NMR spin-rotation relaxation and diffusion of methane

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Abstract

The translational diffusion-coefficient and the spin-rotation contribution to the 1H NMR relaxation rate for methane (CH4) are investigated using MD (molecular dynamics) simulations, over a wide range of densities and temperatures, spanning the liquid, supercritical, and gas phases. The simulated diffusion-coefficients agree well with measurements, without any adjustable parameters in the interpretation of the simulations. A minimization technique is developed to compute the angular velocity for non-rigid spherical molecules, which is used to simulate the autocorrelation function for spin-rotation interactions. With increasing diffusivity, the autocorrelation function shows increasing deviations from the single-exponential decay predicted by the Langevin theory for rigid spheres, and the deviations are quantified using inverse Laplace transforms. The 1H spin-rotation relaxation rate derived from the autocorrelation function using the "kinetic model" agrees well with measurements in the supercritical/gas phase, while the relaxation rate derived using the "diffusion model" agrees well with measurements in the liquid phase. 1H spin-rotation relaxation is shown to dominate over the MD-simulated 1H-1H dipole-dipole relaxation at high diffusivity, while the opposite is found at low diffusivity. At high diffusivity, the simulated spin-rotation correlation time agrees with the kinetic collision time for gases, which is used to derive a new expression for 1H spin-rotation relaxation, without any adjustable parameters.

Original languageEnglish
Article number204504
JournalJournal of Chemical Physics
Volume148
Issue number20
DOIs
StatePublished - May 28 2018
Externally publishedYes

Funding

This work was funded by the Rice University Consortium on Processes in Porous Media and the American Chemical Society Petroleum Research Fund [Grant No. ACS-PRF-58859-ND6]. We gratefully acknowledge the National Energy Research Scientific Computing Center that is supported by the Office of Science of the U.S. Department of Energy [Grant No. DE-AC02-05CH11231] for HPC time and support. We also gratefully acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin (URL: http://www.tacc.utexas.edu) for providing HPC resources and Zeliang Chen for his assistance.

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