A pulse-powered microfluidic infusion pump: Preliminary human subject flow rate data
POSTER
Abstract
We fabricated two-layer, pulse-driven microfluidic pumps using stereolithographic 3D printing and soft lithography techniques. Devices were tested by affixing them to the wrist above the radial pulse found, and the resulting flow rate was measured using video tracking and the Tracker software. Linear regression models were evaluated to correlate the resulting flow rate and channel design, and user variables. Channel design parameters included channel length, width, inlet depth, and outlet depth. User variables included age, race, sex, height, weight, heart rate, blood pressure, subcutaneous fat percentage, wrist circumference, and the three conditions (at rest, during wrist motion, and after jogging) of the subject.
The study found significant differences in the flow rate of the device between resting and wrist rotation (0.00105 μL/s rest, 0.00513 μL/s wrist rotation, p=0.0003) and between resting and after jogging conditions (0.00172 μL/s after jogging, p=0.0421). No other user or channel design variables demonstrated significant differences in the resting device flow rate. In application, the increased flow rate during physical activity or motion could introduce adverse medicinal responses, suggesting a future need for device design refinement. Channel geometry modification offers a design strategy for flow rate control, and we will explore more channel design combinations to identify optimal device designs tailored for specific subjects.
Publication: 1. Zhang, S., Davalos, R., & Staples, A. (2024, November). Pulse-driven microfluidic pumps with varying depth flow channels for fine flow rate tuning. In APS Division of Fluid Dynamics Meeting Abstracts (pp. L11-011).<br>2. Chatterjee, K., Graybill, P. M., Socha, J. J., Davalos, R. V., & Staples, A. E. (2021). Frequency-specific, valveless flow control in insect-mimetic microfluidic devices. Bioinspiration & Biomimetics, 16(3), 036004.
Presenters
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Harsheel S Dhruva
Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, Virginia, United States 24061
Authors
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Harsheel S Dhruva
Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, Virginia, United States 24061
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Kaleb Belcher
Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, Virginia, United States 24061
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Shuyu Zhang
Virginia Tech – Wake Forest School of Biomedical Engineering and Sciences, Blacksburg, VA 24061, Virginia Tech-Wake Forest School of Biomedical Engineering and Sciences, Blacksburg, Virginia, United States 24061
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Benny Li
Department of Statistics, Virginia Tech, Blacksburg, Virginia, United States 24061
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Makayla Woodward
Department of Biological Sciences, Virginia Tech, Blacksburg, Virginia, United States 24061
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Leanna L Hours
Department of Statistics, Virginia Tech, Blacksburg, Virginia, United States 24061
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Rafael V Davalos
Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech – Emory University, Atlanta, GA 30322
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Anne E Staples
Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA, 24061, Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, 24061