(731c) Optimization of Reaction Networks In Zeolites

Zeolites are useful materials for shape-selective catalysis due to their regular microporous structure. Difficulty with comprehensive experimental study has prompted interest in computational approaches to systematically study the pore structure of zeolites. With nearly 200 known zeolite framework types [1] and millions of hypothetical zeolite-like structures [2,3], a quick screening method is desirable to discover those structures with the potential to promote desired chemical pathways.

We recently developed an automated method for the three-dimensional characterization of zeolite porous networks [4,5,6,7]. The method requires only crystallographic data as input and identifies channels and cages that connect to form the network of pores. The results of our characterization approach are used to calculate quantities of interest, including pore size distribution, accessible volume, surface area, largest cavity and pore limiting diameters, and probability of pore occupancy. To facilitate use of the automated method by the scientific community, we have also developed a web tool and online database of structure characterizations, ZEOMICS, which is freely available at: http://helios.princeton.edu/zeomics/

In this presentation, we introduce a novel computational approach for the modeling of reaction networks in zeolites. Large reaction networks are particularly important for the conversion of biomass and can be readily generated as input [8]. Typical biomass reactions of interest include deoxygenation of biofuels, conversion of furfural to its derivatives, and dehydration of carbohydrates. Starting from a prespecified network of reactants, products, and intermediates, transition state structures are identified for each reaction step through quantum calculations [9]. We then consider the relative mobility of all species within a zeolite to determine plausible pathways within the reaction network. The contribution of each pathway to the overall reaction yield is weighted by energetic and occupancy factors. We use our approach to screen databases of structures and identify those that exhibit the highest favorability toward generation of desired products.

References:

[1] Baerlocher, C., McCusker, L.B., 2010. Database of zeolite structures: (http://www.iza-structure.org/databases/).

[2] Deem, M.W., Pophale, R., Cheeseman, P.A., Earl, D.J., 2009. Computational discovery of new zeolite-like materials. Journal of Physical Chemistry C 113, 21353-21360.

[3] Pophale, R., Cheeseman, P. A., and Deem, M. W., 2011. A database of new zeolite-like materials. Physical Chemistry Chemical Physics 13, 12407-12412.

[4] Gounaris, C.E., Floudas, C.A., Wei, J., 2006. Rational design of shape selective separation and catalysis--I: Concepts and analysis. Chemical Engineering Science 61, 7933-7948.

[5] Gounaris, C.E., Wei, J., Floudas, C.A., 2006. Rational design of shape selective separation and catalysis--II: Mathematical model and computational studies. Chemical Engineering Science 61, 7949-7962.

[6] Gounaris, C.E., Wei, J., Floudas, C.A., Ranjan, R., Tsapatsis, M., 2009. Rational design of shape selective separations and catalysis: Lattice relaxation and effective aperture size. AIChE Journal 56, 611-632.

[7] First, E. L., Gounaris, C. E., Wei, J., and Floudas, C. A., 2011. Computational characterization of zeolite porous networks: an automated approach. Physical Chemistry Chemical Physics 13, 17339-17358.

[8] Rangarajan, S., Bhan, A., and Daoutidis, P., 2010. Rule-Based Generation of Thermochemical Routes to Biomass Conversion. Industrial & Engineering Chemistry Research 49, 10459-10470.

[9] Frisch, M.J. et al., Gaussian 09, Revision A.02, Gaussian, Inc., Wallingford CT, 2009.