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A capillary flow-driven microfluidic system for microparticle-labeled immunoassays

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Abstract

A simple, reliable, and self-powered capillary flow-driven microfluidic platform is developed for conducting microparticle-labeled immunoassays. To obtain the washing forces and binding kinetics appropriate for microparticle-labeled immunoassays, both microchannel networks and sample access holes are designed and characterized to confirm the fluidic routes. To demonstrate two different types of immunoassays, serial and parallel capillary-driven microfluidic platforms were developed for mouse immunoglobulin G (IgG) and cardiac troponin I (cTnI) using detection antibody-conjugated microparticles, respectively. With the serial capillary-driven microfluidic platform, we successfully demonstrated IgG quantification using direct immunoassay and achieved a limit of detection (LOD) of 30 pM by using pre-immobilized mouse IgG. In the parallel capillary-driven microfluidic platform, a sandwich immunoassay for detecting cTnI was demonstrated and a clinically relevant LOD as low as 4.2 pM was achieved with minimal human intervention. In both assays, the association rate constants (Ka) were measured to estimate the overall assay time. According to these estimations, microparticle-labeled immunoassays could be conducted in a few minutes using the proposed capillary-driven microfluidic devices. By coupling with various magnetic sensors, these simple immunoassay platforms enable us to achieve a true sample-in-answer-out device that can screen for a variety of targets without relying on external power sources for fluidic manipulation.

Graphical abstract: A capillary flow-driven microfluidic system for microparticle-labeled immunoassays

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Publication details

The article was received on 14 May 2018, accepted on 31 May 2018 and first published on 01 Jun 2018


Article type: Paper
DOI: 10.1039/C8AN00898A
Citation: Analyst, 2018, Advance Article
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    A capillary flow-driven microfluidic system for microparticle-labeled immunoassays

    A. Khodayari Bavil and J. Kim, Analyst, 2018, Advance Article , DOI: 10.1039/C8AN00898A

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