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http://hdl.handle.net/10261/295599
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Campo DC | Valor | Lengua/Idioma |
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dc.contributor.author | Cruz, Carlos | es_ES |
dc.contributor.author | Matatagui, Daniel | es_ES |
dc.contributor.author | Ramírez, Cristina | es_ES |
dc.contributor.author | Badillo-Ramírez, Isidro | es_ES |
dc.contributor.author | De la O-Cuevas, Emmanuel | es_ES |
dc.contributor.author | Saniger, J.M. | es_ES |
dc.contributor.author | Horrillo, Carmen | es_ES |
dc.date.accessioned | 2023-02-22T12:00:27Z | - |
dc.date.available | 2023-02-22T12:00:27Z | - |
dc.date.issued | 2022-02-07 | - |
dc.identifier | doi: 10.3390/s22031261 | - |
dc.identifier | issn: 1424-8220 | - |
dc.identifier.citation | Sensors 22: 1261- (2022) | es_ES |
dc.identifier.uri | http://hdl.handle.net/10261/295599 | - |
dc.description | 13 páginas, 9 figuras, 2 tablas | - |
dc.description.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. | - |
dc.description.sponsorship | Spanish Ministry of Science and Innovation for financing the project RTI2018-095856-B-C22 (AEI/FEDER). D.M. acknowledges the financial support from the Fundación General CSIC via Programa ComFuturo. C.R acknowledge the funding from IJCI-2017-34724 grant. | - |
dc.language | eng | - |
dc.publisher | Multidisciplinary Digital Publishing Institute | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095856-B-C22/ES/DESARROLLO DE MATERIALES MAGNETICOS Y SENSORES PARA APLICACIONES BIOMEDICAS/ | - |
dc.relation.isversionof | Publisher's version | - |
dc.rights | openAccess | - |
dc.subject | Electronic nose | - |
dc.subject | NO2 | - |
dc.subject | carbon nanomaterials | - |
dc.subject | graphene oxide | - |
dc.subject | surface acoustic wave (SAW) | - |
dc.subject | pollutants | - |
dc.subject | Discrimination | - |
dc.subject | classification | - |
dc.subject | Machine Learning (ML) | - |
dc.title | Carbon SH-SAW-Based Electronic Nose to Discriminate and Classify Sub-ppm NO2 | es_ES |
dc.type | artículo | es_ES |
dc.identifier.doi | 10.3390/s22031261 | - |
dc.relation.publisherversion | http://dx.doi.org/10.3390/s22031261 | - |
dc.date.updated | 2023-02-22T12:00:27Z | - |
dc.rights.license | https://creativecommons.org/licenses/by/4.0/ | - |
dc.contributor.funder | Ministerio de Ciencia e Innovación (España) | - |
dc.contributor.funder | Agencia Estatal de Investigación (España) | - |
dc.relation.csic | Sí | es_ES |
dc.identifier.funder | http://dx.doi.org/10.13039/501100004837 | es_ES |
dc.identifier.funder | http://dx.doi.org/10.13039/501100011033 | es_ES |
dc.type.coar | http://purl.org/coar/resource_type/c_6501 | es_ES |
item.cerifentitytype | Publications | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.grantfulltext | open | - |
item.fulltext | With Fulltext | - |
item.openairetype | artículo | - |
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Carbon_SH-SAW_based_electronic_nose.pdf | 3,13 MB | Adobe PDF | Visualizar/Abrir |
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