Abstract:
The detection of biomolecules such as nutrients, disease biomarkers, drugs, and toxins in human biofluids is essential for advancing healthcare and diagnostics. Traditional centralized laboratory assays, like enzyme-linked immunosorbent assays (ELISA), are limited by sample preparation complexity and processing time. To address these challenges, we initially focused on enhancing ELISA sensitivity by incorporating single-atom nanozymes (SANs) as robust catalytic amplifiers. SANs exhibit exceptional stability across a wide pH and temperature range and display superior catalytic activity toward hydrogen peroxide, effectively substituting natural peroxidases.

We developed methods to integrate SANs into ELISA workflows and expanded their application to electrochemical platforms and lateral-flow immunoassays, enabling rapid and sensitive detection of target analytes in various biological matrices. The SAN-based biosensors demonstrated reproducible performance and significant improvements in sensitivity and stability compared to traditional enzyme-based assays.

To further address the growing demand for continuous, on-body health monitoring, we integrated these SAN-based sensing platforms with flexible, wearable energy management modules, creating a self-powered wearable microgrid system. This system ensures continuous and autonomous operation by harvesting biomechanical and environmental energy from the wearer. Additionally, the lightweight and flexible design of the wearable microgrid system provides enhanced comfort and long-term wearability, enabling real-time, high-frequency monitoring of biomarkers directly on the body.

Conclusions:
The SAN-enabled ELSIA demonstrated ten times greater sensitivity compared to a commercial product in detecting one type of Alzheimer's disease. By transitioning from traditional ELISA to SAN-enabled biosensing and integrating with self-powered, flexible wearable systems, our research paves the way for continuous, autonomous biomolecular monitoring, including glucose, lactate, Vitamin C, and levodopa from sweat. This comprehensive approach meets the demands for real-time, user-friendly healthcare monitoring and advanced human–machine interfacing.