Nanothermometer Performance Prediction via Fluorescence Dynamics Model

Fluorescent nanothermometers have garnered widespread attention in the biomedical field due to their high precision, rapid temporal response, and strong tissue penetration capability. However, accurately predicting and regulating the thermometric performance is still hindered by the lack of high-precision dynamics models and data on the temperature dependence of intrinsic parameters. This leads to improved performance of nanothermometers, which heavily relies on extensive experimental exploration, characterized by a high degree of trial and error, labor-intensive efforts, and a lack of mechanistic understanding. Therefore, for fluorescence lifetime-based thermometry, this paper establishes a fluorescence dynamics model and proposes a performance prediction method for nanothermometers based on this model. First, we developed a microscopic rate equation model for core–shell nanostructures, which has enabled a precise description of luminescence for nanoparticles with various structures and doping concentrations of luminescent ions. By selecting the smallest modeling unit and simulating the random doping of luminescent ions, along with comprehensively considering physical processes such as stimulated absorption, radiative relaxation, and energy migration/transfer, we achieved accurate and computationally efficient modeling of nanoparticles with multiple shell layers. Guided by the evaluation criterion for randomness of lattice modeling, a strategy of repeated modeling and computation was proposed to mitigate the effects of such randomness. Second, within the temperature range relevant for biological tissue thermometry (25–70 °C), we experimentally measured the intrinsic parameters of the fluorescence system, including the absorption cross-section, radiative relaxation rate, and energy migration/transfer microparameters. Finally, based on the established model and measured parameters, we proposed a method for predicting the thermometric performance of nanothermometers, and the theoretical predictions of luminescence lifetime show excellent agreement with the experimentally measured values (relative deviation is only 1%). Through the simulation of the spatiotemporal distribution of excitation energy in nanoparticles, we revealed the microscopic physical mechanisms by which particle structure, luminescent ion doping concentration, and excitation conditions regulate thermometric performance. This study provides a theoretical model, fundamental data, and methodological support for the performance prediction and regulation of fluorescent nanothermometers.