Modeling of the dynamics of autonomous catalytic nanomotors using the method of regularized stokeslets

摘要

Catalytic nanomotors move autonomously by deriving energy directly from their environment, mimicking biological nanomotors that perform a wide range of complex functions at the cellular level and drive vital functions such as active transport, muscle contraction, cell mobility, and other movement. With the belief that precise control of microscale and nanoscale motors may eventually permit the design of functional machinery at these scales, researchers have been systematically designing such nanomotors and experimentally investigating their swimming behavior.The complex nature of fluid-structure interaction resulting from complicated surface geometry, the Brownian fluctuations and the contact surface effects, necessitate appropriate theoretical modeling and use of computational methods and tools to accurately simulate particle dynamics. The swimming behavior exhibited by catalytic nanomotors designed by our collaborators Gibbs and Zhao, is numerically simulated based on an accurate representation of the nanomotor geometry, full hydrodynamic interactions of the nanomotor components as well as with the supporting substrate, accurately captured by the method of Regularized Stokeslets. We also account for solid-frictional forces and torques from the substrate, as prescribed by an established velocity-dependent friction model for micro/nanoscale friction. To explain random deviations, the Brownian fluctuations are precisely modeled as the stochastic solution to a Langevin Equation satisfying the fluctuation-dissipation theorem of statistical mechanics. A comparison of our numerically simulated results with the experimental observations is also presented here.