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 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 Ag2 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.

Reliable and Remote Monitoring of Absolute Temperature during Liver Inflammation via Luminescence-Lifetime-Based Nanothermometry

Temperature of tissues and organs is one of the first parameters affected by physiological and pathological processes, such as metabolic activity, acute trauma, or infection-induced inflammation. Therefore, the onset and development of these processes can be detected by monitoring deviations from basal temperature. To accomplish this, minimally invasive, reliable, and accurate measurement of the absolute temperature of internal organs is required. Luminescence nanothermometry is the ideal technology for meeting these requirements. Although this technique has lately undergone remarkable developments, its reliability is being questioned due to spectral distortions caused by biological tissues. In this work, how the use of bright Ag₂ S nanoparticles featuring temperature-dependent fluorescence lifetime enables reliable and accurate measurement of the absolute temperature of the liver in mice subjected to lipopolysaccharide-induced inflammation is demonstrated. Beyond the remarkable thermal sensitivity (≈ 3% °C^-1 around 37 °C) and thermal resolution obtained (smaller than 0.3 °C), the results included in this work set a blueprint for the development of new diagnostic procedures based on the use of intracorporeal temperature as a physiological indicator.