Jump to main content
Jump to site search
Access to RSC content Close the message box

Continue to access RSC content when you are not at your institution. Follow our step-by-step guide.


Issue 31, 2017
Previous Article Next Article

Computational prediction of high methane storage capacity in V-MOF-74

Author affiliations

Abstract

The methane adsorption properties in M-MOF-74 (M = Mg, Ti, V, Cr, Mn, Co, Ni, Cu, and Zn) were investigated for potential adsorbed natural gas (ANG) vehicle applications. In particular, density functional theory (DFT) simulations were conducted to derive the force field parameters that were used in the grand canonical Monte Carlo (GCMC) simulations to obtain the methane adsorption isotherm curves. Our results indicate that commonly used DFT exchange correlation functionals (e.g. vdW-DF, vdW-DF2, PBE+D2) overestimated the methane binding strength to the metal sites, leading to inaccurate description of the adsorption properties. As such, the global scaling factor within the exchange correlation functional, PBE+D2, was optimized to find a suitable functional that leads to good agreement with the available experimental methane adsorption data. From the newly derived force field parameters, our computational simulations predict a methane uptake of 279 cm3 cm−3 in V-MOF-74 at T = 298 K and P = 65 bar (condition relevant to ANG storage operation), which would be higher than the current record holder of HKUST-1 (270 cm3 cm−3). Although the methane working capacity (65–5.8 bar uptake difference) is low due to strong binding of methane with the V-MOF-74, varying the process conditions (e.g. lower adsorption temperature, higher desorption temperature, lower desorption pressure) can lead to a significantly high methane working capacity, towards the goal of meeting the DOE requirements for ANG technology.

Graphical abstract: Computational prediction of high methane storage capacity in V-MOF-74

Back to tab navigation

Supplementary files

Article information


Submitted
29 May 2017
Accepted
19 Jul 2017
First published
27 Jul 2017

Phys. Chem. Chem. Phys., 2017,19, 21132-21139
Article type
Paper

Computational prediction of high methane storage capacity in V-MOF-74

S. Hyeon, Y. Kim and J. Kim, Phys. Chem. Chem. Phys., 2017, 19, 21132
DOI: 10.1039/C7CP03605A

Social activity

Search articles by author

Spotlight

Advertisements