(583a) The Good, the Bad and the Ugly of Combining Theory, Spectroscopy and Experimental Kinetic Studies in Fundamental Electrocatalysis.

To date, many experimental and theoretical methods have been applied to unravel electrocatalytic reaction mechanism on porous and atomically flat catalyst for a myriad of emerging energy transformation. Most electrochemical cells used in fundamental electrocatalytic studies suffer from ill-defined mass and heat transport properties making the measurement of intrinsic kinetics during electrocatalysis difficult. In this presentation, we challenge the emerging approach of combining theory, spectroscopy and experimental electrocatalysis as a means to reveal fundamental knowledge. We elucidate common pitfalls in the development of correlations between theory, experiments and spectroscopy and highligth erroneous assumptions that ultimately bias scientific conclusions. We further motivate theoreticians to challenge experimentalists and spectroscopists to understand how data is actually acquired in the lab so that theory can one day truly lead electrocatalyst discovery.

In this talk, we show that mass transport plays a role in determining product selectivity when more than one product is possible. Changes in selectivity are significant even when all experimental conditions remain constant in terms of electrolyte composition, temperature, pressure and applied potential. We show that this is true for the oxygen reduction, methane oxidation, and the CO₂ reduction reactions. Spectroscopic studies often are decoupled from product detection methods and rely on the assumption that catalyst prepared in a similar manner should retain the same catalytic activities and selectivity regardless of the electrode morphology or the electrochemical cell geometry. We show that in typical spectroscopic cells the primary and secondary current distributions are largely inhomogeneous and concentration overpotentials become significant particularly for non-atomically flat substrates. These leads to observation of reaction intermediates not relevant to kinetic studies.

Using experimental dataset in combination with machine learning and multi-scale modeling, we show hot to develop a complete mechanistic model for complex transformation that accounts for transport effects and intrinsic kinetics.