Motion Analysis and Real-Time Trajectory Prediction of Magnetically Steerable Catalytic Janus Micromotors

Abstract

Chemically driven micromotors display unpredictable trajectories due to the rotational Brownian motion interacting with the surrounding fluid molecules. This hampers the practical applications of these tiny robots, particularly where precise control is a requisite. To overcome the rotational Brownian motion and increase motion directionality, robots are often decorated with a magnetic composition and guided by an external magnetic field. However, despite the straightforward method, explicit analysis and modeling of their motion have been limited. Here, catalytic Janus micromotors are fabricated with distinct magnetizations and a controlled self-propelled motion with magnetic steering is shown. To analyze their dynamic behavior, a dynamic model that can successfully predict the trajectory of micromotors in uniform viscous flows in real time by incorporating a form of state-dependent-coefficient with a robust two-stage Kalman filter is theoretically developed. A good agreement is observed between the theoretically predicted dynamics and experimental observations over a wide range of model parameter variations. The developed model can be universally adopted to various designs of catalytic micro-/nanomotors with different sizes, geometries, and materials, even in diverse fuel solutions. Finally, the proposed model can be used as a platform for biosensing, detecting fuel concentration, or determining small-scale motors' propulsion mechanisms in an unknown environment.