Propane Dehydrogenation on Platinum Catalysts: Identifying the Active Sites through Bayesian Analysis

Uncertainty
quantification, Bayesian statistics, the reported experimental
literature, and density functional theory are synthesized to identify
the active sites for the non-oxidative propane dehydrogenation on
platinum catalysts. This study tests three different platinum surface
models as active sites, Pt(100), Pt(111), and Pt(211), and two different
methodologies for generating uncertainty, using data from four density
functional theory functionals and data from the BEEF–vdW ensembles.
By comparing these three surface facets using two uncertainty sources,
a total of six different computational models were evaluated. Three
experimental data sets, with varying numbers of reported observables,
such as turnover frequencies, selectivity to propylene, apparent activation
energy, and reaction orders, are calibrated and validated for these
six models. This study finds no evidence for Pt(100) as the dominant
active facet and finds that Pt(211) has some evidence for being the
most relevant active site on the catalyst. In addition, all four functional
models were excluded from final data analysis due to poor “goodness-of-fit”.
In contrast, the BEEF–vdW model with ensembles (BMwEs) was
found to pass “goodness-of-fit” for most of the models
tested. Finally, for both Pt(111) and Pt(211), this study finds that
the majority of simulations found the kinetically rate-controlling
step the first dehydrogenation step from propane to C3H7*.