Synchrotron XAFS Analysis of Corrosion on Steel Surface Using Materials Informatics

Abstract

The rust layer changing behavior of initial corrosion on carbon steel was investigated by XAFS analysis using the large synchrotron radiation. Analysis of XANES spectrum and RDF by machine learning showed that the change in valence state and Fe-O covalent bond distance corresponded well with the initial corrosion rate. The distribution of valence and Fe-O bond distance of corrosion products on the steel surface showed a different tendency. These results suggest that the combination of synchrotron radiation analysis and machine learning is effective for elucidating rust formation mechanisms in initial corrosion.