Nuclear Induction Lineshape: Non-Markovian Diffusion with Boundaries

TL;DR

This paper introduces a computational approach combining molecular dynamics and analytic continuation to accurately model NMR lineshapes in non-Markovian, confined viscoelastic fluids by capturing memory effects through pressure tensor correlations.

Contribution

It presents a novel method linking memory functions with thermal transport parameters to compute NMR lineshapes in confined geometries.

Findings

Successfully computes NMR lineshapes considering boundary effects.

Connects memory functions with thermal transport parameters.

Demonstrates applicability to viscoelastic fluids in confinement.

Abstract

The dynamics of viscoelastic fluids are governed by a memory function, essential yet challenging to compute, especially when diffusion faces boundary restrictions. We propose a computational method that captures memory effects by analyzing the time-correlation function of the pressure tensor, a viscosity indicator, through the Stokes-Einstein equation's analytic continuation into the Laplace domain. We integrate this equation with molecular dynamics (MD) simulations to derive necessary parameters. Our approach computes NMR lineshapes using a generalized diffusion coefficient, accounting for temperature and confinement geometry. This method directly links the memory function with thermal transport parameters, facilitating accurate NMR signal computation for non-Markovian fluids in confined geometries.