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.

能量色散 X 射线光谱 (EDX) 通常与用于纳米级物理化学分析的高角度环形暗场扫描透射电子显微镜 (STEM) 同时进行。然而，高质量的 STEM-EDX 断层成像仍然具有挑战性，因为存在一些基本限制，例如扫描时间延长导致样品降解以及 X 射线产生的可能性低。为了解决这个问题，我们提出了一种用于核壳纳米晶体的高质量 3D EDX 断层扫描的无监督深度学习方法，这种方法通常会被长时间的电子束永久损坏。所提出的深度学习 STEM-EDX 断层扫描方法用于准确重建商业显示设备中使用的 Au 纳米颗粒和 InP/ZnSe/ZnS 核壳量子点。此外，