Multivariable linear models of structural parameters to predict methane uptake in metal-organic frameworks

Abstract

A key challenge preventing CH4-driven energy future is the lack of effective, economic and safe on-board CH4 storage systems. In this study, computational tools were utilized to examine CH4 storage capacity of metal-organic frameworks (MOFs) under practical operating conditions. Grand Canonical Monte Carlo (GCMC) simulations were performed to calculate CH4 uptake capacity of 45 MOFs. Results were confirmed with experimental data available in the literature. Motivated from the good agreement between experiments and simulations, a quantitative structure-property relationship (QSPR) analysis was performed. Making use of this analysis, multivariable linear models with one-, two-, three-, and four-variables that can accurately predict CH4 uptake of MOFs at room temperature and pressures ranging from 1 to 65 bar were developed. Model parameters were based on easily measurable/computable structural properties, such as pore volume, surface area, and density. Models that predict CH4 uptake at 5 and 35 bar were studied in detail to investigate the viability of reaching CH4 storage target for vehicular systems set by DOE. At both pressures the models with four variables outperformed other models at the same pressures, and void fraction (V-f) and isostetic heat of adsorption (Q(st)) were found to be the most significant parameters. In order for a material to exceed the DOE target of 0.5 g/g CH4 uptake at a storage pressure of 35 bar, the material should have high gravimetric surface area (S-g), reaching 6000 m(2)/g, void fraction (V-f), as high as 0.9, dominant pore diameter (DPD) approximately 30 angstrom, and Q(st) value around 30 KJ/mol. Results of this work reveal the structural properties controlling the CH4 storage capacity of MOFs. Such information can guide experimental studies to tune the MOFs in the way of designing new materials with desirable structural properties that will reach the storage targets. (C) 2014 Elsevier Ltd. All rights reserved.