Fast Magnetic Micropropellers with Random Shapes.

Studying propulsion mechanisms in low Reynolds number fluid has implications for many fields, ranging from the biology of motile microorganisms and the physics of active matter to micromixing in catalysis and micro- and nanorobotics. The propulsion of magnetic micropropellers can be characterized by a dimensionless speed, which solely depends on the propeller geometry for a given axis of rotation. However, this dependence has so far been only investigated for helical propeller shapes, which were assumed to be optimal.

The triathlon of magnetic actuation: rolling, propelling, swimming with a single magnetic material.

Magnetic actuation of microscopic devices in a liquid environment has been achieved in various ways, which can be grouped into rolling, propelling and swimming. Previous actuators were designed with a focus on one particular type of magnetic actuation. We have shown earlier that efficient magnetic propellers can be selected from randomly shaped magnetic nanostructures synthesized in solution.

Selecting for function: solution synthesis of magnetic nanopropellers.

We show that we can select magnetically steerable nanopropellers from a set of carbon coated aggregates of magnetic nanoparticles using weak homogeneous rotating magnetic fields. The carbon coating can be functionalized, enabling a wide range of applications. Despite their arbitrary shape, all nanostructures propel parallel to the vector of rotation of the magnetic field.

Noise reduction by signal combination in Fourier space applied to drift correction in optical tweezers.

A general method is proposed to reduce noise by combining signals. Different measurements of the same physical quantity often exhibit different noise levels in different frequency ranges. Hence, a single high-fidelity signal can be constructed by combining the low-noise parts of the signals in Fourier space.