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).