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Soft, skin-interfaced microfluidic systems with integrated enzymatic assays for measuring the concentration of ammonia and ethanol in sweat

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Abstract

Eccrine sweat is a rich and largely unexplored biofluid that contains a range of important biomarkers, from electrolytes, metabolites, micronutrients and hormones to exogenous agents, each of which can change in concentration with diet, stress level, hydration status and physiologic or metabolic state. Traditionally, clinicians and researchers have used absorbent pads and benchtop analyzers to collect and analyze the biochemical constituents of sweat in controlled, laboratory settings. Recently reported wearable microfluidic and electrochemical sensing devices represent significant advances in this context, with capabilities for rapid, in situ evaluations, in many cases with improved repeatability and accuracy. A limitation is that assays performed in these platforms offer limited control of reaction kinetics and mixing of different reagents and samples. Here, we present a multi-layered microfluidic device platform with designs that eliminate these constraints, to enable integrated enzymatic assays with demonstrations of in situ analysis of the concentrations of ammonia and ethanol in microliter volumes of sweat. Careful characterization of the reaction kinetics and their optimization using statistical techniques yield robust analysis protocols. Human subject studies with sweat initiated by warm-water bathing highlight the operational features of these systems.

Graphical abstract: Soft, skin-interfaced microfluidic systems with integrated enzymatic assays for measuring the concentration of ammonia and ethanol in sweat

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Publication details

The article was received on 22 Oct 2019, accepted on 20 Nov 2019 and first published on 20 Nov 2019


Article type: Paper
DOI: 10.1039/C9LC01045A
Lab Chip, 2020, Advance Article

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    Soft, skin-interfaced microfluidic systems with integrated enzymatic assays for measuring the concentration of ammonia and ethanol in sweat

    S. B. Kim, J. Koo, J. Yoon, A. Hourlier-Fargette, B. Lee, S. Chen, S. Jo, J. Choi, Y. S. Oh, G. Lee, S. M. Won, A. J. Aranyosi, S. P. Lee, J. B. Model, P. V. Braun, R. Ghaffari, C. Park and J. A. Rogers, Lab Chip, 2020, Advance Article , DOI: 10.1039/C9LC01045A

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