Abstract
Impedance flow cytometers based on coplanar electrodes often have a significant signal dependency on the particle position. In this work we show that supervised machine learning can be employed to accurately predict the particle size of monodisperse polystyrene beads in an inhomogeneous electric field. This approach offers accurate results for the presented irregular signal shape (due to sensor geometry, particle position, and electrode alignment) without the need for signal template fitting and a compensation function.
Original language | English |
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Title of host publication | 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences |
Subtitle of host publication | 10 - 14 October 2021, Palm Springs, CA, USA |
Pages | 1653-1654 |
Publication status | Published - 31 Oct 2021 |
Event | 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences, µTAS 2021 - Palm Springs, United States Duration: 10 Oct 2021 → 14 Oct 2021 Conference number: 25 https://microtas2021.org/ |
Conference
Conference | 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences, µTAS 2021 |
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Abbreviated title | MicroTAS 2021 |
Country/Territory | United States |
City | Palm Springs |
Period | 10/10/21 → 14/10/21 |
Internet address |