Issue 5, 2023

Graphdiyne supported Ag–Cu tandem catalytic scheme for electrocatalytic reduction of CO2 to C2+ products

Abstract

The electrochemical CO2 reduction reaction (CO2RR) to added-value C2+ products is a worthy way to effectively reduce CO2 levels in the atmosphere. Cu nanomaterials have been proposed as efficient CO2RR catalysts for producing C2+ products; however, the difficulties in controlling their efficiency and selectivity hinder their applications. Herein, we propose a simple routine to construct a graphdiyne (GDY) supported Ag–Cu nanocluster as a C2+ product-selective electrocatalyst and optimize the composition by electrochemical performance screening. The synthesized Ag–Cu nanoclusters are uniformly distributed on the surface of GDY with particle sizes constricted to 3.7 nm due to the strong diyne–Cu interaction. Compared to Cu/GDY, Ag–Cu/GDY tandem schemes exhibited superior CO2RR to C2+ performance with a Faraday efficiency (FE) of up to 55.1% and a current density of 48.6 mA cm−2 which remain stable for more than 33 hours. Theoretical calculations show that the adsorption energy of CO is much higher on Cu (−1.066 eV) than on Ag (−0.615 eV), thus promoting the drift of *CO from Ag to Cu. Moreover, the calculations indicate that the key C–C coupling reaction of *CO with *COH is more favored on Ag–Cu/GDY than on the original Cu/GDY which contributes to the formation of C2+ products. Our findings shed light on a new strategy of combining a GDY support with a tandem catalytic scheme for developing new CO2RR catalysts with superior selectivity and activity for C2+ products.

Graphical abstract: Graphdiyne supported Ag–Cu tandem catalytic scheme for electrocatalytic reduction of CO2 to C2+ products

Supplementary files

Article information

Article type
Paper
Submitted
29 Sep 2022
Accepted
31 Dec 2022
First published
02 Jan 2023

Nanoscale, 2023,15, 2106-2113

Graphdiyne supported Ag–Cu tandem catalytic scheme for electrocatalytic reduction of CO2 to C2+ products

Q. Zhu, Y. Hu, H. Chen, C. Meng, Y. Shang, C. Hao, S. Wei, Z. Wang, X. Lu and S. Liu, Nanoscale, 2023, 15, 2106 DOI: 10.1039/D2NR05399C

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