e_g Occupancy as a Predictive Descriptor for Spinel Oxide Nanozymes

Area of Science:

• Materials Science
• Nanotechnology
• Catalysis

Background:

• Nanozymes, functional nanomaterials mimicking natural enzymes, are crucial for catalysis.
• Current nanozyme development relies heavily on trial-and-error due to a lack of predictive descriptors.
• Enzyme-like activity in diverse materials is expanding, necessitating a more systematic approach.

Purpose of the Study:

• To identify effective descriptors for predicting nanozyme activity, specifically peroxidase-like behavior in spinel oxides.
• To enable the rational design of high-performance nanozymes.
• To establish a methodology for discovering new nanozyme descriptors.

Main Methods:

• Identification of 'e_g occupancy' as a descriptor for spinel oxide nanozymes.
• Prediction of optimal e_g values for enhanced peroxidase-like activity (around 0.6).
• Utilizing density functional theory (DFT) to elucidate reaction mechanisms.

Main Results:

• Established e_g occupancy as a reliable descriptor for predicting peroxidase-like activity in spinel oxides.
• Successfully predicted LiCo₂O₄ as a nanozyme with superior activity.
• Achieved over an order of magnitude improvement in catalytic activity for the predicted LiCo₂O₄ nanozyme.

Conclusions:

• The study provides a rational design strategy for high-performance nanozymes using activity descriptors.
• e_g occupancy serves as a key descriptor for spinel oxide nanozymes with peroxidase-like activity.
• The developed methodology can be applied to identify descriptors for other types of nanozymes.