The Effect of Ionic Association on the Electrochemistry of Redox Mediators for Li-O2 Batteries: Developing a Theoretical Framework

Abstract

A theoretical framework to explain how interactions between redox mediators (RMs) and electrolyte components impact electron transfer kinetics, thermodynamics, and catalytic efficiency is presented. Specifically focusing on ionic association, 2,5-di-tert-butyl-1,4-benzoquinone (DBBQ) is used as a case study to demonstrate these effects. Our analytical equations reveal how the observed redox couple potential and electron transfer rate constants evolve with Li⁺ concentration, resulting from different redox activity mechanisms. Experimental validation by cyclic voltammetry measurements shows that DBBQ binds to three Li⁺ ions in its reduced state and one Li⁺ ion in its neutral form, leading to a maximum in the electron transfer kinetic constant at around 0.25M. The framework is extended to account for other phenomena that can play an important role in the redox mechanism of RMs: the effect of Li⁺ ion solvation and its association with the supporting salt counteranion on the redox processes is considered, and the role of “free Li⁺” concentration in determining the electrochemical behaviour is emphasized. The impact of Li⁺ concentration on oxygen reduction reaction (ORR) catalysis was then explored, again using DBBQ and modelling the effects of the Li⁺ concentration on electron transfer and catalytic kinetics. We show that even though the observed catalytic rate constant increases with Li⁺ concentration, the overall catalysis can become more sluggish depending on the electron transfer pathway. Cyclic voltammograms are presented as illustrative examples. The strength of the proposed theoretical framework lies in its adaptability to a wider range of redox mediators and their interactions. By understanding these effects, we open up new avenues to tune electron transfer and catalytic kinetics and thus improve the energy efficiency and rate capability of Li-O₂ batteries. Although exact results may not transfer to different solvents, the predictions of our model will provide a starting point for future studies of similar systems, and the model itself is easily extensible to new chemistries.