Suppr超能文献

高锂亲和力化学剥离二维共价有机框架

High-Lithium-Affinity Chemically Exfoliated 2D Covalent Organic Frameworks.

作者信息

Chen Xiudong, Li Yusen, Wang Liang, Xu Yi, Nie Anmin, Li Qianqian, Wu Fan, Sun Weiwei, Zhang Xiang, Vajtai Robert, Ajayan Pulickel M, Chen Long, Wang Yong

机构信息

Department of Chemical Engineering, School of Environmental and Chemical Engineering, Shanghai University, 99 Shangda Road, Shanghai, 200444, P. R. China.

Tianjin Key Laboratory of Molecular Optoelectronic Science and Department of Chemistry Tianjin University, Tianjin, 300072, P. R. China.

出版信息

Adv Mater. 2019 Jul;31(29):e1901640. doi: 10.1002/adma.201901640. Epub 2019 Jun 2.

Abstract

Covalent organic frameworks (COFs) with reversible redox behaviors are potential electrode materials for lithium-ion batteries (LIBs). However, the sluggish lithium diffusion kinetics, poor electronic conductivity, low reversible capacities, and poor rate performance for most reported COF materials limit their further application. Herein, a new 2D COF (TFPB-COF) with six unsaturated benzene rings per repeating unit and ordered mesoporous pores (≈2.1 nm) is designed. A chemical stripping strategy is developed to obtain exfoliated few-layered COF nanosheets (E-TFPB-COF), whose restacking is prevented by the in situ formed MnO nanoparticles. Compared with the bulk TFPB-COF, the exfoliated TFPB-COF exhibits new active Li-storage sites associated with conjugated aromatic π electrons by facilitating faster ion/electron kinetics. The E-TFPB-COF/MnO and E-TFPB-COF electrodes exhibit large reversible capacities of 1359 and 968 mAh g after 300 cycles with good high-rate capability.

摘要

具有可逆氧化还原行为的共价有机框架(COF)是锂离子电池(LIB)的潜在电极材料。然而,大多数已报道的COF材料的锂扩散动力学缓慢、电子导电性差、可逆容量低以及倍率性能不佳,限制了它们的进一步应用。在此,设计了一种新型二维COF(TFPB-COF),其每个重复单元含有六个不饱和苯环且具有有序介孔(≈2.1 nm)。开发了一种化学剥离策略以获得剥离的少层COF纳米片(E-TFPB-COF),原位形成的MnO纳米颗粒可防止其重新堆叠。与块状TFPB-COF相比,剥离的TFPB-COF通过促进更快的离子/电子动力学,展现出与共轭芳族π电子相关的新的活性锂存储位点。E-TFPB-COF/MnO和E-TFPB-COF电极在300次循环后展现出1359和968 mAh g的大可逆容量以及良好的高倍率性能。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验