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Wearable chemical sensing – optimizing platforms and sensitivity for real-time sweat analysis

Deignan, Jennifer and Florea, Larisa and Coyle, Shirley and Diamond, Dermot (2014) Wearable chemical sensing – optimizing platforms and sensitivity for real-time sweat analysis. In: The 18th International Conference on Miniaturized Systems for Chemistry and Life Sciences, µTAS 2014, 26-30 Oct 2014, San Antonio, TX..

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Wearable sensors have applications in multiple aspects of health monitoring. In particular, real-time physiological and on-body measurements have benefits in many scenarios including monitoring chronic disease conditions, long-term rehabilitation programs, athletic training regimens, and tracking the wellbeing of first responders in highly dangerous environments. Sweat sensing is of particular interest due to the non-invasive nature of sampling. Comfortable wearable devices increase the likelihood of continuous use and therefore its tendency for adoption by users. However, the inherent complexity of chemical sensors has made them difficult to implement in reliable wearable systems. This is mainly due to the need for a representative sample to be in continuous contact with the sensor [1]. While this is a relatively simple task in the laboratory, environmental factors such as movement, temperature and signal interference affect real-time performance. This work presents the optimization of electrical parameters and sampling platforms to maximize the sensitivity of conductivity measurements for applications in wearable sweat sensing. A TraceDec capacitively coupled contactless conductivity detection (C4D) system was used to determine the conductivity of a range of NaCl solutions using a commercially available thin-film interdigitated microarray electrode (Micrux, Figure 1). Both Au and Pt electrodes were tested using the arrangement shown in Figures 2 and 3. Voltage, frequency and gain were adjusted over the ranges 2.5-80Vpp, 75-300kHz, and 50-150%, respectively. During optimization, human safety was taken into account with the intention of using the parameters to create a wearable device. In this context, the minimum voltage required to achieve acceptable sensitivity was selected. In addition, a range of PDMS microchips with different microchannel configurations were tested to find the maximum sensitivity over 10mM-130mM NaCl. The concentration range was chosen to encompass the average sweat conductivities in healthy ([NaCl] < 60 mM) and cystic fibrosis positive ([NaCl] ≥ 60 mM) adults before and during exercise. Multiple channels were created to optimize the design with respect to direction of flow and surface area achieved. The electrodes displayed high sensitivity, repeatability and stability over time. A linear relationship was found between concentration and voltage at high concentrations and low concentrations as shown in Figure 4. Measurements also showed high repeatability over the calibration range (relative standard deviation between 0.0087%-0.0726% for n=3) and high stability over 5 minute measurements (relative standard deviation between 0.0017%-0.0860% for n=5940) as shown in Figure 5. Good performance characteristics like these obtained in laboratory experiments are a necessary prerequisite for achieving reliable real-time on-body measurements. The results of this work will be implemented in a real-time, wearable device for monitoring the sweat conductivity. As sweat conductivity is dominated by NaCl concentration, conductivity can be used as an indicator for cystic fibrosis or to monitor the effectiveness of treatments, although it is not formally accepted as a diagnostic tool [2]. In addition, the device could aid in monitoring hydration levels of athletes in real-time. Most importantly, the optimization of a real-time sweat sampling platform could allow replacement or integration of other detection methods for sodium, or other physiological features.

Item Type:Conference or Workshop Item (Poster)
Event Type:Conference
Subjects:Biological Sciences > Microfluidics
Physical Sciences > Chemical detectors
Biological Sciences > Biosensors
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 under the Insight initiative, grant SFI/12/RC/2289.
ID Code:20316
Deposited On:21 Nov 2014 13:33 by Ms Jennifer Deignan. Last Modified 31 Dec 2015 01:02

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