Electrochemical hydrogenation (ECH) reactions are needed in the post-pyrolysis treatment of bio-oil to convert biomass into fuels or chemicals. A computational approach is taken toward catalyst design, to provide testable hypotheses regarding catalyst composition and its activity and selectivity for experimental work. Despite extensive research done in electrochemical reactions, the environment complexity has hindered a rigorous understanding of electrocatalyst surface chemistry. Density functional theory calculations of species binding and ECH reaction energies were done as a function of voltage on the surface of numerous nanoparticles. Initial results show that Au, Cu, and C catalysts are best suited because they preferably bind organic molecules over hydrogen, and show a large overpotential for H<sub>2</sub> formation and low overpotential for organic reduction. Since only the species at the cathode (surface or nanoparticle on surface) vicinity can undergo electron transfer, classical molecular dynamics simulations of surrogate bio-oil solvent mixtures in an electrolytic cell were performed to assess how species concentrations differ between the solid/liquid interface and bulk regions. Simulations were performed at varying temperatures and voltages, with different cathode surfaces and nanoparticles. Energetic and entropic contributions were analyzed to understand competing surface binding and solvation effects. Initial results show a decrease in organic and increase in water mol fractions at the solid/liquid interface as the voltage in the electrolytic cell increases. This can only be compensated by a strong adhesion of the organic molecule to the electrode surface; therefore organic molecule adsorption is a key parameter. As a result, metal nanoparticles on graphene surfaces exhibit selective reduction of aromatics and appreciably lower activity toward aliphatic molecules. Results are compared with concurrent experiments and implications on catalyst choice and reactor design are discussed. <br/>

Computational modeling of electrochemical bio-oil upgrading

251st National Meeting (2016)

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