(250a) Modeling Adsorption And Desorption In Ordered Mesoporous Materials | AIChE

(250a) Modeling Adsorption And Desorption In Ordered Mesoporous Materials

Authors 

Libby, B. - Presenter, Univ. of Massachusetts
Monson, P. A. - Presenter, Univ. of Massachusetts


The field of porous materials research has been revolutionized by the synthesis of more ordered mesoporous silica materials via templated self-assembly processes[1]. Materials such as MCM-41, MCM-48, SBA-16 etc. exhibit a relatively high degree of geometrical order on the mesopore length scale and are thus excellent test beds for theories of adsorption, particularly those based on molecular concepts. Many studies of these systems have been made using density functional theory [2] and Monte Carlo simulations [3] for molecular models with geometries representative of the real materials. Such studies reveal important insights into the molecular level behavior and provide a basis for new methods for isotherm analysis and characterization. However, it could be argued that none of the calculations made thus far provides truly quantitative agreement with experiment over the full range of the adsorption and desorption isotherms - including any hysteresis present. In this work we explore some of the issues that underlie this and describe how much improved agreement with experiment can be obtained from Monte Carlo simulations of molecular models. We focus on the cases of MCM-41, based on the cylindrical pore geometry, and MCM-48 and SBA-16, based on minimal surface pore geometries.

While these materials display a substantial degree of geometric order, we have found that the inclusion of some disorder into the models is necessary to reproduce the behavior seen in experiments. In our work we do this by including: i) variations in pore size across the sample through averaging over a pore size distribution; ii) surface roughness in the pore walls; iii) constrictions within the pores. We describe an accurate method for interpolating adsorption and desorption isotherms, which makes it possible to efficiently average Monte Carlo simulation results over pore sizes sampled from a distribution.

1. Beck, J.S., et al., J. Am. Chem. Soc., 1992, 114, 10834; Zhao, D.Y., et al., J. Am. Chem. Soc., 1998. 120, 6024. 2. Ravikovitch, P.I., A. Vishnyakov, and A.V. Neimark, Phys. Rev. E, 2001, 64, 011602. 3. Schumacher, C., et al., J. Phys. Chem. B, 2006, 110, 319; Coasne, B., et al., Langmuir, 2006. 22, 194.