Optical aerosol sizing method without prior refractive index.

[Display omitted] • A new method is proposed for sizing aerosols without refractive index. • The LSIF is used to characterize unconstrained PSD and refractive index. • Deep learning is adopted to retrieve unconstrained PSD efficiently (about 100 ms). • An integrated and cost-effective prototype is designed and verified. Existing light-based aerosol size analyzers suffer from measurement errors or even fail to measure particle size distribution (PSD) when the refractive index is unknown. To overcome this limitation, we introduced a new aerosol sizing method without the prior refractive index using the light scattering intensity field (LSIF) and deep learning. The LSIF is the intensity distribution of the light scattered in all directions around aerosol particles, which can be used to simultaneously characterize the PSD and the refractive index. A PSD retrieval approach based on deep learning was developed to efficiently retrieve (about 100 ms) the unconstrained PSD (141 independent channels). Our proof-of-concept prototype was tested on various aerosol samples with different particle sizes and refractive indices, where the relative standard deviation (RSD) of the particle size was 1.94% in the test of monodisperse aerosols and the Kullback-Leibler divergence of PSD was 0.13 in the test of polydisperse aerosols. [ABSTRACT FROM AUTHOR]