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Magnetic Porous Hydrogel-Enhanced Wearable Patch Sensor for Sweat Zinc Ion Monitoring
Yao Chu1, Zhengzhong LvZeng1, Kaijie Lu1
1College of Chemistry and Materials Science, Shanghai Normal University, Shanghai 200234, China.
Sensors (Basel, Switzerland)
|September 14, 2024
Summary
This study introduces a novel hydrogel-based wearable sensor for real-time zinc ion (Zn2+) monitoring in sweat. The advanced sensor overcomes limitations of existing methods, enabling faster and more accurate physiological measurements.
Area of Science:
- Biomedical Engineering
- Materials Science
- Analytical Chemistry
Background:
- Wearable sensors for sweat analysis face challenges in efficient sweat collection and real-time signal detection.
- Current methods require significant exercise to induce sweating, leading to delayed and prolonged detection signals that do not reflect real-time bodily changes.
Purpose of the Study:
- To develop a multifunctional hydrogel-based wearable sensor for real-time monitoring of zinc ions (Zn2+) in human sweat.
- To address the limitations of existing sweat detection technologies, including slow sweat collection and delayed signal recording.
Main Methods:
- Fabrication of a multifunctional hydrogel with magnetic and porous properties using Fe3O4 nanoparticles.
- Integration of the hydrogel with a flexible patch electrode to create a wearable sensing platform.
- Utilizing differential pulsed anodic striping voltammetry (DPASV) for Zn2+ detection.
Main Results:
- The hydrogel's porous structure facilitated rapid sweat absorption, while Fe3O4 nanoparticles enhanced conductivity and structural adjustability.
- The developed sensing platform demonstrated a linear relationship for Zn2+ detection within the concentration range of 0.16–16 µg/mL.
- Successful real-time detection of sweat Zn2+ in four volunteers during both exercise and resting states.
Conclusions:
- The novel hydrogel-based wearable sensor enables rapid and real-time monitoring of sweat Zn2+.
- This technology overcomes key challenges in sweat collection and detection, offering a promising approach for physiological monitoring.
- The platform shows potential for commercial applications in non-invasive health diagnostics.

