摘要
This study presents a flexible, low-power, integrated sensor system for accurate, non-invasive breath monitoring, vital for real-time health assessment and early disease detection. It integrates a piezoelectric barium titanate (BTO) cantilever respiratory flow sensor for respiratory flow measurement and a Na-doped ZnO nanoflower film for selective gas and humidity sensing. The BTO cantilever generates voltage signals in response to the airflow, and the Na-doped ZnO chemoresistive sensor facilitates multi-analyte detection. The integrated sensor system achieved a 0.82 ppm detection limit for acetone, showing a sensitivity of 0.00682/ppm within the 2–10 ppm range, and demonstrated rapid and reversible impedance response to ammonia (20–80 ppm), with significantly faster fall times (30–40 s) compared to commercial sensors (120–130 s). It also showed high sensitivity to humidity (30–80 % RH), all within clinically relevant ranges. This platform offers practical advantages, including fast response/recovery times, low-power ambient temperature operation, and flexible substrate compatibility, addressing critical challenges in current breath sensor technologies. Fabricated via hydrothermal and precipitation methods, the system was validated using BiPAP-generated airflow and human breath, with an AutoRegressive with exogenous input (ARX) model enabling accurate voltage-to-flow rate mapping and providing high-resolution signal mapping for respiratory flow. This compact, multifunctional sensor system is well-suited for continuous respiratory monitoring, wearable diagnostics, and next-generation point-of-care health technologies.
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•First integration of BTO cantilever and Na-ZnO film for multimodal breath sensing.•Targets respiratory flow, humidity, acetone gas, and ammonia gas for diagnostics.•Hierarchical Na-ZnO nanoflowers achieve enhanced selectivity and rapid response.•Sub-ppm acetone detection at ambient conditions with superior analytical performance.•ARX modeling bridges sensor voltage to airflow with high-resolution signal mapping.
