In developing micro- and nanodevices, stiction between their parts is a well-known problem. It is caused by the finite-temperature analogue of the quantum electrodynamical Casimir–Lifshitz forces, which are normally attractive. Repulsive Casimir–Lifshitz forces have been realized experimentally, but their reliance on specialized materials severely limits their applicability and prevents their dynamic control. Here we demonstrate that repulsive critical Casimir forces, which emerge in a critical binary liquid mixture upon approaching the critical temperature, can be used to counteract stiction due to Casimir–Lifshitz forces and actively control microscopic and nanoscopic objects with nanometre precision.
Many systems in biology, physics, and finance exhibit anomalous diffusion dynamics where the mean squared displacement grows with an exponent that deviates from one. When studying time series recording the evolution of these systems, it is crucial to precisely measure the anomalous exponent and confidently identify the mechanisms responsible for anomalous diffusion. These tasks are difficult when only few short trajectories are available, a common scenario in non-equilibrium and living systems. We show that long short-time memory (LSTM) recurrent neural networks excel at characterizing anomalous diffusion from a single short trajectory. The method we developed generalizes to experimental data obtained from subdiffusive colloids trapped in speckle light fields and superdiffusive microswimmers. We discuss the performance of the method in comparison to alternative ones in the context of the Anomalous Diffusion Challenge. In closing, we address the interpretability of the method.
Thermally driven microswimmers self-propel by con- verting a self-generated heat flow to motion. In the last decade, many studies have been performed on Janus col- loids, which absorb laser light through an active cap, resulting in a temperature gradient and corresponding thermodynamic forces along the surface [1]. Particles trapped between two fluid phases, on the other hand, are advected by the Marangoni flow due to the temperature gradient along the interface [2, 3]. Steering along a given trajectory has been implemented by dynamical feedback
[4] or spatial shaping of the laser beam [5]. Active motion arises from the creep flow along the particle surface. Its axisymmetric component results in linear motion of the Janus particle. In various instances, however, active particles show also rotational motion. Thus complex trajectories have been observed for Janus colloids carrying a metal cap of irregular shape or moving in a in non- uniform laser intensity profile [3–6].
Anomalous diffusion occurs in many physical and biological phenomena, when the growth of the mean squared displacement with time has an exponent different from one and can be due to different mechanisms. We show that recurrent neural networks (RNNs) efficiently characterize anomalous diffusion by identifying the mechanism causing it and determining the anomalous exponent from a single short trajectory.
This method outperforms standard techniques and advanced ones when the available data points are limited, as is often the case in experiments. Furthermore, RNNs can handle more complex tasks where there are no standard approaches, such as determining the anomalous diffusion exponent from a trajectory sampled at irregular times, and measuring intermittent systems that switch between different kinds of anomalous diffusion. The method is validated on experimental data obtained from subdiffusive colloids trapped in speckle light fields and superdiffusive microswimmers.
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