Image credit: Hyungjin ChungAbstract
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.
Hyungjin Chung, Ph.D.
Research Scientist
Generative models, Inverse problems, Multimodality, Motion, and more.