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http://dx.doi.org/10.3389/fpubh.2023.1145240 | DOI Listing |
Environ Toxicol Chem
January 2025
Blue Growth Research Lab, Ghent University, Ostend Science Park, Ostend, Belgium.
In contrast to microplastics, studying the interactions of nanoplastics (NPs) with primary producers such as marine microalgae remains challenging. This is attributed to the lack of adequate visualization methods that can distinguish NPs from autofluorescent biological material such as marine algae. The aim of this study was to develop a method for labeling and visualizing nonfluorescent micro- and nanoplastics (MNPs) of various polymer types, shapes, and sizes, in interaction with marine primary producers, which are autofluorescent.
View Article and Find Full Text PDFMikrochim Acta
January 2025
School of Chemical Engineering and Technology, Hebei Key Laboratory of Functional Polymers, Hebei University of Technology, Beichen District, Xiping Road No. 5340, Tianjin, 300401, China.
A kind of sulfur-doped carbon dots was prepared which were encapsulated with polydopamine (S-CDs@PDA) that has fluorescence response on polyethylene (PE) microplastics (MPs). Modified membranes were constructed using S-CDs@PDA for MP detection. Through heating and vacuum filtration process, yellow emission from the modified membrane appeared because of the combination between S-CDs@PDA and PE MPs.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Zoology, University of Sialkot, Sialkot, 51040, Punjab, Pakistan.
Microplastics (MPs) form when plastic debris is released into the aquatic environment, where they decompose and have deleterious effects on aquatic life. This study aimed to examine the harmful impacts of polystyrene MPs (PS-MPs) on the growth, carcass composition, hematology, digestibility, histopathology, and mineral analysis of Catla catla (11.09 ± 0.
View Article and Find Full Text PDFAnal Chem
January 2025
Department of Chemistry, University of Waterloo, 200 University Avenue W., Waterloo, Ontario N2L 3G1, Canada.
Research has shown microplastic particles to be pervasive pollutants in the natural environment, but labor-intensive sample preparation, data acquisition, and analysis protocols continue to be necessary to navigate their diverse chemistry. Machine learning (ML) classification models have shown promise for identifying microplastics from their Raman spectra, but all attempts to date have focused on the lower energy "fingerprint" region of the spectrum. We explore strategies to improve ML classification models based on the -nearest-neighbor algorithm by including other regions of the Raman spectra.
View Article and Find Full Text PDFSci Total Environ
January 2025
Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa 277-8563, Chiba, Japan; Atmosphere and Ocean Research Institute, The University of Tokyo, Kashiwa 277-8564, Chiba, Japan.
In recent decades, microplastics (MPs) have emerged as one of the biggest environmental challenges in aquatic environments. Ingestion and toxicity of MPs in seawater (SW) and freshwater (FW) fish have been studied extensively both in field and laboratory settings. However, the basic mechanism of how fish deal with MPs in SW and FW remains unclear, although physiological conditions of fish differ significantly in the two environments.
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