(677h) Classification of Adsorbed Hydrocarbons Based on Surface Site Stabilities

The design of heterogeneous catalysts can be accelerated by identifying relevant descriptors that accurately and effectively link binding and activation energies to reactivity. Herein, we have investigated scaling relations between binding energies of various hydrocarbon-based adsorbates on three different Pt surfaces and the metal binding energies (ΔE_M) estimated via the recently developed α-scheme model. The coordination based α-scheme model can predict ΔE_M very accurately and efficiently. The predicted ΔE_M shows strong correlation with the hydrocarbon-based metal adsorbate and adsorbate binding energies (ΔE_Pt-A and ΔE_A). We find that the scaling slopes are similar for certain groups of adsorbates which then can be classified based on their spatial and electronic structure. The binding energies of simple hydrocarbons CH_x, x={0,1,2,3,4} and CHCH₂ can be used to identify the binding energies of more complex hydrocarbon-based adsorbates as scaling relations slopes of ΔE_Pt-A and ΔE_A vs. ΔE_Pt for higher hydrocarbons are similar with these adsorbates. Since various classes are linearly correlated with CH it can be used as a simple descriptor for dehydrogenation of hydrocarbons. These are correlated with ΔE_Pt which can be obtained from the α-scheme model (Figure). We introduce this classification to establish a generalizable scheme in which complex hydrogenation/dehydrogenation processes of higher hydrocarbons can be predicted via the binding energies of simpler hydrocarbon-based species and ultimately through the surface site stabilities.

Slight differences in the reaction energies for various hydrocarbons are due to the difference in the linear scaling intercepts which can be explained from differences in bond dissociation energy (BDE) of the C–H bond for the various hydrocarbon intermediates. By using these approaches, it is possible to obtain the reaction energies for dehydrogenation of hydrocarbons on Pt. We believe that the established correlations and classifications will be useful for accurate and efficient screening of a large number of catalysts.