Integrating N-Doped Porous Carbon-Encapsulated Ultrafine SnO2 with MXene Nanosheets via Electrostatic Self-Assembly as a Superior Anode Material for Lithium Ion Capacitors

ACS Applied Energy Materials(2022)

引用 5|浏览3
暂无评分
摘要
MXene nanosheets (MNSs) with high conductivity, high flexibility, and rich surface functional groups have gained renewed attention due to their potential applications for energy storage devices. However, the low theoretical specific capacity and easy self-aggregation of MNSs severely restrict their practical application. Herein, a superior anode material with a unique hierarchical structure and optimal composition (SnO2@NDPC/MNSs-2, NDPC = nitrogen-doped porous carbon) for lithium-ion capacitors (LICs) is successfully fabricated by an electrostatic self-assembly strategy. Specifically, the contrary charges of the SnO2 @NDPC and MNSs enable a feasible and convenient route for the preparation of the as-achieved nanocomposite. Moreover, the SnO2@NDPC with similar to 5 nm SnO2 nanopartides offering a high specific capacity and pores structure can simultaneously prevent the restack of MNSs and withstand the volume expansion during the repeated Li+ insertion-extraction processes, and the two-dimensional MNSs can improve the overall conductivity of the nanocomposite and facilitate the Li+ transport kinetics. As a result, the as-prepared anode material displays excellent rate performance and extraordinary cycle stability, with a high specific capacity of 465 mAh g(-1) after 500 cycles at 2 A g(-1) and 90.2% capacity retention capability. More importantly, an assembled LIC with the biomass-derived nitrogen-rich porous carbon and SnO2@NDPC/MNSs-2 as respective cathode and anode can deliver a high energy density of 55.9 Wh kg(-1) at a high power density of 6097 W kg(-1) and excellent cycle performance, making it potential an anode material for practical LICs with high power and energy densities.
更多
查看译文
关键词
MXene nanosheets,electrostatic self-assembly,N-doped porous carbon,lithium-ion capacitors,tin dioxide
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要