A Comparison of Bayesian Inference Strategies for Parameterisation of Large Amplitude AC Voltammetry Derived from Total Current and Fourier Transformed Versions

Understanding the Decamethylferrocene FeIII/IV Oxidation Process in Tris(pentafluoroethyl)trifluorophosphate-Containing Ionic Liquids at Glassy Carbon and Boron-Doped Diamond Electrodes

Under voltammetric conditions, the neutral decamethylferrocene ([Me10Fc]) to cationic ([Me10Fc]+) FeII/III process is a well-known reversible outer-sphere reaction. A companion cationic [Me10Fc]+ to dicationic [Me10Fc]2+ FeIII/IV process has been reported under direct current (DC) cyclic voltammetric conditions at highly positive potentials in liquid SO2 at low temperatures and in a 1.5:1.0 AlCl3/1-butylpyridinium chloride melt. This study demonstrates that in room-temperature ionic liquids containing the hard to oxidize and hydrophobic tris(pentafluoroethyl)trifluorophosphate anion, the [Me10Fc]+/2+ process can be detected as a quasi-reversible reaction at glassy carbon (GC) and boron-doped diamond (BDD) electrodes. Large amplitude Fourier-transformed alternating current (FT-AC) voltammetry minimizes background current contributions occurring at potentials similar to those of the FeIII/IV process in the second and higher-order harmonics. This enables a straightforward determination of the thermodynamics and kinetics for both the FeII/III and FeIII/IV processes. Unlike the ideal outer-sphere FeII/III process, the parameters of the FeIII/IV process may be impacted by ion-interaction effects. For the faster FeII/III process, heterogeneous rate constants are approximately 10 times smaller at BDD than those at GC electrodes. This electrode dependence is less pronounced for the slower FeIII/IV process. The slower BDD kinetics may be attributed in part to a density of states lower than that at GC.

Inclusion of multiple cycling of potential in the deep neural network classification of voltammetric reaction mechanisms

The use of deep neural networks (DNNs) for the classification of electrochemical mechanisms using simulated voltammograms with one cycle of potential for training has previously been reported. In this paper, it is shown how valuable additional patterns for mechanism distinction become available when a new DNN is trained simultaneously on images obtained from three cycles of potential using tensor inputs. Significant improvements, relative to the single cycle training, in achieving the correct classification of E, EC_1^st and EC_2^nd mechanisms (E = electron transfer step and C_1^st and C_2^nd are first and second order follow up chemical reactions, respectively) are demonstrated with noisy simulated data for conditions where all mechanisms are close to chemically reversible and hence difficult to distinguish, even by an experienced electrochemist. Challenges anticipated in applying the new DNN to the classification of experimental data are highlighted. Directions for future development are also discussed.