of user generated videos in repositories is increasing at a rapid rate. In today's video retrieval systems, a simple query will return many videos which seriously increase the viewing burden. To manage these video retrievals and provide viewers with an efficient way to browse, we introduce a system to automatically generate a summarization from multiple user generated videos and present their salience to viewers in an enjoyable manner. Among multiple consumer videos, we find their qualities to be highly diverse due to various factors such as a photographer's experience or environmental conditions at the time of capture. Such quality inspires us to include a video quality evaluation component into the video summarization since videos with poor qualities can seriously degrade the viewing experience. We first propose a probabilistic model to evaluate the aesthetic quality of each user generated video. This model compares the rich aesthetics information from several well-known photo databases with generic unlabeled consumer videos, under a human perception component indicating the correlation between a video and its constituting frames. Subjective studies were carried out with the results indicating that our method is reliable. Then a novel graph-based formulation is proposed for the multi-video summarization task. Desirable summarization criteria is incorporated as the graph attributes and the problem is solved through a dynamic programming framework. Comparisons with several state-of-the-art methods demonstrate that our algorithm performs better than other methods in generating a skimming video in preserving the essential scenes from the original multiple input videos, with smooth transitions among consecutive segments and appealing aesthetics overall.