Dual-Signal Amplification Strategy for Synchronous Monitoring of Stress Hormones and Metabolic Indicators

Area of Science:

• Biomedical Engineering
• Analytical Chemistry
• Materials Science

Background:

• Synchronous monitoring of stress hormones and metabolic indicators is vital for health management.
• Wearable sensors face challenges due to low biomarker concentrations and signal interference in sweat.
• Existing methods struggle with simultaneous, sensitive detection of multiple analytes.

Purpose of the Study:

• To develop a wearable electrochemical sensor for simultaneous, highly sensitive detection of cortisol, glucose (Glu), and uric acid (UA) in sweat.
• To enhance sensor performance using a dual-signal amplification strategy and machine learning optimization.
• To create a practical, watch-like device for real-time, wireless health monitoring.

Main Methods:

• Fabrication of a CuFe-PBA/CC substrate with a molecularly imprinted polymer (MIP) layer for target enrichment.
• Implementation of a dual-signal amplification pathway using the substrate and an exogenous redox probe ([Fe(CN)6]3-/4-).
• Optimization of sensor preparation parameters using machine learning algorithms.
• Integration of the sensor into a watch-like form factor with wearable electronic modules.

Main Results:

• Achieved high sensitivity with detection limits of 44.78 fM for cortisol, 0.064 μM for glucose, and 0.089 μM for uric acid.
• Demonstrated excellent sensor stability, retaining 88% signal after 100 deformation cycles.
• Successfully performed real-time wireless detection of sweat analytes in ex vivo experiments.
• Reported a significant improvement in signal-to-noise ratio due to the dual-amplification strategy.

Conclusions:

• The developed wearable sensor offers a novel solution for objective stress assessment and metabolic status monitoring.
• The dual-signal amplification strategy and machine learning integration significantly enhance sensor performance.
• The watch-like device demonstrates potential for advancing long-term, non-invasive health management applications.