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| Home > Publications database > Yb$^{3+}$ Luminescence Thermometry Revived: Full‐Spectrum Machine Learning for Sub‐Kelvin Accuracy and Precision |
| Home > Publications database > Yb$^{3+}$ Luminescence Thermometry Revived: Full‐Spectrum Machine Learning for Sub‐Kelvin Accuracy and Precision |
| Journal Article | PUBDB-2026-02669 |
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2026
Wiley-VCH
Weinheim
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Please use a persistent id in citations: doi:10.1002/adom.71578
Abstract: In this manuscript, we redefine the operational limits of Yb3 + -based near-infrared luminescence thermometry. Using Ce3 + /Yb3 + co-doped Y3 Al5 O12 (YAG) nanoparticles, we established a full-spectrum, data-driven thermometric framework that transcends the constraints of the conventional Boltzmann-based luminescence intensity ratio method. By systematically optimizing Yb3 + concentration and leveraging both unsupervised (Principal Component Analysis) and supervised (Random Forest and Gaussian Process Regression) machine learning approaches, we extracted robust, temperature-encoded signatures from highly overlapped Yb3 + Stark-level emissions. This approach enables accurate, full-spectrum thermometry across the entire 100–700 K range, far surpassing the conventional Boltzmann limit ( > 420 K). Gaussian Process Regression yields an exceptional peak performance with an average accuracy of 0.02 K and a precision of 0.11 K. Furthermore, when subjected to a rigorous temperature-block splitting strategy to evaluate genuine generalization, the framework retains sub-kelvin predictive power, demonstrating an absolute accuracy of 0.45 K and a precision of 0.13 K, outperforming all conventional and alternative machine learning methods under disjoint conditions. Beyond the demonstrated performance in Ce3 + /Yb3 + -doped YAG, these results establish Luminescence Thermometry 2.0 as a full-spectrum, data-driven thermometric framework in which spectral complexity is exploited rather than avoided, and in which temperature prediction is evaluated through both precision and genuine predictive generalization.
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