Intelligent diagnostics enabled by optical piezoelectric biosensors based on two-dimensional nanomaterials and driven by machine learning

Recent technological advancements have accelerated the adoption of flexible sensors in wearable healthcare systems for continuous physiological monitoring. Conventional silicon- and glass-based sensors often suffer from limitations such as rigidity, bulkiness, and restricted capability for long-term monitoring of vital parameters, including blood pressure. Two-dimensional (2D) nanomaterials have emerged as promising alternatives because of their high surface-area-to-volume ratio, excellent electrical conductivity, flexibility, lightweight nature, and cost-effectiveness. In particular, 2D piezoelectric nanogenerators (PENGs) demonstrate exceptional sensitivity and mechanical flexibility, making them suitable for detecting physiological signals from the skin surface. Materials such as graphene, transition metal dichalcogenides (TMDs), and MXenes have shown significant potential in flexible blood pressure monitoring applications. Despite these advances, limited research has explored the clinical application of 2D PENGs for disease diagnosis through physiological signal acquisition. When attached to the skin, 2D PENGs can capture three characteristic pulse waveform peaks associated with different physiological conditions, enabling extraction of disease-related biomarkers. This study focuses on developing advanced piezoelectric sensors for point-of-care diagnostics. The proposed intelligent diagnostic system achieves 93.75% disease recognition accuracy in clinical evaluations, successfully identifying nine major diseases. Results demonstrate the strong potential of 2D PENG technology for early disease detection and noninvasive health monitoring.