The characterisation of the fluid motion induced by the acoustic streaming effect is of paramount interest for novel microfluidic devices based on surface acoustic waves (SAWs), e.g. for a detailed description of the achievable mixing efficiency and thus the design of such devices. Here, we present for the first time a quantitative 3D comparison between experimental measurements and numerical simulations of the acoustic streaming induced fluid flow inside a microchannel originating from a SAW. On the one hand, we performed fully three-dimensional velocity measurements using the astigmatism particle tracking velocimetry. On the other hand, we derived a novel streaming force approach solving the damped wave equation, which allows fast and easy 3D simulations of the acoustic streaming induced fluid flow. Furthermore, measurements of the SAW amplitude profile inside the fluid filled microchannel were performed. Based on these results, we obtained a very good agreement between the velocity measurements and the simulations of the fluid flow demonstrating the importance of comprising the actual shape of the SAW amplitude profile for quantitatively reliable simulations. It is shown that the novel streaming force approach is a valid approximation for the simulation of the acoustic streaming induced fluid flow, allowing a rapid and simple estimation of the flow field of SAW based microfluidic devices.
Many applications in chemistry, biology and medicine use microfluidic devices to separate, detect and analyze samples on a miniaturized size-level. Fluid flows evolving in channels of only several tens to hundreds of micrometers in size are often of a 3D nature, affecting the tailored transport of cells and particles. To analyze flow phenomena and local distributions of particles within those channels, astigmatic particle tracking velocimetry (APTV) has become a valuable tool, on condition that basic requirements like low optical aberrations and particles with a very narrow size distribution are fulfilled. Making use of the progress made in the field of machine vision, deep neural networks may help to overcome these limiting requirements, opening new fields of applications for APTV and allowing them to be used by nonexpert users. To qualify the use of a cascaded deep convolutional neural network (CNN) for particle detection and position regression, a detailed investigation was carried out starting from artificial particle images with known ground truth to real flow measurements inside a microchannel, using particles with uniand bimodal size distributions. In the case of monodisperse particles, the mean absolute error and standard deviation of particle depth-position of less than and about 1 µm were determined, employing the deep neural network and the classical evaluation method based on the minimum Euclidean distance approach. While these values apply to all particle size distributions using the neural network, they continuously increase towards the margins of the measurement volume of about one order of magnitude for the classical method, if nonmonodisperse particles are used. Nevertheless, limiting the depth of measurement volume in between the two focal points of APTV, reliable flow measurements with low uncertainty are also possible with the classical evaluation method and polydisperse tracer particles. The results of the flow measurements presented herein confirm this finding. The source code of the deep neural network used here is available on https://github.com/SECSY-Group/DNN-APTV.
This paper presents the dynamic modelling and design of a control strategy for the ZnS precipitation process. During lab-scale experiments, the sulfide concentration in a precipitator was controlled at a prespecified pS value by manipulating the flow from a buffer vessel. Batch tests showed that the optimal condition for zinc sulfide precipitation is at a sulfide concentration of 10 215 mole/l (pS 15). Experiments with the precipitator showed that the sulfide concentration highly deviates from a given setpoint when proportional (P) control is used, but this deviation can be decreased using a Proportional Integral (PI) controller. Moreover, the PI controller was able to handle sudden disturbances in the process conditions (pH, influent flow rate, or zinc and sulfide concentration). Additional precipitation experiments were conducted using effluent from a sulfate reducing gas-lift reactor to determine if the compounds present in the effluent influence the control process. With the gas-lift reactor effluent and a PI
Experimental and numerical studies on the acoustically induced fluid flow at the center of pseudo-standing surface acoustic waves, as typically employed in Lab-on-a-Chip devices for particle separation.
Acoustic tweezers facilitate a noninvasive, contactless, and label-free method for the precise manipulation of micro objects, including biological cells. Although cells are exposed to mechanical and thermal stress, acoustic tweezers...
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