journal article·2021-05

Sulfur-vacancies promoted performance of hierarchical NiCo2S4 nanotubes through electrospinning for supercapacitors

Ying Zhang, Li Ye, Jiahui Guo, Yuanyuan Shang, Fengmei Guo, Yingjiu Zhang, Jie Xu

Journal of Materials Science· Volume 56· Issue 15
Publisher page

Abstract

In this work, one-dimensional (1D) NiCo2S4 nanotubes with sulfur vacancies (Vc-NiCo2S4) were designed effectively via the electrostatic spinning and chemical bath deposition (CBD) process. As-spun polyacrylonitrile (PAN) nanofibers containing surfactant were taken as hard templates. Through the in-situ growth and etching process, NiCo2S4 nanotubes covered with a large number of nanosheets were generated. Sulfur vacancies were introduced by the following chemical reduction. The influence of defects was systematically discussed through structure analyses and electrochemical properties. As-obtained Vc-NiCo2S4 not only maintains the original hierarchical nanotube characteristic, but also boost the electrochemical behaviors. Due to the defect treatment, Vc-NiCo2S4 nanotubes have a higher specific capacity (1046 F g−1 at a current density of 1 A g−1) and enhanced rate capability. A symmetrical supercapacitor (SSCs) device assembled by Vc-NiCo2S4 nanotubes displays a high energy density of 31.8 Wh kg−1 at the power density of 260 W kg−1. These results indicate that the construction of 1D hierarchical nanotubes and sulfur vacancies could provide effective strategy to promote the performance of supercapacitors. 1D NiCo2S4 hierarchical nanotubes were prepared by employing PAN nanofibers as sacrificial templates. The Vc-NiCo2S4 nanotubes after NaBH4 reduction maintain the original morphology and optimize the electrochemical activities. Vc-NiCo2S4 with sulfur vacancies exhibits enhanced electrochemical performance.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Supercapacitor Materials and Fabrication · Advanced battery technologies research · Electrocatalysts for Energy Conversion

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

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