(488d) Modeling Adsorption Capacity of Ag/SSZ-13 Zeolite: A Bayesian Update from Experiments

Three potential adsorption sites within Ag/SSZ-13 zeolite are compared for their ethylene adsorption capacity. The dominant adsorption site of Ag/SSZ-13 is determined as a foundation for the rational design of zeolite adsorbing materials. Ethylene acts as a model hydrocarbon molecule in this work to stand in for vehicle exhaust, because Ag/SSZ-13 is a candidate material for trapping vehicle emissions during cold-start.¹ The three active sites studied are the intended Ag ion exchanged with an H in the zeolite framework, an H site (known as a Brønsted acid site) and Ag₂O which may form as a non-zeolite adsorption site during the Ag ion exchange synthesis. Density functional theory (DFT) calculations are conducted using the BEEF-vdw² functional for up to two ethylene molecules per adsorption site. A microkinetic model parameterized by the DFT predicts the ethylene adsorption capacity for shifting ethylene feed gas concentrations at 100°C. Experimental observations are taken at matching conditions as simulations. The DFT energies and their uncertainties for each adsorption site are updated from experiments using a Bayesian statistical framework. Likewise, an updated ethylene adsorption capacity with uncertainty is obtained for each adsorption site by the Bayesian update. Finally, the adsorption site consisting of the Ag ion of Ag/SSZ-13 is further investigated for water co-adsorption with ethylene. Water is also a feature of vehicle emissions. DFT calculations, experimental observations and a Bayesian update further advance this model for the two-component adsorption.

[1] Lee, J.; Theis, J. R.; Kyriakidou, E. A. Vehicle Emissions Trapping Materials: Successes, Challenges and the Path Forward. Appl. Catal. B- Environ. 2019, 243, 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: Exchange-Correlation Model Development with Bayesian Error Estimation. Phys. Rev. B 2012, 85, 235149.