Molecular simulations of adsorption and diffusion of RDX in IRMOF-1

Abstract

In order to test the feasibility of using metal-organic frameworks (MOFs) to pre-concentrate explosive molecules for detection, molecular simulations of hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) within IRMOF-1 were performed. Grand canonical Monte Carlo (GCMC) simulations were used to generate adsorption isotherms for pure RDX, RDX in dry air, and RDX in wet air. In addition to the isotherms, the GCMC simulations provide adsorption energies and density distributions of the adsorbates within the MOF. Molecular dynamics simulations calculate diffusivities and provide a detailed understanding of the change in conformation of the RDX molecule upon adsorption. The presence of dry air has little influence on the amount of RDX that adsorbs. The presence of wet air increases the amount of RDX that adsorbs due to favourable interactions between RDX and water. We found a Henry's law constant of 21.2 mol/kg/bar for both pure RDX and RDX in dry air. The RDX adsorption sites are located (i) in big cages, (ii) near a vertex, and (iii) between benzene rings. The energy of adsorption of RDX at infinite dilution was found to be − 9.2 kcal/mol. The distributions of bond lengths, bond angles and torsion angles in RDX are uniformly slightly broader in the gas phase than in the adsorbed phase, but not markedly so. The self-diffusivity of RDX in IRMOF-1 is a strong function of temperature, with an activation energy of 6.0 kcal/mol.

Keywords:

• metal-organic framework
• RDX
• adsorption
• diffusion
• molecular simulation

Acknowledgements

The authors gratefully acknowledge the financial support of National Science Foundation (NSF) under grant CMMI-0730207. Work at Oak Ridge National Laboratory (ORNL) was supported by the Center for Nanophase Materials Sciences, sponsored by the Division of Scientific User Facilities, U.S. Department of Energy (USDOE) and used resources of the National Center for Computational Sciences (NCCS), ORNL, supported by the Office of Science, USDOE, as well as resources of the National Institute for Computational Sciences (NICS), ORNL, supported by NSF with agreement number: OCI 07-11134.