RT journal article
T1 Quantitative 3D Characterization of Functionally Relevant Parameters in Heavy-Oxide-Supported 4d Metal Nanocatalysts
A1 Marqueses Rodríguez, José
A1 Manzorro Ureba, Ramón
A1 Grzonka, Justyna María
A1 Jiménez Benítez, Antonio Jesús
A1 Cervera Gontard, Lionel
A1 Hungría Hernández, Ana Belén
A1 Calvino Gámez, José Juan
A1 López Haro, Miguel
A2 Ciencia de los Materiales e Ingeniería Metalúrgica y Química Inorgánica
A2 Física de la Materia Condensada
K1 Nanometrology
K1 Nanometer-sized particles
K1 Nano-catalyst
K1 Metal supported
K1 Metal particle
K1 Electron tomography
K1 Atomic numbers
K1 American Chemical Society
K1 3D characterization
AB Accurate 3D nanometrology of catalysts with small nanometer-sized particles of light 3d or 4d metals supported on high-atomic-number oxides is crucial for understanding their functionality. However, performing quantitative 3D electron tomography analysis on systems involving metals like Pd, Ru, or Rh supported on heavy oxides (e.g., CeO2) poses significant challenges. The low atomic number (Z) of the metal complicates discrimination, especially for very small nanoparticles (1-3 nm). Conventional reconstruction methods successful for catalysts with 5d metals (e.g., Au, Pt, or Ir) fail to detect 4d metal particles in electron tomography reconstructions, as their contrasts cannot be effectively separated from those of the underlying support crystallites. To address this complex 3D characterization challenge, we have developed a full deep learning (DL) pipeline that combines multiple neural networks, each one optimized for a specific image-processing task. In particular, single-image super-resolution (SR) techniques are used to intelligently denoise and enhance the quality of the tomographic tilt series. U-net generative adversarial network algorithms are employed for image restoration and correcting alignment-related artifacts in the tilt series. Finally, semantic segmentation, utilizing a U-net-based convolutional neural network, splits the 3D volumes into their components (metal and support). This approach enables the visualization of subnanometer-sized 4d metal particles and allows for the quantitative extraction of catalytically relevant structural information, such as particle size, sphericity, and truncation, from compressed sensing electron tomography volume reconstructions. We demonstrate the potential of this approach by characterizing nanoparticles of a metal widely used in catalysis, Pd (Z = 46), supported on CeO2, a very high density (7.22 g/cm3) oxide involving a quite high-atomic-number element, Ce (Z = 58).
PB American Chemical Society
SN 0897-4756
YR 2023
FD 2023-09-14
LK http://hdl.handle.net/10498/31416
UL http://hdl.handle.net/10498/31416
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 06-nov-2026