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Wearable chemical sensing – optimizing fluidics for real-time sweat analysis

Deignan, Jennifer and Florea, Larisa and Coyle, Shirley and Diamond, Dermot (2016) Wearable chemical sensing – optimizing fluidics for real-time sweat analysis. In: Conference on Analytical Sciences Ireland 2016, 14-15 Apr 2016, DCU, Dublin, Ireland.

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Abstract

This work presents the optimization of electrical parameters and sampling platforms to maximize the sensitivity of conductivity measurements for applications in wearable sweat sensing. Conductivity in sweat is related to electrolyte content and offers valuable information for hydration, athletic performance and nutrition. The most abundant ions of sweat electrolytes are sodium and chloride, making sweat conductivity directly related to their concentration [1]. Capacitively coupled contactless conductivity detection (C4D) was used to test the response of commercial gold microelectrodes (figure A). This work was done in preparation for the development of an on-body detection system for sweat analysis. On body testing requires sample handling specific to the method of testing, which in this case is non-contact. For this type of handling, polydimethylsiloxane (PDMS) and poly(methyl methacrylate) (PMMA) microchannels of various configurations were tested for their compatibility with the system and the effect of their geometry on signal sensitivity. Figures B-D show the dimensions of one such PMMA channel from top, bottom and expanded side view, respectively.

Item Type:Conference or Workshop Item (Other)
Event Type:Conference
Refereed:No
Subjects:Physical Sciences > Electrochemistry
Medical Sciences > Health
Physical Sciences > Chemistry
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Science and Health > School of Chemical Sciences
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Research Initiatives and Centres > National Centre for Sensor Research (NCSR)
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland, Insight Centre for Data Analytics
ID Code:21203
Deposited On:06 May 2016 14:03 by Ms Jennifer Deignan. Last Modified 01 Mar 2017 01:02

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