Taylor line swimming in microchannels and cubic lattices of obstacles

Münch, Jan L.; Alizadehrad, Davod; Babu, Sujin B.; Stark, Holger

doi:10.1039/c6sm01304j

Cited by 15 publications

(26 citation statements)

References 88 publications

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“…A heterogeneous environment can be realized in different ways, both in experiments and theory, e.g., by regular or irregular patterns of obstacles [20][21][22][23][24][25][26][27][28][29][30][31][32], mazes [33], arrays of funnels [16,[34][35][36][37][38], pinning substrates [39], or patterned light fields, which control the velocity of the microswimmer [40,41]. For a review see [4,42].…”

Section: Introductionmentioning

confidence: 99%

Active Brownian particles moving in a random Lorentz gas

2017

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Abstract. Biological microswimmers often inhabit a porous or crowded environment such as soil. In order to understand how such a complex environment influences their spreading, we numerically study noninteracting active Brownian particles (ABPs) in a two-dimensional random Lorentz gas. Close to the percolation transition in the Lorentz gas, they perform the same subdiffusive motion as ballistic and diffusive particles. However, due to their persistent motion they reach their long-time dynamics faster than passive particles and also show superdiffusive motion at intermediate times. While above the critical obstacle density η c the ABPs are trapped, their long-time diffusion below ηc is strongly influenced by the propulsion speed v 0. With increasing v0, ABPs are stuck at the obstacles for longer times. Thus, for large propulsion speed, the long-time diffusion constant decreases more strongly in a denser obstacle environment than for passive particles. This agrees with the behavior of an effective swimming velocity and persistence time, which we extract from the velocity autocorrelation function.

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Section: Introductionmentioning

confidence: 99%

Active Brownian particles moving in a random Lorentz gas

2017

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“…Large scale cooperative movement is seen in micro-organisms which propagate by virtue of deformations along the cell body. Such swimming strategies are commonly seen in spermatozoa, C. elegans and various flagellated microswimmers [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. Micro-swimmers demonstrate various aggregating patterns such as swarming, clustering or band formation [2][3][4][5][6][7][8].…”

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confidence: 99%

“…In the present study we will analyze a simple system consisting of two types of swimmers which differ only in the swimming velocity. Since for Taylor line the velocity is directly proportional to the beating frequency ν [16], we vary the frequency of actuation in our simulations. The beating frequency of the faster swimmer is given by ν a and that of slower swimmer is given by ν b where we define δν = |ν a −ν b |/ ν .…”

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confidence: 99%

“…Each Taylor line consists of a sequence of N beads each of mass 10m at an equilibrium distance of 1 2 l. These beads interact with the nearest neighbors by Hooke's spring potential and bending potential [16] that keeps the consecutive beads aligned at an angle α i with the bending rigidity κ = pk B T where p is the persistent length. A sinusoidal wave of beating frequency ν is generated along the contour of the Taylor line by varying curvature c(i, t) = α i /l, spontaneously, with time, t and bead position, i.…”

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confidence: 99%

“…In the present study, we consider only hydrodynamic and steric interactions between the swimmers. To model artificial swimmers, we use a two dimensional discretized model of Taylors sheets termed as Taylor Lines [16]. The Taylor Line hydrodynamically interacts with the fluid using a sinusoidal bending wave which moves along the body.…”

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Self-organization in a bimotility mixture of model microswimmers

Agrawal

Babu

2018

Phys. Rev. E

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Cooperation between micro-organisms give rise to novel phenomena like clustering, swarming in suspension. We study the collective behavior of the artificial swimmer called Taylor line at low Reynolds number using multi-particle collision dynamics method. In this paper we have modeled bi-motility mixtures of multiple swimmers in 2 dimension, which differ from each other by the velocity with which they swim. We observe that the swimmers can segregate into slower and faster ones depending on the relative difference in velocity of the 2 type of swimmers. We also observe that contribution of slower swimmers towards clustering, on an average, is much larger compared to faster ones, although we employ a homogeneous mixture. When the difference in velocity is large between the swimmers, the faster ones move away from the slower ones towards the boundary. On the other hand, when the relative difference in velocity is very small, the slower and faster swimmer mix together to form big clusters. At later time even for small difference in velocity the swimmers segregate into fast and slow swimmer clusters.The collective motility of micro-organisms is quintessential in a range of biological activities [1][2][3][4][5][6][7][8][9][10][11]. Large scale cooperative movement is seen in micro-organisms which propagate by virtue of deformations along the cell body. Such swimming strategies are commonly seen in spermatozoa, C. elegans and various flagellated microswimmers [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. Micro-swimmers demonstrate various aggregating patterns such as swarming, clustering or band formation [2][3][4][5][6][7][8]. Aggregation is a consequence of homology in the system and is hydrodynamically favorable as it reduces energy consumption in transport [22]. The real systems comprise of swimmers having a range of different motilities. In addition, a portion of swimmers may be unhealthy or may employ atypical swimming strategies and hence immensely differ in propagating strengths. To study how the cluster formation is favored in case when the species are not perfectly homologous, several simulations have been performed [20][21][22][40][41][42][43][44]. Consequently, a positive feedback between clustering and segregation has been reported [8,40]. Previous works comprising mixture of active and passive self-propelled particles or rods indicate spontaneous segregation in the system [39][40][41][42][43]. These processes occur even in absence of cell to cell signaling or chemotaxis [3, 7, 8, 17, 20, 22, 24, 33-36, 40, 41, 44].In this Letter, we employ numerical simulations to investigate the collective dynamics of microswimmers which show propulsion via planar beating mechanisms in Newtonian fluid. We analyze the cooperation between the swimmers in a bi-motility mixture which results into the aggregation and segregation among their own types. Understanding this cooperation is prerequisite to deeper understanding of collective motion of microswimmers. In the present study, we consider only h...

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Modeling of Spermbots in a Viscous Colloidal Suspension

Khalil¹,

Klingner

Magdanz

et al. 2019

Advcd Theory and Sims

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Spermbots are biohybrid micromachines consisting of single sperm cells captured in artificial magnetic microstructures, and have the potential to act as autonomous tools for minimally invasive medicines and in diverse in vivo applications. This work investigates the hydrodynamic effects of the spermbots in a heterogeneous viscous medium similar to environments encountered in vivo. The propulsion of the spermbots is simulated using a numerical model based on the method of regularized Stokeslets for computing Stokes flows in the presence of immersed obstacles. It is shown that the concentration and size of these obstacles create a pressure gradient along the propulsion axis of the spermbot; hence they influence its effective net motion. In particular, the simulation results herein suggest that the forward and lateral swimming speeds of the spermbot increase with the concentration of the immersed obstacles and decrease with their size.

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Taylor line swimming in microchannels and cubic lattices of obstacles

Cited by 15 publications

References 88 publications

Active Brownian particles moving in a random Lorentz gas

Active Brownian particles moving in a random Lorentz gas

Self-organization in a bimotility mixture of model microswimmers

Modeling of Spermbots in a Viscous Colloidal Suspension

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