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Transition metal–nitrogen–carbon nanostructured catalysts for the oxygen reduction reaction: From mechanistic insights to structural optimization

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Abstract

Accelerating the rate-limiting oxygen reduction reaction (ORR) at the cathode remains the foremost issue for the commercialization of fuel cells. Transition metal–nitrogen–carbon (M–N/C, M = Fe, Co, etc.) nanostructures are the most promising class of non-precious metal catalysts (NPMCs) with satisfactory activities and stabilities in practical fuel cell applications. However, the long-debated nature of the active sites and the elusive structure-performance correlation impede further developments of M–N/C materials. In this review, we present recent endeavors to elucidate the actual structures of active sites by adopting a variety of physicochemical techniques that may provide a profound mechanistic understanding of M–N/C catalysts. Then, we focus on the spectacular progress in structural optimization strategies for M–N/C materials with tailored precursor architectures and modified synthetic routes for controlling the structural uniformity and maximizing the number of active sites in catalytic materials. The recognition of the right active centers and site-specific engineering of the nanostructures provides future directions for designing advantageous M–N/C catalysts.

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Acknowledgements

Financial support by National Natural Science Foundation of China (No. 21635007) and National Key Research and Development Program of China (No. 2016YFA0203200) is gratefully acknowledged.

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  1. State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, 130022, China

    Mengxia Shen, Changting Wei, Kelong Ai & Lehui Lu

  2. University of Chinese Academy of Sciences, Beijing, 100039, China

    Mengxia Shen & Changting Wei

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  1. Mengxia Shen
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Correspondence to Lehui Lu.

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Shen, M., Wei, C., Ai, K. et al. Transition metal–nitrogen–carbon nanostructured catalysts for the oxygen reduction reaction: From mechanistic insights to structural optimization. Nano Res. 10, 1449–1470 (2017). https://doi.org/10.1007/s12274-016-1400-7

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