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Characterization of microplastics by holographic features for automatic detection in heterogeneous samples

Academic Article
Publication Date:
2019
abstract:
Microplastics are worrisome water pollutants that are more and more spread in deep sea and coastal waters. Plastic items can take decades to biodegrade, have the potential to affect the food chain and are harmful to marine life. Hence, there is the urgent need to define protocols and to create reliable tools to map the presence of microplastics in heterogeneous liquid samples. However, well established protocols and tools to identify microplastics in water have not been proposed yet. Here we investigate this class of objects by means of coherent imaging, in particular relying on Digital Holography (DH) microscopy. We provide a DH characterization of the "plastic" class that can be used as a global identifier independently on the plastic material under analysis. We probe microplastics of various materials through our DH microscope and show that the phase contrast map of microplastics can be used to define a fingerprint for the microplastics population. Thanks to the DH flexible refocusing, volumetric counting of microplastics in flow is feasible by DH with high-throughput. Remarkably, field-deployable, cost effective DH microscopes exist that can bring the DH characterization potential out of the lab for in situ environmental monitoring.
Iris type:
01.01 Articolo in rivista
Keywords:
Classification; Digital Holography; Environmental monitoring; Machine Learning; Micro-plastics
List of contributors:
Distante, Cosimo; Ferraro, Pietro; Carcagni', Pierluigi; Paturzo, Melania; Merola, Francesco; Memmolo, Pasquale; Bianco, Vittorio
Authors of the University:
BIANCO VITTORIO
CARCAGNI' PIERLUIGI
DISTANTE COSIMO
FERRARO PIETRO
MEMMOLO PASQUALE
MEROLA FRANCESCO
PATURZO MELANIA
Handle:
https://iris.cnr.it/handle/20.500.14243/385844
Published in:
PROCEEDINGS OF SPIE, THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING
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http://www.scopus.com/record/display.url?eid=2-s2.0-85072633906&origin=inward
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