Carbon SH-SAW-Based Electronic Nose to Discriminate and Classify Sub-ppm NO2

Carlos Cruz*, Daniel Matatagui*, Cristina Ramírez, Isidro Badillo-Ramirez, Emmanuel de la O-Cuevas, José M. Saniger, Mari Carmen Horrillo

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

In this research, a compact electronic nose (e-nose) based on a shear horizontal surface acoustic wave (SH-SAW) sensor array is proposed for the NO2 detection, classification and discrimination among some of the most relevant surrounding toxic chemicals, such as carbon monoxide (CO), ammonia (NH3), benzene (C6H6) and acetone (C3H6O). Carbon-based nanostructured materials (CBNm), such as mesoporous carbon (MC), reduced graphene oxide (rGO), graphene oxide (GO) and polydopamine/reduced graphene oxide (PDA/rGO) are deposited as a sensitive layer with con-trolled spray and Langmuir–Blodgett techniques. We show the potential of the mass loading and elastic effects of the CBNm to enhance the detection, the classification and the discrimination of NO2 among different gases by using Machine Learning (ML) techniques (e.g., PCA, LDA and KNN). The small dimensions and low cost make this analytical system a promising candidate for the on-site discrimination of sub-ppm NO2.
Original languageEnglish
Article number1261
JournalSensors
Volume22
Issue number3
Number of pages13
ISSN1424-8220
DOIs
Publication statusPublished - 2022

Keywords

  • Electronic nose
  • NO2
  • Carbon nanomaterials
  • Graphene oxide
  • Surface acoustic wave (SAW)
  • Pollutants
  • Discrimination
  • Classification
  • Machine Learning (ML)

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