Bird-Inspired Time-Domain Analysis: High-Robustness Intelligent Sensing of Hypochlorite Aerosols

The increasing demand for highly reliable sensors capable of anti-interference and sensitive detection of aerosol-associated hypochlorite (ClO^-) drives progress in luminescence-based sensing technologies. Inspired by collective behavioral patterns in bird flocks, we developed a "bird-like flock" time-domain sensing method through the utilization of functional long afterglow nanomaterials (AGNPs) for ClO^- detection. Particularly, by translating avian aggregation-separation-alignment behaviors into an algorithmic framework, we presented a new sensing mode based on simplifying time decay curves into interpretable temporal metrics. In light of dual-emission AGNPs with impressive luminescence performance, ClO^--stimulated responsiveness, and derived metrics, our approach accurately distinguished ClO^- concentrations down to 95 nM, resulting from the well-refined six temporal parameters. Furthermore, by integrating a portable aerosol generation device via pneumatic atomization and an aerosol-enriched hydrogel sensing matrix, we demonstrated that this time-domain analysis method maintained superior detection accuracy, outperforming traditional methods that often falter with complex water samples. This advancement paves the way for high-contrast sensing while enabling deeper exploration of atmospheric targets. Inspired by the dynamic evolution of avian flock behavior patterns, we developed a high-fidelity analytical method based on time-resolved long-afterglow spectral partitioning for qualitative and quantitative investigation of hypochlorite (ClO^-) in atmospheric aerosols.