Parametric studies on droplet generation reproducibility for applications with biological relevant fluids

3D printing in microfluidics: experimental optimization of droplet size and generation time through flow focusing, phase, and geometry variation

Droplet-based microfluidics systems have become widely used in recent years thanks to their advantages, varying from the possibility of handling small fluid volumes to directly synthesizing and encapsulating various living forms for biological-related applications. The effectiveness of such systems mainly depends on the ability to control some of these systems' parameters, such as produced droplet size and formation time, which represents a challenging task. This work reports an experimental study on tuning droplet size and generation time in a flow-focusing geometry fabricated with stereolithography 3D printing by exploring the interplay of phase and geometrical parameters. We produced droplets at different low flow rates of continuous and dispersed phases to assess the effect of each of these phases on the droplets' size and formation time. We observed that smaller droplets were produced for high viscosity oil and water phase, along with high flow rates. In addition, changing the microfluidics channels' width, and morphology of the orifice has shown a similar effect on droplet size, as shown in the case of high-viscosity solutions. The variation of the bifurcation angle shows a noticeable variation in terms of the achieved droplet size and formation time. We further investigated the impact of modifying the width ratio of the continuous and dispersed phases on droplet formation.

Flexible Toolbox of High-Precision Microfluidic Modules for Versatile Droplet-Based Applications

Although the enormous potential of droplet-based microfluidics has been successfully demonstrated in the past two decades for medical, pharmaceutical, and academic applications, its inherent potential has not been fully exploited until now. Nevertheless, the cultivation of biological cells and 3D cell structures like spheroids and organoids, located in serially arranged droplets in micro-channels, has a range of benefits compared to established cultivation techniques based on, e.g., microplates and microchips. To exploit the enormous potential of the droplet-based cell cultivation technique, a number of basic functions have to be fulfilled. In this paper, we describe microfluidic modules to realize the following basic functions with high precision: (i) droplet generation, (ii) mixing of cell suspensions and cell culture media in the droplets, (iii) droplet content detection, and (iv) active fluid injection into serially arranged droplets. The robustness of the functionality of the Two-Fluid Probe is further investigated regarding its droplet generation using different flow rates. Advantages and disadvantages in comparison to chip-based solutions are discussed. New chip-based modules like the gradient, the piezo valve-based conditioning, the analysis, and the microscopy module are characterized in detail and their high-precision functionalities are demonstrated. These microfluidic modules are micro-machined, and as the surfaces of their micro-channels are plasma-treated, we are able to perform cell cultivation experiments using any kind of cell culture media, but without needing to use surfactants. This is even more considerable when droplets are used to investigate cell cultures like stem cells or cancer cells as cell suspensions, as 3D cell structures, or as tissue fragments over days or even weeks for versatile applications.

Design automation of microfluidic single and double emulsion droplets with machine learning

Droplet microfluidics enables kHz screening of picoliter samples at a fraction of the cost of other high-throughput approaches. However, generating stable droplets with desired characteristics typically requires labor-intensive empirical optimization of device designs and flow conditions that limit adoption to specialist labs. Here, we compile a comprehensive droplet dataset and use it to train machine learning models capable of accurately predicting device geometries and flow conditions required to generate stable aqueous-in-oil and oil-in-aqueous single and double emulsions from 15 to 250 μm at rates up to 12000 Hz for different fluids commonly used in life sciences. Blind predictions by our models for as-yet-unseen fluids, geometries, and device materials yield accurate results, establishing their generalizability. Finally, we generate an easy-to-use design automation tool that yield droplets within 3 μm (<8%) of the desired diameter, facilitating tailored droplet-based platforms and accelerating their utility in life sciences.

Versatility and stability optimization of flow-focusing droplet generators via quality metric-driven design automation

Droplet generation is a fundamental component of droplet microfluidics, compartmentalizing biological or chemical systems within a water-in-oil emulsion. As adoption of droplet microfluidics expands beyond expert labs or integrated devices, quality metrics are needed to contextualize the performance capabilities, improving the reproducibility and efficiency of operation. Here, we present two quality metrics for droplet generation: performance versatility, the operating range of a single device, and stability, the distance of a single operating point from a regime change. Both metrics were characterized in silico and validated experimentally using machine learning and rapid prototyping. These metrics were integrated into a design automation workflow, DAFD 2.0, which provides users with droplet generators of a desired performance that are versatile or flow stable. Versatile droplet generators with stable operating points accelerate the development of sophisticated devices by facilitating integration of other microfluidic components and improving the accuracy of design automation tools.

