A novel manual rotating fluid control mechanism in a microfluidic device with a finger-actuated pump for dual-mode sweat sampling

Abstract

Wearable sweat analysis using microfluidics offers a non-invasive approach for real-time health monitoring, with applications in chronic disease management, athletic performance optimization, and early-stage condition detection. However, most existing wearable sweat microfluidic devices are limited to single-mode operation either real-time or on-demand sampling and often lack precise control over sample volume, which compromises analytical accuracy and utility. To address these limitations, we present a novel wearable microfluidic device featuring a manual rotating fluid control mechanism and a finger-actuated pump for dual-mode sweat sampling. The rotational control mechanism directs sweat either into detection chambers for volume-independent sensor reactions or through the finger-actuated pump for precise volume control. The pump incorporates a dedicated collection chamber, enabling sweat accumulation and controlled delivery in a single actuation, ensuring reproducible sample volumes and facilitating on-demand analysis when required. Additionally, the device integrates two reaction chambers for simultaneous dual biomarker detection. Performance validation during a 40 minute exercise session, using a colorimetric glucose assay, demonstrated reliable sweat sampling and on-demand biochemical analysis. These results highlight the device's potential as a practical tool for personalized health monitoring and field applications.

Graphical abstract: A novel manual rotating fluid control mechanism in a microfluidic device with a finger-actuated pump for dual-mode sweat sampling

Supplementary files

Article information

Article type
Paper
Submitted
19 Feb 2025
Accepted
13 Mar 2025
First published
24 Mar 2025

Lab Chip, 2025, Advance Article

A novel manual rotating fluid control mechanism in a microfluidic device with a finger-actuated pump for dual-mode sweat sampling

M. I. H. Gama, S. Abdullahi, M. O. M. Omer, Z. Yang, X. Wang, Y. B. M. Osman, Y. Liu, J. Li, Y. Li, X. Gao and Z. Nie, Lab Chip, 2025, Advance Article , DOI: 10.1039/D5LC00171D

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