journal article·2019-10

Effective enhancement of electrochemical energy storage of cobalt-based nanocrystals by hybridization with nitrogen-doped carbon nanocages

Qingming Ma, Yuejian Yao, Minglei Yan, Jie Zhao, Chengxuan Ge, Qiang Wu, Lijun Yang, Xizhang Wang, Zheng Hu

Science China Materials· Volume 62· Issue 10
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Abstract

Cobalt-based oxygenic compounds Co(OH)2, CoO and Co3O4 are attractive for electrochemical energy storage owing to their high theoretical capacities and pseudocapacitive properties. Despite the great efforts to their compositional and morphological regulations, the performances to date are still quite limited owing to the low active surface area and sluggish charge transfer kinetics. Herein, different Co-based nanocrystals (Co-NCs) were conveniently anchored on the hierarchical nitrogen-doped carbon nanocages (hNCNCs) with high specific surface area and coexisting micro-meso-macropores to decrease the size and facilitate the charge transfer. Accordingly, a high specific capacity of 1170 F g−1 is achieved at 2 A g−1 for the Co(OH)2/hNCNCs hybrid, in which the capacitance of Co(OH)2 (2214 $${\rm{F\;g}}_{\rm{Co({OH})_2}} - 1$$ ) is approaching to its theoretical maximum (2595 F g−1), demonstrating the high utilization of active materials by the hybridization with N-doped nanocarbons. This study also reveals that these Co-NCs store/release electrical energy via the same reversible redox reaction despite their different pristine compositions. This insight on the energy storage of Co-based nanomaterials suggests that the commonly-employed transformation of the Co-NCs from Co(OH)2 to CoO and Co3O4 on carbon supports is unnecessary and even could be harmful to the energy storage performance. The result is instructive to develop high-energy-density electrodes from transition metal compounds.
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Research context

OpenAlex topics: Supercapacitor Materials and Fabrication · Advanced battery technologies research · Conducting polymers and applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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