Defocus particle tracking (DPT) has gained increasing importance for its use to determine particle trajectories in all three dimensions with a single-camera system, as typical for a standard microscope, the workhorse of today’s ongoing biomedical revolution. DPT methods derive the depth coordinates of particle images from the different defocusing patterns that they show when observed in a volume much larger than the respective depth of field. Therefore it has become common for state-of-the-art methods to apply image recognition techniques. Two of the most commonly and widely used DPT approaches are the application of (astigmatism) particle image model functions (MF methods) and the normalized cross-correlations between measured particle images and reference templates (CC methods). Though still young in the field, the use of neural networks (NN methods) is expected to play a significant role in future and more complex defocus tracking applications. To assess the different strengths of such defocus tracking approaches, we present in this work a general and objective assessment of their performances when applied to synthetic and experimental images of different degrees of astigmatism, noise levels, and particle image overlapping. We show that MF methods work very well in low-concentration cases, while CC methods are more robust and provide better performance in cases of larger particle concentration and thus stronger particle image overlap. The tested NN methods generally showed the lowest performance, however, in comparison to the MF and CC methods, they are yet in an early stage and have still great potential to develop within the field of DPT.

By integrating surface acoustic waves (SAW) into microfluidic devices, microparticle systems can be fractionated precisely in flexible and easily scalable Lab-on-a-Chip platforms. The widely adopted driving mechanism behind this principle is the acoustic radiation force, which depends on the size and acoustic properties of the suspended particles. Superimposed fluid motion caused by the acoustic streaming effect can further manipulate particle trajectories and might have a negative influence on the fractionation result. A characterization of the crucial parameters that affect the pattern and scaling of the acoustically induced flow is thus essential for the design of acoustofluidic separation systems. For the first time, the fluid flow induced by pseudo-standing acoustic wave fields with a wavelength much smaller than the width of the confined microchannel is experimentally revealed in detail, using quantitative three-dimensional measurements of all three velocity components (3D3C). In Part I of this study, we focus on the fluid flow close to the center of the surface acoustic wave field, while in Part II the outer regions with strong acoustic gradients are investigated. By systematic variations of the SAW-wavelength λ_SAW and channel height H, a transition from vortex pairs extending over the entire channel width W to periodic flows resembling the pseudo-standing wave field is revealed. An adaptation of the electrical power, however, only affects the velocity scaling. Based on the experimental data, a validated numerical model was developed in which critical material parameters and boundary conditions were systematically adjusted. Considering a Navier slip length at the substrate-fluid interface, the simulations provide a strong agreement with the measured velocity data over a large frequency range and enable an energetic consideration of the first and second-order fields. Based on the results of this study, critical parameters were identified for the particle size as well as for channel height and width. Progress for the research on SAW-based separation systems is obtained not only by these findings but also by providing all experimental velocity data to allow for further developments on other sites.

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 $$1.14~\upmu \hbox {m}$$1.14μm – $$5.03~\upmu \hbox {m}$$5.03μm 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 $$1.14~\upmu \hbox {m}$$1.14μm and $$5.03~\upmu \hbox {m}$$5.03μm . 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.

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). 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 1% for all examined particle seeding concentrations. Additionally, the performance on experimental images, using four different particle sizes, ranging from 1.14 μ m to 5.03 μ m 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 96% is reached with a high recall above 95%. The error in the depth position remains below 1% 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.

This review comprehensively summarizes working mechanisms of various microfluidic shape-based separation for cells and particles and provides future perspectives based on current challenges for biomedical and clinical applications.

Standing surface acoustic waves (sSAW) emerged as a flexible tool for precise manipulation of spherical and non-spherical objects in Lab-on-a-Chip devices. While the manipulation of suspended particles and cells in acoustofluidic devices is mostly dominated by acoustic forces due to acoustic scattering and the acoustically induced fluid flow, surface acoustic waves are inherently linked to an inhomogeneous electric field. The superimposed effects of dielectrophoretic forces and torques on polarizable particles are less explored in microfluidics using sSAW. In this study, a thorough analysis of the physical interplay of acoustophoresis and dielectrophoresis aims to bridge this gap. In comprehensive experiments, the dielectrophoretic impact on the behavior of spherical and non-spherical particles is distinguished by screening the electric field of the sSAW inside the micro channel locally. As a result, particles are forced into trapping locations across the entire channel height. However, the height position close to the bottom differs between the screened and non-screened region. Regardless of the shape of the particles used in this study, particles are forced towards the bottom at the region with screening, while being levitated at regions without screening. This indicates clearly the influence of the electric field in close vicinity to the substrate surface. Furthermore, the unintuitive preferred orientation of prolate spheroids perpendicular to the pressure nodes of the sSAW recently reported, is confirmed in both region regardless of the presence of the electric field. Based on a three-dimensional numerical model, this orientation results not only due to the acoustic torque but is also caused by the dielectrophoretic torque, which complement each other. The experimental and numerical findings are in excellent agreement and provide deep insights into the underlying physical mechanisms responsible for patterning and orientation of the particles.