Density fluctuations and energy spectra of 3D bacterial suspensions

Liu, Zhengyang; Zeng, Wei; Ma, Xiaolei; Cheng, Xiang

doi:10.1039/d1sm01183a

Cited by 23 publications

(25 citation statements)

References 61 publications

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“…In contrast, ↵ decreases with decreasing for 2D GNF. Notably, for high 5.6%, ↵ stabilizes to 0.33 ± 0.01, quantitatively agreeing with the theoretical prediction of ↵ = 1/3 for 3D suspensions of polar-ordered self-propelled particles with hydrodynamic interactions [20]. Hence, our experiments for the first time quantitatively verify the theory of the GNF of 3D wet active fluids.…”

Section: A Transition To Bacterial Turbulencesupporting

confidence: 87%

“…Finally, these scaling laws for 3D flows, Ẽ(q) ∼ q 0 and Ẽ(q) ∼ q −3 , have been recently found in bacterial suspensions experiments [20] (Fig. 1d).…”

Section: Statistical Geometry Of the Flowsupporting

confidence: 75%

“…The inset shows the velocity correlation function from which the spectrum is obtained. d, Adapted from [19,20]. The different spectra correspond to different volume fractions of bacteria.…”

Section: Bacterial Suspension (E Coli 3d) Dmentioning

confidence: 99%

“…For example, motivated by theoretical predictions, experiments on active nematics have measured the vortex area distribution [24,25,28,31]. The central quantity common to all types of active turbulence is the velocity correlation function or, equivalently, the kinetic energy spectrum, which has been measured in most experimental systems [15,20,21,28,32,33] (Fig. 1).…”

Section: Bacterial Suspension (E Coli 3d) Dmentioning

confidence: 99%

“…1). Examples include different bacterial suspensions [10][11][12][13][14][15][16][17][18][19][20], swarming sperm [21], suspensions of microtubules and molecular motors [22][23][24][25][26][27][28], tissue cell monolayers [29][30][31][32], and suspensions of artificial self-propelled particles [33,34].…”

Section: Introductionmentioning

confidence: 99%

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Active Turbulence

Alert¹,

Casademunt²,

Joanny³

2021

Preprint

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Active fluids exhibit spontaneous flows with complex spatiotemporal structure, which have been observed in bacterial suspensions, sperm cells, cytoskeletal suspensions, self-propelled colloids, and cell tissues. Despite occurring in the absence of inertia, chaotic active flows are reminiscent of inertial turbulence, and hence they are known as active turbulence. Here, we survey the field, providing a unified perspective over different classes of active turbulence. To this end, we divide our review in sections for systems with either polar or nematic order, and with or without momentum conservation (wet/dry). Comparing to inertial turbulence, we highlight the emergence of power-law scaling with either universal or non-universal exponents. We also contrast scenarios for the transition from steady to chaotic flows, and we discuss the absence of energy cascades. We link this feature to both the existence of intrinsic length scales and the self-organized nature of energy injection in active turbulence, which are fundamental differences with inertial turbulence. We close by outlining the emerging picture, remaining challenges, and future directions.

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Section: A Transition To Bacterial Turbulencesupporting

confidence: 87%

“…Finally, these scaling laws for 3D flows, Ẽ(q) ∼ q 0 and Ẽ(q) ∼ q −3 , have been recently found in bacterial suspensions experiments [20] (Fig. 1d).…”

Section: Statistical Geometry Of the Flowsupporting

confidence: 75%

“…The inset shows the velocity correlation function from which the spectrum is obtained. d, Adapted from [19,20]. The different spectra correspond to different volume fractions of bacteria.…”

Section: Bacterial Suspension (E Coli 3d) Dmentioning

confidence: 99%

Section: Bacterial Suspension (E Coli 3d) Dmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Active Turbulence

Alert¹,

Casademunt²,

Joanny³

2021

Preprint

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Swarm Autonomy: From Agent Functionalization to Machine Intelligence

Wang,

Chen,

Xie

et al. 2024

Advanced Materials

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Swarm behaviors are common in nature, where individual organisms collaborate via perception, communication, and adaptation. Emulating these dynamics, large groups of active agents can self‐organize through localized interactions, giving rise to complex swarm behaviors, which exhibit potential for applications across various domains. This review presents a comprehensive summary and perspective of synthetic swarms, to bridge the gap between the microscale individual agents and potential applications of synthetic swarms. We begin by examining active agents, the fundamental units of synthetic swarms, to understand the origins of their motility and functionality in the presence of external stimuli. Then we summarize inter‐agent communications and agent‐environment communications that contribute to the swarm generation. Furthermore, we review the swarm behaviors reported to date and the emergence of machine intelligence within these behaviors. Eventually, the applications enabled by distinct synthetic swarms are summarized. By discussing the emergent machine intelligence in swarm behaviors, we offer insights into the design and deployment of autonomous synthetic swarms for real‐world applications.This article is protected by copyright. All rights reserved

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Scaling Transition of Active Turbulence from Two to Three Dimensions

Wei,

Yang,

Wei

et al. 2024

Advanced Science

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Turbulent flows are observed in low‐Reynolds active fluids, which display similar phenomenology to the classical inertial turbulence but are of a different nature. Understanding the dependence of this new type of turbulence on dimensionality is a fundamental challenge in non‐equilibrium physics. Real‐space structures and kinetic energy spectra of bacterial turbulence are experimentally measured from two to three dimensions. The turbulence shows three regimes separated by two critical confinement heights, resulting from the competition of bacterial length, vortex size and confinement height. Meanwhile, the kinetic energy spectra display distinct universal scaling laws in quasi‐2D and 3D regimes, independent of bacterial activity, length, and confinement height, whereas scaling exponents transition in two steps around the critical heights. The scaling behaviors are well captured by the hydrodynamic model we develop, which employs image systems to represent the effects of confining boundaries. The study suggests a framework for investigating the effect of dimensionality on non‐equilibrium self‐organized systems.

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Density fluctuations and energy spectra of 3D bacterial suspensions

Abstract: This experimental work studies giant number fluctuations and active turbulent flow of dense bulk bacterial suspensions, a prominent example of 3D wet active fluids.

Cited by 23 publications

References 61 publications

Active Turbulence

Active Turbulence

Swarm Autonomy: From Agent Functionalization to Machine Intelligence

Scaling Transition of Active Turbulence from Two to Three Dimensions

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