Analysis of Polarization Curves Under Mixed Activation-Diffusion Control: An Algorithm to Minimize Human Factors

A substantial part of corrosion research relies on the analysis of polarization curves to obtain corrosion currents, Tafel slopes, and other parameters, such as exchange current densities. This, often manual, analysis remains highly subjective, which hampers the reproducibility of corrosion research and makes a comparison of reported Tafel slopes, corrosion rates, or exchange current densities from different literature sources difficult. One reason is that the analysis is strongly influenced by the selected range of the measured data. To improve this, we developed a Python library for the reliable analysis of polarization curves. A particular novelty is an algorithm designed to fit polarization curves under mixed activation-diffusion control, which is a situation often encountered in corrosion research. This algorithm reduces the subjectivity related to the measured or selected potential range. Moreover, the algorithm offers the possibility to diagnose and quantify the accuracy of the fit. We use experimentally measured polarization curves to test the proposed approach and show that for curves without a clear, purely activation-controlled Tafel region in the cathodic branch, accurate and consistent analysis is only possible by applying the mixed activation-diffusion control technique. Re-evaluation of literature data shows that by applying the library the variability in reported Tafel slopes can be greatly reduced. Thus, here the proposed approach and the related open-access Python library for the analysis of polarization curves may foster reproducibility and enhance the comparability of data measured in corrosion research.