文献类型：期刊文献

英文题名：Relieving photobleaching impacts on fluorescence thermometry via neural network predictions

作者：Wang, Jiahao[1];Wu, Binhe[1];Wang, Chunrui[1];Zhou, Jian[2];Sun, Hao[2];Cao, Wenhan[3];Yu, Huimei[4]

机构：[1]Donghua Univ, Dept Appl Phys, 2999 North Renmin Rd, Shanghai 201620, Peoples R China;[2]Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, 865 Changning Rd, Shanghai 200050, Peoples R China;[3]ShanghaiTech Univ, Sch Informat Sci & Technol, 393 Middle Huaxia Rd, Shanghai 201210, Peoples R China;[4]East China Univ Sci & Technol, Sch Mat Sci & Engn, 130 Meilong Rd, Shanghai 200237, Peoples R China

年份：2024

卷号：63

期号：30

起止页码：7857

外文期刊名：APPLIED OPTICS

收录：;EI(收录号：20244417296989);WOS:【SCI-EXPANDED(收录号:WOS:001343310800006)】；

基金：Funding. National Natural Science Foundation of China (61975029, 62205204, 62375172) .

语种：英文

外文关键词：Photobleaching

摘要：The thermal sensitivity of luminescence intensities enables fluorescence thermometry for remote temperature probing with high spatial and temporal resolutions. However, its accuracy suffers from factors such as nonlinear thermal response and the photochemical stability of fluorescence sensors. In this work, we realized thermometric measurements with high spatial resolution at micrometer scale using thin films with europium (Eu) complexes and microscopic measurements. We identified tris(dibenzoylmethane)phenanthroline europium(III)/polystyrene (Eu(DBM)3Phen/PS) thin film as an optimal choice for not only its linear dependence on fluorescence intensity for temperatures of biological interest but also its stronger resistance to the photobleaching effect. More importantly, we show that the latter effect can be effectively compensated via neural network methods. This approach has been validated for surface temperature mapping at the thermal equilibrium, where better uniformity as compared with results without correcting the photobleaching effect was achieved. The temperature elevation of resistive wires due to Joule heating can be clearly identified. This work shows that neural network models are powerful tools in improving the accuracy of fluorescence thermometry and beneficial for applications ranging from biology to nanotechnologies. (c) 2024 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.