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• W4288439488 abstract "Microplastic pollution is one of the greatest environmental concerns for contemporary times and the future. In the last years, the number of publications about microplastic contamination has increased rapidly and the list is daily updated. However, the lack of standard analytical approaches might generate data inconsistencies, reducing the comparability among different studies. The present study investigates the potential of two image processing tools (namely the shapeR package for R and ImageJ 1.52v) in providing an accurate characterization of the shape of microplastics using a restricted set of shape descriptors. To ascertain that the selected tools can measure small shape differences, we perform an experiment to verify the detection of pre-post variations in the shape of different microplastic types (i.e., nylon [NY], polyethylene [PE], polyethylene terephthalate [PET], polypropylene [PP], polystyrene [PS], and polyvinylchloride [PVC]) treated with mildly corrosive chemicals (i.e., 10% KOH at 60 °C, 30% H2O2 at 50 °C, and 15% H2O2 + 5% HNO3 at 40 °C; incubation time ≈ 12 h). Analysis of surface area variations returns results about the vulnerability of plastic polymers to digestive solutions that are aligned with most of the acquired knowledge. The largest decrease in surface area occurs for KOH-treated PET particles, while NY results in the most susceptible polymer to the 30% H2O2 treatment, followed by PVC and PS. PE and PP are the most resistant polymers to all the used treatments. The adopted methods to characterize microplastics seem reliable tools for detecting small differences in the shape and size of these particles. Then, the analytic perspectives that can be developed using such widely accessible and low-cost equipment are discussed." @default.
• W4288439488 created "2022-07-29" @default.
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• W4288439488 date "2022-07-28" @default.
• W4288439488 modified "2023-10-16" @default.
• W4288439488 title "Image processing tools in the study of environmental contamination by microplastics: reliability and perspectives" @default.
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• W4288439488 doi "https://doi.org/10.1007/s11356-022-22128-3" @default.
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