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

The synchronous monitoring of stress hormones and metabolic indicators is crucial for long-term health management, yet it remains a significant challenge for wearable sensors, primarily due to the ultralow abundance of biomarkers in sweat and the vulnerability of weak electrochemical signals to interference. Herein, we report a wearable electrochemical sensor based on a dual-signal amplification strategy for the synchronous and highly sensitive detection of cortisol, glucose (Glu), and uric acid (UA). The CuFe-PBA/CC substrate provides a platform featuring high surface area, electrocatalytic activity, and dual signal amplification capabilities, which are the primary sources of high sensitivity. The MIP layer enhances the effective concentration of target substances near active sites through a local enrichment effect. Combined with the substrate’s high catalytic activity, this amplifies the electrochemical signal specific to the target. It synergizes with an exogenous redox probe ([Fe(CN) 6 ] 3– / 4– ) to establish a dual-amplification pathway, which enhances the current output to the milliampere level and substantially improves the signal-to-noise ratio. To ensure the optimal performance of the sensor, the preparation parameters were optimized via the integration of machine learning. This integrated sensing system exhibits high selectivity, superior sensitivity (with detection limits of 44.78 fM for cortisol, 0.064 μM for Glu, and 0.089 μM for UA), and excellent stability (retaining 88% of the initial signal after 100 cycles of mechanical deformation). Furthermore, we fabricated the sensor into a watch-like form factor, integrated it with high-precision wearable electronic modules, and achieved real-time wireless detection of sweat cortisol, Glu, and UA in ex vivo sweat monitoring experiments. This work provides a novel solution for objective stress assessment and metabolic status monitoring, holding promise for advancing long-term health management applications.