Manipulation of nonmagnetic particles and liquids on a programmable air-ferrofluid interface

P. A. Diluka Harischandra

Research output: ThesisDoctoral ThesisCollection of Articles

Abstract

Magnetic manipulation, a technique that manipulates magnetic objects using magnetic fields has emerged providing significant advantages in a multitude of fields, such as robotics, materials science, and biomedical engineering. Although there have been remarkable advances, traditional magnetic manipulation methods require the manipulated objects to be magnetic. However, most materials in nature are nonmagnetic, and therefore the current magnetic manipulation methods for nonmagnetic materials are insufficient. This thesis addresses the challenges in manipulation of nonmagnetic particles and presents novel methods to enhance the manipulation capabilities for liquid droplets on an air-ferrofluid interface. First, this thesis introduces a novel approach for closed-loop magnetic manipulation of non-magnetic particles on a programmable air-ferrofluid interface. The method uses magnetic fields for the programmable deformation of the air-ferrofluid interface, enabling the movement of particles. The proposed automatic manipulation method can perform path following of nonmagnetic particles in predefined trajectories. This approach overcomes the limitations of state-of-the-art methods that are restricted to closed-loop manipulation of magnetic particles and it broadens the manipulation capabilities at the air-liquid interface for living and non-living matter. Secondly, this thesis presents a novel approach to shape nonmagnetic liquids on a programmable air-ferrofluid interface. Current methods for shaping liquid droplets using magnetic fields require the manipulated droplet to possess magnetic properties. This thesis demonstrates that the deformations created at an air-ferrofluid interface can produce diverse convex and concave shapes of a nonmagnetic droplet at the same interface. Additionally, droplets can also be rotated or stirred. The methods presented in this thesis have significantly enhanced the manipulation capabilities for liquids at the air-liquid interface. Thirdly, this thesis proposes a novel method to predict droplet shape evolution resulting from sequences of actuations and the required actuations for a given shape sequence using a Long Short-Term Memory (LSTM) network. The method can be used to learn sequences of convex or concave shapes of the droplet and allows capturing and retaining the sequence of droplet morphology changes due to actuations. Lastly, this thesis presents a breakthrough in automatic shaping of liquids. Existing methods to automatically shape a droplet are limited to elliptical shape and the droplet must be magnetic. This research introduces a data-efficient online learning method using Bayesian optimization to morph liquid droplets into desired target shapes on the programmable air-ferrofluid interface. Using this method, desired convex or concave target shapes of the droplet can be achieved in a few iterations.
Translated title of the contributionManipulation of nonmagnetic particles and liquids on a programmable air-ferrofluid interface
Original languageEnglish
QualificationDoctor's degree
Awarding Institution
  • Aalto University
Supervisors/Advisors
  • Zhou, Quan, Supervising Professor
  • Zhou, Quan, Thesis Advisor
Publisher
Print ISBNs978-952-64-2141-4
Electronic ISBNs978-952-64-2142-1
Publication statusPublished - 2024
MoE publication typeG5 Doctoral dissertation (article)

Keywords

  • magnetic manipulation
  • shaping
  • air-liquid interface
  • droplet
  • non-magnetic

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