2020
DOI: 10.1017/jfm.2019.1055
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Collisions and rebounds of chemically active droplets

Abstract: Active droplets swim as a result of the nonlinear advective coupling of the distribution of chemical species they consume or release with the Marangoni flows created by their non-uniform surface distribution. Most existing models focus on the self-propulsion of a single droplet in an unbounded fluid, which arises when diffusion is slow enough (i.e. beyond a critical Péclet number, Pec). Despite its experimental relevance, the coupled dynamics of multiple droplets and/or collision with a wall remains mostly une… Show more

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Cited by 42 publications
(99 citation statements)
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References 67 publications
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“…The smaller droplet being exposed to an increased chemical gradient is quickly repelled away, reducing the interaction time with the larger one. Additionally, the increased size (or Pe) of the larger droplet enhances the persistence of its chemical polarity and self-propulsion, as already observed for symmetric collisions [31].…”
supporting
confidence: 53%
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“…The smaller droplet being exposed to an increased chemical gradient is quickly repelled away, reducing the interaction time with the larger one. Additionally, the increased size (or Pe) of the larger droplet enhances the persistence of its chemical polarity and self-propulsion, as already observed for symmetric collisions [31].…”
supporting
confidence: 53%
“…Yet, most experiments report much denser situations; the present Letter thus focuses on such near-field interactions, which are likely critical to the emergence of collective behaviour. Exploiting the simplicity of a two-sphere geometry, we were recently able to fully-resolve the symmetric collision of two droplets [31], and provided a critical insight on how the nonlinear convective transport conditions the collision. To note, controlling experimentally the exact droplet radius, which slowly varies over time [6], is however impossible, giving rise to heterogeneous systems and non-symmetric in practice.…”
mentioning
confidence: 99%
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