(541e) Mapping the Coverage-Dependent Adsorption Energies of Complex Phenolics on Pd(111)

Modeling the hydrodeoxygenation (HDO) of aromatics under realistic conditions is essential as this influences both the catalyst activity and the kinetic mechanisms under which we can produce usable biofuels. Within these systems, there are two dominant interactions that must be captured to ensure for an accurate representation of the system: (1) the surface-mediated interactions resulting from the electronic exchanges between the catalyst and adsorbents, and (2) the through-space-steric interactions between co-adsorbed species. The strength of the latter is highly dependent on the adsorbing species’ configuration and coverage and is influenced by the presence of a solvent environment—a significant note since water is a byproduct of catalytic HDO.

This investigation uses first principles to model the coverage-dependent adsorption energies for a range of aromatics with increasingly complex functional groups on Pd(111) (Figure 1). For most adsorbates tested, the data fits the linear mean-field (MF) model well, where the only adsorbates with Root-Mean-Square-Errors (RMSE) larger than 50 meV are aromatics with carboxylic acids, suggesting that the MF approach does not work for those aromatics. Close-shelled adsorbates all have repulsive interactions, demonstrated by their large, linear MF slopes, whereas open-shelled aromatics (phenoxy, guaioxy) have near-zero slopes, indicating that their energies have a minimal dependence on changing coverage.

We also found that the inclusion of a water environment indicates an increase in the lateral interactions within these systems. This has a larger effect on the adsorption energies of the phenolics at lower coverages compared to near-saturation coverages (Figure 2). From this data, the lateral interactions can be quantified and a parity plot predicting the coverage-dependent adsorption energies can be created, allowing us to both describe and predict the energies of phenolics on selective transition state metals. This all aids in the accurate representation of the behavior of HDO mechanisms to produce renewable biofuel.