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

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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
- Less is more: dimensionality reduction as a general strategy for more precise luminescence thermometry
- Light: Science & Applications 2022年11卷第9期页码：2127-2139
- 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
  中图分类号：
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Erving Ximendes, Riccardo Marin, Luis Dias Carlos, 等. Less is more: dimensionality reduction as a general strategy for more precise luminescence thermometry[J]. Light: Science & Applications, 2022,11(9):2127-2139.

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).
Erving Ximendes, Riccardo Marin, Luis Dias Carlos, 等. Less is more: dimensionality reduction as a general strategy for more precise luminescence thermometry[J]. Light: Science & Applications, 2022,11(9):2127-2139. DOI： 10.1038/s41377-022-00932-3.

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.

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references

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