Application of Bayesian Inference in Fourier-Transformed Alternating Current Voltammetry for Electrode Kinetic Mechanism Distinction

Estimation of parameters of interest in dynamic electrochemical (voltammetric) studies is usually undertaken via heuristic or data optimization comparison of the experimental results with theory based on a model chosen to mimic the experiment. Typically, only single point parameter values are obtained via either of these strategies without error estimates. In this article, Bayesian inference is introduced to Fourier-transformed alternating current voltammetry (FTACV) data analysis to distinguish electrode kinetic mechanisms (reversible or quasi-reversible, Butler-Volmer or Marcus-Hush models) and quantify the errors. Comparisons between experimental and simulated data were conducted across all harmonics using public domain freeware (MECSim).