Neural networks push the limits of luminescence lifetime nanosensing

Luminescence lifetime-based sensing is ideally suited to monitor biological systems due to its minimal invasiveness and remote working principle. Yet, its applicability is limited in conditions of low signal-to-noise ratio (SNR) induced by, e.g., short exposure times and presence of opaque tissues....

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Detalles Bibliográficos
Autores: Ming, Liyan, Zabala Gutierrez, Irene, Rodríguez Sevilla, Paloma, Rubio Retama, Jorge, Jaque García, Daniel, Marin, Riccardo, Ximendes, Erving Clayton
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/709205
Acceso en línea:http://hdl.handle.net/10486/709205
https://dx.doi.org/10.1002/adma.202306606
Access Level:acceso abierto
Palabra clave:Luminescence lifetime
Luminescence thermometry
Machine learning
Neural networks
Sensing
Física
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spelling Neural networks push the limits of luminescence lifetime nanosensingMing, LiyanZabala Gutierrez, IreneRodríguez Sevilla, PalomaRubio Retama, JorgeJaque García, DanielMarin, RiccardoXimendes, Erving ClaytonLuminescence lifetimeLuminescence thermometryMachine learningNeural networksSensingFísicaLuminescence lifetime-based sensing is ideally suited to monitor biological systems due to its minimal invasiveness and remote working principle. Yet, its applicability is limited in conditions of low signal-to-noise ratio (SNR) induced by, e.g., short exposure times and presence of opaque tissues. Herein this limitation is overcome by applying a U-shaped convolutional neural network (U-NET) to improve luminescence lifetime estimation under conditions of extremely low SNR. Specifically, the prowess of the U-NET is showcased in the context of luminescence lifetime thermometry, achieving more precise thermal readouts using Ag2S nanothermometers. Compared to traditional analysis methods of decay curve fitting and integration, the U-NET can extract average lifetimes more precisely and consistently regardless of the SNR value. The improvement achieved in the sensing performance using the U-NET is demonstrated with two experiments characterized by extreme measurement conditions: thermal monitoring of free-falling droplets, and monitoring of thermal transients in suspended droplets through an opaque medium. These results broaden the applicability of luminescence lifetime-based sensing in fields including in vivo experimentation and microfluidics, while, hopefully, spurring further research on the implementation of machine learning (ML) in luminescence sensingThis work was financed by the Spanish Ministerio de Innovación y Ciencias under Project Nos. RTI2018-101050-J-I00, NANONERV PID2019-106211RB-I00, NANOGRANZ PID2021-123318OB-I00, TED2021-132317- I00B, and EIN2020-112419. Additional funding was provided by the European Union Horizon 2020 FETOpen project NanoTBTech (Grant No. 801305) and by the Comunidad Autónoma de Madrid (S2022/BMD7403 REMIN-CM). R.M. is grateful to the Spanish Ministerio de Ciencia e Innovación for support to research through a Ramón y Cajal Fellowship (RYC2021-032913-I). L.M. acknowledges a scholarship from the China Scholarship Council (No. 202108350018). I.Z.-G. thanks UCM-Santander for a predoctoral contract (CT63/19-CT64/19). P.R.-S. is grateful for a Juan de la Cierva-Incorporación scholarship (Grant No. IJC2019-041915-I)WileyDepartamento de Física de MaterialesFacultad de Ciencias20232023-10-03research articlehttp://purl.org/coar/resource_type/c_2df8fbb1VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10486/709205https://dx.doi.org/10.1002/adma.202306606reponame: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:10486/7092052026-06-23T12:46:27Z
dc.title.none.fl_str_mv Neural networks push the limits of luminescence lifetime nanosensing
title Neural networks push the limits of luminescence lifetime nanosensing
spellingShingle Neural networks push the limits of luminescence lifetime nanosensing
Ming, Liyan
Luminescence lifetime
Luminescence thermometry
Machine learning
Neural networks
Sensing
Física
title_short Neural networks push the limits of luminescence lifetime nanosensing
title_full Neural networks push the limits of luminescence lifetime nanosensing
title_fullStr Neural networks push the limits of luminescence lifetime nanosensing
title_full_unstemmed Neural networks push the limits of luminescence lifetime nanosensing
title_sort Neural networks push the limits of luminescence lifetime nanosensing
dc.creator.none.fl_str_mv Ming, Liyan
Zabala Gutierrez, Irene
Rodríguez Sevilla, Paloma
Rubio Retama, Jorge
Jaque García, Daniel
Marin, Riccardo
Ximendes, Erving Clayton
author Ming, Liyan
author_facet Ming, Liyan
Zabala Gutierrez, Irene
Rodríguez Sevilla, Paloma
Rubio Retama, Jorge
Jaque García, Daniel
Marin, Riccardo
Ximendes, Erving Clayton
author_role author
author2 Zabala Gutierrez, Irene
Rodríguez Sevilla, Paloma
Rubio Retama, Jorge
Jaque García, Daniel
Marin, Riccardo
Ximendes, Erving Clayton
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Física de Materiales
Facultad de Ciencias
dc.subject.none.fl_str_mv Luminescence lifetime
Luminescence thermometry
Machine learning
Neural networks
Sensing
Física
topic Luminescence lifetime
Luminescence thermometry
Machine learning
Neural networks
Sensing
Física
description Luminescence lifetime-based sensing is ideally suited to monitor biological systems due to its minimal invasiveness and remote working principle. Yet, its applicability is limited in conditions of low signal-to-noise ratio (SNR) induced by, e.g., short exposure times and presence of opaque tissues. Herein this limitation is overcome by applying a U-shaped convolutional neural network (U-NET) to improve luminescence lifetime estimation under conditions of extremely low SNR. Specifically, the prowess of the U-NET is showcased in the context of luminescence lifetime thermometry, achieving more precise thermal readouts using Ag2S nanothermometers. Compared to traditional analysis methods of decay curve fitting and integration, the U-NET can extract average lifetimes more precisely and consistently regardless of the SNR value. The improvement achieved in the sensing performance using the U-NET is demonstrated with two experiments characterized by extreme measurement conditions: thermal monitoring of free-falling droplets, and monitoring of thermal transients in suspended droplets through an opaque medium. These results broaden the applicability of luminescence lifetime-based sensing in fields including in vivo experimentation and microfluidics, while, hopefully, spurring further research on the implementation of machine learning (ML) in luminescence sensing
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-10-03
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/10486/709205
https://dx.doi.org/10.1002/adma.202306606
url http://hdl.handle.net/10486/709205
https://dx.doi.org/10.1002/adma.202306606
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 Wiley
publisher.none.fl_str_mv Wiley
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
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