Reduced CoNi2S4 nanosheets with enhanced conductivity for high-performance supercapacitors
Reduced CoNi2S4 nanosheets with enhanced conductivity for high-performance supercapacitors
复制标题
用于高性能超级电容器的具有增强导电性的还原CoNi2S4纳米片
DOI：
10.1016/j.electacta.2018.05.030
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发表时间：
2018-07-10
影响因子：
6.6
通讯作者：
Hu, Yong
中科院分区：
文献类型：
--
作者：
Li, Zhipeng;Zhao, Dian;Hu, Yong
过渡金属二硫属化物的缺陷工程被认为是在产生活性位点和增强本征电导率方面改善电化学性能的有效方法。这项研究报告了一种由还原 CoNi2S4 (r-CoNi2S4) 纳米片制成的新型高性能电化学超级电容器，该纳米片是通过简单的适度还原过程合成的。缺硫的r-CoNi2S4纳米片表现出显着增强的电导率，这是由还原反应中形成的丰富的硫空位引起的。与原始CoNi2S4纳米片相比，r-CoNi2S4纳米片具有更高的比容量（2 A g(-1)电流密度下为1117C g(-1)）、优异的倍率性能和稳定的循环性能。第一原理分析证实，还原产生的硫空位会改善 Ni 和 Co d 态以及接近费米能级的 S p 态之间的杂化，从而增强 CoNi2S4 纳米结构的电导率。此外，在非对称超级电容器配置中，在8 kW kg(-1)的功率密度下获得了55.4Wh kg(-1)的超高能量密度，并且即使在10000次循环后超级电容器仍然保持80%的电容。 (C) 2018 Elsevier Ltd. 保留所有权利。
Defect engineering on transition metal dichalcogenides has been regarded as an effective method to improve electrochemical properties in terms of generating active sites and enhancing the intrinsic conductivity. This study reports a new high-performance electrochemical supercapacitor made of reduced CoNi2S4 (r-CoNi2S4) nanosheets, which are synthesized via a facile moderate-reduction process. The sulfur-deficient r-CoNi2S4 nanosheets exhibit significantly enhanced conductivity which is induced by abundant sulfur vacancies formed in the reduction reaction. Compared with the pristine CoNi2S4 nanosheets, the r-CoNi2S4 nanosheets are characterized with a higher specific capacity (1117C g(-1) at current density of 2 A g(-1)) as well as excellent rate capability and stable cycling performance. Firstprinciple analysis confirms that the sulfur vacancies originating from the reduction lead to improve hybridization between the Ni and Co d states and the S p states close to the fermi level, and consequently enhance conductivity with the CoNi2S4 nanostructure. Moreover, an ultrahigh energy density of 55.4Wh kg(-1) at the power density of 8 kW kg(-1) is obtained in an asymmetric supercapacitor configuration, and 80% capacitance of the supercapacitor remains even after 10000 cycles. (C) 2018 Elsevier Ltd. All rights reserved.