4.8 Article

Probing Fast Transformation of Magnetic Colloidal Microswarms in Complex Fluids

Journal

ACS NANO
Volume 16, Issue 11, Pages 19025-19037

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acsnano.2c07948

Keywords

colloidal microswarm; swarm control; pattern transformation; magnetic actuation; collective behavior

Funding

  1. Hong Kong Research Grants Council (RGC) - HKSAR Innovation and Technology Commission (ITC) [14300621, E-CUHK401/20, RFS21224S03]
  2. Croucher Foundation [CAS20403]
  3. CUHK
  4. National Natural Science Foundation of China [52205590]
  5. Natural Science Foundation of Jiangsu Province [BK20220834]
  6. Fundamental Research Funds for the Central Universities [2242022R10051]
  7. SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems
  8. Multiscale Medical Robotics Center (MRC), InnoHK, at the Hong Kong Science Park
  9. ITF project - HKSAR Innovation and Technology Commission (ITC) [MRP/036/18X]

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This study reports a strategy for optimizing pattern transformation rates of colloidal microswarms by coordinating the inner interactions. The influences of magnetic field parameters on pattern transformation rates are studied, and the feasibility of the strategy is validated in different fluid environments, showing promising potential for practical applications in targeted delivery tasks.
The rapidly transformed morphology of natural swarms enables fast response to environmental changes. Artificial microswarms can reconfigure their swarm patterns like natural swarms, which have drawn extensive attention due to their active adaptability in complex environments. However, as a prerequisite for biomedical applications of microswarms in confined environments, achieving on-demand control of pattern transformation rates remains a challenge. In this work, we report a strategy for optimizing pattern transformation rates of colloidal microswarms by coordinating the inner interactions. The influences of magnetic field parameters on pattern transformation rates are theoretically and experimentally studied, which elucidates the mechanism for optimal transformation rate control. The feasibility of the strategy is then validated in viscous Newtonian fluids and non-Newtonian biofluids. Moreover, the strategy is further validated in dynamic flow environments, exhibiting a promising future for practical applications in targeted delivery tasks with an optimal pattern transformation manner.

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