Less is more: dimensionality reduction as a general strategy for more precise luminescence thermometry

Erving Ximendes; Riccardo Marin; Luis Dias Carlos; Daniel Jaque

doi:10.1038/s41377-022-00932-3

Go to Nature Website

Your Location：

Home >

Browse articles >

Less is more: dimensionality reduction as a general strategy for more precise luminescence thermometry

Original Articles

- Less is more: dimensionality reduction as a general strategy for more precise luminescence thermometry
- Light: Science & Applications Vol. 11, Issue 9, Pages: 2127-2139(2022)
- Affiliations：
  
  1.NanoBIG, Departamento de Fısica de Materiales, Facultad de Ciencias, Universidad Autónoma de Madrid, C/Francisco Tomás y Valiente 7, Madrid, 28049, Spain
  2.NanoBIG, Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Ctra. Colmenar km. 9.100, Madrid, 28034, Spain
  3.Phantom-g, CICECO – Aveiro Institute of Materials, Department of Physics, University of Aveiro, Aveiro, 3810-193, Portugal
- Author bio：
  
  Erving Ximendes (erving.ximendes@uam.es)
  Riccardo Marin (riccardo.marin@uam.es)
- Funds：
- DOI：10.1038/s41377-022-00932-3
  CLC：
Scan for full text
Ximendes, E. et al. Less is more: dimensionality reduction as a general strategy for more precise luminescence thermometry. Light: Science & Applications, 11, 2127-2139 (2022).
DOI：

Ximendes, E. et al. Less is more: dimensionality reduction as a general strategy for more precise luminescence thermometry. Light: Science & Applications, 11, 2127-2139 (2022). DOI： 10.1038/s41377-022-00932-3.

摘要

Abstract

Thermal resolution (also referred to as temperature uncertainty) establishes the minimum discernible temperature change sensed by luminescent thermometers and is a key figure of merit to rank them. Much has been done to minimize its value via probe optimization and correction of readout artifacts, but little effort was put into a better exploitation of calibration datasets. In this context, this work aims at providing a new perspective on the definition of luminescence-based thermometric parameters using dimensionality reduction techniques that emerged in the last years. The application of linear (Principal Component Analysis) and non-linear (t-distributed Stochastic Neighbor Embedding) transformations to the calibration datasets obtained from rare-earth nanoparticles and semiconductor nanocrystals resulted in an improvement in thermal resolution compared to the more classical intensity-based and ratiometric approaches. This, in turn, enabled precise monitoring of temperature changes smaller than 0.1 ℃. The methods here presented allow choosing superior thermometric parameters compared to the more classical ones, pushing the performance of luminescent thermometers close to the experimentally achievable limits.

关键词

Keywords

references

M. Marquardt . Paul Ehrlich , :New York Schuman , 1951 .

H. L. Liu , 等 . Intracellular temperature sensing: an ultra-bright luminescent Nanothermometer with non-sensitivity to pH and ionic strength . Sci. Rep. , 2015 . 5 14879 DOI:10.1038/srep14879http://doi.org/10.1038/srep14879 .

K. Okabe , 等 . Intracellular temperature mapping with a fluorescent polymeric thermometer and fluorescence lifetime imaging microscopy . Nat. Commun. , 2012 . 3 705 DOI:10.1038/ncomms1714http://doi.org/10.1038/ncomms1714 .

H. H. Suo , 等 . Rational design of Ratiometric luminescence thermometry based on thermally coupled levels for bioapplications . Laser Photonics Rev. , 2021 . 15 2000319 DOI:10.1002/lpor.202000319http://doi.org/10.1002/lpor.202000319 .

R. Piñol , 等 . Real-time intracellular temperature imaging using lanthanide-bearing polymeric micelles . Nano Lett. , 2020 . 20 6466 -6472 . DOI:10.1021/acs.nanolett.0c02163http://doi.org/10.1021/acs.nanolett.0c02163 .

