JOURNAL ARTICLE
Discover the Evolution: A Comprehensive Review of Transition and Rare Earth Metals for Oxygen Reduction Reaction, from Mono to High‐Entropy Catalysts.
Published In: Chemical Record, 2025, v. 25, n. 7. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Bib Khan, Jala; Liang, Yuan‐Chang 3 of 3
Abstract
Green energy, including metal‐air batteries and fuel cells, is the key solution to climate change. The efficiency of these energy technologies depends on the oxygen reduction reaction (ORR) at the cathode, which is a slow process requiring expensive noble metal catalysts, like platinum, for improvement. The high cost of this catalyst restricts its widespread use in producing metal‐air batteries and fuel cells. An alternative approach is to utilize non‐noble metals, such as transition and rare earth metal catalysts, which are more cost‐effective and demonstrate comparable durability and effectiveness to noble metals. With their affordability and distinct electronic structure, these non‐noble metals have the potential to revolutionize the industry. Transition and rare earth metals can enhance the effectiveness of ORR catalysts by manipulating the electronic and surface molecular makeup through ′doping′ and ′synergistic effects′. This article discusses the roles of various non‐noble metals in the ORR process, covering fundamental to advanced levels, as well as the progression from mono to high‐entropy systems (systems with increasing complexity and potential for improved performance), including bi‐, tri‐, and tetra‐metallic catalysts in a comprehensive manner, and emphasizes opportunities for researchers to propose innovative strategies for optimizing the ORR process. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Chemical Record. 2025/07, Vol. 25, Issue 7, p1
- Document Type:Article
- Subject Area:Geology
- Publication Date:2025
- ISSN:1527-8999
- DOI:10.1002/tcr.202500032
- Accession Number:186995718
- Copyright Statement:Copyright of Chemical Record 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.)
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