Advanced data analysis tools such as mathematical optimisation, Bayesian inference and machine learning have the capability to revolutionise the field of quantitative voltammetry.

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).

Large-amplitude Fourier transform alternating current (FTAC) voltammetry has been used to parameterize the electrode kinetics associated with the reduction of α-[S2W18O62]4– in acetonitrile containing [n-Bu4N]­[PF6] as the supporting electrolyte at glassy carbon (GC), gold (Au), and platinum (Pt) electrodes by experimenter-based heuristic and computer-assisted automated approaches. The electron-transfer kinetics described by the Butler–Volmer relationship are faster at GC than at the metal electrodes. Progressively increasing departures from ideality in the experimental versus simulated data comparisons were found with reduction processes that occur at more negative potentials and with higher electrolyte concentrations. Ion pairing between α-[S2W18O62]4– or its reduced forms and the electrolyte cation may contribute to nonconformance between theory and experiment. Electrochemical quartz crystal microbalance experiments along with other experiments reveal that adsorption of more extensively reduced species may modify the electrode surface and contribute to the asymmetry found in the reduction and oxidation components of the FTAC voltammetric data. Enhanced double-layer effect at negative potentials could also explain why the level of nonideality increases with reduction processes that occur at more negative potentials. The findings in this study are expected to apply to the voltammetric reduction of other negatively charged polyoxometalates.

The use of Deep Neural Networks (DNNs) for the classification of electrochemical mechanisms based on training with simulations of the initial cycle of potential have been reported. In this paper,...

No abstract