title: Lévy fluctuations and mixing in dilute suspensions of algae and bacteria.
abstract: Swimming micro-organisms rely on effective mixing strategies to achieve efficient nutrient influx. Recent experiments, probing the mixing capability of unicellular biflagellates, revealed that passive tracer particles exhibit anomalous non-Gaussian diffusion when immersed in a dilute suspension of self-motile Chlamydomonas reinhardtii algae. Qualitatively, this observation can be explained by the fact that the algae induce a fluid flow that may occasionally accelerate the colloidal tracers to relatively large velocities. A satisfactory quantitative theory of enhanced mixing in dilute active suspensions, however, is lacking at present. In particular, it is unclear how non-Gaussian signatures in the tracers' position distribution are linked to the self-propulsion mechanism of a micro-organism. Here, we develop a systematic theoretical description of anomalous tracer diffusion in active suspensions, based on a simplified tracer-swimmer interaction model that captures the typical distance scaling of a microswimmer's flow field. We show that the experimentally observed non-Gaussian tails are generic and arise owing to a combination of truncated Lévy statistics for the velocity field and algebraically decaying time correlations in the fluid. Our analytical considerations are illustrated through extensive simulations, implemented on graphics processing units to achieve the large sample sizes required for analysing the tails of the tracer distributions.
authors.affiliation: Rudolf Peierls Centre for Theoretical Physics, University of Oxford, 1 Keble Road, Oxford OX1 3NP, UK. irwin.zaid@physics.ox.ac.uk
authors.forename: Irwin M
authors.initials: IM
authors.lastname: Zaid
authors.forename: Jörn
authors.initials: J
authors.lastname: Dunkel
authors.forename: Julia M
authors.initials: JM
authors.lastname: Yeomans
mesh_terms: D018407:Bacterial Physiological Phenomena; D000460:Chlorophyta / Q000502:physiology*; D003198:Computer Simulation; D008954:Models, Biological; D013269:Stochastic Processes