Super‐Resolution Near‐Infrared Fluorescence Microscopy of Single‐Walled Carbon Nanotubes Using Deep Learning

Abstract

Single-walled carbon nanotubes (SWCNTs) have unique optical and physical properties, with numerous biomedical imaging and sensing applications, owing to their near-infrared (nIR) fluorescence which overlaps with the biological transparency window. However, their longer emission wavelengths compared to emitters in the visible range result in a lower resolution due to the diffraction limit. Moreover, the elongated high-aspect-ratio structure of SWCNTs poses an additional challenge on super-resolution techniques that assume point emitters. Utilizing the advantages of deep learning and convolutional neural networks, along with the super-resolution radial fluctuation (SRRF) algorithm for network training, a fast, parameter-free, computational method is offered for enhancing the spatial resolution of nIR fluorescence images of SWCNTs. An average improvement of 22% in the resolution and 47% in signal-to-noise ratio (SNR) compared to the original images is shown, whereas SRRF leads to only 24% SNR improvement. The approach is demonstrated for a variety of SWCNT densities and length distributions, and a wide range of imaging conditions with challenging SNRs, including real-time videos, without compromising the temporal resolution. The results open the path for accelerated and accessible super-resolution of nIR fluorescent SWCNTs images, further advancing their applicability as nanoscale optical probes.