Mechanistic Insights into Catalytic Reduction of N2O by CO over Cu-Embedded Graphene: A Density Functional Theory Perspective

In this study, the mechanism of N2O reduction by CO over Cu-embedded graphene(CuG) surface was examined through Density Functional Theory(DFT) with Grimme-D2 dispersion correction. Cu-embedded graphene networks can be synthesized experimentally, and are less costly than plain graphene by virtue of the limited use of Cu atoms. Cu atoms strongly bond to defective structures and make the structure more stable. The binding energy between the defective graphene structure and the Cu atom was calculated as -3.92 eV. The Bader analysis was performed for CuG surface characteristics, and adsorption geometries of N2O and electron density difference maps were created. The results showed that the charge density of Cu atoms provided a high catalytic activity for reduction reactions. O* atom adsorbed to the surface renders O transfer easier. The results indicated that there were 0.16 divide e divide and 0.02 divide e divide electron were transferred from the surface to the N-terminated and O-terminated N2O molecule, respectively. The calculations proved that the surface possessed a high catalytic activity on O*+N2O -> N-2 + O-2 and CO + N2O -> CO2 + N-2 reduction reactions. This study paves the way for tailoring a high-performance electrocatalyst for NO2 reduction reaction by considering the high electrocatalytic activity and superior physicochemical properties of Cu-embedded graphene.

Automated summary

This study investigated the mechanism of N2O reduction by CO over Cu-embedded graphene surface using Density Functional Theory. The results revealed that Cu-embedded graphene surface exhibited high catalytic activity for reduction reactions. Copper atoms strongly bonded to defective structures, enhancing the stability and catalytic performance of the surface.