Combining Deep Learning and Compressed Sensing Methods for the 3D Characterization of Ultra‐Thin Epitaxial Layers Grown on Controlled‐Shape Nano‐Oxides

Abstract

Using a nanostructured platform (a controlled‐shape nano‐oxide) and conventional wet impregnation techniques, powder‐type materials have been prepared in which atomically thin surface layers are deposited under very mild conditions. More importantly, an advanced methodology, combining energy dispersive X‐ray spectroscopy‐scanning transmission electron tomography (STEM‐EDX ET) and deep learning denoising techniques, has been developed for the 3D compositional characterization of these unique nanosystems. The complex case of LaOx‐coated CeO2 nanocubes is illustrated. For these, aberration corrected 2D STEM‐EDX evidence that ceria nanocubes become covered with a 2–4 atom‐thick layer of a La, Ce‐mixed oxide with spatially varying composition. However, STEM‐EDX ET reveals that this layer distributes unevenly, patching most of the available nanocube surface. The large flexibility and spread availability of the involved synthetic techniques enables, using the tools here developed, a wide exploration of the wealth of questions and applications of these intriguing, atomically thin, surface oxide phases.

Keywords

• compressed sensing
• deep learning
• electron tomography
• energy dispersive X-ray spectroscopy
• ultra-thin layers