Neural Networks Push The Limits of Luminescence Lifetime Nanosensing.

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 we overcome this limitation applying a U-shaped convolutional neural network (U-NET) to improve luminescence lifetime estimation under conditions of extremely low SNR. Specifically, we showcase the prowess of the U-NET 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 thermometric performance using the U-NET is showcased 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 in luminescence sensing. This article is protected by copyright. All rights reserved.