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Scientific reports, 2024-01, Vol.14 (1), p.2355-2355, Article 2355
2024
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Autor(en) / Beteiligte
Titel
High-throughput microplastic assessment using polarization holographic imaging
Ist Teil von
  • Scientific reports, 2024-01, Vol.14 (1), p.2355-2355, Article 2355
Ort / Verlag
England: Nature Publishing Group
Erscheinungsjahr
2024
Quelle
EZB Electronic Journals Library
Beschreibungen/Notizen
  • Microplastic (MP) pollution has emerged as a global environmental concern due to its ubiquity and harmful impacts on ecosystems and human health. MP assessment has therefore become increasingly necessary and common in environmental and experimental samples. Microscopy and spectroscopy are widely employed for the physical and chemical characterization of MPs. However, these analytical methods often require time-consuming pretreatments of samples or expensive instrumentation. In this work, we develop a portable and cost-effective polarization holographic imaging system that prominently incorporates deep learning techniques, enabling efficient, high-throughput detection and dynamic analysis of MPs in aqueous environments. The integration enhances the identification and classification of MPs, eliminating the need for extensive sample preparation. The system simultaneously captures holographic interference patterns and polarization states, allowing for multimodal information acquisition to facilitate rapid MP detection. The characteristics of light waves are registered, and birefringence features are leveraged to classify the material composition and structures of MPs. Furthermore, the system automates real-time counting and morphological measurements of various materials, including MP sheets and additional natural substances. This innovative approach significantly improves the dynamic monitoring of MPs and provides valuable information for their effective filtration and management.
Sprache
Englisch
Identifikatoren
ISSN: 2045-2322
eISSN: 2045-2322
DOI: 10.1038/s41598-024-52762-5
Titel-ID: cdi_doaj_primary_oai_doaj_org_article_8496858c00184d7c813e1505779cdf78

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