Lithium (Li) metal is an ideal anode material for rechargeable batteries due to its extremely high theoretical specific capacity (3860 mA h g À1 ), low density (0.59 g cm À3 ) and the lowest negative electrochemical potential (À3.040 V vs. the standard hydrogen electrode). Unfortunately, uncontrollable dendritic Li growth and limited Coulombic efficiency during Li deposition/stripping inherent in these batteries have prevented their practical applications over the past 40 years. With the emergence of post-Li-ion batteries, safe and efficient operation of Li metal anodes has become an enabling technology which may determine the fate of several promising candidates for the next generation energy storage systems, including rechargeable Li-air batteries, Li-S batteries, and Li metal batteries which utilize intercalation compounds as cathodes. In this paper, various factors that affect the morphology and Coulombic efficiency of Li metal anodes have been analyzed. Technologies utilized to characterize the morphology of Li deposition and the results obtained by modelling of Li dendrite growth have also been reviewed.Finally, recent development and urgent need in this field are discussed.

Rechargeable lithium metal batteries are considered the "Holy Grail" of energy storage systems. Unfortunately, uncontrollable dendritic lithium growth inherent in these batteries (upon repeated charge/discharge cycling) has prevented their practical application over the past 40 years. We show a novel mechanism that can fundamentally alter dendrite formation. At low concentrations, selected cations (such as cesium or rubidium ions) exhibit an effective reduction potential below the standard reduction potential of lithium ions. During lithium deposition, these additive cations form a positively charged electrostatic shield around the initial growth tip of the protuberances without reduction and deposition of the additives. This forces further deposition of lithium to adjacent regions of the anode and eliminates dendrite formation in lithium metal batteries. This strategy may also prevent dendrite growth in lithium-ion batteries as well as other metal batteries and transform the surface uniformity of coatings deposited in many general electrodeposition processes.

Facial attribute editing aims to manipulate single or multiple attributes of a face image, i.e., to generate a new face with desired attributes while preserving other details. Recently, generative adversarial net (GAN) and encoder-decoder architecture are usually incorporated to handle this task with promising results. Based on the encoder-decoder architecture, facial attribute editing is achieved by decoding the latent representation of the given face conditioned on the desired attributes. Some existing methods attempt to establish an attributeindependent latent representation for further attribute editing. However, such attribute-independent constraint on the latent representation is excessive because it restricts the capacity of the latent representation and may result in information loss, leading to over-smooth and distorted generation. Instead of imposing constraints on the latent representation, in this work we apply an attribute classification constraint to the generated image to just guarantee the correct change of desired attributes, i.e., to "change what you want". Meanwhile, the reconstruction learning is introduced to preserve attribute-excluding details, in other words, to "only change what you want". Besides, the adversarial learning is employed for visually realistic editing. These three components cooperate with each other forming an effective framework for high quality facial attribute editing, referred as AttGAN. Furthermore, our method is also directly applicable for attribute intensity control and can be naturally extended for attribute style manipulation. Experiments on CelebA dataset show that our method outperforms the state-of-the-arts on realistic attribute editing with facial details well preserved.

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