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An aberration-free line scan confocal Raman imager and type classification and distribution detection of microplastics
Centre for Optical and Electromagnetic Research, Zhejiang University, Hangzhou 310058, China; Taizhou Hospital, Zhejiang University. Taizhou, China.
Centre for Optical and Electromagnetic Research, Zhejiang University, Hangzhou 310058, China.
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Communication Systems, CoS, Optical Network Laboratory (ON Lab). KTH, School of Engineering Sciences (SCI), Centres, Zhejiang-KTH Joint Research Center of Photonics, JORCEP. KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electromagnetic Engineering and Fusion Science. Taizhou Hospital, Zhejiang University. Taizhou, China; National Engineering Research Center for Optical Instruments, Zhejiang University, Hangzhou 310058, China; Department of Electromagnetic Engineering, School of Electrical Engineering, Royal Institute of Technology, Electrum 229, 16440 Kista, Sweden.ORCID iD: 0000-0002-3401-1125
2024 (English)In: Journal of Hazardous Materials, ISSN 0304-3894, E-ISSN 1873-3336, Vol. 470, article id 134191Article in journal (Refereed) Published
Abstract [en]

An aberration-free line scanning confocal Raman imager (named AFLSCRI) is developed to achieve rapid Raman imaging. As an application example, various types and sizes of MPs are identified through Raman imaging combined with a machine learning algorithm. The system has excellent performance with a spatial resolution of 2 µm and spectral resolution of 4 cm−1. Compared to traditional point-scanning Raman imaging systems, the detection speed is improved by 2 orders of magnitude. The pervasive nature of MPs results in their infiltration into the food chain, raising concerns for human health due to the potential for chemical leaching and the introduction of persistent organic pollutants. We conducted a series of experiments on various types and sizes of MPs. The system can give a classification accuracy of 98% for seven different types of plastics, and Raman imaging and species identification for MPs as small as 1 µm in diameter were achieved. We also identified toxic and harmful substances remaining in plastics, such as Dioctyl Phthalate (DOP) residues. This demonstrates a strong performance in microplastic species identification, size recognition and identification of hazardous substance contamination in microplastics.

Place, publisher, year, edition, pages
Elsevier B.V. , 2024. Vol. 470, article id 134191
Keywords [en]
Aberration free, Confocal Raman imager, Deep learning algorism, Microplastics, Plastifier
National Category
Environmental Sciences
Identifiers
URN: urn:nbn:se:kth:diva-345737DOI: 10.1016/j.jhazmat.2024.134191ISI: 001224079700001PubMedID: 38579584Scopus ID: 2-s2.0-85189516570OAI: oai:DiVA.org:kth-345737DiVA, id: diva2:1852513
Note

QC 20240604

Available from: 2024-04-18 Created: 2024-04-18 Last updated: 2024-06-04Bibliographically approved

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He, Sailing

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Optical Network Laboratory (ON Lab)Zhejiang-KTH Joint Research Center of Photonics, JORCEPElectromagnetic Engineering and Fusion Science
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