Carbon SH-SAW-Based Electronic Nose to Discriminate and Classify Sub-ppm NO2
Authors
Cruz de la Torre, Carlos; Matatagui, Daniel; Ramírez, Cristina; Badillo-Ramírez, Isidro; de la O-Cuevas, Emmanuel; [et al.]Identifiers
Permanent link (URI): http://hdl.handle.net/10017/58766DOI: 10.3390/s22031261
ISSN: 1424-8220
Publisher
MDPI
Date
2022-02-07Bibliographic citation
Cruz de la Torre, C. [et al.], 2022, "Carbon SH-SAW-Based Electronic Nose to Discriminate and Classify Sub-ppm NO2", Sensors, vol. 22, no. 3, art. no. 1261, pp. 1-13.
Keywords
Electronic nose
NO2
Carbon nanomaterials
Graphene oxide
Surface acoustic wave (SAW)
Pollutants
Discrimination
Classification
Machine Learning (ML)
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/publishedVersion
Publisher's version
https://doi.org/10.3390/s22031261Rights
Attribution 4.0 International (CC BY 4.0)
Access rights
info:eu-repo/semantics/openAccess
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 controlled 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.
Files in this item
Files | Size | Format |
|
---|---|---|---|
Carbon_Cruz_Sensors_2022.pdf | 3.060Mb |
|
Files | Size | Format |
|
---|---|---|---|
Carbon_Cruz_Sensors_2022.pdf | 3.060Mb |
|
Collections
- ELECTRON - Artículos [245]