Poster J12 - Liquid-based colorimetric assay development for microfluidic wearable sweat sensing applications

Friday, October 25, 2024

3:30 PM - 4:30 PM EST

Location: Exhibit Hall E, F & G

Presenting Author(s)

Anthony Sukendro

Bioengineering Student Researcher
Santa Clara University
Franklin Township, New Jersey, United States

Co-Author(s)

Andrea Y. Pader

Undergraduate Researcher
Santa Clara University
Santa Clara, California, United States
RA

Rana Altay, MSc (she/her/hers)

PhD Student
Santa Clara University
SANTA CLARA, California, United States

Primary Investigator(s)

IA

Ismail E. Araci, PhD

Associate Prof.
Santa Clara University, United States

Introduction: Wearable microfluidic sweat sensors have been of academic and commercial interest due to their capabilities as non-obstructive, convenient, and noninvasive health monitoring devices. Human sweat is found to have correlated metabolite concentrations with human serum [1]. Most research in this field has revolved around the development of electrochemical sensors with continuous and multiplexed biochemical measurement capability. As a simpler and lower-cost alternative, colorimetric measurements can provide information at a single time point or at discrete time points (i.e., through chrono-sampling) [2, 3]. However, chrono-sampling methods are limited in terms of the data points that it can provide (e.g., 5-15 data points [1]) due to chip size limitations [4]. We propose a liquid-based colorimetric assay to analyze the sweat with better time-resolution and with smaller patches. Such an approach requires the ability to generate liquid flow on the skin in a power-free manner The power-free skin-mountable pumps developed in our group can facilitate liquid (e.g., reagents, sweat) extraction and movement within such a chip [5]. Therefore, we can use an image-based colorimetric sweat sensor method using liquid reagents to gather information about the user’s sweat composition and its dynamics through the measurement of color gradient variations in a microfluidic channel,

Here we report on a liquid-based colorimetric assay concept and its feasibility to create an observable gradient from which the user can extract information over a period of exercise via smartphone imaging and analysis. Figure 1a shows the device we have designed to test this concept on the benchtop.

Materials and

Methods: Device Fabrication:
The polydimethylsiloxane (PDMS) chip is fabricated using a 3D-printed clear resin mold (designed by SolidWorks and printed using Formlabs). A 10:1 (A:B) ratio of PDMS is poured atop the mold and cured for 30 minutes in an 80oC oven with 0.5 mm diameter holes at the two inlets and outlet. A 20:1 ratio of PDMS is spun on a glass microscope slide at 200 rpm. The base was cured for 7 minutes in the 80oC oven. The two layers are then thermal bonded for 45 minutes.

Characterization of Gradient Formation
We characterized the gradient formed in the observation channel on the benchtop by applying pressure (Elveflow OB1) to sealed cryotubes to drive liquid into the chip inlets. On the reagent side, we applied a 50:50 ratio of pH reagent (mixture of ethanol, phenolphthalein, bromothymol blue, and methyl red) and vegetable glycerin for added viscosity to assist in color-retention. On the sample side, we applied samples with physiologically realistic pH values; 1g/100mL solution of baking soda in deionized (DI) water (sample 1, pH = 8.9) and pure DI water (sample 2, pH= 5.3). We used baking soda as a substitute for sweat during exercise, and DI water as passive sweat. applied a continuous pressure value into each inlet and let the chip fill. After the chip was filled, we took an image, and we recorded the chip image every 5 minutes, up to 60 minutes. These images were analyzed using ImageJ, NIH analysis tools.

Results, Conclusions, and Discussions: Using the above-mentioned method, we observed consistent mixing in the T-junction chip and that it can produce a gradient when sample 1 is input followed by sample 2. The yellow-blue gradient formed with varying intensities depending on the flow rates. Higher flow rates of the sample that correspond to unrealistically high sweat rates produced fainter gradients. We have used a sample flow rate of 2.2 µL/min and reagent flow rate of 2.9 µL/min.

We tested different volumes of sample 1 to simulate different sweat time dynamics based on physiological sweat rates in previous literature [6]. The different sample 1 sweat volumes (3, 6, and 9 µL) followed by sample 2 produced gradients with different sizes and locations within the observation channels as seen in Figures 2a-c. Figure 2d introduces the concept of a herringbone pattern chaotic mixer to help with the mixing process to create a more consistent hue across large regions of the same pH.

Figure 3 shows how changes in sample 1 volume (i.e., sweat composition dynamics) affect the color gradient location and size (slope) within a microfluidic channel. This gradient pattern is retained within the channel without a significant change for more than 40 minutes (i.e., a typical exercise period), This data suggests that we can use the gradients’ size and location captured via smartphone imaging to characterize the user’s sweat patterns.

The user can actuate the chip to induce assay mixing within the chip via strain-actuated pumping systems. This allows us to use a refillable liquid reagent in small conservative quantities to continuously test concentrations of sweat samples in vitro. Once the chip fills, the color gradient established in the serpentine observation channels should show not only the concentrations of the sweat, but also how long those metabolite concentrations last. We have also explored the merits of a herringbone-pattern chaotic mixer as shown in Figures 1b and 1c and its effects on assay mixing and gradient formation to produce results that are more consistent.

Acknowledgements (Optional): This project was supported by the Kuehler grant and NSF program 19-582, grant 2045087. The devices were fabricated in the Center for Nanostructures at Santa Clara University.

References
[1] A. Hauke et al., “Complete validation of a continuous and blood-correlated sweat biosensing device with integrated sweat stimulation,” Lab on a Chip, vol. 18, no. 24, pp. 3750–3759, 2018, doi: https://doi.org/10.1039/c8lc01082j.

[2] W. Liu, H. Cheng, and X. Wang, “Skin-interfaced colorimetric microfluidic devices for on-demand sweat analysis,” npj Flexible Electronics, vol. 7, no. 1, Sep. 2023, doi: https://doi.org/10.1038/s41528-023-00275-y.

[3] Y. Sasaki, X. Lyu, and T. Minami, “A Highly Accurate pH Detection Method for Sweat Analysis using a Printed 96‐Microwell Colorimetric Sensor Array,” Analysis & Sensing, vol. 3, no. 5, Dec. 2022, doi: https://doi.org/10.1002/anse.202200097.

[4] Sung Wan Kim et al., “Super‐Absorbent Polymer Valves and Colorimetric Chemistries for Time‐Sequenced Discrete Sampling and Chloride Analysis of Sweat via Skin‐Mounted Soft Microfluidics,” Small, vol. 14, no. 12, pp. 1703334–1703334, Mar. 2018, doi: https://doi.org/10.1002/smll.201703334.

[5] C. B. Askar, N. Cmager, R. Altay, and I. E. Araci, “Human Activity Recording Based on Skin-Strain-Actuated Microfluidic Pumping in Asymmetrically Designed Micro-Channels,” Sensors, vol. 24, no. 13, p. 4207, Jan. 2024, doi: https://doi.org/10.3390/s24134207.

[6] K. Kwon et al., “An on-skin platform for wireless monitoring of flow rate, cumulative loss and temperature of sweat in real time,” Nature Electronics, vol. 4, no. 4, pp. 302–312, Apr. 2021, doi: https://doi.org/10.1038/s41928-021-00556-2.