Aqueous batteries that use metal anodes exhibit maximum anodic capacity, whereas the energy density is still unsatisfactory partially due to the high redox potential of the metal anode. Current metal anodes are plagued by the dilemma that the redox potential of Zn is not low enough, whereas Al, Mg, and others with excessively low redox potential cannot work properly in aqueous electrolytes. Mn metal with a suitably low redox potential is a promising candidate, which was rarely explored before. Here, we report a rechargeable aqueous Mn‐metal battery enabled by a well‐designed electrolyte and robust inorganic–organic interfaces. The inorganic Sn‐based interface with a bottom‐up microstructure was constructed to preliminarily suppress water decomposition. With this bubble‐free interface, the organic interface can be formed via an esterification reaction of sucrose triggered by acyl chloride in the electrolyte, generating a dense physical shield that isolates water while permitting Mn2+ diffusion. Hence, a Mn symmetric cell achieves a superior plating/stripping stability for 200 hours, and a Mn||V2O5 battery maintains approximately 100 % capacity after 200 cycles. Moreover, the Mn||V2O5 battery realizes a much higher output voltage than that of the Zn||V2O5 battery, evidencing the possibility of increasing the energy density through using a Mn anode. This work develops a systematic strategy to stabilize a Mn‐metal anode for Mn‐metal batteries, opening a new door towards enhanced voltage of aqueous batteries.

API recommendation in real-time is challenging for dynamic languages like Python. Many existing API recommendation techniques are highly effective, but they mainly support static languages. A few Python IDEs provide API recommendation functionalities based on type inference and training on a large corpus of Python libraries and third-party libraries. As such, they may fail to recommend or make poor recommendations when type information is missing or target APIs are projectspecific. In this paper, we propose a novel approach, PyART, to recommending APIs for Python programs in real-time. It features a light-weight analysis to derive so-called optimistic data-flow, which is neither sound nor complete, but simulates the local data-flow information humans can derive. It extracts three kinds of features: data-flow, token similarity, and token co-occurrence, in the context of the program point where a recommendation is solicited. A predictive model is trained on these features using the Random Forest algorithm. Evaluation on 8 popular Python projects demonstrates that PyART can provide effective API recommendations. When historic commits can be leveraged, which is the target scenario of a state-of-theart tool ARIREC, our average top-1 accuracy is over 50% and average top-10 accuracy over 70%, outperforming APIREC and Intellicode (i.e., the recommendation component in Visual Studio) by 28.48%-39.05% for top-1 accuracy and 24.41%-30.49% for top-10 accuracy. In other applications such as when historic comments are not available and cross-project recommendation, PyART also shows better overall performance. The time to make a recommendation is less than a second on average, satisfying the real-time requirement.

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