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Review and analysis of performance metrics of droplet microfluidics systems

Microfluidics and Nanofluidics volume 16, pages 921–939 (2014)Cite this article

Abstract

Droplet microfluidics has enabled many recent applications in high-throughput screening and diagnostics. Little work has been done, however, to analyze the performance of droplet-based assays. This review aims to apply what is known in the literature to the analysis of the performance metrics of droplet-based assays, with specific relevance to diagnostic and biomedical applications based on two processes: enzymatic reactions and cell culture in droplets. By considering the physical scaling of individual processes—droplet generation, reaction kinetics, cell growth, and droplet interrogation—it is possible to extract a practical relationship between input parameters (e.g., droplet size and droplet polydispersity) and the output characteristics (e.g., throughput, dynamic range, and accuracy) of the assay. This review can serve as a guide to the design of droplet-based assays for achieving desired performance. While the focus is on assays based on enzymatic reactions and cell cultures, a similar analysis can be applied to other assays based on polymerase chain reaction and the detection of nucleic acids.

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Acknowledgments

We acknowledge funding from the Stanford Center for Innovation in Global Health, Stanford Woods Institute for the Environment, the California Sea Grant Project No. R/CONT-219 through NOAA’s National Sea Grant College Program, 3M, and the Donors of the American Chemical Society Petroleum Research Fund for partial support of this research. We acknowledge Y. Chen for initial help with the manuscript.

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  1. Department of Mechanical Engineering, Stanford University, Stanford, CA, 94305, USA

    Liat Rosenfeld, Tiras Lin & Sindy K. Y. Tang

  2. Department of Chemistry, University of Alberta, Edmonton, Canada

    Ratmir Derda

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  1. Liat Rosenfeld
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Correspondence to Sindy K. Y. Tang.

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Rosenfeld, L., Lin, T., Derda, R. et al. Review and analysis of performance metrics of droplet microfluidics systems. Microfluid Nanofluid 16, 921–939 (2014). https://doi.org/10.1007/s10404-013-1310-x

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