Fluid heterogeneity detection based on the asymptotic distribution of the time-averaged mean squared displacement in single particle tracking experiments

TL;DR

This paper develops statistical tests based on the asymptotic distribution of the time-averaged mean squared displacement to detect heterogeneity in viscoelastic fluids from single particle tracking data, applicable to various fractional Gaussian processes.

Contribution

It introduces novel asymptotic distribution-based methods for detecting physical heterogeneity in anomalously diffusive particle paths, covering a broad class of fractional Gaussian processes.

Findings

Methods are valid for 0 < α < 3/2.

Successfully applied to experimental biofilm data.

Provides a statistical framework for heterogeneity detection in complex fluids.

Abstract

A tracer particle is called anomalously diffusive if its mean squared displacement grows approximately as $\sigma^2 t^{\alpha}$ as a function of time $t$ for some constant $\sigma^2$, where the diffusion exponent satisfies $\alpha \neq 1$. In this article, we use recent results on the asymptotic distribution of the time-averaged mean squared displacement (Didier and Zhang (2017)) to construct statistical tests for detecting physical heterogeneity in viscoelastic fluid samples starting from one or multiple observed anomalously diffusive paths. The methods are asymptotically valid for the range $0 < \alpha < 3/2$ and involve a mathematical characterization of time-averaged mean squared displacement bias and the effect of correlated disturbance errors. The assumptions on particle motion cover a broad family of fractional Gaussian processes, including fractional Brownian motion and many fractional instances of the generalized Langevin equation framework. We apply the proposed methods in experimental data from treated $P.\ aeruginosa$ biofilms generated by the collaboration of the Hill and Schoenfisch Labs at UNC-Chapel Hill.