(621ca) Importance of the Materials Gap for the Rational Design of Pd Catalysts for Complete Methane Oxidation
The structural complexity of catalysts, particularly under working conditions, makes it a formidable task to study catalysis at the atomic level with traditional surface science or modern quantum mechanical methods. Hence, the materials gap has been only bridged in part by employing systems of increasing complexity in both experimental and theoretical studies.1,2 For the latter, such an approach is only viable to a certain extent before the computational cost becomes prohibitive. The need for computational efficiency is particularly pronounced for computational catalyst design based on reactivity trends. Therefore, understanding the materials gap is crucial for choosing catalyst models that allow for accurate trend predictions at feasible computational costs.
In this study, we investigated the importance of the materials gap for complete methane oxidation over supported Pd/γ-Al2O3 catalysts using a combination of Density Functional Theory (DFT) calculations and Temperature Programmed Oxidation (TPO) experiments. This catalytic system is a good benchmark, because the reaction is structure sensitive, Pd may be present as metal or metal-oxide depending on the reaction conditions, and the choice of support affects catalytic performance. The active site of Pd/γ-Al2O3 was approximated by four popular computational models with increasing complexity: Pd(100), Pd(211), PdO(101), and Pd10/γ-Al2O3(110). Among these, Pd(100) and Pd(211) distinguish between the terrace and step sites of a metallic Pd catalyst particle, respectively. PdO(101), on the other hand, has been shown to be the most stable and active phase of Pd during complete methane oxidation.3 The most complex Pd10/γ-Al2O3(110) model, which consists of a 10-atom Pd nanocluster supported on γ-Al2O3, was employed to model the metal/support interface. The first C-H bond cleavage, or methane activation, is known to determine the rate of methane oxidation and we used its activation energy barrier as the computational activity descriptor.4To predict reactivity trends for each active site representation we modified each active site model with metal promoters under consideration of their thermodynamic stability.
Although the unpromoted Pd model surfaces exhibit different activities for methane activation, our DFT results indicate that their promoted counterparts predict a consistent trend irrespective of the active site representation. Specifically, Pt and Ni are shown to lower the methane activation energy barrier, whereas Cu and Co are detrimental to catalytic performance. TPO experiments were carried out for a series of promoted Pd/γ-Al2O3catalyst candidates and the results obtained for three catalytic cycles agree well with the theoretical predictions. While the quantitative results vary between surface models, our study suggests that catalytic trend predictions are less sensitive to the choice of active site model and the associated materials gap. Overall, this work supports the validity of commonly practiced oversimplifications during computational catalysts screening based on reactivity trends, and exceptions prove the rule.
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(2) Saavedra, J.; Doan, H. A.; Pursell, C. J.; Grabow, L. C.; Chandler, B. D. Science. 2014, 345, 1599–1602.
(3) Hellman, A.; Resta, A.; Martin, N. M.; Gustafson, J.; Trinchero, A.; Carlsson, P.-A.; Balmes, O.; Felici, R.; Rijn, R. Van; Frenken, J. W. M.; Andersen, J. N.; Lundgren, E.; Gr, H.; van Rijn, R.; Grönbeck, H. J. Phys. Chem. Lett. 2012, 3 (6), 678–682.
(4) Trinchero, A.; Hellman, A.; Grönbeck, H. Surf. Sci. 2013, 616, 206–213.