Discrimination of microplastics and phytoplankton using impedance cytometry

Butement, Jonathan T.; Wang, Xiang; Siracusa, Fabrizio; Miller, Emily; Pabortsava, Katsiaryna; Mowlem, Matthew; Spencer, Daniel; Morgan, Hywel. 2024 Discrimination of microplastics and phytoplankton using impedance cytometry. ACS Sensors. https://doi.org/10.1021/acssensors.4c01353

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Official URL: http://dx.doi.org/10.1021/acssensors.4c01353

Abstract/Summary

ABSTRACT: Both microplastics and phytoplankton are found together in the ocean as suspended microparticles. There is a need for deployable technologies that can identify, size, and count these particles at high throughput to monitor plankton community structure and microplastic pollution levels. In situ analysis is particularly desirable as it avoids the problems associated with sample storage, processing, and degradation. Current technologies for phytoplankton and microplastic analysis are limited in their capability by specificity, throughput, or lack of deployability. Little attention has been paid to the smallest size fraction of microplastics and phytoplankton below 10 μm in diameter, which are in high abundance. Impedance cytometry is a technique that uses microfluidic chips with integrated microelectrodes to measure the electrical impedance of individual particles. Here, we present an impedance cytometer that can discriminate and count microplastics sampled directly from a mixture of phytoplankton in a seawater-like medium in the1.5−10 μm size range. A simple machine learning algorithm was used to classify microplastic particles based on dual-frequency impedance measurements of particle size (at 1 MHz) and cell internal electrical composition (at 500 MHz). The technique shows promise for marine deployment, as the chip is sensitive, rugged, and mass producible.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1021/acssensors.4c01353
ISSN:	2379-3694
Additional Keywords:	microplastics, phytoplankton, impedance cytometry, impedance spectroscopy, machine learning, lab-on-a-chip
Date made live:	05 Sep 2024 16:13 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/537972

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1021/acssensors.4c01353

ISSN:

2379-3694

Additional Keywords:

microplastics, phytoplankton, impedance cytometry, impedance spectroscopy, machine learning, lab-on-a-chip

Date made live:

05 Sep 2024 16:13 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/537972