The systematic manipulation of components of multimodal particle solutions is a key for the design of modern industrial products and pharmaceuticals with highly customized properties. In order to optimize innovative particle separation devices on microfluidic scales, a particle size recognition with simultaneous volumetric position determination is essential. In the present study, the astigmatism particle tracking velocimetry is extended by a deterministic algorithm and a deep neural network (DNN) to include size classification of particles of multimodal size distribution. Without any adaptation of the existing measurement setup, a reliable classification of bimodal particle solutions in the size range of $$1.14~\upmu \hbox {m}$$ 1.14 μ m –$$5.03~\upmu \hbox {m}$$ 5.03 μ m is demonstrated with a precision of up to 99.9 %. Concurrently, the high detection rate of the particles, suspended in a laminar fluid flow, is quantified by a recall of 99.0 %. By extracting particle images from the experimentally acquired images and placing them on a synthetic background, semi-synthetic images with consistent ground truth are generated. These contain labeled overlapping particle images that are correctly detected and classified by the DNN. The study is complemented by employing the presented algorithms for simultaneous size recognition of up to four particle species with a particle diameter in between $$1.14~\upmu \hbox {m}$$ 1.14 μ m and $$5.03~\upmu \hbox {m}$$ 5.03 μ m . With the very high precision of up to 99.3 % at a recall of 94.8 %, the applicability to classify multimodal particle mixtures even in dense solutions is confirmed. The present contribution thus paves the way for quantitative evaluation of microfluidic separation and mixing processes.

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We investigate the behaviour of accelerating contact lines in an unsteady quasi-capillary channel flow. The configuration consists of a liquid column that moves along a vertical 2D channel, open to the atmosphere and driven by a controlled pressure head. Both advancing and receding contact lines were analyzed to test the validity of classic models for dynamic wetting and to study the flow field near the interface. The operating conditions are characterized by a large acceleration, thus dominated by inertia. The shape of the moving meniscus was retrieved using Laser-Induced Fluorescence (LIF)-based image processing while the flow field near was analyzed via Time-Resolved Particle Image Velocimetry (TR-PIV). The TR-PIV measurements were enhanced in the post-processing, using a combination of Proper Orthogonal Decomposition (POD) and Radial Basis Functions (RBF) to achieve superresolution of the velocity field. Large counter-rotating vortices were observed, and their evolution was monitored in terms of the maximum intensity of the Q-field. The results show that classic contact angle laws based on interface velocity cannot describe the evolution of the contact angle at a macroscopic scale. Moreover, the impact of the interface dynamics on the flow field is considerable and extends several capillary lengths below the interface.

We present an experimental analysis of the flow field near an accelerating contact line using time-resolved Particle Image Velocimetry (TR-PIV). Both advancing and receding contact lines are investigated. The analyzed configuration consists of a liquid column that moves along a vertical 2D channel, open to the atmosphere and driven by a controlled pressure head. Large counter-rotating vortices were observed and analyzed in terms of the maximum intensity of the Q-field. To compute smooth spatial derivatives and improve the measurement resolution in the post-processing stage, we propose a combination of Proper Orthogonal Decomposition (POD) and Radial Basis Functions (RBF). The RBFs are used to regress the spatial and temporal structures of the leading POD modes, so that “high-resolution” modes are obtained. These can then be combined to reconstruct high-resolution fields that are smooth and robust against measurement noise and amenable to analytic differentiation. The results show significant differences in the flow topology between the advancing and the receding cases despite velocity and acceleration of contact lines are comparable in absolute values. This suggests that the flow dynamics are tightly linked to the shape of the interface, which significantly differs in the two cases.

Microfluidic flows feature typically fully three-dimensional velocity fields. However, often the optical access for measurements is limited. Astigmatism or defocus particle tracking velocimetry is a technique that enables the 3D position determination of individual particles by the analysis of astigmatic/defocused particle images. The classification and position determination of particles is a task well suited to deep neural networks (DNNs). In this work, two DNNs are used to extract the class and in-plane position (object detection) as well as the depth position (regression network). The performance of both DNNs is assessed by the position uncertainties as well as the precision of the size classes and the amount of recalled particles. The DNNs are evaluated on a synthetic dataset and establish a new benchmark of DNNs in defocus tracking applications. The recall is higher than compared to classic methods and the in-plane errors are always subpixel accurate. The relative uncertainty in the depth position is below \SI{1}{\percent} for all examined particle seeding concentrations. Additionally, the performance on experimental images, using four different particle sizes, ranging from 1.14 to \SI{5.03}{\micro\meter} is analyzed. The particle images are systematically rearranged to produce comprehensive datasets of varying particle seeding concentrations. The distinction between particles of similar size is more challenging but the DNNs still show very good results. A precision above \SI{96}{\percent} is reached with a high recall above \SI{95}{\percent}. The error in the depth position remains below \SI{1}{\percent} and the in-plane errors are subpixel accurate with respect to the labels. The work shows that first, DNNs can be trained with artificially rearranged data sets based on individual experimental images and are therefore easily adaptable to various experimental setups and applicable by non experts. Second, the DNNs can be successfully adapted to determine additional variables as in this case the size of the suspended particles.