Title:
A Critical Look at Advanced Nano-To-Macro Scale Characterization Techniques to Study Passivity and Corrosion of Steel in Concrete
Author(s):
Pouria Ghods and O. Burkan Isgor
Publication:
Symposium Paper
Volume:
312
Issue:
Appears on pages(s):
1-18
Keywords:
carbon steel reinforcement, corrosion, chloride, depassivation, passivity, transmission electron microscopy (TEM), x-ray photo-electron spectroscopy (XPS)
DOI:
10.14359/51689368
Date:
10/1/2016
Abstract:
Many analytical surface characterization techniques exist to study steel passivity and corrosion in concrete. Some of these techniques, such as EIS and polarization resistance methods, have been used for decades to provide macro-scale data to characterize electrochemical activity on metal surfaces. More recently, advanced nanoscale spectroscopic methods, such as XPS and EELS, as well as analytical TEM have been shown to be quite useful to study compositional and crystallographic structures of oxides that form on steel. Despite these advances,
researchers studying the interface between reinforcing steel and concrete in different scales are faced with several challenges with respect to the selection and the utilization of the available techniques. Each technique has advantages and disadvantages when compared with others; however, literature on these are quite limited in the study of steel/concrete interface. Since most of these techniques are not performed in situ, the interpretation of the data they provide requires careful examination. In addition, since most analytical studies to study passivity and corrosion in concrete are conducted in simulated environments, representativeness of the test setups are generally questioned. This paper presents a review of the commonly-used electrochemical as well as more recent analytical surface characterization techniques for the study of steel passivity and corrosion in concrete structures. The paper addresses the challenges with respect to the selection and the use of these techniques, pitfalls related to interpretation of the data, and common errors in test setups.