Predicting Spinel Disorder and Its Effect on Oxygen Transport Kinetics in Hercynite

The iron aluminate spinel hercynite (FeAl₂O₄) is a promising redox material for solar thermochemical hydrogen production (STCH). Although it has a high H₂ production capacity, the kinetics of its oxidation and reduction may be too slow to be practical for STCH. However, our results suggest that Fe-rich hercynite may have substantially faster redox kinetics, which could make hercynite competitive with other materials for STCH. We used density functional theory to investigate the origin of hercynite's slow kinetic behavior and show that it arises from the high activation barrier of 2.46 eV for oxygen vacancy (V_O) diffusion in normal hercynite. To model the effect of disorder caused by spinel inversion, we examined 11 of the most common cation arrangements and found a near 1:1 correlation between the diffusion barrier and V_O formation energy, both of which decrease by 0.6 eV for each additional nearest-neighbor Fe atom. To examine this trend, we used integrated crystal orbital Hamilton population (ICOHP) analysis to estimate the difference in the metal-oxygen bond strengths of cations neighboring V_O and the diffusion transition state. The ICOHP predicted bond strengths correlate to both the diffusion barrier and V_O formation energy. We also computed the effect of the charge state of the oxygen vacancy and found that positively charged vacancies are stable at low Fermi energies and have a diffusion barrier of only 0.79 eV, 1.67 eV lower than that of the neutral vacancy, demonstrating that stabilizing these charged vacancies may enable faster oxidation and reduction kinetics in hercynite. We show that uncompensated Fe antisite defects, which are present in Fe-rich hercynite, provide redox flexibility that stabilizes the charged V_O and thereby increase the rate of V_O diffusion. Finally, we predict that at higher V_O concentrations the diffusion barrier depends on the relative positions of the vacancies and decreases when they are next-nearest neighbors.