The emerging optical multiplexing within nanoscale shows super-capacity in encoding information by using lifetime fingerprints from luminescent nanoparticles. However, the optical diffraction limit compromises the decoding accuracy and throughput of the nanoparticles during conventional widefield imaging. This, in turn, challenges the quality of nanoparticles to afford the modulated excitation condition and further retain the multiplexed optical fingerprints for super-resolution multiplexing. Here we report a tailor-made multiplexed super-resolution imaging method using the lifetime-engineered upconversion nanoparticles. We demonstrate that the nanoparticles are bright, uniform, and stable under structured illumination, which supports a lateral resolution of 185 nm, less than 1/4th of the excitation wavelength. We further develop a deep learning algorithm to coordinate with super-resolution images for more accurate decoding compared to a numeric algorithm. We demonstrate a three-channel super-resolution imaging based optical multiplexing with decoding accuracies above 93% for each channel and larger than 60% accuracy for potential seven-channel multiplexing. The improved resolution provides high throughput by resolving the particles within the diffraction-limited spots, which enables higher multiplexing capacity in space. This lifetime multiplexing super-resolution method opens a new horizon for handling the growing amount of information content, disease source, and security risk in modern society.
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http://dx.doi.org/10.1039/d1na00765c | DOI Listing |
Nat Biotechnol
January 2025
Department of Automation, Tsinghua University, Beijing, China.
Super-resolution (SR) neural networks transform low-resolution optical microscopy images into SR images. Application of single-image SR (SISR) methods to long-term imaging has not exploited the temporal dependencies between neighboring frames and has been subject to inference uncertainty that is difficult to quantify. Here, by building a large-scale fluorescence microscopy dataset and evaluating the propagation and alignment components of neural network models, we devise a deformable phase-space alignment (DPA) time-lapse image SR (TISR) neural network.
View Article and Find Full Text PDFNano Lett
January 2025
Department of Physics, Indian Institute of Technology Delhi, New Delhi 110016, India.
Structured illumination microscopy (SIM) is a robust wide-field optical nanoscopy technique. Several approaches are implemented to improve SIM's resolution capability (∼2-fold). However, achieving a high resolution with a large field of view (FOV) is still challenging.
View Article and Find Full Text PDFBiol Cell
January 2025
CNRS, Univ Rennes, IGDR [(Institut de Génétique et Développement de Rennes)]-UMR 6290, Rennes, France.
Understanding the spatiotemporal organization of components within living systems requires the highest resolution possible. Microscopy approaches that allow for a resolution below 250 nm include electron and super-resolution microscopy (SRM). The latter combines advanced imaging techniques and the optimization of image processing methods.
View Article and Find Full Text PDFSpatial anti-bunching, in contrast to the well-known bunching behavior observed in classical light sources, describes a situation where photons tend to avoid each other in space, resulting in a reduced probability of detecting two or more photons in proximity. This anti-bunching effect, a hallmark of nonclassical light, signifies a deviation from classical intensity fluctuations and has been observed not only in free electrons and entangled photon pairs but also in chaotic-thermal light. This work investigates the generation mechanism of spatial anti-bunching correlation in random light fields, leveraging the wandering of light centers to induce a second-order coherence degree below unity.
View Article and Find Full Text PDFNano Lett
January 2025
Institute of Nanochemistry and Nanobiology, School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, P.R. China.
Crystals with three-dimensional (3D) stereoscopic structures, characterized by diverse shapes, crystallographic planes, and morphologies, represent a significant advancement in catalysis. Differentiating and quantifying the catalytic activity of specific surface facets and sites at the single-particle level is essential for understanding and predicting catalytic performance. This study employs super-resolution radial fluctuations electrogenerated chemiluminescence microscopy (SRRF-ECLM) to achieve high-resolution mapping of electrocatalytic activity on individual 3D CuO crystals, including cubic, octahedral, and truncated octahedral structures.
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