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Issue 10, 2017

Eyeglasses based wireless electrolyte and metabolite sensor platform

Author affiliations

Abstract

The demand for wearable sensors has grown rapidly in recent years, with increasing attention being given to epidermal chemical sensing. Here, we present the first example of a fully integrated eyeglasses wireless multiplexed chemical sensing platform capable of real-time monitoring of sweat electrolytes and metabolites. The new concept has been realized by integrating an amperometric lactate biosensor and a potentiometric potassium ion-selective electrode into the two nose-bridge pads of the glasses and interfacing them with a wireless electronic backbone placed on the glasses' arms. Simultaneous real-time monitoring of sweat lactate and potassium levels with no apparent cross-talk is demonstrated along with wireless signal transduction. The electrochemical sensors were screen-printed on polyethylene terephthalate (PET) stickers and placed on each side of the glasses' nose pads in order to monitor sweat metabolites and electrolytes. The electronic backbone on the arms of the glasses' frame offers control of the amperometric and potentiometric transducers and enables Bluetooth wireless data transmission to the host device. The new eyeglasses system offers an interchangeable-sensor feature in connection with a variety of different nose-bridge amperometric and potentiometric sensor stickers. For example, the lactate bridge-pad sensor was replaced with a glucose one to offer convenient monitoring of sweat glucose. Such a fully integrated wireless “Lab-on-a-Glass” multiplexed biosensor platform can be readily expanded for the simultaneous monitoring of additional sweat electrolytes and metabolites.

Graphical abstract: Eyeglasses based wireless electrolyte and metabolite sensor platform

Supplementary files

Article information


Submitted
24 Feb 2017
Accepted
25 Apr 2017
First published
26 Apr 2017

Lab Chip, 2017,17, 1834-1842
Article type
Paper

Eyeglasses based wireless electrolyte and metabolite sensor platform

J. R. Sempionatto, T. Nakagawa, A. Pavinatto, S. T. Mensah, S. Imani, P. Mercier and J. Wang, Lab Chip, 2017, 17, 1834 DOI: 10.1039/C7LC00192D

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