Pore structure and connectivity determine how microstructured materials perform in applications such as catalysis, fluid storage and transport, filtering, or as reactors. We report a model study on silica aerogel using a recently introduced time-of-flight (TOF) magnetic resonance imaging technique to characterize the flow field and elucidate the effects of heterogeneities in the pore structure on gas flow and dispersion with Xe-129 as the gas-phase sensor. The observed chemical shift allows the separate visualization of unrestricted xenon and xenon confined in the pores of the aerogel. The asymmetrical nature of the dispersion pattern alludes to the existence of a stationary and a flow regime in the aerogel. An exchange time constant is determined to characterize the gas transfer between them. As a general methodology, this technique provides new insights into the dynamics of flow in porous media where multiple phases or chemical species may be present.

We present a method to measure self-diffusion across membranes without the need for concentration or pressure gradients. Hyperpolarized xenon in combination with remote detection of NMR allows the measurement of membrane permeation, even in the gas phase. The resulting images allow quantification of the amount of fluid diffused through the membrane, and represent an alternative, potentially more precise way of measuring a membrane diffusion coefficient. The use of remote detection of NMR allows for non-invasive signal encoding coupled to sensitive detection, making this approach ideal for the study of diffusion in intact devices such as fuel cells or separation systems.

A time-of-flight imaging technique is introduced to visualize fluid flow and dispersion through porous media using NMR. As the fluid flows through a sample, the nuclear spin magnetization is modulated by RF pulses and magnetic field gradients to encode the spatial coordinates of the fluid. When the fluid leaves the sample, its magnetization is recorded by a second RF coil. This scheme not only facilitates a time-dependent imaging of fluid flow, it also allows a separate optimization of encoding and detection subsystems to enhance overall sensitivity. The technique is demonstrated by imaging gas flow through a porous rock.