Screening of First-Row Transition Metal Complexes for Aqueous Redox Flow Batteries: Experimental and Density Functional Theory Approaches

Abstract

The development of redox flow batteries took new directions in the last decades. From initial metal-based systems, it evolved towards organic and hybrid approaches in aqueous media. Furthermore, redox targeting flow batteries are an emerging alternative to the traditional redox flow battery architecture which offer improved energy density via an added electroactive solid ‘booster’. Whatever the system, there is a need of soluble electroactive species with potentials allowing large cell potentials within the water stability window or imperative redox potentials matching between soluble redox mediator and the solid for redox targeting. Transition metal complexes are a promising class of redox electroactive centers due to the tunability of their solubility and electrochemical potential, and the ability to take into account sustainability1. Recent reports underlined for instance that either the modification of the ligand2 or using heteroleptic complexes3, can lead to iron complexes with solubilities higher than 1 M and potentials higher than 1 V vs SHE. Furthermore, reliable and time-efficient prediction tools for the redox potentials of transition metal complexes are needed to help the search for new potential systems. While density functional theory usually provides a good trade-off between computational cost and accuracy, calculations on transition metal complexes often result in large errors. We thus developed a procedure and compared different solvation methods and levels of theory using an initial experimental data set based on aqueous iron complexes with bidentate ligands (Figure 1).In the present contribution, we will illustrate this coordination approach through a screening of new complexes based on non-toxic and affordable transition metal complexes. In this perspective, electrochemical characterization at different pH, coupled with NMR and UV-Visible studies will be presented, to screen for new candidates as electrolyte in redox flow batteries. Finally, we will show how the calculations corrected using a simple linear regression using training data yields a good prediction of redox potentials (mean average error =0.09 V).