Single atomic dispersed M-N-C (M = Fe, Co, Ni, Cu, etc.) composites display excellent performance for catalytic reactions. However, the analysis and understanding of neighboring M-N-C centers at the atomic level are still insufficient. Here, FeCo-N-doped hollow carbon nanocages (FeCo-N-HCN) with neighboring Fe-N 4 -C and Co-N 4 -C dual active centers as efficient catalysts are reported. Spherical aberration-corrected high angle annular darkfield scanning transmission electron microscopy, small area (1 nm 2 ) electron energy loss spectroscopy, and X-ray absorption spectroscopy data analysis and fitting prove the neighboring Fe-N 4 -C and Co-N 4 -C dual active structure in FeCo-N-HCN. Experimental tests and density functional theory calculation results reveal that the FeCo-N-HCN catalyst displays better catalytic activity than Fe single-metal catalyst for oxygen reduction reaction (ORR), which is attributed to the synergistic effect of Fe-N 4 -C and Co-N 4 -C dual active centers reducing the reaction energy barriers for ORR. Although the catalytic performance of the FeCo-N-HCN catalyst is not comparable to the-state-of-art catalysts reported due to the low metal contents (Fe: 1.96 wt% and Co: 1.31 wt%), these results can refresh the understanding of neighboring M-N-C centers at the atomic level and provide guidance for the design of catalysts in the future.

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The task of precipitation nowcasting is significant in the operational weather forecast. The radar echo map extrapolation plays a vital role in this task. Recently, deep learning techniques such as Convolutional Recurrent Neural Network (ConvRNN) models have been designed to solve the task. These models, albeit performing much better than conventional optical flow based approaches, suffer from a common problem of underestimating the high echo value parts. The drawback is fatal to precipitation nowcasting, as the parts often lead to heavy rains that may cause natural disasters. In this paper, we propose a novel interaction dual attention long short-term memory (IDA-LSTM) model to address the drawback. In the method, an interaction framework is developed for the ConvRNN unit to fully exploit the short-term context information by constructing a serial of coupled convolutions on the input and hidden states. Moreover, a dual attention mechanism on channels and positions is developed to recall the forgotten information in the long term. Comprehensive experiments have been conducted on CIKM AnalytiCup 2017 data sets, and the results show the effectiveness of the IDA-LSTM in addressing the underestimation drawback. The extrapolation performance of IDA-LSTM is superior to that of the state-of-the-art methods.

Oxygen reduction electrocatalysts play important roles in metal–air batteries. Herein, Fe3C-TiN heterostructural quantum dots loaded on carbon nanotubes (FCTN@CNTs) are prepared as electrocatalysts for the oxygen reduction reaction (ORR) through a one-pot pyrolysis. The Fe3C-TiN quantum dots with a diameter of 2–5 nm show the unique characteristic of heterostructural interface. The as-prepared FCTN@CNTs display Pt/C comparable ORR performance (E onset 1.06 and E 1/2 0.95 V) in alkaline medium, which is ascribed to the heterostructural interface between TiN and Fe3C. Furthermore, the Al–air batteries with the FCTN@CNT catalyst display superior discharge performance, demonstrating good feasibility for practical application. This work provides an effective new method to synthesize affordable and efficient oxygen reduction reaction catalysts.

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