L. Labrador-Páez , 等 . Reliability of rare-earth-doped infrared luminescent nanothermometers . Nanoscale , 2018 . 10 22319 -22328 . DOI:10.1039/C8NR07566Bhttp://doi.org/10.1039/C8NR07566B .

Y. L. Shen , 等 . In vivo spectral distortions of infrared luminescent nanothermometers compromise their reliability . ACS Nano , 2020 . 14 4122 -4133 . DOI:10.1021/acsnano.9b08824http://doi.org/10.1021/acsnano.9b08824 .

A. D. Pickel , 等 . Apparent self-heating of individual upconverting nanoparticle thermometers . Nat. Commun. , 2018 . 9 4907 DOI:10.1038/s41467-018-07361-0http://doi.org/10.1038/s41467-018-07361-0 .

Y. L. Shen , 等 . Ag2S Nanoheaters with multiparameter sensing for reliable thermal feedback during in vivo tumor therapy . Adv. Funct. Mater. , 2020 . 30 2002730 DOI:10.1002/adfm.202002730http://doi.org/10.1002/adfm.202002730 .

J. Lifante , 等 . Reaching deeper: absolute in vivo thermal reading of liver by combining superbright Ag2S nanothermometers and in silico simulations . Adv. Sci. , 2021 . 8 2003838 DOI:10.1002/advs.202003838http://doi.org/10.1002/advs.202003838 .

Y. L. Shen , 等 . Reliable and remote monitoring of absolute temperature during liver inflammation via luminescence‐lifetime‐based nanothermometry . Adv. Mater. , 2022 . 34 2107764 DOI:10.1002/adma.202107764http://doi.org/10.1002/adma.202107764 .

L. M. Liz-Marzán , C. R. Kagan , J. E. Millstone . Reproducibility in nanocrystal synthesis? Watch out for impurities! . ACS Nano , 2020 . 14 6359 -6361 . DOI:10.1021/acsnano.0c04709http://doi.org/10.1021/acsnano.0c04709 .

J. Rocha , C. D. S. Brites , L. D. Carlos . Lanthanide organic framework luminescent thermometers . Chem. A Eur. J. , 2016 . 22 14782 -14795 . DOI:10.1002/chem.201600860http://doi.org/10.1002/chem.201600860 .

J. R. Casar , 等 . Lanthanide-based nanosensors: refining nanoparticle responsiveness for single particle imaging of stimuli . ACS Photonics , 2021 . 8 3 -17 . DOI:10.1021/acsphotonics.0c00894http://doi.org/10.1021/acsphotonics.0c00894 .

C. Lewis , 等 . Use of machine learning with temporal photoluminescence signals from cdte quantum dots for temperature measurement in microfluidic devices . ACS Appl. Nano Mater. , 2020 . 3 4045 -4053 . DOI:10.1021/acsanm.0c00065http://doi.org/10.1021/acsanm.0c00065 .

T. Cai , 等 . Acquisition of kHz-frequency two-dimensional surface temperature field using phosphor thermometry and proper orthogonal decomposition assisted long short-term memory neural networks . Int. J. Heat. Mass Transf. , 2021 . 165 120662 DOI:10.1016/j.ijheatmasstransfer.2020.120662http://doi.org/10.1016/j.ijheatmasstransfer.2020.120662 .

Š. Dragutin , 等 . Temperature effects on luminescent properties of Sr2CeO4: Eu3+ nanophosphor: a machine learning approach . Tehnika , 2020 . 75 279 -283 . DOI:10.5937/tehnika2003279Shttp://doi.org/10.5937/tehnika2003279S .

A. Bednarkiewicz , 等 . Standardizing luminescence nanothermometry for biomedical applications . Nanoscale , 2020 . 12 14405 -14421 . DOI:10.1039/D0NR03568Hhttp://doi.org/10.1039/D0NR03568H .

