In the past half-century, surface plasmon resonance in noble metallic nanoparticles has been an important research subject. Recent advances in the synthesis, assembly, characterization, and theories of traditional and non-traditional metal nanostructures open a new pathway to the kaleidoscopic applications of plasmonics. However, accurate and precise models of plasmon resonance are still challenging, as its characteristics can be affected by multiple factors. We herein summarize the recent advances of plasmonic nanoparticles and their applications, particularly regarding the fundamentals and applications of surface plasmon resonance (SPR) in Au nanoparticles, plasmon-enhanced upconversion luminescence, and plasmonic chiral metasurfaces.

We present a hardware-friendly deep learning architecture with one-dimensional convolutional neural networks (1D CNN) for fast analyzing fluorescence lifetime imaging (FLIM) data. A 1D CNN shows unparalleled advantages; they are more straightforward, quicker to train, and faster than high dimensional CNNs. 1D CNNs can be easily applied to multi-exponential fluorescence decay models. Compared with traditional least-square methods, superior performances of 1D CNNs on fluorescence lifetime image reconstruction have been validated using simulated data. We also employ the proposed 1D CNN to analyze two-photon FLIM images of functionalized gold nanoprobes in Hek293 and human prostate cancer cells. The results further demonstrate that 1D CNNs are fast and can accurately extract lifetime parameters from fluorescence signals. Our study shows that 1D CNNs have great potential in various real-time FLIM applications.

We present a deep learning (DL) framework, termed few-photon fluorescence lifetime imaging (FPFLI), for fast analysis of fluorescence lifetime imaging (FLIM) data under highly low-light conditions with only a few photons per pixel. FPFLI breaks the conventional pixel-wise lifetime analysis paradigm and fully exploits the spatial correlation and intensity information of fluorescence lifetime images to estimate lifetime images, pushing the photon budget to an unprecedented low level. The DL framework can be trained by synthetic FLIM data and easily adapted to various FLIM systems. FPFLI can effectively and robustly estimate FLIM images within seconds using synthetic and experimental data. The fast analysis of low-light FLIM images made possible by FPFLI promises a broad range of potential applications.

We present a fast and accurate analytical method for fluorescence lifetime imaging microscopy (FLIM), using the extreme learning machine (ELM). We used extensive metrics to evaluate ELM and existing algorithms. First, we compared these algorithms using synthetic datasets. The results indicate that ELM can obtain higher fidelity, even in low-photon conditions. Afterwards, we used ELM to retrieve lifetime components from human prostate cancer cells loaded with gold nanosensors, showing that ELM also outperforms the iterative fitting and non-fitting algorithms. By comparing ELM with a computational efficient neural network, ELM achieves comparable accuracy with less training and inference time. As there is no back-propagation process for ELM during the training phase, the training speed is much higher than existing neural network approaches. The proposed strategy is promising for edge computing with online training.

We present a deep learning approach to obtain high-resolution (HR) fluorescence lifetime images from low-resolution (LR) images acquired from fluorescence lifetime imaging (FLIM) systems. We first proposed a theoretical method for training neural networks to generate massive semi-synthetic FLIM data with various cellular morphologies, a sizeable dynamic lifetime range, and complex decay components. We then developed a degrading model to obtain LR-HR pairs and created a hybrid neural network, the spatial resolution improved FLIM net (SRI-FLIMnet) to simultaneously estimate fluorescence lifetimes and realize the nonlinear transformation from LR to HR images. The evaluative results demonstrate SRI-FLIMnet’s superior performance in reconstructing spatial information from limited pixel resolution. We also verified SRI-FLIMnet using experimental images of bacterial infected mouse raw macrophage cells. Results show that the proposed data generation method and SRI-FLIMnet efficiently achieve superior spatial resolution for FLIM applications. Our study provides a solution for fast obtaining HR FLIM images.

Fluorescence lifetime imaging (FLIM) is a powerful tool that provides unique quantitative information for biomedical research. In this study, we propose a multi-layer-perceptron-based mixer (MLP-Mixer) deep learning (DL) algorithm named FLIM-MLP-Mixer for fast and robust FLIM analysis. The FLIM-MLP-Mixer has a simple network architecture yet a powerful learning ability from data. Compared with the traditional fitting and previously reported DL methods, the FLIM-MLP-Mixer shows superior performance in terms of accuracy and calculation speed, which has been validated using both synthetic and experimental data. All results indicate that our proposed method is well suited for accurately estimating lifetime parameters from measured fluorescence histograms, and it has great potential in various real-time FLIM applications.