journal article·2024-11

Vanadium nitride/nitrogen-doped porous carbon prepared by one-pot molten salt pyrolysis for all-solid-state asymmetric supercapacitors

Liang Liu, Xiaoshuang Zhou, Wei Wang, Kewei Zhang, Xiankai Li, Long Chen, Yanhui Li

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Abstract

Cubic vanadium nitride (VN) holds substantial promise as an anode material for supercapacitors, owing to its high conductivity, theoretical capacity, and a wide negative voltage window. However, the tendency of VN to agglomerate, coupled with poor cycling stability from irreversible volume expansion during charge-discharge cycles, hinders its application in asymmetric supercapacitors. In this work, we reported a one-pot molten salt pyrolysis method to prepare VN/nitrogen doped porous carbon (VN/NPC) materials. The nitrogen-doped carbon material with 3D hierarchical porous structure not only facilitates ion diffusion and charge transport, but also serve as a matrix to in-situ synthesis VN nanoparticles to improve the cycling performance. The VN/NPC exhibits a specific capacitance of 272.3 F/g at 2 mV/s, and 268 F/g at 0.5 A/g. In addition, the VN/NPC demonstrates an excellent cycling stability with only 7 % capacity decay after 10,000 cycles. Furthermore, the all-solid-state asymmetric supercapacitors (ASCs) constructed using VN/NPC as anode and NiCo-layered double hydroxides (NiCo-LDH) as cathode deliver remarkable energy densities of 61.1 and 22.9 Wh/kg at power densities of 700.4 and 7496.6 W/kg, respectively. In addition, this ASCs exhibits excellent cycling stability with 83 % capacity retention after 10,000 cycles. This work provides a green and facile method to prepare anode materials with high capacitance and excellent cycle performance for high energy density ASCs devices.
Source: expertdata · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Supercapacitor Materials and Fabrication · Advancements in Battery Materials · Catalytic Processes in Materials Science

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

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