Abstract
Reliable operation of lab-on-a-chip systems depends on user-friendly, precise, and predictable fluid management tailored to particular sub-tasks of the microfluidic process protocol and their required sample fluids. Pressure-driven flow control, where the sample fluids are delivered to the chip from pressurized feed vessels, simplifies the fluid management even for multiple fluids. The achieved flow rates depend on the pressure settings, fluid properties, and pressure-throughput characteristics of the complete microfluidic system composed of the chip and the interconnecting tubing. The prediction of the required pressure settings for achieving given flow rates simplifies the control tasks and enables opportunities for automation. In our work, we utilize a fast-running, Kirchhoff-based microfluidic network simulation that solves the complete microfluidic system for in-line prediction of the required pressure settings within less than 200 ms. The appropriateness of and benefits from this approach are demonstrated as exemplary for creating multi-component laminar co-flow and the creation of droplets with variable composition. Image-based methods were combined with chemometric approaches for the readout and correlation of the created multi-component flow patterns with the predictions obtained from the solver.
Highlights
We demonstrate the operation of microfluidic devices at user-defined flow rates utilizing the flow-focusing unit (FFU) of the Leibniz-IPHT
We demonstrate the operation of microfluidicofdevices at userdevices at user-defined flow rates utilizing the flow-focusing unit PDG2 as a proof-ofdefined flow rates utilizing the flow-focusing unit PDG2 as a proof-of-concept and referconcept and reference
The mfnSolver is comparably robust to length variations, as long as the of thebeen pressure can be assigned todesign the interconnectWhile much workmajority has already donedrop towards microfluidic automation ing tubes
Summary
Microfluidics is the backbone of lab-on-a-chip devices [1]. Complex laboratory workflows have been implemented through on-chip technologies [2,3,4,5], ranging from the generation of chemical gradients [6,7], over microfluidic-based nanoparticle fabrication [8], their surface modification [9,10] to point-of-care diagnostics [11] and environmental analysis [12]. The performance requirements of microfluidic devices are increasing, and reliable control and prediction of flow rate and operating pressure are crucial. Pattern-based microfluidics [13] require mechanical designs tailored to their specific application, which require new and optimized approaches for the design [13,14,15] and control of microfluidic networks
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