Experiments in Fluids

, 47:849 | Cite as

Wavefront sensing for three-component three-dimensional flow velocimetry in microfluidics

  • S. Chen
  • N. Angarita-Jaimes
  • D. Angarita-Jaimes
  • B. Pelc
  • A. H. Greenaway
  • C. E. Towers
  • D. Lin
  • D. P. TowersEmail author
Research Article


We present the application of wavefront sensing to three-component, three-dimensional micro particle tracking velocimetry (μPTV). The technique is based upon examining the defocus of the wavefront scattered by a tracer particle and from such information establishing the 3-D tracer location. The imaging system incorporates a cylindrical lens acting as an anamorphic element that creates different magnifications in the two orthogonal axes. A single anamorphic image is obtained from each tracer, which contains sufficient information to reconstruct the wavefront defocus and uniquely identify the tracer’s axial position. A mathematical model of the optical system is developed and shows that the lateral and depth performance of the sensor can be largely independently varied across a wide range. Hence, 3-D image resolution can be achieved from a single viewpoint, using simple and inexpensive optics and applied to a wide variety of microfluidic or biological systems. Our initial results show that an uncertainty in depth of 0.18 μm was achieved over a 20-μm range. The technique was employed to measure the 3-D velocity field of micron-sized fluorescent tracers in a flow within a micro channel, and an uncertainty of 2.8 μm was obtained in the axial direction over a range of 500 μm. The experimental results were in agreement with the expected fluid flow when compared to the corresponding CFD model. Thus, wavefront sensing proved to be an effective approach to obtain quantitative measurements of three-component three-dimensional flows in microfluidic devices.


Particle Image Velocimetry Particle Image Measurable Range Cylindrical Lens Particle Tracking Velocimetry 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank EPSRC for funding this research through grant GR/S96555 and GR/S61720.


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Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • S. Chen
    • 1
  • N. Angarita-Jaimes
    • 1
  • D. Angarita-Jaimes
    • 1
  • B. Pelc
    • 1
  • A. H. Greenaway
    • 2
  • C. E. Towers
    • 1
  • D. Lin
    • 3
  • D. P. Towers
    • 1
    Email author
  1. 1.School of Mechanical EngineeringUniversity of LeedsLeedsUK
  2. 2.School of Engineering and Physical SciencesHeriot-Watt UniversityEdinburghUK
  3. 3.Optoelectronics Research CentreUniversity of SouthamptonSouthamptonUK

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