Erving Ximendes

Luminescence lifetime‐based sensing is ideally suited to monitor biological systems due to its minimal invasiveness and remote working principle. Yet, its applicability is limited in conditions of low signal‐to‐noise ratio (SNR) induced by, e.g., short exposure times and presence of opaque tissues. Herein this limitation is overcome by applying a U‐shaped convolutional neural network (U‐NET) to improve luminescence lifetime estimation under conditions of extremely low SNR. Specifically, the prowess of the U‐NET is showcased in the context of luminescence lifetime thermometry, achieving more precise thermal readouts using Ag 2 S nanothermometers. Compared to traditional analysis methods of decay curve fitting and integration, the U‐NET can extract average lifetimes more precisely and consistently regardless of the SNR value. The improvement achieved in the sensing performance using the U‐NET is demonstrated with two experiments characterized by extreme measurement conditions: thermal monitoring of free‐falling droplets, and monitoring of thermal transients in suspended droplets through an opaque medium. These results broaden the applicability of luminescence lifetime‐based sensing in fields including in vivo experimentation and microfluidics, while, hopefully, spurring further research on the implementation of machine learning (ML) in luminescence sensing.