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

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 ),...

Descripción completa

Detalles Bibliográficos
Autores: Cruz, Carlos, Matatagui Cruz, Daniel, Ramírez, Cristina, Badillo Ramírez, Isidro, Cuevas, Emmanuel de la O, Saniger, José M., Horrillo, Mari Carmen
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/711145
Acceso en línea:http://hdl.handle.net/10486/711145
https://dx.doi.org/10.3390/s22031261
Access Level:acceso abierto
Palabra clave:electronic nose
NO2
carbon nanomaterials
graphene oxide
surface acoustic wave (SAW)
pollutants
discrimination
classification
Machine Learning (ML)
Informática
id ES_2b6b4aced551fb06510fa7bf7b74200e
oai_identifier_str oai:repositorio.uam.es:10486/711145
network_acronym_str ES
network_name_str España
repository_id_str
spelling Carbon SH-SAW-Based Electronic Nose to Discriminate and Classify Sub-ppm NO2Cruz, CarlosMatatagui Cruz, DanielRamírez, CristinaBadillo Ramírez, IsidroCuevas, Emmanuel de la OSaniger, José M.Horrillo, Mari Carmenelectronic noseNO2carbon nanomaterialsgraphene oxidesurface acoustic wave (SAW)pollutantsdiscriminationclassificationMachine Learning (ML)InformáticaIn 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 .Spanish Ministry of Science and Innovation for financing the project RTI2018-095856-B-C22 (AEI/FEDER).MDPIDepartamento de Ingeniería InformáticaEscuela Politécnica Superior20222022-02-07research articlehttp://purl.org/coar/resource_type/c_2df8fbb1VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10486/711145https://dx.doi.org/10.3390/s22031261reponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/7111452026-06-23T12:46:27Z
dc.title.none.fl_str_mv Carbon SH-SAW-Based Electronic Nose to Discriminate and Classify Sub-ppm NO2
title Carbon SH-SAW-Based Electronic Nose to Discriminate and Classify Sub-ppm NO2
spellingShingle Carbon SH-SAW-Based Electronic Nose to Discriminate and Classify Sub-ppm NO2
Cruz, Carlos
electronic nose
NO2
carbon nanomaterials
graphene oxide
surface acoustic wave (SAW)
pollutants
discrimination
classification
Machine Learning (ML)
Informática
title_short Carbon SH-SAW-Based Electronic Nose to Discriminate and Classify Sub-ppm NO2
title_full Carbon SH-SAW-Based Electronic Nose to Discriminate and Classify Sub-ppm NO2
title_fullStr Carbon SH-SAW-Based Electronic Nose to Discriminate and Classify Sub-ppm NO2
title_full_unstemmed Carbon SH-SAW-Based Electronic Nose to Discriminate and Classify Sub-ppm NO2
title_sort Carbon SH-SAW-Based Electronic Nose to Discriminate and Classify Sub-ppm NO2
dc.creator.none.fl_str_mv Cruz, Carlos
Matatagui Cruz, Daniel
Ramírez, Cristina
Badillo Ramírez, Isidro
Cuevas, Emmanuel de la O
Saniger, José M.
Horrillo, Mari Carmen
author Cruz, Carlos
author_facet Cruz, Carlos
Matatagui Cruz, Daniel
Ramírez, Cristina
Badillo Ramírez, Isidro
Cuevas, Emmanuel de la O
Saniger, José M.
Horrillo, Mari Carmen
author_role author
author2 Matatagui Cruz, Daniel
Ramírez, Cristina
Badillo Ramírez, Isidro
Cuevas, Emmanuel de la O
Saniger, José M.
Horrillo, Mari Carmen
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería Informática
Escuela Politécnica Superior
dc.subject.none.fl_str_mv electronic nose
NO2
carbon nanomaterials
graphene oxide
surface acoustic wave (SAW)
pollutants
discrimination
classification
Machine Learning (ML)
Informática
topic electronic nose
NO2
carbon nanomaterials
graphene oxide
surface acoustic wave (SAW)
pollutants
discrimination
classification
Machine Learning (ML)
Informática
description 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 .
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-02-07
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10486/711145
https://dx.doi.org/10.3390/s22031261
url http://hdl.handle.net/10486/711145
https://dx.doi.org/10.3390/s22031261
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869405143256006656
score 15,301603

Similares en LA Referencia