Optimizing the use of a gas diffusion electrode setup for CO2 electrolysis imitating a zero-gap MEA design

Alinejad, Shima; Quinson, Jonathan; Li, Yao; Kong, Ying; Reichenberger, Sven; Barcikowski, Stephan; Broekmann, Peter; Arenz, Matthias (2024). Optimizing the use of a gas diffusion electrode setup for CO2 electrolysis imitating a zero-gap MEA design. Journal of catalysis, 429, p. 115209. Elsevier 10.1016/j.jcat.2023.115209

[img]
Preview
Text
1-s2.0-S0021951723004542-main.pdf - Published Version
Available under License Creative Commons: Attribution (CC-BY).

Download (4MB) | Preview

The lack of a robust and standardized experimental test bed to investigate the performance of catalyst materials for the electrochemical CO2 reduction reaction (ECO2RR) is one of the major challenges in this field of research. To best reproduce and mimic commercially relevant conditions for catalyst screening and testing, gas diffusion electrode (GDE) setups attract rising attention as an alternative to conventional aqueous-based setups such as the H-cell configuration. Zero-gap electrolyzer designs show promising features for upscaling to the commercial scale. In this study, we scrutinize further our recently introduced “zero-gap GDE” setup or more correct half-cell MEA design for the CO2RR. Using an Au electrocatalyst as a model system we simulate the anode conditions in a zero-gap electrolyzer and identify/report the key experimental parameters to control the catalyst layer preparation to optimize the activity and selectivity of the catalyst. Among others, it is demonstrated that supported Au nanoparticles (NPs) result in significantly higher current densities when compared to unsupported counterparts, however, the supporting also renders the NPs prone to agglomeration during electrolysis.

Item Type:

Journal Article (Original Article)

Division/Institute:

08 Faculty of Science > Department of Chemistry, Biochemistry and Pharmaceutical Sciences (DCBP)

UniBE Contributor:

Alinejad Khabaz, Shima, Kong, Ying, Broekmann, Peter, Arenz, Matthias

Subjects:

500 Science > 570 Life sciences; biology
500 Science > 540 Chemistry
000 Computer science, knowledge & systems

ISSN:

0021-9517

Publisher:

Elsevier

Language:

English

Submitter:

Peter Broekmann

Date Deposited:

03 Jan 2024 16:03

Last Modified:

14 Jan 2024 02:43

Publisher DOI:

10.1016/j.jcat.2023.115209

BORIS DOI:

10.48350/191051

URI:

https://boris.unibe.ch/id/eprint/191051

Actions (login required)

Edit item Edit item
Provide Feedback