RT journal article
T1 Combining Deep Learning and Compressed SensingMethods for the 3D Characterization of Ultra-ThinEpitaxial Layers Grown on Controlled-Shape Nano-Oxides
A1 Grzonka, Justyna María
A1 Marqueses Rodríguez, José
A1 Fernández García, Susana
A1 Chen, Xiaowei
A1 Calvino Gámez, José Juan
A1 López Haro, Miguel
A2 Ciencia de los Materiales e Ingeniería Metalúrgica y Química Inorgánica
K1 compressed sensing
K1 deep learning
K1 electron tomography
K1 energy dispersive X-ray spectroscopy
K1 ultra-thin layers
AB 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
PB Wiley
SN 2640-4567
YR 2023
FD 2023-01-19
LK http://hdl.handle.net/10498/29359
UL http://hdl.handle.net/10498/29359
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 11-nov-2026