Electrode Material Dependence of the Electron Transfer Kinetics Associated with the [SVW11O40]3−/4− (VV/IV) and [SVW11O40]4−/5− (WVI/V) Processes in Dimethylformamide

Recent advances and future perspectives for automated parameterisation, Bayesian inference and machine learning in voltammetry

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.

Impact of the Lithium Cation on the Voltammetry and Spectroscopy of [XVM11O40]n- (X = P, As (n = 4), S (n = 3); M = Mo, W): Influence of Charge and Addenda and Hetero Atoms

Polyoxometalates (POMs) have been proposed as electromaterials for lithium-based batteries because they provide access to multiple electron transfer reactions coupled to fast lithium ion transport processes and high stability over many redox cycles. Consequently, knowledge of reversible potentials and Li⁺ cation-POM anion interactions provides a strategic basis for their further development. In this study, detailed cyclic voltammetric studies of a series of [XV^VM₁₁O₄₀]^n- (XVM₁₁^n-) POMs (where X (heteroatom) = P (n = 4), As (n = 4), and S (n = 3) and M (addenda atom) = Mo, W) have been undertaken in CH₃CN in the presence of LiClO₄, with n-Bu₄NPF₆ also present when required to keep the ionic strength close to constant value of 0.1 M. An analysis of the data has allowed the impact of the POM charge, and addenda and hetero atoms on the reversible potentials and the interaction between Li⁺ and the oxidized XV^VM₁₁^n- and reduced XV^IVM₁₁^(n+1)- forms of the V^V/IV redox couple to be determined. The SV^V/IVM₁₁^3-/4- process is independent of the Li⁺ concentration, implying the absence of the association of this cation with either SV^VM₁₁^3- or SV^IVM₁₁^4- redox levels. However, lithium-ion association constants for both V^V and V^IV redox levels were obtained from a comparison of simulated and experimental cyclic voltammograms for the reduction of the more negatively charged XV^VM₁₁^4- (X = P, As; M = Mo, W), since the Li⁺ interaction with these more negatively charged POMs is much stronger. The interaction between Li⁺ and the oxidized, XV^VM₁₁^n-, and reduced, XV^IVM₁₁^(n+1)-, forms was also investigated by ⁵¹V NMR and EPR spectroscopy, respectively, and it was confirmed that, due to their lower charge density, SV^VM₁₁^3- and SV^IVM₁₁^4- interact significantly less strongly with the lithium ion than XV^VM₁₁^4- and XV^IVM₁₁^5- (X = P, As). The lithium-POM association constants are substantially smaller than the corresponding proton association constants reported previously, which is attributed to a smaller surface charge density. The much stronger impact of Li⁺ on the W^VI/V- and Mo^VI/V-based reductions that occur at more negative potentials than the V^V/IV process also has been qualitatively evaluated.

Automatically Identifying Electrode Reaction Mechanisms Using Deep Neural Networks

At present, electrochemical mechanisms are most commonly identified subjectively based on the experience of the researcher. This subjectivity is reflected in bias to particular mechanisms as well as lack of quantifiable confidence in the chosen mechanism compared to potential alternative mechanisms. In this paper we demonstrate that a deep neural network trained to recognize dc cyclic voltammograms for three commonly encountered mechanisms provides correct classifications within 5 ms without the problem of subjectivity. To mimic experimental data, the impact of noise, uncompensated resistance, and dependence on scan rate, factors that are relevant to practical studies, has also been investigated. Outcomes with two experimental data sets are also presented.