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The emerging optical multiplexing within nanoscale shows super-capacity inencoding information by using the time-domain fingerprints from uniformnanoparticles. However, the optical diffraction limit compromises the decodingthroughput and accuracy of the nanoparticles during wide-field imaging. This,in turn, challenges the quality of nanoparticles to afford the modulatedexcitation condition, and further to retain the multiplexed opticalfingerprints for super-resolution multiplexing. Here we report a tailor-madetime-domain super-resolution method with the lifetime-engineered upconversionnanoparticles for multiplexing. We demonstrate that the nanoparticles arebright, uniform, and stable under structured illumination, which supports alateral resolution of 186 nm, less than 1/4th of the excitation wavelength. Wefurther develop a deep learning algorithm to coordinate with super-resolutionimages for more accurate decoding compared to a numeric algorithm. Wedemonstrate a three-channel sub-diffraction-limit imaging-based opticalmultiplexing with decoding accuracies above 93% for each channel, and largerthan 60% accuracies for potential seven-channel multiplexing. The improvedresolution provides high throughput by resolving the particles within theoptical limit, which enables higher multiplexing capacity in space. Thistime-domain super-resolution multiplexing opens a new horizon for handling thegrowing amount of information content, diseases source, and security risk inmodern society

Multiplexed structured illumination super-resolution imaging with time-domain upconversion nanoparticles