Positional Information‐Based Organization of Surfactant Droplet Swarms Emerging from Competition Between Local and Global Marangoni Effects

Positional information is key for particles to adapt their behavior based on their position in external concentration gradients, and thereby self-organize into complex patterns. Here, position-dependent behavior of floating surfactant droplets that self-organize in a pH gradient is demonstrated, using the Marangoni effect to translate gradients of surface-active molecules into motion. First, fields of surfactant microliter-droplets are generated, in which droplets floating on water drive local, outbound Marangoni flows upon dissolution of surfactant and concomitantly grow myelin filaments. Next, a competing surfactant based on a hydrolysable amide is introduced, which is more surface active than the myelin surfactant and thereby inhibits the local Marangoni flows and myelin growth from the droplets. Upon introducing a pH gradient, the amide surfactant hydrolyses in the acidic region, so that the local Marangoni flows and myelin growth are reestablished. The resulting combination of local and global surface tension gradients produces a region of myelin-growing droplets and a region where myelin growth is suppressed, separated by a wave front of closely packed droplets, of which the position can be controlled by the pH gradient. Thereby, it is shown how "French flag"-patterns, in synthetic settings typically emerging from reaction-diffusion systems, can also be established via surfactant droplet systems.

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Publication Details

Journal
Small
Published
2024-08-21
DOI
https://doi.org/10.1002/smll.202403720
Citations
7
Primary Topic
Micro and Nano Robotics
Type
article
Field-Weighted Citation Impact
2.18

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article

Positional Information‐Based Organization of Surfactant Droplet Swarms Emerging from Competition Between Local and Global Marangoni Effects

Peter A. Korevaar, P. Visser, Max T. G. M. Derks, Mink Neeleman et al.
7 citations
Small
Micro and Nano Robotics
2.18
article

Positional Information‐Based Organization of Surfactant Droplet Swarms Emerging from Competition Between Local and Global Marangoni Effects

Peter A. Korevaar, P. Visser, Max T. G. M. Derks, Mink Neeleman, Pim F. J. Dankloff
article en
7 citations

Abstract

Positional information is key for particles to adapt their behavior based on their position in external concentration gradients, and thereby self-organize into complex patterns. Here, position-dependent behavior of floating surfactant droplets that self-organize in a pH gradient is demonstrated, using the Marangoni effect to translate gradients of surface-active molecules into motion. First, fields of surfactant microliter-droplets are generated, in which droplets floating on water drive local, outbound Marangoni flows upon dissolution of surfactant and concomitantly grow myelin filaments. Next, a competing surfactant based on a hydrolysable amide is introduced, which is more surface active than the myelin surfactant and thereby inhibits the local Marangoni flows and myelin growth from the droplets. Upon introducing a pH gradient, the amide surfactant hydrolyses in the acidic region, so that the local Marangoni flows and myelin growth are reestablished. The resulting combination of local and global surface tension gradients produces a region of myelin-growing droplets and a region where myelin growth is suppressed, separated by a wave front of closely packed droplets, of which the position can be controlled by the pH gradient. Thereby, it is shown how "French flag"-patterns, in synthetic settings typically emerging from reaction-diffusion systems, can also be established via surfactant droplet systems.

SmallVol. 20(47)
Radboud University Nijmegen (NL)
Radboud Universiteit, Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Clean water and sanitation
Openalex Percentile: Top 12%
Micro and Nano Robotics
2.18
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Positional Information‐Based Organization of Surfactant Droplet Swarms Emerging from Competition Between Local and Global Marangoni Effects — Peter A. Korevaar, P. Visser, et al. · Small (2024) | TGRS Research Map | TGRS