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Luminescence thermometry has been extensively exploited in the last decades both from the fundamental and applied point of views. The application of photoluminescent nanoparticles on the microscopic level based on rare-earth doped (RED) nanostructures is yet a challenge. Distinct underlying physical mechanisms in the RED nanomaterials have been exploited, such as intensity ratio between radiative transitions associated with thermally coupled energy levels, energy peak and lifetime of an excited state variations with the temperature. The drawbacks of such systems are the relatively low thermal sensitivity (Sr), and the large temperature uncertainty. To overcome that, several research groups have been seeking new functionalized materials. The majority of the efforts have been directed towards increasing Sr with record around 10 %{\deg}C-1, which is, however, considered unsatisfactory. We propose the use of an automated machine learning tool to retrieve an ideal pipeline improving the response of photoluminescence thermometers. As a proof-of-concept, we used Nd3+-doped YAG nanoparticles, excited at 760 nm, and the photoluminescence spectra in the range from 860 nm to 960 nm as input parameters. In addition to the improvement in the accuracy (> 5.5x over traditional methods), the implementation is very simple, without the requirement of any deconvolution procedure or knowledge of any underlying physical mechanism. Our findings demonstrate that this approach is resilient to natural variances across various spectral acquisitions, which may otherwise lead to an inaccurate estimation of temperature, opening the door for real-time applications. Our open-source code is designed to be accessible to individuals without programming experience.

In the anti-Stokes excitation of trivalent rare-earth ions (RE3+), the excitation photons energy is smaller than that of a given absorption transition, and the energy mismatch can be compensated by phonons annihilation from the host lattice. Since the phonon occupation number increases with temperature, heating the system generally increases the efficiency of anti-Stokes excitation. Here, we exploited the intrinsic heating associated with light-to-heat conversion in the interaction of excitation laser light with metallic nanoparticles (Ag or Au) on the surface of submicrometric particles of NdxY1.00-xAl3(BO3)4 (x = 0.10, 0.20, and 1.00) in order to enhance the efficiency of the anti-Stokes excitation at 1064 nm. Several upconversion emissions are observed from 600 nm to 880 nm, the most intense being at 750 nm due to the Nd3+ transition {4F7/2, 4S3/2} - 4I9/2. Giant enhancements are demonstrated, when compared to undecorated NdxY1.00-xAl3(BO3)4 particles. The present results can be expanded to other luminescent materials as well as excitation wavelengths.