JOURNAL ARTICLE
Study of effects of rare earth elements on the corrosion behavior of weathering steels under a simulated immersion environment and a real atmospheric environment.
Published In: Materials & Corrosion / Werkstoffe und Korrosion, 2024, v. 75, n. 11. P. 1539 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Fan, Zengwei; Xi, Zhong; Liu, Tengshi; Lian, Xintong; Wei, Xicheng; Dong, Han 3 of 3
Abstract
The effects of rare earth elements (REEs) on the corrosion behavior of weathering steels under a simulated immersion environment and a real atmospheric environment have been investigated in this paper. Although the corrosion rate of the indoor accelerated corrosion experiment (0.01 mol/L NaHSO3) is much higher than that of the real atmospheric exposure experiment, the addition of REEs can highly improve the corrosion resistance of test steels under both conditions. The improvement of the corrosion resistance can be attributed to the quick transformation of γ‐FeOOH into α‐FeOOH and the accelerated formation of a stable rust layer by REEs. Furthermore, the segregation of the REE inner rust layer increases the density of the rust layer and prevents corrosive particles from eroding the matrix. REEs can also promote the segregation of Cu, Cr, and other alloying elements in the rust layer, thus blocking the cracks and holes and enabling the formation of a continuous rust layer with good adhesion. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Materials & Corrosion / Werkstoffe und Korrosion. 2024/11, Vol. 75, Issue 11, p1539
- Document Type:Article
- Subject Area:Earth and Atmospheric Sciences
- Publication Date:2024
- ISSN:0947-5117
- DOI:10.1002/maco.202414447
- Accession Number:180680309
- Copyright Statement:Copyright of Materials & Corrosion / Werkstoffe und Korrosion 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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