(150g) Trapping Properties of Ag/SSZ-13 Zeolite: Modeling Adsorption Capacity
AIChE Annual Meeting
2019
2019 AIChE Annual Meeting
Catalysis and Reaction Engineering Division
Modeling and Analysis of Chemical Reactors I: Steady-State Behavior
Monday, November 11, 2019 - 2:30pm to 2:50pm
Trapping
materials prevent greenhouse gases from being released before a vehicle’s
catalytic converter activates during start-up. A major consideration for
any trapping candidate material is the adsorption capacity of the material for cold
(below the catalyst operating temperature) gases emitted from the engine during
startup before the gases sufficiently heat to the catalyst operating
temperature.1 The trapping candidate material chosen for
evaluation in this work is the zeolite SSZ-13 ion exchanged with Ag (Figure
1). Ethylene and water, two components present in vehicle exhaust which
compete for adsorption sites are studied from density functional theory (DFT)
calculations. The BEEF-vdw2 functional is used in DFT
calculations, which provides an estimate of DFT binding energy uncertainty.
Previously, DFT models in the literature have been limited to energies and not
adsorption capacity predictions. Adsorption capacities are key to the
experimental testing of candidate trapping materials. This uncertainty is
propagated through two adsorption capacity models. The two models are
competitive Langmuir adsorption and a mean-field microkinetic model. Both
models give qualitatively similar results.
Figure
1. The zeolite material SSZ-13 with Ag ions is modeled (2 unit cells displayed)
for its adsorption capacity of two vehicle engine emissions gases: ethylene and
water.
[1] Lee,
J.; Theis, J. R.; Kyriakidou, E. A. Vehicle Emissions Trapping Materials:
Successes, Challenges and the Path Forward. Appl. Catal. B- Environ. 243
(2019) 397-414.
[2]
Wellendorff, J.; Lundgaard, K. T.; Møgelhøj, A.; Petzold, V.; Landis, D. D.; Nørskov,
J. K.; Bligaard, T.; Jacobsen, K. W. Density Functionals for Surface Science:
Exhange-Correlation Model Development with Bayesian Error Estimation. Phys.
Rev. B 85 (2012) 235149.