Deep learning STEM-EDX tomography of nanocrystals

Energy-dispersive X-ray spectroscopy (EDX) is often performed simultaneously with high-angle annular dark-field scanning transmission electron microscopy (STEM) for nanoscale physico-chemical analysis. However, high-quality STEM-EDX tomographic imaging is still challenging due to fundamental limitations such as sample degradation with prolonged scan time and the low probability of X-ray generation. To address this, we propose an unsupervised deep learning method for high-quality 3D EDX tomography of core-shell nanocrystals, which can be usually permanently dammaged by prolonged electron beam. The proposed deep learning STEM-EDX tomography method was used to accurately reconstruct Au nanoparticles and InP/ZnSe/ZnS core-shell quantum dots, used in commercial display devices. Furthermore, the shape and thickness uniformity of the reconstructed ZnSe/ZnS shell closely correlates with optical properties of the quantum dots, such as quantum efficiency and chemical stability. Advanced electron microscopy and spectroscopy techniques can reveal useful structural and chemical details at the nanoscale. An unsupervised deep learning approach helps to reconstruct 3D images and observe the relationship between optical and structural properties of semiconductor nanocrystals, of interest in optoelectronic applications.

Automated summary

This study proposes an unsupervised deep learning method for high-quality 3D EDX tomography, addressing fundamental limitations such as sample degradation and low X-ray generation probability during prolonged scan. The method successfully reconstructed Au nanoparticles and InP/ZnSe/ZnS core-shell quantum dots.