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Issue 18, 2013
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Flow switching in microfluidic networks using passive features and frequency tuning

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Abstract

Manipulating fluids in microchips remains a persistent challenge in the development of inexpensive and portable point-of-care diagnostic tools. Flow in microfluidic chips can be controlled via frequency tuning, wherein the excitation frequency of a pressure source is matched with the characteristic frequencies of network branches. The characteristic frequencies of each branch arise from coupling between fluid in the channels and passive deformable features, and can be programmed by adjusting the dimensions and stiffness of the features. In contrast to quasi-static ‘on–off’ valves, such networks require only a single active element and relatively small dynamic displacements. To achieve effective flow switching between different pathways in the chip, well-separated peak frequencies and narrow bandwidths are required (such that branches are independently addressable). This paper illustrates that high selectivity can be achieved in fluidic networks that exploit fluidic inertia, with flow driven selectively at peak frequencies between ∼1–100 Hz with bandwidths less than ∼25% of the peak frequency. Precise frequency-based flow switching between two on-chip microchannels is demonstrated. A simple theoretical framework is presented that predicts the characteristic frequencies in terms of feature properties, thus facilitating the design of networks with specific activation frequencies. The approach provides a clear pathway to simplification and miniaturization of flow-control hardware for microchips with several fluidic domains.

Graphical abstract: Flow switching in microfluidic networks using passive features and frequency tuning

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Publication details

The article was received on 17 Apr 2013, accepted on 25 Jun 2013 and first published on 26 Jun 2013


Article type: Paper
DOI: 10.1039/C3LC50481F
Lab Chip, 2013,13, 3668-3674

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    Flow switching in microfluidic networks using passive features and frequency tuning

    R. R. Collino, N. Reilly-Shapiro, B. Foresman, K. Xu, M. Utz, J. P. Landers and M. R. Begley, Lab Chip, 2013, 13, 3668
    DOI: 10.1039/C3LC50481F

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