Magneto-capillary particle dynamics at curved interfaces: inference and criticism of dynamical models

Livitz, Dimitri; Dhatt-Gauthier, Kiran; Bishop, Kyle J. M.

doi:10.1039/d3sm01256e

Soft Matter

2023

DOI: 10.1039/d3sm01256e

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Magneto-capillary particle dynamics at curved interfaces: inference and criticism of dynamical models

Dimitri Livitz,

Kiran Dhatt-Gauthier,

Kyle J. M. Bishop

Abstract: We use 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.

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2024

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Self-Guided Navigation of Magnetic Microspheres on Topographic Landscapes

Wu,

Ramos,

Bishop

2024

ACS Appl. Eng. Mater.

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The directed propulsion of magnetic microrobots through structured environments often requires real-time feedback between external sensors and the applied field. This requirement, however, can be relaxed to enable self-guided propulsion by coupling field-driven motion to gradients in the local environment. We show that rotating fields direct the migration of ferromagnetic spheres up local gradients in the topography of a solid substrate. We quantify the speed and direction of particle migration as a function of the rotation frequency and incline angle. These observations are explained by a dynamic model that describes particle motion through the fluid due to the magnetic torque and gravitational force. We demonstrate how "topotaxis" can direct the simultaneous navigation of multiple particles on patterned arrays of concave bowls and convex domes without knowledge of the particle locations or the surface topography. These results highlight opportunities for designing timevarying fields to achieve other self-guided behaviors conditioned on local environmental cues.

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Self-Guided Navigation of Magnetic Microspheres on Topographic Landscapes

Wu,

Ramos,

Bishop

2024

ACS Appl. Eng. Mater.

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show abstract

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Magneto-capillary particle dynamics at curved interfaces: inference and criticism of dynamical models

Abstract: We use 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.

Cited by 1 publication

References 39 publications

Self-Guided Navigation of Magnetic Microspheres on Topographic Landscapes

Self-Guided Navigation of Magnetic Microspheres on Topographic Landscapes

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