Integration of heuristic and automated parametrization of three unresolved two-electron surface-confined polyoxometalate reduction processes by AC voltammetry

Martin Robinson, Kontad Ounnunkad, Jie Zhang, David Gavaghan, Alan Bond

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

Abstract

The thermodynamic and electrode kinetic parameters that describe each of the three unresolved proton-coupled two-electron transfer processes of surface-confined Keggin-type phosphomolybdate, [PMo12O40]3− adsorbed onto glassy carbon electrode in 1.0 M H2SO4 have been elucidated by comparison of experimental and simulated AC voltammmetric data. Modelling of this problem requires the introduction of over 30 parameters, although this may be reduced to about half this number when intelligent forms of data analysis are introduced. Heuristic (i. e., an experimenter based trial and error method) and automated data optimization approaches are integrated in this very extensive parameter estimation exercise. However, obtaining a unique solution remains challenging for reasons that are outlined. In the final analysis and using the automated strategy, estimates of six reversible potentials, lower limits of the six electron transfer rate constants, the double layer capacitance, uncompensated resistance and surface coverage are reported, with others (such as the charge transfer co-efficient) present in the model being unobtainable for reasons that are provided. The fit to experimental data using parameters obtained by automated data optimisation is excellent and slightly superior to that obtained by heuristic analysis. The parameters obtained by either method account for differences in shapes and current magnitudes of each of the overall two electron processes.

Original languageEnglish
Pages (from-to)3771-3785
Number of pages15
JournalChemElectroChem
Volume5
Issue number23
DOIs
Publication statusPublished - 3 Dec 2018

Keywords

  • AC voltammetry
  • bayesian
  • electrochemistry
  • heuristic and automated parameter fitting
  • polyoxometalates

Cite this

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title = "Integration of heuristic and automated parametrization of three unresolved two-electron surface-confined polyoxometalate reduction processes by AC voltammetry",
abstract = "The thermodynamic and electrode kinetic parameters that describe each of the three unresolved proton-coupled two-electron transfer processes of surface-confined Keggin-type phosphomolybdate, [PMo12O40]3− adsorbed onto glassy carbon electrode in 1.0 M H2SO4 have been elucidated by comparison of experimental and simulated AC voltammmetric data. Modelling of this problem requires the introduction of over 30 parameters, although this may be reduced to about half this number when intelligent forms of data analysis are introduced. Heuristic (i. e., an experimenter based trial and error method) and automated data optimization approaches are integrated in this very extensive parameter estimation exercise. However, obtaining a unique solution remains challenging for reasons that are outlined. In the final analysis and using the automated strategy, estimates of six reversible potentials, lower limits of the six electron transfer rate constants, the double layer capacitance, uncompensated resistance and surface coverage are reported, with others (such as the charge transfer co-efficient) present in the model being unobtainable for reasons that are provided. The fit to experimental data using parameters obtained by automated data optimisation is excellent and slightly superior to that obtained by heuristic analysis. The parameters obtained by either method account for differences in shapes and current magnitudes of each of the overall two electron processes.",
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Integration of heuristic and automated parametrization of three unresolved two-electron surface-confined polyoxometalate reduction processes by AC voltammetry. / Robinson, Martin; Ounnunkad, Kontad; Zhang, Jie; Gavaghan, David; Bond, Alan.

In: ChemElectroChem, Vol. 5, No. 23, 03.12.2018, p. 3771-3785.

Research output: Contribution to journalArticleResearchpeer-review

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