Characterizing transport through a crowded environment with different obstacle sizes
Author(s) -
Adam Ellery,
Matthew J. Simpson,
Scott W. McCue,
Ruth E. Baker
Publication year - 2014
Publication title -
the journal of chemical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.071
H-Index - 357
eISSN - 1089-7690
pISSN - 0021-9606
DOI - 10.1063/1.4864000
Subject(s) - mean squared displacement , obstacle , displacement (psychology) , random walk , population , anomalous diffusion , diffusion , statistical physics , fick's laws of diffusion , differential equation , constant (computer programming) , work (physics) , mathematics , mathematical analysis , physics , statistics , computer science , innovation diffusion , thermodynamics , law , psychology , knowledge management , demography , quantum mechanics , sociology , political science , psychotherapist , programming language , molecular dynamics
Transport through crowded environments is often classified as anomalous, rather than classical, Fickian diffusion. Several studies have sought to describe such transport processes using either a continuous time random walk or fractional order differential equation. For both these models the transport is characterized by a parameter α, where α = 1 is associated with Fickian diffusion and α < 1 is associated with anomalous subdiffusion. Here, we simulate a single agent migrating through a crowded environment populated by impenetrable, immobile obstacles and estimate α from mean squared displacement data. We also simulate the transport of a population of such agents through a similar crowded environment and match averaged agent density profiles to the solution of a related fractional order differential equation to obtain an alternative estimate of α. We examine the relationship between our estimate of α and the properties of the obstacle field for both a single agent and a population of agents; we show that in both cases, α decreases as the obstacle density increases, and that the rate of decrease is greater for smaller obstacles. Our work suggests that it may be inappropriate to model transport through a crowded environment using widely reported approaches including power laws to describe the mean squared displacement and fractional order differential equations to represent the averaged agent density profiles
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