Semi-quantitative categorization method for the corrosion behavior of metals based on immersion test
File(s)metals-14-00409-v2.pdf (2.8 MB)
Published version
Author(s)
Malaret, Francisco
Type
Journal Article
Abstract
Corrosion processes are complex in nature and their studies have become an interdisciplinary research field, combining fundamental sciences and engineering. As the quantification of corrosion processes is affected by many variables, standard guidelines to study such phenomena had been developed, such as ASME and ISO, and are broadly used in industry and academics. They describe methods to perform immersion test experiments and to quantify the corrosion rates of metals exposed to corrosive environments, but do not provide any guidelines for post-exposure analysis of the as-obtained corroded samples, which might provide useful information to understand the underlying physicochemical mechanisms of corrosion. This knowledge is useful for selecting optimal construction materials and developing corrosion prevention strategies. In this work, a semi-quantitative categorization method of the corrosion behavior of metals exposed to a corrosive medium based on their mass loss and aspect is presented. For each category, the mathematical aspects of gravimetric measurements of mass change rate and the analytical techniques that can be used for the characterization of materials are discussed. The following method does not intend to replace industrial standards, but to expand them in order to maximize the amount of information that can be extracted from immersion tests.
Date Issued
2024-04
Date Acceptance
2024-03-19
Citation
Metals, 2024, 14 (4)
ISSN
2075-4701
Publisher
MDPI AG
Journal / Book Title
Metals
Volume
14
Issue
4
Copyright Statement
© 2024 by the author.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
License URL
Identifier
https://www.mdpi.com/2075-4701/14/4/409#metrics
Publication Status
Published
Article Number
409
Date Publish Online
2024-03-29