High-throughput microflow cytometry has become a focal point of research in recent years. In particular, droplet microflow cytometry (DMFC) enables the analysis of cells reacting to different stimuli in chemical isolation due to each droplet acting as an isolated microreactor. Furthermore, at high flow rates, the droplets allow massive parallelization, further increasing the throughput of droplets. However, this novel methodology poses unique challenges related to commonly used fluorometry and fluorescent microscopy techniques. We review the optical sensor technology and light sources applicable to DMFC, as well as analyze the challenges and advantages of each option, primarily focusing on electronics. An analysis of low-cost and/or sufficiently compact systems that can be incorporated into portable devices is also presented.

Droplet microfluidics enables studying large cell populations in chemical isolation, at a single-cell resolution. Applications include studying cellular response to drugs, cell-to-cell interaction studies. Such applications need a reliable and repeatable droplet generation with high monodispersity. Most systems used in research rely on manual tuning of flow parameters on off-the-shelf instruments. Setups are highly customized, limiting reproduction of experimental results. We propose an integrated, modular system for automated aqueous droplet generation with high monodispersity. The system provides dynamic feedback control of droplet size and input pressure. Input pressure is generated by two piezoelectric micropumps. Droplet sizes are determined via light intensity measurement in an LED-photodiode setup. The system is capable of wireless communication and has a low enough power consumption for battery-powered operation. We report on the assembly and the underlying working principle, as well as an in-depth experimental evaluation of the performance of the proof-of-concept prototype in aqueous droplet generation. Evaluation was performed on a modular as well as on a system level. During module-level evaluations, aqueous droplets were generated in a light mineral oil + Span 80 surfactant carrier medium, using 3 different flow-focusing junction geometries. The presented prototype had a significantly faster pressure stabilization time (10 s) compared to a syringe pump-based reference setup (120 s). During system-level evaluation, deionized water droplets were generated in a carrier medium of HFE7500 + PEG-PFPE triblock surfactant. Resultant droplet sizes were benchmarked with microscopy. The system was able to repeatedly generate mono- and polydisperse droplets on demand, with CVs between 5-10% in the ~50-200 μm droplet diameter range.

This study describes the construction of a compact empirical mathematical model for a flow-focusing microfluidic droplet generator. The application case is a portable, low-cost flow cytometry system for microbiological applications, with water droplet sizes of 50-70 micrometer range and droplet generation rates of 500-1500 per second. In this study, we demonstrate that for the design of reliable microfluidic systems, the availability of an empirical model of droplet generation is a mandatory precondition that cannot be achieved by time-consuming simulations based on detailed physical models. When introducing the concept of a compact empirical model, we refer to a mathematical model that considers general theoretical estimates and describes experimental behavioral trends with a minimal set of easily measurable parameters. By interpreting the experimental results for different water- and oil-phase flow rates, we constructed a minimal 3-parameter droplet generation rate model for every fixed water flow rate by relying on submodels of the water droplet diameter and effective ellipticity. As a result, we obtained a compact model with an estimated 5-10% accuracy for the planned typical application modes. The main novelties of this study are the demonstration of the applicability of the linear approximation model for droplet diameter suppression by the oil flow rate, introduction of an effective ellipticity parameter to describe the droplet form transformation from a bullet-like shape to a spherical shape, and introduction of a machine learning correction function that could be used to fine-tune the model during the real-time operation of the system.

Good quality of single droplet image acquisition in imaging flow cytometry (IFC) is crucial for a wide range of biological analyses. Recently, there have been significant advances in droplet microfluidic data analysis; however, acquiring blur-free single object images is still a great challenge because of the tradeoff between high flow rate and hardware setup complexity and cost. State-of-the-art hardware setups for blur-free single image acquisition are often complex, cumbersome, and not portable, limiting their suitability for point-of-care diagnostics. Moreover, motion blur and duplicate droplet image acquisition can occur with flow rate variation. To address these issues, this paper proposes a lightweight imaging pipeline for acquiring blur-free single droplet images for portable applications; this pipeline is capable of acquiring every single droplet image. While most of the existing literature focuses on complex hardware setups, utilizing high frame rate cameras that are not cost effective and complex optical solutions for droplet focusing, our pipeline utilizes minimum hardware and a lightweight algorithm for detecting, counting, and acquiring single object images from the video stream. The proposed pipeline was evaluated experimentally using videos of fast-moving droplets in which the input fluid flow rate was as high as 67.7 μL/min. The proposed pipeline achieves 100% counting accuracy on the tested videos and 2 ms, 25 ms and 10 ms processing time for each droplet on a desktop PC, single-board computer Raspberry Pi-4, and Nvidia Jetson Nano, respectively. This yields a maximum of 500, 40, and 100 blur free detected droplets per second (DPS), respectively. The Jetson Nano implementation, achieving100 DPS with processing time of 10 ms, is faster than existing similar studies and fast enough for the target application. The results suggest that the proposed lightweight pipeline is suitable for efficient single object image acquisition in IFC on an embedded portable platform.