Microbiologically influenced corrosion and current mitigation strategies: A state of the art review
By R. Jia, T. Unsal, Dake Xu, Yassir Lekbach, T. Gu. International Biodeterioration & Biodegradation, 2019
419 citations recorded by the source corpus.
Abstract In nature, microbes tend to form synergistic biofilms on a wide range of materials using extracellular polymeric substances to embed sessile cells. Problematic biofilms provide environments conducive to the occurrence of microbiologically influenced corrosion (MIC) in many industries. MIC includes corrosion caused by microbes as well as corrosion by another pre-existing corrosion agent that is accelerated by microbes. There are several distinct types of MIC mechanisms associated with different microbial species and metabolism types. Biofilms employ different defense mechanisms to protect themselves from environmental hazards. This makes sessile cells in biofilms much more resistant to treatment than planktonic cells. Biofilms are often treated with biocides together with physical scrubbing. Various approaches have been developed to boost biocide efficacy. New emerging technologies such as bacterial phages, quorum sensing inhibitors, and special chemicals to enhance biocides have been proposed in recent years. This review evaluates these different treatment methods and various techniques used to assess biocide treatment outcome including microbiology, molecular biology, corrosion testing and electrochemical methods.
Subfields: Health, Toxicology and Mutagenesis, Materials Chemistry, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/bb248148e152fe1d43399386ceb79b39f734c0cd
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Health, Toxicology and Mutagenesis
In Materials Chemistry
In Molecular Biology
Citations in the corpus
- Electrochemical Impedance Spectroscopy for the Measurement of the Corrosion Rate of Magnesium Alloys: Brief Review and Challenges (2020, methodology): “In spite of the obvious advantages of using EIS, there are certain limitations of the technique including the following: The major problem with EIS analysis is that, often, different equivalent circuit models all giving low chi-squared (χ2) error values can be used to fit the same EIS data [5,35,4”