Accurate and Fast Thermal Sensing via Phase-Responsive Nanothermometers and Neural Networks

Accurate, rapid, and remote temperature sensing at the nanoscale is essential for applications ranging from monitoring cellular thermodynamics to thermal management of microelectronic devices. Luminescent nanothermometers are promising candidates; however, their deployment is hindered by limited thermal sensitivity and cross-sensitivity to environmental factors that mimic temperature-induced luminescence changes. We introduce fluorescent chromatic nanoswitchers (CNSs), comprising silica nanocapsules incorporating a fluorescent dye within a thermoresponsive matrix. The matrix undergoes a solid-to-liquid phase transition, yielding an exceptional fluorescence lifetime sensitivity of 19% °C^-1 at 37 °C. Crucially, the lifetime-based thermal readout provided by CNSs is resistant to environmental interference, ensuring reliable, reproducible temperature measurements. To enhance CNS performance, we integrate artificial neural networks (ANNs) for advanced lifetime signal processing, enabling faster and robust thermal readouts. Proof-of-concept experiments show that the synergy between high-sensitivity lifetime-based nanothermometers and ANN-driven analysis paves the way for next-generation thermal sensing technologies, offering improved responsiveness, real-time capabilities, and enhanced accuracy.