Microfluidics is commonly ruled by pressure driven flows enabling the transport of material on large scales incorporating different kinds of functionality for sensing flow control or chemical synthesis. Yet, a local control of fluids and dissolved species is difficult due to the macroscopic nature of the exerted pressure gradients.
Here we present our efforts to control liquids and dissolved species at the microscale using thermo-fluidic approaches. We employ optically controlled thermo-osmotic, thermophoretic, and thermoviscous flows to induce fluid flow to sense, localise, or separate different species in solution. We introduce different spectroscopic and microscopic signals to report on the local properties and composition of the solution with the help of machine learning approaches to track and classify species in real time to provide a feedback to steer the system into desired directions.
We introduce a toolbox to calibrate microscopic force fields by analyzing the trajectories of a Brownian particle using machine learning, namely, recurrent neural networks. We demonstrate that this machine-learning approach outperforms standard methods when characterizing the force fields generated by harmonic potentials if the available data are limited. More importantly, it provides a tool to calibrate force fields in situations for which there are no standard methods, such as non-conservative and time-varying force fields. In order to make this method readily available for other users, we provide a Python software package named DeepCalib, which can be easily personalized and optimized for specific force fields and applications.
There is a very limited number of methods to analyze experimental trajectories of systems with feedback and time delay. In most cases, an analytical approach is not even possible. In this study, we show that the feedback parameters and the delay can be accurately characterized using machine learning, namely recurrent neural networks. We demonstrate that our method can dramatically expand the number of time-delayed feedback scenarios that we can characterize. We exemplify our findings on different numerical and experimental scenarios.
Amyloid fibrils are highly stable and organized peptide or protein structures that on the one hand can cause partially severe diseases such as Alzheimer's disease and on the other hand play fundamental roles during a plethora of biological processes. Nevertheless, there are still plenty open questions concerning their formation. We present a thermophoretic trap which is able to confine the Brownian motion of single amyloid fibrils via temperature gradients. The time-resolved tracking of the fibrils' rotational diffusion coefficients in presence of monomers permits to extract their growth rates or to directly observe secondary growth processes as fragmentation.
The calibration of physical force fields from particle trajectories is important for experiments in soft matter, biophysics, active matter, and colloidal science. However, it is not always possible to have a standard method to characterize a force field, especially for systems that are out of equilibrium. Here, we introduce a generic toolbox for calibrating any kind of conservative or non-conservative, fixed or time-varying potentials that is powered by recurrent neural networks (RNN). We show that with the help of neural networks, we can outperform standard methods as well as analyze systems that cannot be approached by existing methods. We provide a software package that is available online for free access.
We demonstrate the long time trapping of single DNA molecules in liquids by feedback driven dynamic temperature fields. By spatially and temporally varying the temperature at a plasmonic nanostructure, thermophoretic drifts are induced that are used to trap single nano-objects. A feedback controlled switching of local temperature fields allows us to confine the motion of a single DNA molecule for minutes. The DNA conformation and conformation dynamics are analyzed in terms of a principle component analysis. Current results are in agreement with previous measurements in thermal equilibrium and suggest only a weak influence of the inhomogeneous temperature rise on the structure and dynamics in the trap.
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