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A Love-type acoustic wave sensor (AT-cut quartz substrate, SiO2 guiding layer) with a center frequency of approximately 120 MHz was used to detect a simulant of pathogenic botulinum neurotoxin type A—recombinant of BoNT-A light chain—in liquid samples. The sensor was prepared by immobilizing monoclonal antibodies specific for botulinum neurotoxin via a thiol monolayer deposited on a gold substrate. Studies have shown that the sensor enables selective analyte detection within a few minutes. In addition, the sensor can be used several times (regeneration of the sensor is possible using a low pH buffer). Nevertheless, the detectability of the analyte is relatively low compared to other analytical techniques that can be used for rapid detection of botulinum neurotoxin. The obtained results confirm the operation of the proposed sensor and give hope for further development of this label-free technique for detecting botulinum neurotoxin.

We present results from a study evaluating the utility of supervised machine learning to classify single particle ultraviolet laser-induced fluorescence (UV-LIF) signatures to investigate airborne primary biological aerosol particle (PBAP) concentrations in a busy, multifunctional building using a Multiparameter Bioaerosol Spectrometer. First we introduce and demonstrate a gradient boosting ensemble decision tree algorithm’s ability to accurately classify laboratory generated PBAP samples into broad taxonomic classes with a high level of accuracy. We then develop a framework to appraise the classification accuracy and performance using the Hellinger distance metric to compare product parameter probability density function similarity; this framework showed that key training classes were sufficiently different in terms of particle fluorescence and morphology to facilitate classification. We also demonstrate the utility of including advanced morphological parameters to minimise inter-class conflation and improve classification confidence, where relying on the fluorescent spectra alone would likely result in misattribution. Finally, we apply these methods to ambient data collected within a large multi-functional building where ambient bacterial- and fungal-like classes were identified to display trends corresponding to human activity; fungal-like classes displayed a consistent diurnal trend with a maximum at midday and hourly peaks correlating to movements within the building; bacteria-like aerosol displayed complex, episodic events during opening hours. All PBAP classes fell to low baseline concentrations when the building was unoccupied overnight and at weekends.