Deep-Learning-Driven Dual Sensors Based on Single-Trapped Upconverting Particles

Abstract

Upconverting lanthanide-doped particles are widely used as nanoscale temperature sensors, particularly in biological and photonic environments. However, extending their functionality to simultaneously measure temperature and light intensity remains a challenge, as the luminescent response is highly sensitive to both the environment composition and its temperature. In this work, we combine deep-learning methods with large-scale spectral data sets to investigate the reliability of Er/Yb-doped particles to function as dual sensors. Spectral data are collected from single particles levitating in vacuum and ensembles of particles in application-relevant wet and dry media. Although traditional ratiometric methods reliably retrieve temperature irrespective of the environment and specific experimental apparatus, they fail to disentangle the complex dependencies needed for dual sensing. Our trained models overcome these limitations, accurately predicting both temperature and laser power despite environmental variability. This approach opens a new route toward robust, data-driven sensing with upconverting particles in complex or fluctuating conditions.