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Microsystem Technologies

, Volume 23, Issue 9, pp 3901–3908 | Cite as

SU-8 microchannels for live cell dielectrophoresis improvements

  • Pavel Fikar
  • Gaelle Lissorgues
  • Lionel Rousseau
  • Olivier Francais
  • Bruno Le Pioufle
  • Feriel S. Hamdi
  • Vjaceslav Georgiev
  • Daniel Georgiev
Technical Paper
  • 247 Downloads

Abstract

In this work a novel highly precise SU-8 fabrication technology is employed to construct microfluidic devices for sensitive dielectrophoretic (DEP) manipulation of budding yeast cells. A benchmark microfluidic live cell sorting system is presented, and the effect of microchannel misalignment above electrode topologies on live cell DEP is discussed in detail. Simplified model of budding Saccharomyces cerevisiae yeast cell is presented and validated experimentally in fabricated microfluidic devices. A novel fabrication process enabling rapid prototyping of microfluidic devices with well-aligned integrated electrodes is presented and the process flow is described. Identical devices were produced with standard soft-lithography processes. In comparison to standard PDMS based soft-lithography, an SU-8 layer was used to construct the microchannel walls sealed by a flat sheet of PDMS to obtain the microfluidic channels. Direct bonding of PDMS to SU-8 surface was achieved by efficient wet chemical silanization combined with oxygen plasma treatment of the contact surface. The presented fabrication process significantly improved the alignment of the microstructures. While, according to the benchmark study, the standard PDMS procedure fell well outside the range required for reasonable cell sorting efficiency. In addition, PDMS delamination above electrode topologies was significantly decreased over standard soft-lithography devices. The fabrication time and costs of the proposed methodology were found to be roughly the same.

Keywords

PDMS Microfluidic Device Benchmark Problem Electrode Structure Oxygen Plasma Treatment 
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.

Notes

Acknowledgments

This work was supported by the European Regional Development Fund (ERDF), project “NTIS-New Technologies for the Information Society”, European Centre of Excellence, CZ.1.05/1.1.00/02.0090 and the Ministry of Education of the Czech Republic within the SGS Project No. SGS-2013-041.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Pavel Fikar
    • 1
    • 2
    • 3
  • Gaelle Lissorgues
    • 3
  • Lionel Rousseau
    • 3
  • Olivier Francais
    • 4
  • Bruno Le Pioufle
    • 4
  • Feriel S. Hamdi
    • 4
  • Vjaceslav Georgiev
    • 1
  • Daniel Georgiev
    • 2
    • 5
  1. 1.Department of Applied Electronics and Telecommunications, Faculty of Electrical EngineeringUniversity of West BohemiaPilsenCzech Republic
  2. 2.New Technologies for the Information Society European Centre of Excellence, Faculty of Applied SciencesUniversity of West Bohemia in PilsenPilsenCzech Republic
  3. 3.Universite Paris Est, ESYCOM EA2552Noisy Le GrandFrance
  4. 4.Ecole Normale Superieure de Cachan, CNRS, SATIE, UMR 8029CachanFrance
  5. 5.Department of CyberneticsUniversity of West BohemiaPilsenCzech Republic

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