Self-organization in a bimotility mixture of model microswimmers

Abstract

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...

Methods for suspensions of passive and active filaments

“…with a similar waveform. Clustering is also observed in planar monodisperse suspensions of model sperm cells (Schoeller & Keaveny, 2018;Yang et al, 2008) and slender undulatory swimmers (Yang et al, 2010), as well as in planar bidisperse suspensions (Agrawal & Babu, 2018) of undulatory swimmers. We first observe swimmers from p = 1 forming clusters with each other, while the slower swimmers from p = 2 remain dispersed in the surrounding fluid.…”

“…The suspension consists of two populations (indexed by p = 1 and p = 2) that have different swimming gaits. Similar simulations (Agrawal & Babu, 2018) have been performed using multiparticle collision dynamics for two swimmer populations with different frequencies, but the same waveform. All swimmers have length L and are formed of N = 30 segments.…”

Methods for suspensions of passive and active filaments

“…The suspension consists of two populations (indexed by p = 1 and p = 2) that have different swimming gaits. Similar simulations (Agrawal & Babu, 2018) have been performed using multiparticle collision dynamics for two swimmer populations with different frequencies, but the same waveform.…”

The narrow escape problem of a chiral active particle (CAP): an optimal scheme

“…Henceforth, the study of active systems confined within enclosed spaces has garnered substantial attention from various groups since the past few decades. 10–20 Furthermore, various research groups 21–23 have explored geometrical confinement as a means to segregate a mixture of active particles with different activities.…”