Going Above and Beyond: A Tenfold Gain in the Performance of Luminescence Thermometers Joining Multiparametric Sensing and Multiple Regression
Autor(a) principal: | |
---|---|
Data de Publicação: | 2021 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1002/lpor.202100301 http://hdl.handle.net/11449/233418 |
Resumo: | Luminescence thermometry has substantially progressed in the last decade, rapidly approaching the performance of concurrent technologies. Performance is usually assessed through the relative thermal sensitivity, Sr, and temperature uncertainty, δT. Until now, the state-of-the-art values at ambient conditions do not exceed maximum Sr of 12.5% K−1 and minimum δT of 0.1 K. Although these numbers are satisfactory for most applications, they are insufficient for fields that require lower thermal uncertainties, such as biomedicine. This has motivated the development of materials with an improved thermal response, many of them responding to the temperature through distinct photophysical properties. This paper demonstrates how the performance of multiparametric luminescent thermometers can be further improved by simply applying new analysis routes. The synergy between multiparametric readouts and multiple linear regression makes possible a tenfold improvement in Sr and δT, reaching a world record of 50% K−1 and 0.05 K, respectively. This is achieved without requiring the development of new materials or upgrading the detection system as illustrated by using the green fluorescent protein and Ag2S nanoparticles. These results open a new era in biomedicine thanks to the development of new diagnosis tools based on the detection of super-small temperature fluctuations in living specimens. |
id |
UNSP_5044018356a3d79116414054f5d4af03 |
---|---|
oai_identifier_str |
oai:repositorio.unesp.br:11449/233418 |
network_acronym_str |
UNSP |
network_name_str |
Repositório Institucional da UNESP |
repository_id_str |
2946 |
spelling |
Going Above and Beyond: A Tenfold Gain in the Performance of Luminescence Thermometers Joining Multiparametric Sensing and Multiple Regressiongreen fluorescent proteinsluminescence nanothermometrymultiple linear regressionsilver sulfideLuminescence thermometry has substantially progressed in the last decade, rapidly approaching the performance of concurrent technologies. Performance is usually assessed through the relative thermal sensitivity, Sr, and temperature uncertainty, δT. Until now, the state-of-the-art values at ambient conditions do not exceed maximum Sr of 12.5% K−1 and minimum δT of 0.1 K. Although these numbers are satisfactory for most applications, they are insufficient for fields that require lower thermal uncertainties, such as biomedicine. This has motivated the development of materials with an improved thermal response, many of them responding to the temperature through distinct photophysical properties. This paper demonstrates how the performance of multiparametric luminescent thermometers can be further improved by simply applying new analysis routes. The synergy between multiparametric readouts and multiple linear regression makes possible a tenfold improvement in Sr and δT, reaching a world record of 50% K−1 and 0.05 K, respectively. This is achieved without requiring the development of new materials or upgrading the detection system as illustrated by using the green fluorescent protein and Ag2S nanoparticles. These results open a new era in biomedicine thanks to the development of new diagnosis tools based on the detection of super-small temperature fluctuations in living specimens.Phantom-g CICECO – Aveiro Institute of Materials Department of Physics University of AveiroInstitute of Chemistry São Paulo State University (UNESP)Nanomaterials for Bioimaging Group Universidade Autónoma de MadridDepartment of Chemical Engineering Massachusetts Institute of TechnologyInstitute of Chemistry São Paulo State University (UNESP)University of AveiroUniversidade Estadual Paulista (UNESP)Universidade Autónoma de MadridMassachusetts Institute of TechnologyMaturi, Fernando E. [UNESP]Brites, Carlos D. S.Ximendes, Erving C.Mills, CarolynOlsen, BradleyJaque, DanielRibeiro, Sidney J. L. [UNESP]Carlos, Luís D.2022-05-01T08:44:36Z2022-05-01T08:44:36Z2021-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1002/lpor.202100301Laser and Photonics Reviews.1863-88991863-8880http://hdl.handle.net/11449/23341810.1002/lpor.2021003012-s2.0-85113141998Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLaser and Photonics Reviewsinfo:eu-repo/semantics/openAccess2022-05-01T08:44:36Zoai:repositorio.unesp.br:11449/233418Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:18:00.736159Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Going Above and Beyond: A Tenfold Gain in the Performance of Luminescence Thermometers Joining Multiparametric Sensing and Multiple Regression |
