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Microbial Iron Respiration Can Protect Steel from Corrosion

Open access
Dubiel, M., Hsu, C. H., Chien, C. C., Mansfeld, F., Newman, D. K.·2002·Applied and Environmental Microbiology

Abstract

Microbiologically influenced corrosion (MC) of steel has been attributed to the activity of biofilms that include anaerobic microorganisms such as iron-respiring bacteria, yet the mechanisms by which these organisms influence corrosion have been unclear. To study this process, we generated mutants of the iron-respiring bacterium Shewanella oneidensis strain MR-1 that were defective in biofilm formation and/or iron reduction. Electrochemical impedance spectroscopy was used to determine changes in the corrosion rate and corrosion potential as a function of time for these mutants in comparison to the wild type. Counter to prevailing theories of MC, our results indicate that biofilms comprising iron-respiring bacteria may reduce rather than accelerate the corrosion rate of steel. Corrosion inhibition appears to be due to reduction of ferric ions to ferrous ions and increased consumption of oxygen, both of which are direct consequences of microbial respiration.

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Metadata

Authors
Dubiel, M., Hsu, C. H., Chien, C. C., Mansfeld, F., & Newman, D. K.
Venue
Applied and Environmental Microbiology
Published
2002-03
Volume
68
Issue
3
Pages
1440-1445
ISSN
  • 0099-2240 (ISSN-L)
  • 1098-5336
OpenAlex
W2113802994
Cited by
227
References
0
FWCI
5.15
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Microbial Iron Respiration Can Protect Steel from Corrosion. https://app.cochat.ai/papers/10.1128/aem.68.3.1440-1445.2002
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Dubiel, M., Hsu, C. H., Chien, C. C., Mansfeld, F., & Newman, D. K. (2002). Microbial Iron Respiration Can Protect Steel from Corrosion. Applied and Environmental Microbiology. https://doi.org/10.1128/aem.68.3.1440-1445.2002

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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.