Studying the Interaction of Mass Transport and Electrochemical Reaction Kinetics by Species Frequency Response Analysis

Electrochemical macrokinetics contains the interaction of electrode reactions with transport phenomena. To disentangle the individual processes, dynamic techniques such as electrochemical impedance spectroscopy are widely used. Additional information can be obtained when further quantities besides current and potential are recorded. Here, we present and analyze a method to observe the dynamics of the flux of volatile species, i.e. mass transfer, in porous electrodes during electrochemical reactions with a high time resolution. We call this technique species frequency response analysis (sFRA). It is experimentally demonstrated with electrochemical methanol oxidation reaction on a porous Pt/Ru electrode. The dynamic relationship between current, potential and the flux of the gaseous reaction product CO$_{2}$ is measured by differential electrochemical mass spectrometry. The resulting transfer function that relates current density with CO$_{2}$ flux is analysed in detail by means of a one-dimensional mathematical model. It is demonstrated how the influence of reaction and transport phenomena can be separated in the sFRA Nyquist plot. Practical aspects such as sensitivity and accessible frequency range are discussed as well as the overall prospects and limitations of the technique

Identifying the oxygen evolution mechanism by microkinetic modelling of cyclic voltammograms

Electrocatalytic water splitting is currently one of the most promising reactions to produce “green” hydrogen in a decarbonized energy system. Its bottleneck reaction, the oxygen evolution reaction (OER), is catalysed by hydrous iridium, a stable and active catalyst material. Improving the OER requires a better and especially quantitative understanding of the reaction mechanism as well as its kinetics. In this work, we present an experimentally validated microkinetic model that allows to quantify the mechanistic pathways, emerging surface species prior and during the OER, the reaction rates for the single steps and essential thermodynamic properties. Therefore, two mechanisms based on density functional theory and experimental findings are evaluated on which only simulation results of the theory-based one are found to be in full accordance with cyclic voltammograms even at different potential rates and, thus, able to describe the catalytic system. The simulation implies that oxygen is evolving mostly via a fast single site pathway (∗OO → ∗ + O2 ) with an effective reaction rate, which is several orders of magnitude faster compared to the slow dual site (2∗ O → 2∗ + O2) pathway rate. Intermediate states of roughly 7% Ir(III), 25% Ir(IV) and 63% Ir(V) are present at typical OER potentials of 1.6 V vs RHE. We are able to explain counterintuitive experimental findings of a reduced iridium species during highly oxidizing potentials by the kinetic limitation of water adsorption. Although water adsorption is in general thermodynamically favourable, it is kinetically proceeding slower than the electrochemical steps at high potential. In the lower potential range from 0.05 to 1.5 V vs RHE the stepwise oxidation of the iridium is accompanied with van der Waals like ad- and desorption processes, which leads in comparison to Langmuir-type adsorption to a broadened peak shape in the cyclic voltammograms. Overall, our analysis shows that the dynamic microkinetic modelling approach is a powerful tool to analyse catalytic microkinetics in depth and to bridge the gap between thermodynamic calculations and experiments

Hybrid Process Models in Electrochemical Syntheses under Deep Uncertainty

Chemical process engineering and machine learning are merging rapidly, and hybrid process models have shown promising results in process analysis and process design. However, uncertainties in first-principles process models have an adverse effect on extrapolations and inferences based on hybrid process models. Parameter sensitivities are an essential tool to understand better the underlying uncertainty propagation and hybrid system identification challenges. Still, standard parameter sensitivity concepts may fail to address comprehensive parameter uncertainty problems, i.e., deep uncertainty with aleatoric and epistemic contributions. This work shows a highly effective and reproducible sampling strategy to calculate simulation uncertainties and global parameter sensitivities for hybrid process models under deep uncertainty. We demonstrate the workflow with two electrochemical synthesis simulation studies, including the synthesis of furfuryl alcohol and 4-aminophenol. Compared with Monte Carlo reference simulations, the CPU-time was significantly reduced. The general findings of the hybrid model sensitivity studies under deep uncertainty are twofold. First, epistemic uncertainty has a significant effect on uncertainty analysis. Second, the predicted parameter sensitivities of the hybrid process models add value to the interpretation and analysis of the hybrid models themselves but are not suitable for predicting the real process/full first-principles process model’s sensitivities