Experimental studies on droplet characteristics in a microfluidic flow focusing droplet generator: effect of continuous phase on droplet encapsulation

The efficacy of droplet-based microfluidic assays depends on droplet size, pattern, generation rate, etc. The size of the droplet is affected by numerous variables as flow rate ratio, viscosity ratio, microchannel geometry, surfactants, nature of fluids and other dimensionless numbers. This work reports rigorous analysis and optimization of the behavior of droplets with change in flow rate ratio and viscosity ratio in a flow-focusing device. Droplets were produced for different flow rate ratios maintaining a constant aqueous phase and varying the continuous phase, to have capillary numbers ranging from 0.01 to 0.1. It was observed that the droplet size decreased with the increase in flow rate ratio, and vice versa. It was noted that as the viscosity ratio was increased, the dispersed phase elongated before the complete breakup and long droplets were formed in the microchannel. Smaller droplets were formed for lower viscosity ratios with a combination of higher flow rate ratios. An empirical relation has been developed to predict the droplet length in terms of capillary number and flow rate ratio for different viscosity ratios. In addition, microparticle encapsulation in individual droplets was attempted to realize the effect of flow rate of the continuous phase for various flow rate ratios on encapsulation efficiency.

Machine learning enables design automation of microfluidic flow-focusing droplet generation

Droplet-based microfluidic devices hold immense potential in becoming inexpensive alternatives to existing screening platforms across life science applications, such as enzyme discovery and early cancer detection. However, the lack of a predictive understanding of droplet generation makes engineering a droplet-based platform an iterative and resource-intensive process. We present a web-based tool, DAFD, that predicts the performance and enables design automation of flow-focusing droplet generators. We capitalize on machine learning algorithms to predict the droplet diameter and rate with a mean absolute error of less than 10 μm and 20 Hz. This tool delivers a user-specified performance within 4.2% and 11.5% of the desired diameter and rate. We demonstrate that DAFD can be extended by the community to support additional fluid combinations, without requiring extensive machine learning knowledge or large-scale data-sets. This tool will reduce the need for microfluidic expertise and design iterations and facilitate adoption of microfluidics in life sciences.

A review of manufacturing capabilities of cell spheroid generation technologies and future development

Spheroid culture provides cells with a three-dimensional environment that can better mimic physiological conditions compared to monolayer culture. Technologies involved in the generation of cell spheroids are continuously being innovated to produce spheroids with enhanced properties. In this paper, we review the manufacturing capabilities of current cell spheroid generation technologies. We propose that spheroid generation technologies should enable tight and robust process controls to produce spheroids of consistent and repeatable quality. Future technology development for the generation of cell spheroids should look into improvement in process control, standardization, scalability and monitoring, in addition to advanced methods of spheroid transfer and characterization.

Performance tuning of microfluidic flow-focusing droplet generators

The required step in all droplet-based devices is droplet formation. A droplet generator must deliver an application-specific performance that includes a prescribed droplet size and generation frequency while producing monodisperse droplets. The desired performance is usually reached through several cost- and time-inefficient design iterations. To address this, we take advantage of a low-cost rapid prototyping method and provide a framework that enables researchers to make informed decisions on how to change geometric parameters and flow conditions to tune the performance of a microfluidic flow-focusing droplet generator. We present the primary and secondary parameters necessary for fine-tuning droplet formation over a wide range of capillary numbers and flow rate ratios. Once the key parameters are identified, we demonstrate the effect of geometric parameters and flow conditions on droplet size, generation rate, polydispersity, and generation regime. Using this framework, a wide range of droplet diameters (i.e., 30-400 μm) and generation rates (i.e., 0.5-800 Hz) was achieved.