Use of Bayesian Inference for Parameter Recovery in DC and AC Voltammetry

David J. Gavaghan, Jonathan Cooper, Aidan C. Daly, Christopher Gill, Kathryn Gillow, Martin Robinson, Alexandr N. Simonov, Jie Zhang, Alan M. Bond

Research output: Contribution to journalArticleResearchpeer-review

16 Citations (Scopus)


We describe the use of Bayesian inference for quantitative comparison of voltammetric methods for investigating electrode kinetics. We illustrate the utility of the approach by comparing the information content in both DC and AC voltammetry at a planar electrode for the case of a quasi-reversible one electron reaction mechanism. Using synthetic data (i.e. simulated data based on Butler-Volmer electrode kinetics for which the true parameter values are known and to which realistic levels of simulated experimental noise have been added), we are able to show that AC voltammetry is less affected by experimental noise (so that in effect it has a greater information content then the corresponding DC measurement) and hence yields more accurate estimates of the experimental parameters for a given level of noise. Significantly, the AC approach is shown to be able to distinguish higher values of the rate constant. The results of using synthetic data are then confirmed for an illustrative case of experimental data for the [Fe(CN)6]3-/4- process.

Original languageEnglish
Pages (from-to)917–935
Number of pages19
Issue number6
Publication statusPublished - 2017


  • Bayesian Inference
  • Inverse Problems
  • Parameter Estimation
  • Voltammetry

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