Theoretical Design of Electrocatalysts Inspired by Enzymes

The objective is to develop computational procedures based on density functional theory (DFT) for the design and evaluation of heterogenous electrocatalyst.

We will continue the development of DFT based local surface properties and apply them in the design of catalysts for specific applications. The design principles are inspired by the functions of natural enzymes.

The first application involves catalysts based on chiral nanocrystals for use in medical sensors, e.g. noninvasive glucose sensing.

To obtain working catalysts we will collaborate with Korean scientists that are experts on synthesizing well-defined chiral gold nanocrystals.

The catalyst should stabilize the transition state (TS) by providing a binding site that is complementary to the TS in terms of shape and electrostatic potential.

We will use local surface properties together with machine learning and DFT reaction modeling to develop methods for identifying and characterizing binding sites at nanocrystal surfaces. The second application is catalysts for the nitrogen reduction reaction to produce ammonia.

This will involve identifying functional groups that break scaling relationships and selectively stabilize the rate-determining steps.

Catalysts will be based on inexpensive main group elements and/or first row transition metals, and in the catalyst design we will use local surface properties together with advanced DFT modeling.