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Visualization and numerical modelling of microfluidic on-chip injection processes.

David Sinton1, Liqing Ren, Dongqing Li

  • 1Department of Mechanical & Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, Ontario, Canada M5S 3G8.

Journal of Colloid and Interface Science
|April 11, 2003
PubMed
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Microfluidic-cross chips enable precise sample injection. Larger, concentrated samples are well-suited for transport but sensitive to pressure effects during loading, especially from reservoir meniscus disturbances.

Area of Science:

  • Microfluidics
  • Analytical Chemistry
  • Biotechnology

Background:

  • Microfluidic devices are crucial for precise fluid handling.
  • Efficient sample injection is vital for various analytical applications.
  • Understanding fluid behavior in microchannels is key to optimizing performance.

Purpose of the Study:

  • To investigate sample injection processes using a microfluidic-cross chip.
  • To analyze the formation and manipulation of different sample geometries.
  • To evaluate the suitability of large, concentration-dense samples for transport.

Main Methods:

  • Experimental investigation using fluorescent dye for visualization.
  • Numerical simulations to predict sample behavior.
  • Varying electric fields and dye mobilities to control sample geometry.

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  • Direct visualization to verify numerical predictions.

    • Main Results:

    • Achieved different sample geometries by controlling electric fields and dye properties.
    • Demonstrated the ability to load and dispense large axial extent samples.
    • Larger samples showed lower concentration gradients, improving transport and reducing diffusion sensitivity.
    • Identified increased sensitivity of larger samples to pressure effects during loading.

    Conclusions:

    • Microfluidic-cross chips can effectively manage sample injection for diverse geometries.
    • Large axial extent samples offer advantages in transport but require careful management during loading.
    • Laplace pressure from reservoir meniscus curvature is a significant factor affecting sample loading.