Ammonia gas optical sensor based on lossy mode resonances

This letter presents the fabrication and characterization of an ammonia (NH 3) gas optical sensor based on lossy mode resonances (LMRs). A chromium (III) oxide (Cr 2 O 3) thin film deposited onto a planar waveguide was used as LMR supporting coating. The obtained LMR shows a maximum attenuation wave...

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Authors: Armas, Dayron, Zubiate Orzanco, Pablo, Ruiz Zamarreño, Carlos, Matías Maestro, Ignacio
Format: article
Status:Versión aceptada para publicación
Publication Date:2023
Country:España
Institution:Universidad Pública de Navarra
Repository:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
OAI Identifier:oai:academica-e.unavarra.es:2454/46397
Online Access:https://hdl.handle.net/2454/46397
Access Level:Open access
Keyword:Sensor materials
Ammonia gas sensor
Lossy mode resonance (LMR)
Machine learning
Planar waveguides
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oai_identifier_str oai:academica-e.unavarra.es:2454/46397
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spelling Ammonia gas optical sensor based on lossy mode resonancesArmas, DayronZubiate Orzanco, PabloRuiz Zamarreño, CarlosMatías Maestro, IgnacioSensor materialsAmmonia gas sensorLossy mode resonance (LMR)Machine learningPlanar waveguidesThis letter presents the fabrication and characterization of an ammonia (NH 3) gas optical sensor based on lossy mode resonances (LMRs). A chromium (III) oxide (Cr 2 O 3) thin film deposited onto a planar waveguide was used as LMR supporting coating. The obtained LMR shows a maximum attenuation wavelength or resonance wavelength centered at 673 nm. The optical properties of the coating can be modified as a function of the presence and concentration of NH 3 in the external medium. Consequently, the refractive index of the Cr 2 O 3 thin film will change, producing a red-shift of the resonance wavelength. Obtained devices were tested for different concentrations of NH 3 as well as repetitive cycles. Concentrations as low as 10 ppbv of NH 3 were detected at room temperature. Machine learning regression models were used to mitigate the cross-sensitivity of the device under temperature and humidity fluctuations.This work was supported in part by the Spanish Ministry of Science and Innovation under Grant FPI PRE2020-091797, in part by the Spanish Agencia Estatal de Investigacion under Grant PID2022-137437OB-I00, and in part by the European Union's Horizon 2020 Research and Innovation Programme (Stardust-Holistic and Integrated Urban Model for Smart Cities) under Grant 774094.IEEEIngeniería Eléctrica, Electrónica y de ComunicaciónInstitute of Smart Cities - ISCIngeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren2023info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttps://hdl.handle.net/2454/46397reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarrainstname:Universidad Pública de NavarraInglésinfo:eu-repo/grantAgreement/European Commission/Horizon 2020 Framework Programme/774094info:eu-repo/grantAgreement/AEI//PRE2020-091797info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-137437OB-I00© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work.info:eu-repo/semantics/openAccessoai:academica-e.unavarra.es:2454/463972026-06-17T12:41:47Z
dc.title.none.fl_str_mv Ammonia gas optical sensor based on lossy mode resonances
title Ammonia gas optical sensor based on lossy mode resonances
spellingShingle Ammonia gas optical sensor based on lossy mode resonances
Armas, Dayron
Sensor materials
Ammonia gas sensor
Lossy mode resonance (LMR)
Machine learning
Planar waveguides
title_short Ammonia gas optical sensor based on lossy mode resonances
title_full Ammonia gas optical sensor based on lossy mode resonances
title_fullStr Ammonia gas optical sensor based on lossy mode resonances
title_full_unstemmed Ammonia gas optical sensor based on lossy mode resonances
title_sort Ammonia gas optical sensor based on lossy mode resonances
dc.creator.none.fl_str_mv Armas, Dayron
Zubiate Orzanco, Pablo
Ruiz Zamarreño, Carlos
Matías Maestro, Ignacio
author Armas, Dayron
author_facet Armas, Dayron
Zubiate Orzanco, Pablo
Ruiz Zamarreño, Carlos
Matías Maestro, Ignacio
author_role author
author2 Zubiate Orzanco, Pablo
Ruiz Zamarreño, Carlos
Matías Maestro, Ignacio
author2_role author
author
author
dc.contributor.none.fl_str_mv Ingeniería Eléctrica, Electrónica y de Comunicación
Institute of Smart Cities - ISC
Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
dc.subject.none.fl_str_mv Sensor materials
Ammonia gas sensor
Lossy mode resonance (LMR)
Machine learning
Planar waveguides
topic Sensor materials
Ammonia gas sensor
Lossy mode resonance (LMR)
Machine learning
Planar waveguides
description This letter presents the fabrication and characterization of an ammonia (NH 3) gas optical sensor based on lossy mode resonances (LMRs). A chromium (III) oxide (Cr 2 O 3) thin film deposited onto a planar waveguide was used as LMR supporting coating. The obtained LMR shows a maximum attenuation wavelength or resonance wavelength centered at 673 nm. The optical properties of the coating can be modified as a function of the presence and concentration of NH 3 in the external medium. Consequently, the refractive index of the Cr 2 O 3 thin film will change, producing a red-shift of the resonance wavelength. Obtained devices were tested for different concentrations of NH 3 as well as repetitive cycles. Concentrations as low as 10 ppbv of NH 3 were detected at room temperature. Machine learning regression models were used to mitigate the cross-sensitivity of the device under temperature and humidity fluctuations.
publishDate 2023
dc.date.none.fl_str_mv 2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2454/46397
url https://hdl.handle.net/2454/46397
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/European Commission/Horizon 2020 Framework Programme/774094
info:eu-repo/grantAgreement/AEI//PRE2020-091797
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-137437OB-I00
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
instname:Universidad Pública de Navarra
instname_str Universidad Pública de Navarra
reponame_str Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
collection Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
repository.name.fl_str_mv
repository.mail.fl_str_mv
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