A colloidal motor driven by surface tension forces is theoretically designed by encapsulating an active Janus particle in a liquid drop which is immiscible in the suspending medium. The Janus particle produces an asymmetric flux of a solute species which induces surface tension gradients along the liquid–liquid interface between the drop and the surrounding fluid. The resulting Marangoni forces at the interface propel the compound drop/Janus particle system. The propulsion speeds of the motor are evaluated for a range of relative sizes and positions of the drop and the particle and across a range of transport properties of the drop and the suspending medium. It is demonstrated that the proposed design can produce higher propulsion velocities than the traditional Janus-particle-based colloidal motors propelled by neutral diffusiophoresis.
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Magneto-capillary particle dynamics at curved interfaces: inference and criticism of dynamical models
Time-varying fields drive the motion of magnetic particles adsorbed on liquid drops due to interfacial constraints that couple magnetic torques to capillary forces. Such magneto-capillary particle dynamics and the associated fluid flows are potentially useful for propelling drop motion, mixing drop contents, and enhancing mass transfer between phases. The design of such functions benefits from the development and validation of predictive models. Here, we apply methods of Bayesian data analysis to identify and validate a dynamical model that accurately predicts the field-driven motion of a magnetic particle adsorbed at the interface of a spherical droplet. Building on previous work, we consider candidate models that describe particle tilting at the interface, field-dependent contributions to the magnetic moment, gravitational forces, and their combinations. The analysis of each candidate is informed by particle tracking data for a magnetic Janus sphere moving in a precessing field at different frequencies and angles. We infer the uncertain parameters of each model, criticize their ability to describe and predict experimental data, and select the most probable candidate, which accounts for gravitational forces and the tilting of the Janus sphere at the interface. We show how this favored model can predict complex particle trajectories with micron-level accuracy across the range of driving fields considered. We discuss how knowledge of this “best” model can be used to design experiments that inform accurate parameter estimates or achieve desired particle trajectories.
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- Award ID(s):
- 1935228
- PAR ID:
- 10502007
- Publisher / Repository:
- RSC
- Date Published:
- Journal Name:
- Soft Matter
- Volume:
- 19
- Issue:
- 46
- ISSN:
- 1744-683X
- Page Range / eLocation ID:
- 9017 to 9026
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/D3SM01256E
