1. Neural Networks Push the Limits of Luminescence Lifetime Nanosensing.
- Author
-
Ming L, Zabala-Gutierrez I, Rodríguez-Sevilla P, Retama JR, Jaque D, Marin R, and Ximendes E
- Subjects
- Neural Networks, Computer, Luminescence, Thermometry
- Abstract
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., (© 2023 The Authors. Advanced Materials published by Wiley-VCH GmbH.)- Published
- 2023
- Full Text
- View/download PDF