The emerging field of self-propelling micro/nanorobots is teeming with a wide variety of novel micro/nanostructures, which are tested here for self-propulsion in a liquid environment. As the size of these microscopic movers diminishes into the fully nanosized region, the ballistic paths of an active micromotor become a random walk of colloidal particles. To test such colloidal samples for self-propulsion, the commonly adopted “golden rule” is to refer to the mean squared displacement (MSD) function of the measured particle tracks. The practical significance of the result strongly depends on the amount of collected particle data and the sampling rate of the particle track. Because micro/nanomotor preparation methods are mostly low-yield, the amount of used experimental data in published results is often on the edge of reproducibility. To address the situation, we perform MSD analysis on an experimental as well as simulated dataset. These data are used to explore the effects of MSD analysis on limited data and several situations where the lack of data can lead to insignificant results.
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