Description
Airborne pathogen plays an essential role for environment monitoring, infectious-disease warning, and public health surveillance; however, direct and reliable detection in air remains challenging. Unlike liquid-phase samples, airborne biological particles are sparsely and stochastically deposited, making it difficult to generate a stable optical response. As a result, the light-matter interaction between airborne pathogens and conventional optical sensors often produces weak, transient, and fluctuating spectral signals. Herein, we developed an bioinspired photonic fibre microlaser sensor for robust airborne airborne pathogens monitoring. Inspired by the functionality of spider web fabrics, the hybrid fibre integrates two complementary functional regions within a single fibre architecture. First, the roughened fibre regions provide an enlarged interfacial area and surface heterogeneity, which promote interaction with airborne airborne pathogenss and aerosol droplets. Seocondly, the smooth spindle-knot regions function as optical microcavities that support whispering-gallery-mode microlaser emission with sharp spectral features. This bioinspired structural design allows the fibre to combine airborne pathogens capture, local interaction, and optical readout in one platform. To address the instability of airborne signals, we further introduce an AI-assisted spectral analysis strategy. Instead of relying on a single wavelength shift or intensity change, multiple time-dependent spectral features are extracted from the microlaser emission, including variations in peak position, emission intensity, linewidth, and dynamic fluctuation patterns. These multidimensional features contain richer information about airborne pathogens deposition and its interaction with fibre microcavity. By analysing these features with an AI model, the sensor can distinguish airborne pathogens-induced spectral responses from random fluctuations and environmental noise, thereby improving signal recognition under unstable airborne conditions. This strategy provides a promising route toward real-time, robust, and intelligent environmental monitoring using fibre-integrated microlaser sensors.
| I am the presenting author | Yes |
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