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This study introduces a real-time simulator for controlling magnetic nanoparticle microswarms. The simulator enables human interaction and accurately models microswarm navigation in vascular networks, validated against experimental data.

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Area of Science:

  • Robotics and Control Systems
  • Biomedical Engineering
  • Nanotechnology

Background:

  • Magnetic nanoparticle microswarms offer promising applications in various fields.
  • Existing simulations for microswarms are accurate but time-consuming, lacking real-time human input.
  • Real-time simulators allow for valuable user interaction data collection.

Purpose of the Study:

  • To develop and validate a real-time, two-dimensional microswarm simulator.
  • To enable human interaction for controlling microswarms.
  • To collect user interaction data for microswarm control research.

Main Methods:

  • Development of a real-time two-dimensional microswarm simulator.
  • Utilized a haptic device for human control of the microswarm.
  • Steered the microswarm through a vascular network to a selected outlet.
  • Validated the simulator against real-world experimental data.

Main Results:

  • The simulator demonstrated an 8% deviation when verified against experimental data.
  • Reduced magnetic gradients significantly impacted performance (1000 mT/m to 100 mT/m decreased performance from 100% to 30.8%).
  • Increased fluid flow velocities considerably affected performance (0.005 m/s to 0.06 m/s decreased performance from 100% to 35.3%).
  • Initial particle arrangement influenced performance, with a drop to 59% under varying fluid flow.

Conclusions:

  • The developed real-time simulator is a viable tool for studying human interaction in microswarm control.
  • Magnetic gradient and fluid flow are critical parameters influencing microswarm navigation performance.
  • Simulator validation against experimental data confirms its accuracy and utility for parametric studies.