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Contactless conductivity sensor for wearable sweat monitoring

Deignan, Jennifer, Florea, Larisa orcid logoORCID: 0000-0002-4704-2393, Coyle, Shirley orcid logoORCID: 0000-0003-0493-8963 and Diamond, Dermot orcid logoORCID: 0000-0003-2944-4839 (2016) Contactless conductivity sensor for wearable sweat monitoring. In: Analytical and Nanoanalytical Methods for Biomedical and Environmental Sciences, 29 Jun - 1 Jul 2016, Brasov, Romania.

Abstract
Analysis of sweat offers a wealth of information related to hydration, nutrition, and athletic performance. The conductivity of sweat can be directly related to sodium chloride concentration, as these are the most abundant electrolyte ions present in sweat [1]. Individuals affected by cystic fibrosis contain a higher concentration of Cl- ions in their sweat. Prescription medications for the disease reduce Cl- concentrations in sweat, and as a result, the efficacy of these medications can be monitored non-invasively with sweat collection. In previous work, we have demonstrated the use of capacitively coupled contactless conductivity detection (C4D) for testing the response of commercial gold microelectrodes to NaCl solutions using multiple sampling platforms [2]. This work presents the optimization of channel and sampling volumes to calculate and minimize the sensor’s response time for applications in wearable sweat sensing. In preparation for on-body testing, the functionality of the chip was optimized for relevant flow rates of sweat. Sweat rate can vary drastically depending upon the subject and body part. Additionally, those affected by cystic fibrosis have difficulty exercising for extended periods of time. Due to these restrictions, the volume of sweat needed to produce a signal is of critical importance. In this work, PDMS microchannels were created which minimized platform volume for on-body analysis. Using varying concentrations of NaCl solutions (10 mM - 130 mM) and the average flow rates for the arm (730 g/m2h), back (797 g/m2h) and forehead (894 g/m2h), a calibration curve was created and the average response time was calculated for each body location. Finally, tests were completed with artificial sweat and compared to the calibration curves. [1] Lezana, J. L.; Vargas, M. H.; Karam-Bechara, J.; Aldana, R. S. and Furuya, E. Y. J. Cyst. Fibros., 2003, (pp. 1-7). [2] Deignan, J.; Florea, L; Coyle, S; and Diamond, D. MicroTAS 2014, 2014 (pp. 2184-2186). Science foundation Ireland under the Insight initiative, grant SFI/12/RC/2289
Metadata
Item Type:Conference or Workshop Item (Speech)
Event Type:Conference
Refereed:Yes
Subjects:Medical Sciences > Exercise
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 Institutes and Centres > INSIGHT Centre for Data Analytics
Research Institutes 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 under the Insight initiative, grant SFI/12/RC/2289
ID Code:21277
Deposited On:26 Jul 2016 10:31 by Ms Jennifer Deignan . Last Modified 10 Jan 2022 14:42
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