ANALYZING PARTICULATE MATTERS VIA SURFACTANT-ASSISTED MICROFLUIDIC IONIC CURRENT SENSING WITH MACHINE LEARNING-DRIVEN IDENTIFICATION

Keiko Fujino, Taisuke Shimada, Takao Yasui, Kazuki Nagashima, Takashi Yanagida, Noritada Kaji, Yoshinobu Baba

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Exposures to particulate matters (PMs) are one of important factors for human health, however, their risks are little known due to lacks of comprehensive sensing methods that can access their physicochemical properties. Here, we developed a microfluidics-based method to characterize size and compositions of PMs via combining surfactant-assisted single particle detection, presented on MicroTAS 2020 [1], with machine learning (ML)-driven identifications. Both of hydrophilic and hydrophobic particles were sensed and their electrical signals were discriminated with 98% accuracy at the single level. Our method will comprehensively sense real PMs to characterize their physicochemical properties, enabling to understand health risks.

Original languageEnglish
Title of host publicationMicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences
PublisherChemical and Biological Microsystems Society
Pages1481-1482
Number of pages2
ISBN (Electronic)9781733419031
Publication statusPublished - 2021
Event25th International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2021 - Palm Springs, Virtual, United States
Duration: Oct 10 2021Oct 14 2021

Publication series

NameMicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences

Conference

Conference25th International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2021
Country/TerritoryUnited States
CityPalm Springs, Virtual
Period10/10/2110/14/21

All Science Journal Classification (ASJC) codes

  • Bioengineering
  • Chemical Engineering (miscellaneous)

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