NMR spin-rotation relaxation and diffusion of methane

Area of Science:

• Physical Chemistry
• Computational Chemistry
• Chemical Physics

Background:

• Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful tool for probing molecular dynamics.
• Understanding relaxation mechanisms, such as spin-rotation and dipole-dipole interactions, is crucial for interpreting NMR data.
• Methane (CH4) serves as a fundamental model system for studying molecular behavior in various phases.

Purpose of the Study:

• To investigate the translational diffusion coefficient and spin-rotation contribution to 1H NMR relaxation rates in methane.
• To explore these properties across liquid, supercritical, and gas phases using molecular dynamics (MD) simulations.
• To develop and validate new methods for calculating molecular properties from simulation data.

Main Methods:

• Utilized molecular dynamics (MD) simulations to model methane behavior over a wide range of densities and temperatures.
• Developed a minimization technique to compute angular velocity for non-rigid spherical molecules.
• Simulated autocorrelation functions for spin-rotation interactions and analyzed deviations from theoretical models.
• Quantified deviations from single-exponential decay using inverse Laplace transforms.

Main Results:

• Simulated diffusion coefficients for methane show excellent agreement with experimental measurements.
• The spin-rotation relaxation rate derived using the kinetic model matches experimental data in supercritical/gas phases.
• The diffusion model accurately predicts spin-rotation relaxation rates in the liquid phase.
• Spin-rotation relaxation dominates over dipole-dipole relaxation at high methane diffusivity.

Conclusions:

• MD simulations provide accurate predictions of methane's translational diffusion and NMR relaxation properties.
• The study highlights the importance of phase and diffusivity in determining dominant NMR relaxation mechanisms.
• A new, parameter-free expression for 1H spin-rotation relaxation was derived, enhancing predictive capabilities.