A team of scientists at Zhejiang University has created a computationally enhanced wearable system for continuous cortisol monitoring (CWSCCM). This innovative system combines sophisticated technologies such as molecularly imprinted polymers (MIPs), organic electrochemical transistors (OECTs), iontophoretic sweat stimulation, microfluidic sampling, and wireless data transmission. The study was published in Science Bulletin.
Schematics of CWSCCM for sweat analysis. Image Credit: Science China Press
Validated through on-body experiments, the CWSCCM exhibits great sensitivity, selectivity, and regeneration capabilities, making it a promising real-time, non-invasive stress monitoring tool.
Bridging the Gap Between Lab-Based Cortisol Testing and Daily Monitoring
Cortisol, the key stress biomarker, has circadian fluctuations and is strongly connected to a variety of psychological and metabolic diseases. Current gold-standard methods—liquid chromatography-mass spectrometry (LC-MS/MS) and immunoassays—require intrusive blood sample and complicated instrumentation, limiting their utility in everyday life.
The CWSCCM solves these constraints by allowing real-time cortisol monitoring in sweat, which is triggered via iontophoresis and evaluated using an OECT-based biosensor, with the results communicated to a mobile application.
Computational Chemistry Enables Rational MIP Design and On-Body Regeneration
To tackle the difficulty of receptor regeneration in continuous biosensing, the researchers used density functional theory (DFT) to select seven monomers for the best binding affinity to cortisol. Pyrrole was chosen because of its balanced affinity and electrochemical activity.
The MIP layer was proven to be electrically regenerable by applying a mild negative potential, allowing for many sensing cycles without chemical washing. This method assures both reusability and consistent signal integrity.
OECT Transistor Optimization Achieves 85-Fold Signal Amplification
The scientists created OECTs with various channel geometries (W/L ratios) and found that a ratio of 40 maximizes transconductance (~1.8 mS). This shape provides much enhanced signal amplification, lower power consumption, and greater manufacturing scalability via screen-printing than typical electrochemical sensors. The devices run in depletion mode and continue to perform well even after being integrated with the MIP recognition layer.
Robust Detection in Complex Fluids, Validated Against ELISA
The MIP-OECT biosensor was tested using phosphate-buffered saline, artificial sweat, and saliva. In every medium, the device demonstrated a clear, concentration-dependent drop in channel current, with a low limit of detection (LOD) of 0.36 nmol/L in sweat. It also showed strong selectivity against structurally related molecules and interfering agents. Correlation with ELISA data validated the sensor's analytical accuracy across a variety of biofluids.
Real-Time Monitoring of Circadian Cortisol Fluctuations and Exercise Response
The CWSCCM is designed for full system integration and includes a screen-printed iontophoretic sweat stimulator and a vertical microfluidic chamber for regulated sample collection. A 3D-printed soft enclosure protects electronics against moisture-induced corrosion.
The method accurately caught circadian cortisol dynamics in human volunteers from 7 a.m. to 11 p.m., as well as acute changes after aerobic exercise. Data from the wearable platform demonstrated strong agreement with ELISA results, indicating that it can be used for dynamic in-situ biosignal tracking.
Toward Closed-Loop Stress Monitoring and Personalized Health Management
The CWSCCM is a big step in combining computational design with scalable bioelectronics for real-world health monitoring. This platform, which combines MIP-based regenerative sensing, OECT amplification, and flexible system integration, provides a robust tool for continuous stress-related biomarker testing.
The technology shows potential for customized medicine applications such as chronic disease management, mental health tracking, and closed-loop therapeutic systems.
Journal Reference:
Liu, Y., et al. (2025) Computationally-assisted wearable system for continuous cortisol monitoring. Science Bulletin. doi.org/10.1016/j.scib.2025.03.060