Identifying plastics with photoluminescence spectroscopy and machine learning

Identifying plastics with photoluminescence spectroscopy and machine learning

A quantitative understanding of the worldwide plastics distribution is required not only to assess the extent and possible impact of plastic litter on the environment but also to identify possible counter measures. A systematic collection of data characterizing amount and composition of plastics has...

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Autors principals: Lotter, Benjamin, Konde, Srumika, Nguyen, Johnny, Grau, Michael, Koch, Martin, Lenz, Peter
Format: Article
Idioma:anglès
Publicat: Philipps-Universität Marburg 2022
Matèries:
Physik
Physics
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Signatura: urn:nbn:de:hebis:04-es2024-02697
Data de publicació: 2024-01-11
Font: Erstveröffentlichung: Lotter, B., Konde, S., Nguyen, J. et al. Identifying plastics with photoluminescence spectroscopy and machine learning. Sci Rep 12, 18840 (2022). https://doi.org/10.1038/s41598-022-23414-3
Downloads: 30 (2024)
Lizenz: https://creativecommons.org/licenses/by/4.0
URL d'accés: https://archiv.ub.uni-marburg.de/es/2024/0269
https://doi.org/10.1038/s41598-022-23414-3

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