Field-effect transistor array modified by a stationary phase to generate informative signal patterns for machine learning-assisted recognition of gas-phase chemicals

Toshihiro Yoshizumi, Tatsuro Goda, Rui Yatabe, Akio Oki, Akira Matsumoto, Hiroaki Oka, Takashi Washio, Kiyoshi Toko, Yuji Miyahara

Research output: Contribution to journalArticle

Abstract

We propose an artificial intelligence-based chemical-sensing system integrating a porous gate field-effect transistor (PGFET) array modified by gas chromatography stationary phase materials and machine-learning techniques. The chemically sensitive PGFET array generates cross-reactive signals for computational analysis and shows potential for applications to compact intelligent sensing devices, including mobile electronic noses.

Original languageEnglish
Pages (from-to)386-389
Number of pages4
JournalMolecular Systems Design and Engineering
Volume4
Issue number2
DOIs
Publication statusPublished - Apr 2019

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Gates (transistor)
Field effect transistors
Learning systems
Gases
Mobile devices
Gas chromatography
Artificial intelligence
Electronic nose

All Science Journal Classification (ASJC) codes

  • Chemistry (miscellaneous)
  • Chemical Engineering (miscellaneous)
  • Biomedical Engineering
  • Energy Engineering and Power Technology
  • Process Chemistry and Technology
  • Industrial and Manufacturing Engineering
  • Materials Chemistry

Cite this

Field-effect transistor array modified by a stationary phase to generate informative signal patterns for machine learning-assisted recognition of gas-phase chemicals. / Yoshizumi, Toshihiro; Goda, Tatsuro; Yatabe, Rui; Oki, Akio; Matsumoto, Akira; Oka, Hiroaki; Washio, Takashi; Toko, Kiyoshi; Miyahara, Yuji.

In: Molecular Systems Design and Engineering, Vol. 4, No. 2, 04.2019, p. 386-389.

Research output: Contribution to journalArticle

Yoshizumi, Toshihiro ; Goda, Tatsuro ; Yatabe, Rui ; Oki, Akio ; Matsumoto, Akira ; Oka, Hiroaki ; Washio, Takashi ; Toko, Kiyoshi ; Miyahara, Yuji. / Field-effect transistor array modified by a stationary phase to generate informative signal patterns for machine learning-assisted recognition of gas-phase chemicals. In: Molecular Systems Design and Engineering. 2019 ; Vol. 4, No. 2. pp. 386-389.
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