Time-domain methods for diffusive transport in soft matter

John Fricks, Lingxing Yao, Timothy C. Elston, M. Gregory Forest

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

Passive microrheology [T. G. Mason and D. A. Weitz, Phys. Rev. Lett., 74 (1995), pp. 1250-1253] utilizes measurements of noisy, entropic fluctuations (i.e., diffusive properties) of micron-scale spheres in soft matter to infer bulk frequency-dependent loss and storage moduli. Here, we are concerned exclusively with diffusion of Brownian particles in viscoelastic media, for which the Mason-Weitz theoretical-experimental protocol is ideal and the more challenging inference of bulk viscoelastic moduli is decoupled. The diffusive theory begins with a generalized Langevin equation (GLE) with a memory drag law specified by a kernel. We start with a discrete formulation of the GLE as an autoregressive stochastic process governing microbead paths measured by particle tracking. For the inverse problem (recovery of the memory kernel from experimental data) we apply time series analysis (maximum likelihood estimators via the Kalman filter) directly to bead position data, an alternative to formulas based on mean-squared-displacement statistics in frequency space. For direct modeling, we present statistically exact GLE algorithms for individual particle paths as well as statistical correlations for displacement and velocity. Our time-domain methods rest upon a generalization of well-known results for a single-mode exponential kernel to an arbitrary M-mode exponential series, for which the GLE is transformed to a vector Ornstein-Uhlenbeck process.

Original languageEnglish (US)
Pages (from-to)1277-1308
Number of pages32
JournalSIAM Journal on Applied Mathematics
Volume69
Issue number5
DOIs
StatePublished - 2009
Externally publishedYes

Keywords

  • Anomalous diffusion
  • Generalized Langevin equation
  • Kalman filter
  • Maximum likelihood
  • Microrheology
  • Time series analysis

ASJC Scopus subject areas

  • Applied Mathematics

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