Molecular dynamics simulations of propane in slit shaped silica nano-pores: direct comparison with quasielastic neutron scattering experiments

Abstract

Molecular motion under confinement has important implications for a variety of applications including gas recovery and catalysis. Propane confined in mesoporous silica aerogel as studied using quasielastic neutron scattering (QENS) showed anomalous pressure dependence in its diffusion coefficient (J. Phys. Chem. C, 2015, 119, 18188). Molecular dynamics (MD) simulations are often employed to complement the information obtained from QENS experiments. Here, we report an MD simulation study to probe the anomalous pressure dependence of propane diffusion in silica aerogel. Comparison is attempted based on the self-diffusion coefficients and on the time scales of the decay of the simulated intermediate scattering functions. While the self-diffusion coefficients obtained from the simulated mean squared displacement profiles do not exhibit the anomalous pressure dependence observed in the experiments, the time scales of the decay of the intermediate scattering functions calculated from the simulation data match the corresponding quantities obtained in the QENS experiment and thus confirm the anomalous pressure dependence of the diffusion coefficient. The origin of the anomaly in pressure dependence lies in the presence of an adsorbed layer of propane molecules that seems to dominate the confined propane dynamics at low pressure, thereby lowering the diffusion coefficient. Further, time scales for rotational motion obtained from the simulations explain the absence of rotational contribution to the QENS spectra in the experiments. In particular, the rotational motion of the simulated propane molecules is found to exhibit large angular jumps at lower pressure. The present MD simulation work thus reveals important new insights into the origin of anomalous pressure dependence of propane diffusivity in silica mesopores and supplements the information obtained experimentally by QENS data.