The large volume of hydrocarbons that is utilized for fuel throughout the world has had a large impact on the amount of CO2 present in the atmosphere which is too much to be properly sequestered by natural means through the carbon cycle. The challenge of large-scale carbon sequestration has been put forth by many organizations and many technologies have been developed for such a task. To date, numerous electrocatalysts have been developed to convert CO2 into more usable, value-added products with metal-organic frameworks (MOFs) appearing quite recently and Cu-based materials appearing quite often especially in regards to C2+ products such as ethanol. The examination of bimetallic, Cu-based MOFs from a theoretical standpoint allows for a more efficient screening of possible CO2 reduction catalysts prior to the need for MOF synthesis. Using density functional theory (DFT), the reaction networks of CO2 reduction have been constructed as a function of the support metal on CuX(BTC) (X = Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn) clusters and an in-depth examination of the networks has been performed in order to better understand the role of the support metal. In addition, these networks can be further mapped and analyzed using machine learning to predict reaction networks without the need for DFT calculations.

Density functional theory examination of bimetallic, Cu-based, metal-organic frameworks (MOFs) as effective CO2 reduction electrocatalysts

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