Microfluidic Lab-on-a-Chip Platforms: Requirements, Characteristics and Applications

  • D. Mark
  • S. Haeberle
  • G. Roth
  • F. Von Stetten
  • R. Zengerle
Conference paper
Part of the NATO Science for Peace and Security Series A: Chemistry and Biology book series (NAPSA)


This review summarizes recent developments in microfluidic platform approaches. In contrast to isolated application-specific solutions, a microfluidic platform provides a set of fluidic unit operations, which are designed for easy combination within a well-defined fabrication technology. This allows the implementation of different application-specific (bio-) chemical processes, automated by microfluidic process integration [1]. A brief introduction into technical advances, major market segments and promising applications is followed by a detailed characterization of different microfluidic platforms, comprising a short definition, the functional principle, microfluidic unit operations, application examples as well as strengths and limitations. The microfluidic platforms in focus are lateral flow tests, linear actuated devices, pressure driven laminar flow, microfluidic large scale integration, segmented flow microfluidics, centrifugal microfluidics, electro-kinetics, electrowetting, surface acoustic waves, and systems for massively parallel analysis. The review concludes with the attempt to provide a selection scheme for microfluidic platforms which is based on their characteristics according to key requirements of different applications and market segments. Applied selection criteria comprise portability, costs of instrument and disposable, sample throughput, number of parameters per sample, reagent consumption, precision, diversity of microfluidic unit operations and the flexibility in programming different liquid handling protocols.


Unit Operation Surface Acoustic Wave Microfluidic Chip Microfluidic Channel Microfluidic Platform 
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.



We would like to thank our colleagues Junichi Miwa and Sven Kerzenmacher for their helpful suggestions and assistance during the preparation of this manuscript.


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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • D. Mark
    • 1
  • S. Haeberle
    • 1
    • 2
  • G. Roth
    • 1
    • 2
  • F. Von Stetten
    • 1
    • 2
  • R. Zengerle
    • 1
    • 2
  1. 1.HSG-IMIT - Institut für Mikro- und InformationstechnikVillingen-SchwenningenGermany
  2. 2.Laboratory for MEMS Applications, Department of Microsystems Engineering (IMTEK)University of FreiburgFreiburgGermany

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