On the relationship between the structure of metal–organic frameworks and the adsorption and diffusion of hydrogen

Abstract

In this work, the adsorptive and diffusive behaviours of molecular hydrogen in 10 different isoreticular metal–organic frameworks (IRMOFs) are studied using molecular-level simulation. Hydrogen adsorption isotherms and heats of adsorption at 77 and 300 K were generated for 10 MOFs at low-pressure conditions (up to 10 bar) using Path Integral Grand Canonical Monte Carlo simulations. Self-diffusivities and activation energies for diffusion were generated using molecular dynamics simulation. Density distributions showing the location and the shape of the adsorption sites are also provided. Statistical correlations for all of the properties as a function of surface area (SA), accessible volume (AV) and binding energy are provided. Based on this work, we observe that at pressures up to 10 bar at 300 K, the adsorption process is virtually completely governed by entropic considerations, resulting in a strong correlation between the amount of hydrogen adsorbed and the AV of the adsorbent. At 77 K, we observe more than one adsorption regime. At low pressures, the adsorption process is governed by energetic considerations, resulting in a strong correlation between the amount of hydrogen adsorbed and the energy of adsorption. At the high end of the pressure range, the adsorption becomes a process dominated by entropic considerations, again resulting in a strong correlation between the amount of hydrogen adsorbed and the AV. Only in the intermediate regime does one observe that an increase in SA results in an increase in the amount of hydrogen adsorbed. The self-diffusivity of hydrogen at infinite dilution is highly correlated with both the energy of adsorption and the AV. The diffusion in larger IRMOFs is faster because of an entropic advantage and specifically not because of a lower activation energy for diffusion.

Keywords:

• hydrogen
• adsorption
• diffusion
• metal–organic framework
• molecular simulation

Acknowledgements

This research was supported by the Sustainable Energy and Education Research Center at the University of Tennessee, by a grant from the National Science Foundation (DGE-0801470) and by the US Department of Energy, Office of Basic Energy Sciences, Division of Materials Sciences and Engineering. This research project used resources of the National Institute for Computational Sciences (NICS) supported by NSF under agreement number: OCI 07-11134.5.