<i>e</i>_g Occupancy as a Predictive Descriptor for Spinel Oxide Nanozymes.

Abstract

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 <i>e</i>_g occupancy as an effective descriptor for spinel oxides with peroxidase-like activity and successfully predicted that the <i>e</i>_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.