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

Abstract

Although the great potential of droplet based microfluidic technologies for routine applications in industry and academia has been successfully demonstrated over the past years, its inherent potential is not fully exploited till now. Especially regarding to the droplet generation reproducibility and stability, two pivotally important parameters for successful applications, there is still a need for improvement. This is even more considerable when droplets are created to investigate tissue fragments or cell cultures (e.g. suspended cells or 3D cell cultures) over days or even weeks. In this study we present microfluidic chips composed of a plasma coated polymer, which allow surfactants‐free, highly reproducible and stable droplet generation from fluids like cell culture media. We demonstrate how different microfluidic designs and different flow rates (and flow rate ratios) affect the reproducibility of the droplet generation process and display the applicability for a wide variety of bio(techno)logically relevant media.

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

“…To achieve this, the spheroid manufacturing process should be made reproducible. The more reproducible the manufacturing process is, the easier the process transfer is and cell spheroids with more consistent properties could be attained (Das et al, 2016; Hurrell et al, 2018; Wiedemeier et al, 2017). It is noted that the reproducibility of the manufacturing process is directly related to the process control and the complexity of the manufacturing technology.…”

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

“…T-junction, coflow, flow-focusing, and step-emulsification geometries alongside pressure-driven or flow-rate-driven fluidic systems are commonly used for droplet generation with varying degrees of performance range (droplet sizes and generation rates) and parameter sensitivity. [16][17][18] Flow-focusing devices can deliver a wide range of diameters and generation rates while maintaining high droplet monodispersity, often making them more desirable in comparison to other geometries. 16,19 Flow-focusing geometries are traditionally designed by choosing an orifice width close to the desired droplet diameter.…”

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

“…Several geometries including T-junction 7 , step-emulsification 24 , co-flow 25 , and flow-focusing 26 can be used to generate droplets. Flowfocusing geometries offer a wider range of deliverable performance (i.e., droplet diameter and generation rate) in comparison to the other geometries [27][28][29] . Nonetheless, due to the large number of effective parameters and the complex fluid dynamics involved, analytical solutions or generalizable scaling laws are yet to be determined for flow-focusing droplet generation 18,20 .…”