JOURNAL ARTICLE
Iridium‐Based Alkaline Hydrogen Oxidation Reaction Electrocatalysts.
Published In: Chemistry - A European Journal, 2024, v. 30, n. 37. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Lv, Qingqing; Liu, Di; Zhu, Wei; Zhuang, Zhongbin 3 of 3
Abstract
The hydroxide exchange membrane fuel cells (HEMFCs) are promising but lack of high‐performance anode hydrogen oxidation reaction (HOR) electrocatalysts. The platinum group metals (PGMs) have the HOR activity in alkaline medium two to three orders of magnitude lower than those in acid, leading to the high required PGMs amount on anode to achieve high HEMFC performance. The mechanism study demonstrates the hydrogen binding energy of the catalyst determines the alkaline HOR kinetics, and the adsorbed OH and water on the catalyst surface promotes HOR. Iridium (Ir) has a unique advantage for alkaline HOR due to its similar hydrogen binding energy to Pt and enhanced adsorption of OH. However, the HOR activity of Ir/C is still unsatisfied in practical HEMFC applications. Further fine tuning the adsorption of the intermediate on Ir‐based catalysts is of great significance to improve their alkaline HOR activity, which can be reasonably realized by structure design and composition regulation. In this concept, we address the current understanding about the alkaline HOR mechanism and summarize recent advances of Ir‐based electrocatalysts with enhanced alkaline HOR activity. We also discuss the perspectives and challenges on Ir‐based electrocatalysts in the future. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Chemistry - A European Journal. 2024/07, Vol. 30, Issue 37, p1
- Document Type:Article
- Subject Area:Geology
- Publication Date:2024
- ISSN:0947-6539
- DOI:10.1002/chem.202400838
- Accession Number:178211631
- Copyright Statement:Copyright of Chemistry - A European Journal is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.