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Use of Bayesian inference for parameter recovery in DC and AC voltammetry

Abstract:
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.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1002/celc.201700678

Authors

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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Oxford college:
New College
Role:
Author
ORCID:
0000-0001-8311-3200
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Computer Science
Role:
Author
ORCID:
0000-0001-6009-3542
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
MPLS Doctoral Training Centre
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Mathematical Institute
Role:
Author


Publisher:
Wiley
Journal:
ChemElectroChem More from this journal
Volume:
5
Issue:
6
Pages:
917-935
Publication date:
2017-09-08
Acceptance date:
2017-09-08
DOI:
EISSN:
2196-0216


Keywords:
Pubs id:
pubs:742103
UUID:
uuid:845343ec-92d0-4675-9f0b-e11d3ac4e3ba
Local pid:
pubs:742103
Source identifiers:
742103
Deposit date:
2018-10-20
ARK identifier:

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