(50d) Mechanism of Polyol Hydrodeoxygenation on Molybdenum Carbide Supported Copper Catalyst: A Combined DFT and Microkinetic Modeling Study

Producing valuable chemicals from renewable biomass has received consistent attention. For highly oxygenated biomass-derived chemicals, the selective hydrodeoxygenation without C/C cleavage is one of the desired reactions preventing loss of carbon as CO or CO₂. With the goal of designing non-precious metal catalysts, the Mo₂C supported Cu has been investigated for the hydrodeoxygenation of oxygenates using density functional theory and microkinetic modeling. Results are correlated to experimental surface science and reactor data.

Previously, we determined that a Cu modifier on a molybdenum carbide (Mo₂C) support reduces the oxophilicity of Mo₂C, such that the selectivity with regards to the number of C/O cleavages can be controlled with the Cu coverage. Here, we focused on a monolayer Cu/Mo₂C and a Cu(111) model to understand the support effect of Mo₂C on the reaction mechanism and on the active site performance under various reaction conditions. Our microkinetic analysis predicted the Cu/Mo₂C surface was more active for a single C/O dissociation of glycerol versus Cu(111) where C/O scission is slow and dehydrogenated products are primarily produced. The enhanced activity of the Cu/Mo₂C surface for the glycerol activation was attributed to electronic modification of Cu atoms by the Mo₂C support, which enhanced the adsorption strength of intermediates and reduced the activation barrier for C/O cleavage reactions.

Next, we examined the mechanism of the conversion of 1,2- and 1,3-propanediol on Cu/Mo₂C to understand the role of intramolecular hydrogen bonding on the selective C/O cleavage. We observed that one C/O cleavage can occur for both species which is consistent with our glycerol study. Furthermore, in agreement to experimental studies, 1,2-propanediol favors terminal C/O cleavage to produce acetone. Finally, we extended our C3 studies on Mo₂C supported Cu catalysts to larger linear polyols. Here, we extrapolate the energetics from our C3 studies to larger molecules via machine learning methods.