Luminescence Thermometry with Eu3+-Doped Y2Mo3O12: Comparison of Performance of Intensity Ratio and Machine Learning Temperature Read-Outs
| Autor: | Tamara Gavrilović, Vesna Đorđević, Jovana Periša, Mina Medić, Zoran Ristić, Aleksandar Ćirić, Željka Antić, Miroslav D. Dramićanin |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: |
luminescence thermometry
luminescence intensity ratio principal component analysis europium Y2Mo3O12 Technology Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85 |
| Zdroj: | Materials, Vol 17, Iss 21, p 5354 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1996-1944 |
| DOI: | 10.3390/ma17215354 |
| Popis: | Accurate temperature measurement is critical across various scientific and industrial applications, necessitating advancements in thermometry techniques. This study explores luminescence thermometry, specifically utilizing machine learning methodologies to enhance temperature sensitivity and accuracy. We investigate the performance of principal component analysis (PCA) on the Eu3+-doped Y2Mo3O12 luminescent probe, contrasting it with the traditional luminescence intensity ratio (LIR) method. By employing PCA to analyze the full emission spectra collected at varying temperatures, we achieve an average accuracy (ΔT) of 0.9 K and a resolution (δT) of 1.0 K, significantly outperforming the LIR method, which yielded an average accuracy of 2.3 K and a resolution of 2.9 K. Our findings demonstrate that while the LIR method offers a maximum sensitivity (Sr) of 5‰ K⁻1 at 472 K, PCA’s systematic approach enhances the reliability of temperature measurements, marking a crucial advancement in luminescence thermometry. This innovative approach not only enriches the dataset analysis but also sets a new standard for temperature measurement precision. |
| Databáze: | Directory of Open Access Journals |
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