e_g Occupancy as a Predictive Descriptor for Spinel Oxide Nanozymes

Functional nanomaterials offer an attractive strategy to mimic the catalysis of natural enzymes, which are collectively called nanozymes. Although the development of nanozymes shows a trend of diversification of materials with enzyme-like activity, most nanozymes have been discovered via trial-and-error methods, largely due to the lack of predictive descriptors. To fill this gap, this work identified e_g occupancy as an effective descriptor for spinel oxides with peroxidase-like activity and successfully predicted that the e_g value of spinel oxide nanozymes with the highest activity is close to 0.6. The LiCo₂O₄ with the highest activity, which is finally predicted, has achieved more than an order of magnitude improvement in activity. Density functional theory provides a rationale for the reaction path. This work contributes to the rational design of high performance nanozymes by using activity descriptors and provides a methodology to identify other descriptors for nanozymes.