期刊
JOURNAL OF LUMINESCENCE
卷 256, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.jlumin.2022.119637
关键词
Photoluminescence; Convolutional neural network; Luminescence thermometry; Cr3+
类别
A novel strategy using convolutional neural network for luminescence thermometry is proposed, which can autonomously select and extract multiple temperature-dependent features for regression temperature calculation, fully utilizing the temperature-dependent spectral data. Compared with traditional ratiometric technology and multiple linear regression method, the proposed method exhibits significantly higher accuracy, indicating the potential of deep learning in the field of luminescence thermometry.
Luminescence thermometry has emerged as an effective way to implement non-invasive thermal reading and finds promising applications in many fields. Nevertheless, traditional luminescence thermometric methods, such as the luminescence intensity ratio, are generally carried out based on one single temperature-dependent spectral feature. As a result, other thermal related spectral features were ignored, which radically restricts the sensing performance. Herein, we preliminarily propose a novel strategy for driving luminescence thermometry via convolutional neural network. The trained network is able to autonomously select and extract multiple temperature-dependent features for regression temperature, so the temperature-dependent spectral data can be fully utilized. Using Y3Al5O12: Cr3+ as the temperature sensing material, the proposed thermometry method exhibits high accuracy and strong generalization over the additional test set. The maximum measurement error (emax) in the range of 35-315 degrees C is about 0.77 degrees C, accompanied with the average error (eaver) of 0.20 degrees C. The accuracy of the proposed approach is significantly superior to those achieved by the classical ratiometric technology (emax approximate to 4.05 degrees C, eaver -1.31 degrees C) and the widely used multiple linear regression method (emax approximate to 4.27 degrees C, eaver -1.69 degrees C), indicating deep learning has the potential to open up a new era of luminescence thermometry.
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