Articles | Volume 17, issue 23
https://doi.org/10.5194/amt-17-6945-2024
https://doi.org/10.5194/amt-17-6945-2024
Research article
 | 
11 Dec 2024
Research article |  | 11 Dec 2024

Merging holography, fluorescence, and machine learning for in situ continuous characterization and classification of airborne microplastics

Nicholas D. Beres, Julia Burkart, Elias Graf, Yanick Zeder, Lea Ann Dailey, and Bernadett Weinzierl

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Cited articles

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Short summary
We tested a method to identify airborne microplastics (MPs), merging imaging, fluorescence, and machine learning of single particles. We examined whether combining imaging and fluorescence data enhances classification accuracy compared to using each method separately and tested these methods with other particle types. The tested MPs have distinct fluorescence, and a combined imaging and fluorescence method improves their detection, making meaningful progress in monitoring MPs in the atmosphere.
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