Design Automation of Microfluidics
- Author(s): Wang, Junchao
- Advisor(s): Grover, William H
- et al.
What can microfluidics do? For the past decades, researchers from physics, chemistry, biotechnology, and other fields have used microfluidics to design numerous chips for different applications. As new microfluidic chips keep emerging, we ask ourselves, ``what else? Could we develop any microfluidic chips that human beings cannot design or imagine? Can we design better microfluidic chips with the help of computer algorithms?''
This thesis presents several projects that demonstrate the potential that computer algorithms, simulations, and conventional microfluidics together will help researchers find better microfluidic chips, better design methods, and help us explore new phenomena. In Chapter 1, we start with a small review of how microfluidic chips are designed in the past decades and discuss the advantages and disadvantages of the conventional design method. In Chapter 2, we present a “random design” method to design a function microfluidic solute generator, which is able to generate three arbitrary concentrations at the same time. In Chapter 3, we present a microfluidics-optimized particle simulation algorithm (MOPSA) that simulates the trajectories of cells, droplets, and other particles in microfluidic chips with more lifelike results than particle tracers in existing commercial software. In Chapter 4, we present a microfluidic simulation method that can simulate the behavior of fluids and particles in typical microfluidic chips instantaneously (in around one second), which is able to accelerate the simulation of microfluidic chips. In Chapter 5, we present a microfluidic particle sorter which is designed by the three algorithms in Chapter 2 - 4. In Chapter 6, we automated designed and optimized a microfluidic mixer using Non-dominated Sorting Genetic Algorithm II (NSGA-II). In Chapter 7, we discuss the potential impacts of the projects presented in this thesis and the future directions of computer algorithms that can help microfluidics evolve into the next generation.