Issue 12, 2022

Atomically dispersed Fe–Nx species within a porous carbon framework: an efficient catalyst for Li–CO2 batteries

Abstract

Li–CO2 batteries are a promising energy storage system, while their practical application is still restricted by a lack of high-performance electrocatalysts for CO2 reduction and evolution reaction. Herein, we propose a metal–organic-framework-derived Fe–N–C electrocatalyst for Li–CO2 batteries. Within the Fe–N–C electrocatalyst, abundant Fe–Nx active sites at the molecular level were formed in the porous carbon framework, profiting from a host–guest chemistry strategy between Fe–mIm nanoclusters and metal organic framework precursors in the pyrolysis process. The confinement effect of the metal organic framework host was beneficial to limit the Fe–mIm nanoclusters at the molecular level, thus resulting in the formation of Fe–Nx sites with the high catalytic activity. Moreover, the as-prepared Fe–N–C catalyst is composed of dodecahedral nanoparticles stacking to form a unique three-dimensional structure with a large specific surface area and sufficient space, which not only favored the electron transport and CO2/Li+ diffusion but also promoted the deposition of discharge product Li2CO3 to ensure a high capacity. Therefore, the Fe–N–C based Li–CO2 battery exhibits high specific capacity (13 238 mA h g−1), good rate capability and excellent cyclability (140 cycles). Therefore, these encouraging results suggest an effective approach to obtain high-performance Fe–N–C electrocatalysts for Li–CO2 batteries.

Graphical abstract: Atomically dispersed Fe–Nx species within a porous carbon framework: an efficient catalyst for Li–CO2 batteries

Supplementary files

Article information

Article type
Paper
Submitted
20 Dec. 2021
Accepted
27 Janv. 2022
First published
27 Janv. 2022

Nanoscale, 2022,14, 4511-4518

Atomically dispersed Fe–Nx species within a porous carbon framework: an efficient catalyst for Li–CO2 batteries

J. Ding, H. Xue, R. Xiao, Y. Xu, L. Song, H. Gong, X. Fan, K. Chang, X. Huang, T. Wang and J. He, Nanoscale, 2022, 14, 4511 DOI: 10.1039/D1NR08354F

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