Optimal Control of Objects on the Micro- and Nano-Scale by Electrokinetic and Electromagnetic Manipulation: for Bio-Sample Preparation, Quantum Information Devices and Magnetic Drug Delivery
Abstract
In this thesis I show achievements for precision feedback control of objects inside micro-fluidic systems and for magnetically guided ferrofluids. Essentially, this is about doing flow control, but flow control on the microscale, and further even to nanoscale accuracy, to precisely and robustly manipulate micro and nano-objects (i.e. cells and quantum dots). Target applications include methods to miniaturize the operations of a biological laboratory (lab-on-a-chip), i.e. presenting pathogens to on-chip sensing cells or extracting cells from messy bio-samples such as saliva, urine, or blood; as well as non-biological applications such as deterministically placing quantum dots on photonic crystals to make multi-dot quantum information systems. The particles are steered by creating an electrokinetic fluid flow that carries all the particles from where they are to where they should be at each time step. The control loop comprises sensing, computation, and actuation to steer particles along trajectories. Particle locations are identified in real-time by an optical system and transferred to a control algorithm that then determines the electrode voltages necessary to create a flow field to carry all the particles to their next desired locations. The process repeats at the next time instant. I address following aspects of this technology. First I explain control and vision algorithms for steering single and multiple particles, and show extensions of these algorithms for steering in three dimensional (3D\) spaces. Then I show algorithms for calculating power minimum paths for steering multiple particles in actuation constrained environments. With this microfluidic system I steer biological cells and nano particles \201quantum dots\202 to nano meter precision.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2010
- Accession Number
- ADA637093
Entities
People
- Roland Probst
Organizations
- University of Maryland