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Gundry L, Guo SX, Kennedy G, Keith J, Robinson M, Gavaghan D, Bond AM, Zhang J. Recent advances and future perspectives for automated parameterisation, Bayesian inference and machine learning in voltammetry. Chem Commun (Camb) 2021; 57:1855-1870. [PMID: 33529293 DOI: 10.1039/d0cc07549c] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Advanced data analysis tools such as mathematical optimisation, Bayesian inference and machine learning have the capability to revolutionise the field of quantitative voltammetry. Nowadays such approaches can be implemented routinely with widely available, user-friendly modern computing languages, algorithms and high speed computing to provide accurate and robust methods for quantitative comparison of experimental data with extensive simulated data sets derived from models proposed to describe complex electrochemical reactions. While the methodology is generic to all forms of dynamic electrochemistry, including the widely used direct current cyclic voltammetry, this review highlights advances achievable in the parameterisation of large amplitude alternating current voltammetry. One significant advantage this technique offers in terms of data analysis is that Fourier transformation provides access to the higher order harmonics that are almost devoid of background current. Perspectives on the technical advances needed to develop intelligent data analysis strategies and make them generally available to users of voltammetry are provided.
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Affiliation(s)
- Luke Gundry
- School of Chemistry, Monash University, Clayton, Vic. 3800, Australia.
| | - Si-Xuan Guo
- School of Chemistry, Monash University, Clayton, Vic. 3800, Australia.
| | - Gareth Kennedy
- School of Chemistry, Monash University, Clayton, Vic. 3800, Australia.
| | - Jonathan Keith
- School of Mathematics, Monash University, Clayton, Vic. 3800, Australia
| | - Martin Robinson
- Department of Computer Science, University of Oxford, Wolfson Building, Parks Road, Oxford, OX1 3QD, UK
| | - David Gavaghan
- Department of Computer Science, University of Oxford, Wolfson Building, Parks Road, Oxford, OX1 3QD, UK
| | - Alan M Bond
- School of Chemistry, Monash University, Clayton, Vic. 3800, Australia.
| | - Jie Zhang
- School of Chemistry, Monash University, Clayton, Vic. 3800, Australia.
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Guin SK, Ambolikar AS, Das S, Poswal AK. Advantage of Fractional Calculus Based Hybrid‐Theoretical‐Computational‐Experimental Approach for Alternating Current Voltammetry. ELECTROANAL 2020. [DOI: 10.1002/elan.201900552] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Saurav K. Guin
- Fuel Chemistry DivisionBhabha Atomic Research Centre, Trombay Mumbai 400085 India
| | - Arvind S. Ambolikar
- Fuel Chemistry DivisionBhabha Atomic Research Centre, Trombay Mumbai 400085 India
- Homi Bhabha National InstituteAnushakti Nagar Mumbai 400094 India
| | - Shantanu Das
- Reactor Contol System Design SectionBhabha Atomic Research Centre, Trombay Mumbai 400085 India
| | - Ashwini K. Poswal
- Atomic & Molecular Physics DivisionBhabha Atomic Research Centre, Trombay Mumbai 400085 India
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Blutstein H, Bond A, Norris A. Simultaneous measurement of the in-phase and quadrature components of the signal in a.c. polarography using multiplier circuitry. J Electroanal Chem (Lausanne) 1978. [DOI: 10.1016/s0022-0728(78)80032-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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