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Related Concept Videos

Photoluminescence: Applications01:14

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Photoluminescence offers a wide range of applications due to its inherent sensitivity and selectivity. This technique allows for both direct and indirect analyses of the analyte. Direct quantitative analysis is possible when the analyte exhibits a favorable quantum yield for fluorescence or phosphorescence. However, an indirect analysis may be feasible if the analyte is not fluorescent or phosphorescent, or if the quantum yield is unfavorable. Indirect methods include reacting the analyte with...
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Flame photometry, also known as flame emission spectrometry, is a technique used for the qualitative and quantitative analysis of elements present in a sample using a flame as the source of excitation energy. The concept of flame photometry was realized in the early 1860s by Kirchhoff and Bunsen, who discovered that specific elements emit characteristic radiation when excited in flames. The first instrument developed for this purpose was used to measure sodium (Na) in plant ash using a Bunsen...
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Luminescence thermometry driven by a support vector machine: a strategy toward precise thermal sensing.

Wei Xu, Chenglong Xu, Junqi Cui

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    |February 1, 2024
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    Machine learning enhances luminescence thermometry precision. A support vector machine (SVM) significantly improves non-contact temperature measurements using Gd3Ga5O12:Er3+-Yb3+, outperforming traditional methods.

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    Area of Science:

    • Materials Science
    • Spectroscopy
    • Machine Learning

    Background:

    • Luminescence thermometry offers non-contact temperature sensing but faces precision and reliability challenges.
    • Existing methods like luminescence intensity ratio (LIR) and multiple linear regression (MLR) have limitations in accuracy and robustness.

    Purpose of the Study:

    • To develop an advanced thermal sensing strategy for luminescence thermometry using machine learning.
    • To compare the performance of a support vector machine (SVM) against LIR and MLR methods for temperature measurement.

    Main Methods:

    • Utilized Gadolinium Gallium Garnet doped with Erbium and Ytterbium (Gd3Ga5O12:Er3+-Yb3+) as the luminescent sensing material.
    • Employed a support vector machine (SVM) to correlate upconversion emission spectra with temperature.
    • Compared SVM performance with LIR and MLR methods across a broad temperature range (303-853 K).

    Main Results:

    • The SVM method achieved significantly lower maximum (0.38 K) and mean (0.12 K) errors compared to LIR (3.75 K, 1.37 K) and MLR (1.82 K, 0.43 K).
    • SVM-based thermometry demonstrated high robustness against spectral distortions caused by environmental interferences, where LIR and MLR proved ineffective.

    Conclusions:

    • Support vector machine (SVM) is a powerful tool for advancing luminescence thermometry.
    • This machine learning approach enables highly precise, reliable, and robust non-contact temperature measurements.