Spontaneous segregation of self-propelled particles with different motilities

McCandlish, Samuel R.; Baskaran, Aparna; Hagan, Michael F.

doi:10.1039/c2sm06960a

Cited by 189 publications

(220 citation statements)

References 71 publications

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“…We observe that due to the small noise, the particles tend to form large clusters with velocity magnitude , except at domain edges where the local density takes smaller values. There is also some evidence of a phase separation between "slow" and "fast" SPP, in analogy with the results found in [34] for a mixture of SPP with different speeds. Unlike the domains found in the shear simulations, though, the slow and fast clusters later on merge into a giant cluster of slow particles and some isolated clusters of faster 0 .…”

Section: Density-dependent Movementsupporting

confidence: 65%

Equilibrium and dynamical behavior in the Vicsek model for self-propelled particles under shear

et al. 2012

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Abstract:The effects of an externally imposed linear shear profile in the Vicsek model of self-propelled particles is investigated via computer simulations. We find that the applied field changes in a relevant way both the equilibrium and dynamical properties of the original model. Indeed, short time dynamics analysis shows that the order-disordered phase transition disappears under shear, because the flow acts as a symmetry breaking field. Moreover, the coarsening of particle domains is arrested at a characteristic length-scale inversely proportional to shear rate. A generalization of the original Vicsek model where the velocity of particles depends on the local value of the density is also introduced and shows to affect the domain formation.

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Section: Density-dependent Movementsupporting

confidence: 65%

Equilibrium and dynamical behavior in the Vicsek model for self-propelled particles under shear

et al. 2012

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“…Here the passive particles would play the role of the solvent and optical tweezers [30], "electric bottles" [32] or suitable microfluidic devices [33] could be used for selective confinement. Suspensions of active colloids are currently attracting much attention for their novel phase behavior [34][35][36][37][38][39][40][41], and active-passive mixtures should show even richer physics [42]. Osmotic effects are very likely to play a key role in the behavior of such systems.…”

Section: Discussionmentioning

confidence: 99%

Osmosis with active solutes

Lion¹,

Allen²

2014

EPL

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Despite much current interest in active matter, little is known about osmosis in active systems. Using molecular dynamics simulations, we investigate how active solutes perturb osmotic steady states. We find that solute activity increases the osmotic pressure, and can also expel solvent from the solution -i.e. cause reverse osmosis. The latter effect cannot be described by an effective temperature, but can be reproduced by mapping the active solution onto a passive one with the same degree of local structuring as the passive solvent component. Our results provide a basic framework for understanding active osmosis, and suggest that activity-induced structuring of the passive component may play a key role in the physics of active-passive mixtures.

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“…On the other hand, models of self-propelled particle systems and active fluids suggest that behavioral parameters of individuals are often critical to collective dynamics [11,15,16,18]. For example, Chaté et al studied the role of noise in active nematic systems and found that correlation length diverges (i.e., quasi-long-range order emerges) below a critical noise strength [89]; McCandlish et al and Hinz et al modeled mixtures of self-propelled particles with different motilities, and found that such systems displayed phase separation [90] and could give rise to dynamical phases [91]; Nagai et al found that selfpropelled particles with memory of past trajectories (i.e., with underdamped angular dynamics) displayed a rich variety of phases not observed in systems without memory, such as vortex lattices and active foam [92]. To understand the connections between individual cell's behavior and the emergent collective behavior, and to verify model predictions in experimental systems of bacterial collective motion, it is desirable to control the behavior of individual cells as well as their physicochemical environment.…”

Section: Controlling Bacterial Collective Motion In Two Dimensions: Amentioning

confidence: 99%

Collective motion of bacteria in two dimensions

2016

Quant. Biol.

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Collective motion can be observed in biological systems over a wide range of length scales, from large animals to bacteria. Collective motion is thought to confer an advantage for defense and adaptation. A central question in the study of biological collective motion is how the traits of individuals give rise to the emergent behavior at population level. This question is relevant to the dynamics of general self-propelled particle systems, biological self-organization, and active fluids. Bacteria provide a tractable system to address this question, because bacteria are simple and their behavior is relatively easy to control. In this mini review we will focus on a special form of bacterial collective motion, i.e., bacterial swarming in two dimensions. We will introduce some organization principles known in bacterial swarming and discuss potential means of controlling its dynamics. The simplicity and controllability of 2D bacterial behavior during swarming would allow experimental examination of theory predictions on general collective motion.Keywords: bacterial swarming; biofilm; flagellar motility; gliding motility; biological self-organization; emergent behavior INTRODUCTION Collective motion is the coordinated self-organized movement of many individuals arising from physical, chemical, or social interactions between these individuals [1]. Collective motion is ubiquitous in nature. Familiar examples include fish schooling [2], bird flocking [3,4], and insect swarming [5]. At microscale, animal cells and microorganisms also display collective motion, such as cancer invasion and tissue development [6], phytoplankton (algae) blooms in surface waters [7], amoebae aggregation under starvation [8], and bacterial swarming during the formation of biofilms and fruiting bodies [9,10]. Collective motion is thought to confer an advantage for defending predators [2,3] and for adapting to the environment [9,10].Despite the vast differences in length scale and propulsion mechanism, collective motion across biological systems shares a similar feature: global order arises spontaneously from local interactions between individuals that do not have access to global information. Intriguingly, the traits of individuals determine the emergent global order in a non-intuitive manner; change of individuals' traits may lead to dramatically different collective behavior. Therefore, a central aim in the study of biological collective motion is to reveal its organization principles, i.e., how the traits of individuals give rise to the emergent behavior at population level. This question is not only important to biology, but also of great interest to other disciplines, such as physics, engineering, and computer science. For example, biological collective motion provides insights for understanding the physics of self-organization in non-equilibrium systems [1]; organization principles revealed in biological collective motion have inspired the design of aerial robots that can perform autonomous group flights [11], the design of swarming robots ...

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Spontaneous segregation of self-propelled particles with different motilities

Cited by 189 publications

References 71 publications

Equilibrium and dynamical behavior in the Vicsek model for self-propelled particles under shear

Equilibrium and dynamical behavior in the Vicsek model for self-propelled particles under shear

Osmosis with active solutes

Collective motion of bacteria in two dimensions

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