Feedback-induced oscillations in one-dimensional colloidal transport

Lichtner, Ken; Pototsky, Andrey; Klapp, Sabine H. L.

doi:10.1103/physreve.86.051405

Cited by 17 publications

(19 citation statements)

References 48 publications

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“…Noise in Brownian systems has been found in theory to induce anomalous diffusion [42] and stochastic resonance [43][44][45], and rocking-ratchet like potentials have been used in optical and magnetic systems [46,47]. The possibility of resonance has also been explored in systems with feedback [48] and random pinning potentials [49]. Recent work studied the transport properties of a system of magnetically driven colloidal particles [50].…”

Section: Introductionmentioning

confidence: 99%

Dynamic mode locking in a driven colloidal system: experiments and theory

et al. 2017

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In this article we examine the dynamics of a colloidal particle driven by a modulated force over a sinusoidal optical potential energy landscape. Coupling between the competing frequencies of the modulated drive and that of particle motion over the periodic landscape leads to synchronisation of particle motion into discrete modes. This synchronisation manifests as steps in the average particle velocity, with mode locked steps covering a range of average driving velocities. The amplitude and frequency dependence of the steps are considered, and compared to results from analytic theory, Langevin dynamics simulations, and dynamic density functional theory. Furthermore, the critical driving velocity is studied, and simulation used to extend the range of conditions accessible in experiments alone. Finally, state diagrams from experiment, simulation, and theory are used to show the extent of the dynamically locked modes in two dimensions, as a function of both the amplitude and frequency of the modulated drive.

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Section: Introductionmentioning

confidence: 99%

Dynamic mode locking in a driven colloidal system: experiments and theory

et al. 2017

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“…In free diffusion, their mean-squared displacement ∆x 2 (t) increases linearly with time t; ∆x 2 (t) ∝ t µ with µ = 1. Particle-external potential (as well as particle-particle) interactions can modify the dynamics significantly leading to µ = 1 [14,[36][37][38][39][40][41][42]. Often the dynamics slow down; on an intermediate time scale subdiffusion (µ < 1) is observed, while at long times diffusion is reestablished with a reduced (long-time) diffusion coefficient D ∞ .…”

Section: Introductionmentioning

confidence: 99%

Colloids in light fields: Particle dynamics in random and periodic energy landscapes

Evers

Hanes

Zunke

et al. 2013

Eur. Phys. J. Spec. Top.

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The dynamics of colloidal particles in potential energy landscapes have mainly been investigated theoretically. In contrast, here we discuss the experimental realization of potential energy landscapes with the help of light fields and the observation of the particle dynamics by video microscopy.The experimentally observed dynamics in periodic and random potentials are compared to simulation and theoretical results in terms of, e.g. the mean-squared displacement, the time-dependent diffusion coefficient or the non-Gaussian parameter. The dynamics are initially diffusive followed by intermediate subdiffusive behaviour which again becomes diffusive at long times. How pronounced and extended the different regimes are, depends on the specific conditions, in particular the shape of the potential as well as its roughness or amplitude but also the particle concentration. Here we focus on dilute systems, but the dynamics of interacting systems in external potentials, and thus the interplay between particle-particle and particle-potential interactions, is also mentioned briefly. Furthermore, the observed dynamics of dilute systems resemble the dynamics of concentrated systems close to their glass transition, with which it is compared. The effect of certain potential energy landscapes on the dynamics of individual particles appears similar to the effect of interparticle interactions in the absence of an external potential.

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“…Reynolds numbers to control the chaotic Taylor-Couette flow [21] and most recently in colloidal systems [22][23][24] and in liquid crystals [25]. Most often, in extended systems a local feedback scheme is used, which acts on each variable.…”

Section: Introductionmentioning

confidence: 99%

Feedback control of flow vorticity at low Reynolds numbers

2015

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Abstract. Our aim is to explore strategies of feedback control to design and stabilize novel dynamic flow patterns in model systems of complex fluids. To introduce the control strategies, we investigate the simple Newtonian fluid at low Reynolds number in a circular geometry. Then, the fluid vorticity satisfies a diffusion equation. We determine the mean vorticity in the sensing area and use two control strategies to feed it back into the system by controlling the angular velocity of the circular boundary. Hysteretic feedback control generates self-regulated stable oscillations in time, the frequency of which can be adjusted over several orders of magnitude by tuning the relevant feedback parameters. Time-delayed feedback control initiates unstable vorticity modes for sufficiently large feedback strength. For increasing delay time, we first observe oscillations with beats and then regular trains of narrow pulses. Close to the transition line between the resting fluid and the unstable modes, these patterns are relatively stable over long times.

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Feedback-induced oscillations in one-dimensional colloidal transport

Cited by 17 publications

References 48 publications

Dynamic mode locking in a driven colloidal system: experiments and theory

Dynamic mode locking in a driven colloidal system: experiments and theory

Colloids in light fields: Particle dynamics in random and periodic energy landscapes

Feedback control of flow vorticity at low Reynolds numbers

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