Achieving high-accuracy of multi-feature temperature sensing in chromium(III)-doped nanophosphors using machine learning

Abstract

Cr³⁺-doped near-infrared luminescence thermometers, recognized for their tunable emission spectra and high temperature sensitivity, are extensively researched. Nonetheless, investigations into their temperature measurement capabilities have predominantly concentrated on ranges either below or above ambient temperature, with limited examination of broad-range measurements. Moreover, temperature assessments based on single spectral features are subject to uncertainties, whereas the integration of multiple features can enhance the temperature sensing accuracy. In this work, K₂NaGaF₆:Cr³⁺ nanophosphors were synthesized via a hydrothermal method and its near-infrared luminescence was significantly enhanced through high-temperature annealing. Emission spectra were evaluated across a temperature span of 83 K to 573 K, and multiple spectral features were extracted for temperature sensing. Employing the auto-sklearn machine learning (ML) techniques, three spectral features—full width at half maximum (FWHM), peak intensity ratio, and integral area—were combined for temperature prediction. The optimized three-feature model achieved a temperature measurement root mean squared error (RMSE) of 0.52 K within the 223 K–323 K range, surpassing the performance of single- and two-feature models. Furthermore, the model also maintained an accuracy of RMSE < 1 K over a wider measured temperature range. Our work demonstrates the superiority of the high-accuracy of temperature sensing based on the multi-feature, and it can be used to measure the temperature in micro(nano)-scale applications.