Oxygen evolution reaction is a key process for enabling solar or electricity-driven water splitting. IrO₂ and RuO₂ are among the most active catalysts for OER, but material scarcity and high cost limits their usage in large-scale applications.^[1] Here, we propose a new strategy for self-forming a range of electrocatalysts through electrochemical lithiation of functional metal oxides by potential control. The lithiated phases provide a strategy to tune the strain and electronic structure and thereby leading to efficient electrocatalysts. The most suitable reaction to demonstrate this strategy is the oxygen evolution reaction, which occurs in highly oxidizing environments.

In this work, we perform a thermodynamic analysis involving density functional theory (DFT) calculations to identify the effect of lithium intercalation into functional metal oxides. The intercalation of lithium ions induces both strain and compositional change. Small strains have been induced through core-shell catalysts and interlayers to significantly enhance the activity of Pt for Oxygen Reduction Reaction (ORR).^[2],[3] We will report on the possibility of using various functional layered metal oxides such as MnO₂, CoO₂, etc.

It has been shown that extraction of intercalated Li⁺ ions from LiCoO₂ (during charging) causes expansion. When the Li⁺ ions intercalate, the lattice reversibly returns to its original spacing.^[4]  Even small change in lattice volume is sufficient to produce enough strain in the lattice to alter its activity for a reaction. We illustrate the concept through the intercalation on CoO₂ layer, as shown in the Figure. Upon lithiation, the binding strength of intermediates is affected by the compressive strain induced. We will report on calculations employing the Bayesian Error Estimation function with van der Waals exchange correlation to determine the O^*, OOH^* and OH^* adsorption energies. This is used to determine the theoretical limiting potential, which is determined by U_L= max [(ΔG_O* - ΔG_HO*), 3.2-(ΔG_O* - ΔG_HO*)]. Promising metal oxide catalysts with corresponding optimal degrees of lithiation will be identified using this framework to maximize activity. We will also use ensemble error estimation capability within BEEF to report on uncertainties associated with these calculations.^[5] 

References:

¹ Lee, Y. et.al. J. Phys. Chem. Lett., 3(3):399–404, 2012.

² Friebel, D. et. al. J. Am. Chem. Soc., 134 (23), 9664–9671, 2012.

³ Strickler, A.L. ACS Energy Lett., 2 (1), 244-249, 2017

⁴ Wang, H. et. al. Science, 354(6315):1031–1036, 2016.

⁵ Wellendorff J. et. al. Phys. Rev. B, 85, 235149, 2012.