As above, so below, and also in between: mesoscale active matter in fluids

Soft Matter ◽  
2019 ◽  
Vol 15 (44) ◽  
pp. 8946-8950 ◽  
Author(s):  
Daphne Klotsa
Keyword(s):  
Collective Behavior ◽  
Active Matter

What is the collective behavior of mesoscopic (size ≈ 0.5 mm–50 cm) organisms/robots that swim or fly, such as plankton and small insects? In this perspective article we discuss the importance of studying active matter in fluids with finite inertia.

scholarly journals Oscillating collective motion of active rotors in confinement

2020 ◽  
Vol 117 (22) ◽  
pp. 11901-11907 ◽  
Author(s):  
Peng Liu ◽  
Hongwei Zhu ◽  
Ying Zeng ◽  
Guangle Du ◽  
Luhui Ning ◽  
...  
Keyword(s):  
Collective Behavior ◽  
Collective Motion ◽  
Dynamic Structure ◽  
Packing Fraction ◽  
Frictional Stress ◽  
Active Matter ◽  
Active Particles ◽  
Particle Level ◽  
Inhomogeneous Density ◽  
Oscillation Properties

Due to its inherent out-of-equilibrium nature, active matter in confinement may exhibit collective behavior absent in unconfined systems. Extensive studies have indicated that hydrodynamic or steric interactions between active particles and boundary play an important role in the emergence of collective behavior. However, besides introducing external couplings at the single-particle level, the confinement also induces an inhomogeneous density distribution due to particle-position correlations, whose effect on collective behavior remains unclear. Here, we investigate this effect in a minimal chiral active matter composed of self-spinning rotors through simulation, experiment, and theory. We find that the density inhomogeneity leads to a position-dependent frictional stress that results from interrotor friction and couples the spin to the translation of the particles, which can then drive a striking spatially oscillating collective motion of the chiral active matter along the confinement boundary. Moreover, depending on the oscillation properties, the collective behavior has three different modes as the packing fraction varies. The structural origins of the transitions between the different modes are well identified by the percolation of solid-like regions or the occurrence of defect-induced particle rearrangement. Our results thus show that the confinement-induced inhomogeneity, dynamic structure, and compressibility have significant influences on collective behavior of active matter and should be properly taken into account.

scholarly journals Synthetic Chemotaxis and Collective Behavior in Active Matter

2018 ◽  
Vol 51 (12) ◽  
pp. 2982-2990 ◽  
Author(s):  
Benno Liebchen ◽  
Hartmut Löwen
Keyword(s):  
Collective Behavior ◽  
Active Matter

scholarly journals Collective forces in scalar active matter

Soft Matter ◽  
2020 ◽  
Vol 16 (11) ◽  
pp. 2652-2663 ◽  
Author(s):  
Thomas Speck
Keyword(s):  
Collective Behavior ◽  
Large Scale ◽  
Particle Interactions ◽  
Active Matter ◽  
Microscopic Particle ◽  
Active Particles

Large-scale collective behavior in suspensions of active particles can be understood from the balance of statistical forces emerging beyond the direct microscopic particle interactions.

scholarly journals Entropy production fluctuations encode collective behavior in active matter

2021 ◽  
Vol 103 (1) ◽  
Author(s):  
Trevor GrandPre ◽  
Katherine Klymko ◽  
Kranthi K. Mandadapu ◽  
David T. Limmer
Keyword(s):  
Entropy Production ◽  
Collective Behavior ◽  
Active Matter

Active Matter of Malt, or Maltin and Diastase

1879 ◽  
Vol 8 (208supp) ◽  
pp. 3313-3313
Author(s):  
M. Dubrunfaut
Keyword(s):  
Active Matter

Voronoi Analysis of a Soccer Game

2004 ◽  
Vol 9 (3) ◽  
pp. 233-240 ◽  
Author(s):  
S. Kim
Keyword(s):  
Collective Behavior ◽  
Quantitative Assessment ◽  
The Other ◽  
Analysis Method ◽  
Random System ◽  
Soccer Game ◽  
Voronoi Polygons ◽  
The Mean

This paper describes a Voronoi analysis method to analyze a soccer game. It is important for us to know the quantitative assessment of contribution done by a player or a team in the game as an individual or collective behavior. The mean numbers of vertices are reported to be 5–6, which is a little less than those of a perfect random system. Voronoi polygons areas can be used in evaluating the dominance of a team over the other. By introducing an excess Voronoi area, we can draw some fruitful results to appraise a player or a team rather quantitatively.

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