Particle Size Determination via supervised machine learning in microfluidic impendance spectroscopy

Douwe Sietse de Bruijn*, Henricus R.A. ten Eikelder, Vasileios Papadimitriou, Wouter Olthuis, Albert van den Berg

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

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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 languageEnglish
Title of host publication25th International Conference on Miniaturized Systems for Chemistry and Life Sciences
Subtitle of host publication10 - 14 October 2021, Palm Springs, CA, USA
Pages1653-1654
Publication statusPublished - 31 Oct 2021
Event25th International Conference on Miniaturized Systems for Chemistry and Life Sciences, µTAS 2021 - Palm Springs, United States
Duration: 10 Oct 202114 Oct 2021
Conference number: 25
https://microtas2021.org/

Conference

Conference25th International Conference on Miniaturized Systems for Chemistry and Life Sciences, µTAS 2021
Abbreviated titleMicroTAS 2021
Country/TerritoryUnited States
CityPalm Springs
Period10/10/2114/10/21
Internet address

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