Quantifying the velocity field near the wall in microfluidic devices is important because surface effects become significant at micro- to nanometer scales. Recent studies have suggested that the "no-slip" boundary condition breaks down in microscale flows of Newtonian liquids, where the amount of slip is usually extrapolated from velocity components measured far from the wall. This doctoral thesis presents a new technique, multilayer nano-particle image velocimetry (MnPIV), for measuring the tangential velocity components at different distances from and within about 500 nm of the wall and its application to measuring slip.
The feasibility of MnPIV was demonstrated using synthetic images of plane Couette flow incorporating Brownian diffusion and imaging noise. The errors in MnPIV data were then quantified with Brownian dynamics simulations. Calibration experiments were used to correlate the image intensity of the tracer to its distance from the wall z.
Multilayer nPIV was then used to determine the z-positions and distribution of the particles for z < 500 nm in experimental studies of microscale Poiseuille flow. The tracers were divided into three distinct layers based on their image intensities, and the average velocity of each layer was placed at the average z-position sampled by the particles in that layer. The resultant velocity gradients were within 6% on average of analytical predictions for 2D Poiseuille flow. Finally, the results of MnPIV studies of aqueous solutions flowing through microchannels with hydrophilic and hydrophobically-coated fused silica surfaces suggest that if the slip lengths are nonzero for both of these surfaces, they are less than the uncertainty in these results, or 27 nm and 31 nm for the hydrophilic and hydrophobic channels, respectively.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/26472 |
| Date | 17 November 2008 |
| Creators | Li, Haifeng |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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