C. D. S. Brites , S. Balabhadra , L. D. Carlos . Lanthanide‐based thermometers: at the cutting‐edge of luminescence thermometry . Adv. Optical Mater. , 2019 . 7 1801239 DOI:10.1002/adom.201801239http://doi.org/10.1002/adom.201801239 .

F. E. Maturi , 等 . Going above and beyond: a tenfold gain in the performance of luminescence thermometers joining multiparametric sensing and multiple regression . Laser Photonics Rev. , 2021 . 15 2100301 DOI:10.1002/lpor.202100301http://doi.org/10.1002/lpor.202100301 .

R. R. Zebari , 等 . A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction . J. Appl. Sci. Technol. Trends , 2020 . 1 56 -70 . DOI:10.38094/jastt1224http://doi.org/10.38094/jastt1224 .

Pudil, P. & Novovičová, J. Novel methods for feature subset selection with respect to problem knowledge. In Feature Extraction, Construction and Selection (eds Liu, H. & Motoda, H. ) 101–116 (Boston: Springer, 1998), https://doi.org/10.1007/978-1-4615-5725-8_7https://doi.org/10.1007/978-1-4615-5725-8_7.

N. Renard , S. Bourennane , J. Blanc-Talon . Denoising and dimensionality reduction using multilinear tools for hyperspectral images . IEEE Geosci. Remote Sens. Lett. , 2008 . 5 138 -142 . DOI:10.1109/LGRS.2008.915736http://doi.org/10.1109/LGRS.2008.915736 .

Kuhn, R. et al. Eigenfaces and eigenvoices: dimensionality reduction for specialized pattern recognition. 1998 IEEE Second Workshop on Multimedia Signal Processing (Cat. No. 98EX175) 71–76 (Redondo Beach: IEEE, 1998), https://doi.org/10.1109/MMSP.1998.738915https://doi.org/10.1109/MMSP.1998.738915.

N. S. Ibrahim , D. A. Ramli . I-vector extraction for speaker recognition based on dimensionality reduction . Procedia Computer Sci. , 2018 . 126 1534 -1540 . DOI:10.1016/j.procs.2018.08.126http://doi.org/10.1016/j.procs.2018.08.126 .

T. Cheng , 等 . Small and bright lithium-based upconverting nanoparticles . J. Am. Chem. Soc. , 2018 . 140 12890 -12899 . DOI:10.1021/jacs.8b07086http://doi.org/10.1021/jacs.8b07086 .

K. B. Beć , J. Grabska , C. W. Huck . Near-infrared spectroscopy in bio-applications . Molecules , 2020 . 25 2948 DOI:10.3390/molecules25122948http://doi.org/10.3390/molecules25122948 .

A. Escudero , 等 . Rare earth based nanostructured materials: synthesis, functionalization, properties and bioimaging and biosensing applications . Nanophotonics , 2017 . 6 881 -921 . DOI:10.1515/nanoph-2017-0007http://doi.org/10.1515/nanoph-2017-0007 .

A. Skripka , 等 . Double rare-earth nanothermometer in aqueous media: opening the third optical transparency window to temperature sensing . Nanoscale , 2017 . 9 3079 -3085 . DOI:10.1039/C6NR08472Ahttp://doi.org/10.1039/C6NR08472A .

Y. L. Shen , 等 . Perspectives for Ag2S NIR-II nanoparticles in biomedicine: from imaging to multifunctionality . Nanoscale , 2019 . 11 19251 -19264 . DOI:10.1039/C9NR05733Ahttp://doi.org/10.1039/C9NR05733A .

D. C. Yu , 等 . One ion to catch them all: targeted high-precision Boltzmann thermometry over a wide temperature range with Gd3+ . Light Sci. Appl. , 2021 . 10 236 DOI:10.1038/s41377-021-00677-5http://doi.org/10.1038/s41377-021-00677-5 .

