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      Self-Diffusiophoresis in the Advection Dominated Regime

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          Abstract

          In both biological and artificial systems, concentration gradients can serve as a convenient mechanism for manipulating particles and generating motility. Particles that interact with a solute will move along its gradient; if they themselves generate the gradient, this mechanism provides a means of self-propulsion. We consider a version of this type of motility appropriate to certain biological systems where polymeric filaments provide the concentration gradient. As the filament diffusion is small, this corresponds to a regime of large P\'eclet number where the motion is dominated by the effects of fluid advection. The nature of such concentration-gradient-driven motion in the advective regime differs in certain fundamental respects from the same process at low P\'eclet number. In particular, we show that out of four broad scenarios of steady state motion at low P\'eclet number, only two remain viable in the strongly advecting limit.

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          Colloid Transport by Interfacial Forces

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            Self-motile colloidal particles: from directed propulsion to random walk

            The motion of an artificial micro-scale swimmer that uses a chemical reaction catalyzed on its own surface to achieve autonomous propulsion is fully characterized experimentally. It is shown that at short times, it has a substantial component of directed motion, with a velocity that depends on the concentration of fuel molecules. At longer times, the motion reverts to a random walk with a substantially enhanced diffusion coefficient. Our results suggest strategies for designing artificial chemotactic systems.
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              On the squirming motion of nearly spherical deformable bodies through liquids at very small reynolds numbers

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                Author and article information

                Journal
                19 July 2011
                Article
                1107.3851
                e9554750-e2ae-4159-ae0d-81c15b4baa71

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                4 pages, 2 figures
                cond-mat.soft cond-mat.stat-mech

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