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

This paper is dedicated to the memory of Jean-Michel SavéantUse of carefully designed computer supported parameterisation methods in voltammetric studies can provide highly robust and accurate methods for simultaneously quantifying the large number of parameters present in complex electrochemical reactions. In this study, a computer program has been developed to parameterise large amplitude AC voltammetric data using mathematical optimisation in combination with Bayesian inference algorithms for calculating posterior distributions of parameters and hence uncertainties in parameter values. The computer program has been applied to objective functions, relevant to total AC current, frequency domain data in the form of the power spectrum derived from Fourier transformation and multivariate based methods using the resolved harmonic data. The robustness of the objective functions have been confirmed and Bayesian inference methods have been validated using "noisy" synthetic and experimental data for the [Fe(CN) 6 ] 3À /4À reduction process in aqueous 3.0 M KCl electrolyte at a gold electrode. It was found that the harmonic based Bayesian inference methods outperformed other methods in parameterisation of the thermodynamics and electrode kinetics of the close to reversible [Fe(CN) 6 ] 3À /4À process due to their ability to compensate for non-ideality in the modelling and the superior parameter sensitivities available in the higher harmonics. The computer supported and heuristic methods were compared. Their advantages and limitations are discussed.

No abstract

Yi-Lun Ying opened discussion of the introductory lecture by Justin Gooding: The nanointerface needs more comprehensive models to describe the interaction networks among water and ions. Would you please comment on how these models and understanding guide the applications of nanoelectrochemistry? Justin Gooding answered: This is a very difficult question which I don't think we know enough yet to give you a clear answer. What we do know is that with surfaces inside the substrate channels of our enzyme-like nanoparticles we have two to three layers of ice like water. Inside the channels that might mean that all the water is in an ice-like state. How this affects transport in the channel and the electrical double layer in electrochemical systems are big questions for us. Certainly, regarding transport, there is evidence for proton migration inside these channels and possibly sodium and potassium if they are present. But in our systems our materials are not well dened enough to tease out such effects. This was what my point was about regarding needing better dened materials to really understand the nature of the nanointerface in nanoparticle systems. There is however a lot of information we can extract from studies performed in the transport of species through nanochannels. I don't think we yet have an understanding though for case that I described where the nanochannels themselves are also reactive. 1,2