M. Suta , A. Meijerink . A theoretical framework for ratiometric single ion luminescent thermometers—thermodynamic and kinetic guidelines for optimized performance . Adv. Theory Simul. , 2020 . 3 2000176 DOI:10.1002/adts.202000176http://doi.org/10.1002/adts.202000176 .

Wang, J. Z. Geometric Structure of High-Dimensional Data and Dimensionality Reduction (Berlin Heidelberg: Springer, 2012), https://doi.org/10.1007/978-3-642-27497-8https://doi.org/10.1007/978-3-642-27497-8.

M. Wattenberg , F. Viégas , I. Johnson . How to use t-SNE effectively . Distill , 2016 . 1 e2 DOI:10.23915/distill.00002http://doi.org/10.23915/distill.00002 .

J. A. Lee , M. Verleysen . Nonlinear Dimensionality Reduction , :New York Springer , 2007 . DOI:10.23915/distill.00002http://doi.org/10.23915/distill.00002 .

Å. Rinnan , F. Van Den Berg , S. B. Engelsen . Review of the most common pre-processing techniques for near-infrared spectra . TrAC Trends Anal. Chem. , 2009 . 28 1201 -1222 . DOI:10.1016/j.trac.2009.07.007http://doi.org/10.1016/j.trac.2009.07.007 .

L. Van Der Maaten , G. Hinton . Visualizing data using t-SNE . J. Mach. Learn. Res. , 2008 . 9 2579 -2605. .

C. F. Dormann , 等 . Collinearity: a review of methods to deal with it and a simulation study evaluating their performance . Ecography , 2013 . 36 27 -46 . DOI:10.1111/j.1600-0587.2012.07348.xhttp://doi.org/10.1111/j.1600-0587.2012.07348.x .

A. S. Gwelo . Principal components to overcome multicollinearity problem . Oradea J. Bus. Econ. , 2019 . 4 79 -91 . DOI:10.47535/1991ojbe062http://doi.org/10.47535/1991ojbe062 .

A. Wibowo , M. R. Ridha . Comparison of logistic regression model and MARS using multicollinearity data simulation . JTAM (J. Teor. dan. Aplikasi Matematika) , 2020 . 4 39 DOI:10.31764/jtam.v4i1.1801http://doi.org/10.31764/jtam.v4i1.1801 .

E. Ximendes , 等 . Quo vadis, nanoparticle-enabled in vivo fluorescence imaging? . ACS Nano , 2021 . 15 1917 -1941 . DOI:10.1021/acsnano.0c08349http://doi.org/10.1021/acsnano.0c08349 .

T. P. Van Swieten , A. Meijerink , F. T. Rabouw . Impact of noise and background on measurement uncertainties in luminescence thermometry . ACS Photonics , 2022 . 9 1366 -1374 . DOI:10.1021/acsphotonics.2c00039http://doi.org/10.1021/acsphotonics.2c00039 .

T. Munro , 等 . CdSe/ZnS quantum dot fluorescence spectra shape-based thermometry via neural network reconstruction . J. Appl. Phys. , 2016 . 119 214903 DOI:10.1063/1.4953223http://doi.org/10.1063/1.4953223 .

L. W. Liu , 等 . Fluorescence spectra shape based dynamic thermometry . Appl. Phys. Lett. , 2014 . 104 031902 DOI:10.1063/1.4862921http://doi.org/10.1063/1.4862921 .

J. Zhou , 等 . Advances and challenges for fluorescence nanothermometry . Nat. Methods , 2020 . 17 967 -980 . DOI:10.1038/s41592-020-0957-yhttp://doi.org/10.1038/s41592-020-0957-y .

Ximendes, E. et al. Dimensionality Reduction in Luminescence Thermometry. Zenodo. https://doi.org/10.5281/zenodo.6507880https://doi.org/10.5281/zenodo.6507880 (2022).

Van Der Maaten, L. Learning a parametric embedding by preserving local structure. In Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics. 384–391 (Clearwater Beach, Florida, USA, 2009).

Views

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

⁰