Modeling of the electrochemical impedance spectroscopic behavior of passive iron using a genetic algorithm approach

Samin Sharifi-Asl, Matthew L. Taylor, Zijie Lu, George R. Engelhardt, Bruno Kursten, Digby Donald Macdonald

Research outputpeer-review

79 Scopus citations
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Abstract

In order to predict the general corrosion damage to metals and alloys, development of deterministic models and the acquisition of values for various model parameters are of paramount importance. In the present work, the Point Defect Model (PDM) was further developed to account for the properties of the passive film on pure iron in deaerated solutions. The model parameter values were extracted from the electrochemical impedance spectroscopic (EIS) data collected for iron in borate buffer solution [0.3 M H3BO3 + 0.075 M Na2B4O7, pH = 8.15 at 21 C] + 0.001 M EDTA [Ethylenediaminetetraacetic acid, EDTA, disodium salt] at 21 C by optimization of the PDM on the experimental EIS data using an Genetically inspired, Differential Evolution Algorithm (GDEA). EDTA effectively suppresses the formation of the outer layer of the passive film, therefore rendering the barrier layer amenable to direct examination. Comparison of the experimental and calculated data demonstrates that the impedance model based on the PDM provides a good account of the growth of the passive film on iron and the extracted model parameters can be used to predict the corrosion evolution of the sample as a function of time.

Original languageEnglish
Pages (from-to)161-173
Number of pages13
JournalElectrochimica Acta
Volume102
DOIs
StatePublished - 2013

ASJC Scopus subject areas

  • General Chemical Engineering
  • Electrochemistry

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