Selective and high-rate CO2 electroreduction by metal-doped covalent triazine frameworks: a computational and experimental hybrid approach

6Citations
Citations of this article
21Readers
Mendeley users who have this article in their library.

Abstract

The electrochemical CO2 reduction reaction (CO2RR) has attracted intensive attention as a technology to achieve a carbon-neutral society. The use of gas diffusion electrodes (GDEs) enables the realization of high-rate CO2RRs, which is one of the critical requirements for social implementation. Although both a high reaction rate and good selectivity are simultaneously required for electrocatalysts on GDEs, no systematic study of the relationship among active metal centers in electrocatalysts, reaction rate, and selectivity under high-rate CO2RR conditions has been reported. In the present study, we employed various metal-doped covalent triazine frameworks (M-CTFs) as platforms for CO2 reduction reaction (CO2RR) electrocatalysts on GDEs and systematically investigated them to deduce sophisticated design principles using a combined computational and experimental approach. The Ni-CTF showed both high selectivity (faradaic efficiency (FE) > 98% at −0.5 to −0.9 V vs. reversible hydrogen electrode) and a high reaction rate (current density < −200 mA cm−2) for CO production. By contrast, the Sn-CTF exhibited selective formic acid production, and the FE and partial current density reached 85% and 150 mA cm−2, respectively. These results for the CO2RR activity and selectivity at high current density with respect to metal centers correspond well with predictions based on first-principles calculations. This work is the first demonstration of a clear relationship between the computational adsorption energy of intermediates depending on metal species and the experimental high-rate gaseous CO2RR.

Cite

CITATION STYLE

APA

Kato, S., Hashimoto, T., Iwase, K., Harada, T., Nakanishi, S., & Kamiya, K. (2022). Selective and high-rate CO2 electroreduction by metal-doped covalent triazine frameworks: a computational and experimental hybrid approach. Chemical Science, 14(3), 613–620. https://doi.org/10.1039/d2sc03754h

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free