(142g) Control Based Techniques for Microfluidic Particle Trapping
The ability to trap and control single particles in free solution has led to major advances in science and engineering. In this talk, we report the development of a multiplexed microfluidic trap for arbitrary control over an arbitrary number of small particles in a microfluidic device. In recent work, we have been designing and building “smart” microfluidic devices by coupling feedback control with microfluidics, thereby enabling new routes for the fluidic-directed assembly of particles. In the first part of the talk, we discuss a control-based characterization of the hydrodynamic trap in regards to controlling a single particle. Here, we study the response of trapped particles actuated using a combination of proportional, integral, and derivative controllers (PID control),1 which extends beyond our prior work where we utilized a simple proportional controller for 2-D manipulation of particles in free solution.2,3 We will also discuss the development of a control model to simulate the hydrodynamic trapping process using a combination of simulation and experimental studies to investigate the effect of controller gains, flow rate (Peclet number), and feedback response times on the robustness of trapping. In the second part of the talk, we discuss the development of methods and models to achieve multiplexed microfluidic trapping of an arbitrary number of particles. We present a simulation of the process and explore the experimental challenges and implementation. In particular, a Hele-Shaw microfluidic cell is used to generate hydrodynamic forces on particles in a viscous-dominated flow defined by the microdevice geometry and imposed peripheral flow rates. We employ a model-predictive control algorithm to manipulate particles in arbitrary directions. Our work provides a solid framework for understanding the response of a microfluidic-based hydrodynamic trap to controller type and system parameters, which will be useful for guiding the design of next-generation, automated on-chip assays.
1. A. Shenoy, M. Tanyeri and C. M. Schroeder (2014), submitted.
2. M. Tanyeri, E. M. Johnson-Chavarria, and C. M. Schroeder, Appl. Phys. Lett. (2010), 96.
3. M. Tanyeri and C. M. Schroeder, Nano Lett. (2013), 13, 2357–2364.