Integrated microfluidic colorimetric patch with auto-framing APP for multiplex temporal detection of ketone bodies in sweat

Abstract

Ketone bodies are key products of fat metabolism, primarily consisting of acetoacetate (AcAc), β-hydroxybutyrate (BHB), and acetone (acetone). Monitoring the concentration of ketone bodies in sweat can reflect the metabolic status of the body; it is also particularly significant in areas such as diabetes management, exercise monitoring, and the evaluation of the ketogenic diet. This paper presents a microfluidic patch for sweat collection and multiplex detection of AcAc, BHB and glucose. The microfluidic patch can achieve time-sequential sensing through Tesla valves, hydrophilic coatings, and unique chamber structural design. The concentrations of the three substances are quantified using colorimetric methods. Additionally, this study has designed a colorimetric app which can achieve automatic framing and detect the grayscale value of the colored area. Experimental results show that the patch can accurately detect changes in the concentrations of the three substances within specific ranges. The linear detection range for AcAc is 0.25 mM to 8 mM, the limit of detection (LOD) is 0.08 mM; for BHB, the linear detection range is 0.05 mM to 0.80 mM, the LOD is 0.02 mM; and for glucose, the linear detection range is 62.50 μM to 1000 μM, the LOD is 20.83 μM. In the future, this technology is expected to be applied to portable metabolic monitoring devices, offering a convenient solution for personal health management.

Graphical abstract: Integrated microfluidic colorimetric patch with auto-framing APP for multiplex temporal detection of ketone bodies in sweat

Supplementary files

Article information

Article type
Paper
Submitted
24 Feb 2025
Accepted
13 Apr 2025
First published
16 Apr 2025

Lab Chip, 2025, Advance Article

Integrated microfluidic colorimetric patch with auto-framing APP for multiplex temporal detection of ketone bodies in sweat

T. Xue, J. Shen, W. Lin, J. Zhou, X. Zhang, C. Chen, J. Liu and G. Zhu, Lab Chip, 2025, Advance Article , DOI: 10.1039/D5LC00189G

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