Discrimination of Microplastics and Phytoplankton\nUsing Impedance Cytometry

Both microplastics\nand phytoplankton are found together in the\nocean as suspended microparticles. There is a need for deployable\ntechnologies that can identify, size, and count these particles at\nhigh throughput to monitor plankton community structure and microplastic\npollution levels. In situ analysis is particularly desirable as it\navoids the problems associated with sample storage, processing, and\ndegradation. Current technologies for phytoplankton and microplastic\nanalysis are limited in their capability by specificity, throughput,\nor lack of deployability. Little attention has been paid to the smallest\nsize fraction of microplastics and phytoplankton below 10 μm\nin diameter, which are in high abundance. Impedance cytometry is a\ntechnique that uses microfluidic chips with integrated microelectrodes\nto measure the electrical impedance of individual particles. Here,\nwe present an impedance cytometer that can discriminate and count\nmicroplastics sampled directly from a mixture of phytoplankton in\na seawater-like medium in the 1.5–10 μm size range. A\nsimple machine learning algorithm was used to classify microplastic\nparticles based on dual-frequency impedance measurements of particle\nsize (at 1 MHz) and cell internal electrical composition (at 500 MHz).\nThe technique shows promise for marine deployment, as the chip is\nsensitive, rugged, and mass producible.