(740c) Structure Sensitive Approaches to Understanding Deactivation in Ethane Dehydrogenation | AIChE

(740c) Structure Sensitive Approaches to Understanding Deactivation in Ethane Dehydrogenation


Roling, L. - Presenter, Iowa State University
Wright, J., Iowa State University
Wright, D., Iowa State University
Dehydrogenation reactions of light alkanes are among the most important platform transformations in industry; however, transition metal catalysts used to perform these are prone to deactivation through coking at the elevated temperatures needed to drive the endothermic processes.1 Subsequent catalyst regeneration and/or replacement is costly and inefficient. A number of experimental and theoretical studies have attempted to elucidate the nature of and mitigate this deactivation, and have identified undercoordinated catalyst atoms as potential active sites for coke nucleation and/or formation.2 However, attempts to quantitatively predict deactivation through dynamic simulation remain relatively limited.

In this presentation, we describe the use of density functional theory (DFT) calculations and a structure-sensitive energy scaling technique to understand catalyst deactivation in ethane dehydrogenation as determined by catalyst composition and morphology. Our methods, based on relating the adsorption strength of molecular species to the stability of the metal surface site, allow the direct prediction of surface site reactivity on nanoparticles based on a parameterization from relatively simple slab models.3 We thereby predict the reactivity of metal nanoparticles of generic shapes, moving away from idealized surface models and toward a more realistic representation of catalyst deactivation in the context of ethane dehydrogenation. We describe the reactivity of various nanostructured fcc transition metals (Pt, Pd, Au) to elucidate the nature of the selectivity challenge. As metal nanoparticles will reconstruct at elevated temperatures and present myriad transient nanoparticle structures, we finally show how our method offers a computationally-feasible approaches to future dynamic simulations of catalyst evolution due to its efficiency.

  1. Sattler, Ruiz-Martinez, Santillan-Jimenez, and Weckhuysen, Chemical Reviews 114, 10613 (2014)
  2. Cybulskis et al., ACS Catalysis 7, 4173 (2017)
  3. Roling and Abild-Pedersen, ChemCatChem 10, 1643 (2018)