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The physical, chemical, and biological properties of an aerosol droplet/particle are dependent on the morphology of the droplet/particle itself; for example, a liquid droplet will be processed by oxidants in the gas phase in a fundamentally different way than a solid particle. Additionally, given their small size, aerosol droplets may change phase over timescales in the order of milliseconds (e.g., deliquescence or crystallisation). Thus, ability to rapidly and easily estimate the morphology of a droplet/particle is critical, especially in the interpretation of complex aerosol processes such as spray drying and dissolution. To be reported here is a novel method that uses the forward scattered light (∼32° < θ < ∼58°) passed through a droplet to determine the droplet/particles morphology. The algorithm was developed through the qualitative analysis of over one million individual phase functions of various particle morphologies. The algorithm can differentiate between four different morphologies: homogeneous, core/shell, with inclusions, and non-spherical/inhomogeneous. The algorithm is applicable to droplets between ∼5 to ∼30 microns in radius. The rate of phase analysis is dependent on the rate in which the light scatter can be collected, in the data presented here a particle’s morphology is reported every 10 milliseconds. The accuracy of the phase identification with the algorithm proposed in this work is very high (>90%); its utility is strengthened by the high frequency of the collection of scattered light, which allows an individual droplet to be probed upwards of over 100 times per second. Although not absolute on every phase function analysis, when coupled with repetition and high throughput, the algorithm presented here can be a valuable tool to easily and readily determine particle morphology in dynamic aerosol systems.
Haddrell, A. E., Rovelli, G., Lewis, D., Church, T., & Reid, J. P. (2019). Identifying time-dependent changes in the morphology of an individual aerosol particle from their light scattering patterns. Aerosol Science and Technology, 53(11), 1334-1351. https://doi.org/10.1080/02786826.2019.1661351