Microfluidic Network Simulations Enable On-Demand Prediction of Control Parameters for Operating Lab-on-a-Chip-Devices

Reliable operation of lab-on-a-chip systems depends on user-friendly, precise, and predictable fluid management tailored to particular sub-tasks the microfluidic process protocol their required sample fluids. Pressure-driven flow control, where fluids are delivered chip from pressurized feed vessels, simplifies even for multiple The achieved rates depend pressure settings, properties, pressure-throughput characteristics complete system composed interconnecting tubing. prediction settings achieving given control tasks enables opportunities automation. In our work, we utilize a fast-running, Kirchhoff-based network simulation that solves in-line within less than 200 ms. appropriateness benefits this approach demonstrated as exemplary creating multi-component laminar co-flow creation droplets with variable composition. Image-based methods were combined chemometric approaches readout correlation created patterns predictions obtained solver.