Pore size distribution analysis of selected hexagonal mesoporous silicas by grand canonical Monte Carlo simulations

Carmelo Herdes, Miguel A. Santos, Francisco Medina, Lourdes F. Vega

Research output: Contribution to journalArticlepeer-review

18 Citations (SciVal)


We combine here a regularization procedure with individual adsorption isotherms obtained from grand canonical Monte Carlo simulations in order to obtain reliable pore size distributions. The methodology is applied to two hexagonal high-ordered silica materials: SBA-15 and PHTS, synthesized in our laboratory. Feasible pore size distributions are calculated through an adaptable procedure of deconvolution over the adsorption integral equation, with two necessary inputs: the experimental adsorption data and individual adsorption isotherms, assuming the validity of the independent pore model. The application of the deconvolution procedure implies an adequate grid size evaluation (i.e., numbers of pores and relative pressures to be considered for the inversion, or kernel size), the fulfillment of the discret Picard condition, and the appropriate choice of the regularization parameter (L-curve criteria). Assuming cylindrical geometry for both porous materials, the same set of individual adsorption isotherms generated from molecular simulations can be used to construct the kernel to obtain the PSD of SBA-15 and PHTS. The PSD robustness is measured imposing random errors over the experimental data. Excellent agreement is found between the calculated and the experimental global adsorption isotherms for both materials. Molecular simulations provide new insights into the studied systems, pointing out the need of high-resolution isotherms to describe the presence of complementary microporosity in these materials.

Original languageEnglish
Pages (from-to)8733-8742
Number of pages10
Issue number19
Publication statusPublished - 13 Sept 2005


Dive into the research topics of 'Pore size distribution analysis of selected hexagonal mesoporous silicas by grand canonical Monte Carlo simulations'. Together they form a unique fingerprint.

Cite this