image: Schematics of CWSCCM for sweat analysis.
Credit: ©Science China Press
Wearable System Enables Continuous Cortisol Monitoring for Stress Management
A research group from Zhejiang University has developed a computationally-assisted wearable system for continuous cortisol monitoring (CWSCCM), integrating advanced technologies including molecularly imprinted polymers (MIPs), organic electrochemical transistors (OECTs), iontophoretic sweat stimulation, microfluidic sampling, and wireless data transmission. Validated through on-body trials, the CWSCCM demonstrates high sensitivity, selectivity, and regenerative capabilities, offering a promising solution for real-time, non-invasive stress monitoring.
Bridging the Gap Between Lab-Based Cortisol Testing and Daily Monitoring
Cortisol, the primary biomarker for stress, exhibits circadian fluctuations and is tightly linked to various psychological and metabolic disorders. Current gold-standard methods—liquid chromatography-mass spectrometry (LC-MS/MS) and immunoassays—require invasive blood sampling and complex instrumentation, limiting their use in daily life. The CWSCCM addresses these limitations by enabling real-time monitoring of cortisol in sweat, stimulated via iontophoresis, and analyzed using an OECT-based biosensor with results transmitted to a mobile application.
Computational Chemistry Enables Rational MIP Design and On-Body Regeneration
To overcome the challenge of receptor regeneration in continuous biosensing, the researchers employed density functional theory (DFT) to screen seven monomers for optimal binding affinity to cortisol. Pyrrole was selected for its balanced affinity and electrochemical activity. The MIP layer was shown to be electrically regenerable via application of a mild negative potential, enabling multiple sensing cycles without chemical washing. This approach ensures both reusability and reliable signal integrity.
OECT Transistor Optimization Achieves 85-Fold Signal Amplification
The team fabricated a series of OECTs with varying channel geometries (W/L ratios), identifying a ratio of 40 as optimal for maximizing transconductance (~1.8 mS). Compared to traditional electrochemical sensors, this geometry delivers significantly improved signal amplification, lower power consumption, and greater production scalability using screen-printing. The devices operate in depletion mode and retain excellent performance even after integration with the MIP recognition layer.
Robust Detection in Complex Fluids, Validated Against ELISA
The MIP-OECT biosensor was evaluated in phosphate-buffered saline, artificial sweat, and saliva. In all media, the device showed a clear, concentration-dependent decrease in channel current with a low limit of detection (LOD) down to 0.36 nmol/L in sweat. It also demonstrated high selectivity against structurally similar molecules and interfering agents. Correlation with ELISA measurements further confirmed the sensor’s analytical accuracy across different biofluids.
Real-Time Monitoring of Circadian Cortisol Fluctuations and Exercise Response
Designed for full-system integration, the CWSCCM incorporates a screen-printed iontophoretic sweat stimulator and a vertical microfluidic chamber for controlled sample collection. A 3D-printed soft enclosure protects the electronics from moisture-induced corrosion. The system successfully captured circadian cortisol dynamics from 7 a.m. to 11 p.m. in human subjects, as well as acute fluctuations following aerobic exercise. Data from the wearable platform showed strong agreement with ELISA results, validating its use for dynamic in-situ bio-signal tracking.
Toward Closed-Loop Stress Monitoring and Personalized Health Management
The CWSCCM represents a significant step forward in integrating computational design with scalable bioelectronics for real-world health monitoring. By combining MIP-based regenerative sensing, OECT amplification, and flexible system integration, this platform provides a robust tool for continuous assessment of stress-related biomarkers. The technology holds promise for personalized medicine applications, including chronic disease management, mental health tracking, and closed-loop therapeutic systems.
Journal
Science Bulletin