Automated control of individual droplets in microfluidic channels offers tremendous potential for applications requiring high accuracy and minimal user involvement.
Microfluidics has progressed tremendously as a field over the last two decades. Various areas of microfluidics developed in fully-fledged domains of their own such as organ-on-a-chip, digital and paper microfluidics. Nevertheless, the technological advancement of microfluidics as a field has not yet reached end-users for independent use. This is the key objective that is kept as a lens throughout this review. The ultimate goal is for microfluidics to be simply considered as a tool for application-focused research. A modular automated platform is envisioned to provide the stacking and modularity required to lower the knowledge barrier for end-users. The literature considered in this review is limited to active microfluidics and the analysis focuses on the potential for end-users to independently leverage the platforms for research in various fields such as cell assays, biochemistry, materials, and environmental factors monitoring.
Microfluidics typically uses either a syringe pump that regulates the flow rate in microchannels or a pressure pump that controls the inlet pressures to drive the flow. In the context of pressure-driven flow, a reservoir holder containing liquid samples is normally used to interface the pressure pump with the microfluidic chip via soft tubing. The tubing connecting the pump and holder transports the pressurized air while the tubing connecting the holder and chip transports the liquid samples. The pressure output from the pump is usually assumed to be stable and the same as that applied to the liquid in the chip; however, in practice this assumption is often incorrect and may negatively impact chip performance. This assumption is critically challenged when applied to microfluidic chips involving dynamic control of fluids since the pressures are constantly varied (at > 10 Hz). This study presents a method for investigating, quantifying and modelling the pump stability and the dynamics of the air tubing using two pressure sensors. The relationship between the pressure output from the pump and the reservoir holder pressure is generalized as a first-order linear system. This relationship allows the software that controls the pressure pump to output the required pressure to the reservoir holder and thus to the microfluidic chip. These results should significantly improve the performance of microfluidic chips using active fluid control, and may also benefit passive fluid control applications.
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