Suppr超能文献

二维双金属金属有机骨架在 MXene 上组装的杂化结构用于高性能超级电容器。

A heterostructure of a 2D bimetallic metal-organic framework assembled on an MXene for high-performance supercapacitors.

机构信息

School of Materials Science & Chemical Engineering, Ningbo University, Ningbo, Zhejiang 315211, P. R. China.

出版信息

Dalton Trans. 2023 Feb 21;52(8):2455-2462. doi: 10.1039/d2dt03872b.

Abstract

Two-dimensional (2D) MXenes (transition metal carbide or carbonitride) and metal-organic frameworks (MOFs) have emerged as appealing electrode materials for supercapacitors due to the advantages of each material and a 2D structure. However, a solitary MXene or MOF suffers from either inadequate redox reactive sites or low electronic conductivity and instability. Here, NiCo-MOF/MXene heterostructures are fabricated by assembling ultrathin 2D bimetallic NiCo-MOF nanosheets on exfoliated MXene nanosheets by a simple room-temperature ultrasonic method. The 2D/2D NiCo-MOF/MXene heterostructures combine the advantages of a MOF, MXene and hierarchical structure, a large surface area, a highly electrically conductive network, rapid ion diffusion and structural stability. As a result, the optimal NiCo-MOF/M electrode exhibits a highly improved capacitance (1176.8 F g 653.4 F g) and cycle life (72.5% 50.5%), compared with the pristine NiCo-MOF. Moreover, a two-electrode cell using NiCo-MOF/M as the cathode shows outstanding energy storage capability. This study provides an opportunity to enhance energy storage by designing 2D heterostructures.

摘要

二维(2D)MXenes(过渡金属碳化物或氮化物)和金属有机骨架(MOFs)由于各自材料和 2D 结构的优势,已成为超级电容器有吸引力的电极材料。然而,单一的 MXene 或 MOF 存在氧化还原反应活性位点不足或电子电导率和稳定性低的问题。在此,通过简单的室温超声方法将超薄二维双金属 NiCo-MOF 纳米片组装在剥离的 MXene 纳米片上,制备了 NiCo-MOF/MXene 异质结构。二维/二维 NiCo-MOF/MXene 异质结构结合了 MOF、MXene 和分层结构的优点,具有大的比表面积、高度导电的网络、快速的离子扩散和结构稳定性。结果表明,与原始的 NiCo-MOF 相比,优化后的 NiCo-MOF/M 电极具有更高的电容(1176.8 F g 653.4 F g)和循环寿命(72.5% 50.5%)。此外,使用 NiCo-MOF/M 作为阴极的两电极电池表现出出色的储能能力。本研究为通过设计 2D 异质结构来提高能量存储提供了机会。

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