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Cited by 284

The DOI matches its Crossref record; 135 of the 135 references it cites were found in a registry or an index.

Selecting medium for corrosion testing of bioabsorbable magnesium and other metals – A critical review

Open access
Mei, Di, Lamaka, Sviatlana V., Lu, Xiaopeng, Zheludkevich, Mikhail L.·2020·Corrosion Science

Abstract

Corrosion testing of bioabsorbable metallic materials is important at material selection stage and for understanding their degradation mechanisms. A large variety of different corrosive media has been reported for evaluating degradation performance. This review takes magnesium as an example and focuses on discussing the advantages, shortcomings and applicability of commonly used corrosive media. The discrepancies between the published results disclosing Mg corrosion behavior often originate from non-comparable test media. This indicates the urgent need for verified corrosion test protocols. The media selection criteria are outlined. Additionally, the discussion extends to the other bioabsorbable metallic materials, such as zinc and iron.

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Crossref · 10.1016/j.corsci.2020.108722

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Metadata

Authors
Mei, D., Lamaka, S. V., Lu, X., & Zheludkevich, M. L.
Venue
Corrosion Science
Publisher
Elsevier BV
Published
2020-07
Volume
171
Pages
108722
ISSN
0010-938X (ISSN-L)
OpenAlex
W3023053585
Cited by
284
References
135
FWCI
8.04
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Selecting medium for corrosion testing of bioabsorbable magnesium and other metals – A critical review. https://app.cochat.ai/papers/10.1016/j.corsci.2020.108722
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Mei, D., Lamaka, S. V., Lu, X., & Zheludkevich, M. L. (2020). Selecting medium for corrosion testing of bioabsorbable magnesium and other metals – A critical review. Corrosion Science. https://doi.org/10.1016/j.corsci.2020.108722

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.