title |
Going Above and Beyond: A Tenfold Gain in the Performance of Luminescence Thermometers Joining Multiparametric Sensing and Multiple Regression |
spellingShingle |
Going Above and Beyond: A Tenfold Gain in the Performance of Luminescence Thermometers Joining Multiparametric Sensing and Multiple Regression Maturi, Fernando E. [UNESP] green fluorescent proteins luminescence nanothermometry multiple linear regression silver sulfide |
title_short |
Going Above and Beyond: A Tenfold Gain in the Performance of Luminescence Thermometers Joining Multiparametric Sensing and Multiple Regression |
title_full |
Going Above and Beyond: A Tenfold Gain in the Performance of Luminescence Thermometers Joining Multiparametric Sensing and Multiple Regression |
title_fullStr |
Going Above and Beyond: A Tenfold Gain in the Performance of Luminescence Thermometers Joining Multiparametric Sensing and Multiple Regression |
title_full_unstemmed |
Going Above and Beyond: A Tenfold Gain in the Performance of Luminescence Thermometers Joining Multiparametric Sensing and Multiple Regression |
title_sort |
Going Above and Beyond: A Tenfold Gain in the Performance of Luminescence Thermometers Joining Multiparametric Sensing and Multiple Regression |
author |
Maturi, Fernando E. [UNESP] |
author_facet |
Maturi, Fernando E. [UNESP] Brites, Carlos D. S. Ximendes, Erving C. Mills, Carolyn Olsen, Bradley Jaque, Daniel Ribeiro, Sidney J. L. [UNESP] Carlos, Luís D. |
author_role |
author |
author2 |
Brites, Carlos D. S. Ximendes, Erving C. Mills, Carolyn Olsen, Bradley Jaque, Daniel Ribeiro, Sidney J. L. [UNESP] Carlos, Luís D. |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
University of Aveiro Universidade Estadual Paulista (UNESP) Universidade Autónoma de Madrid Massachusetts Institute of Technology |
dc.contributor.author.fl_str_mv |
Maturi, Fernando E. [UNESP] Brites, Carlos D. S. Ximendes, Erving C. Mills, Carolyn Olsen, Bradley Jaque, Daniel Ribeiro, Sidney J. L. [UNESP] Carlos, Luís D. |
dc.subject.por.fl_str_mv |
green fluorescent proteins luminescence nanothermometry multiple linear regression silver sulfide |
topic |
green fluorescent proteins luminescence nanothermometry multiple linear regression silver sulfide |
description |
Luminescence thermometry has substantially progressed in the last decade, rapidly approaching the performance of concurrent technologies. Performance is usually assessed through the relative thermal sensitivity, Sr, and temperature uncertainty, δT. Until now, the state-of-the-art values at ambient conditions do not exceed maximum Sr of 12.5% K−1 and minimum δT of 0.1 K. Although these numbers are satisfactory for most applications, they are insufficient for fields that require lower thermal uncertainties, such as biomedicine. This has motivated the development of materials with an improved thermal response, many of them responding to the temperature through distinct photophysical properties. This paper demonstrates how the performance of multiparametric luminescent thermometers can be further improved by simply applying new analysis routes. The synergy between multiparametric readouts and multiple linear regression makes possible a tenfold improvement in Sr and δT, reaching a world record of 50% K−1 and 0.05 K, respectively. This is achieved without requiring the development of new materials or upgrading the detection system as illustrated by using the green fluorescent protein and Ag2S nanoparticles. These results open a new era in biomedicine thanks to the development of new diagnosis tools based on the detection of super-small temperature fluctuations in living specimens. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-01 2022-05-01T08:44:36Z 2022-05-01T08:44:36Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1002/lpor.202100301 Laser and Photonics Reviews. 1863-8899 1863-8880 http://hdl.handle.net/11449/233418 10.1002/lpor.202100301 2-s2.0-85113141998 |
url |
http://dx.doi.org/10.1002/lpor.202100301 http://hdl.handle.net/11449/233418 |
identifier_str_mv |
Laser and Photonics Reviews. 1863-8899 1863-8880 10.1002/lpor.202100301 2-s2.0-85113141998 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Laser and Photonics Reviews |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128788183121920 |