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Quantitative 3D Characterization of Functionally Relevant Parameters in Heavy-Oxide-Supported 4d Metal Nanocatalysts

[Image: see text] 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...

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Autores principales: Marqueses-Rodríguez, José, Manzorro, Ramón, Grzonka, Justyna, Jiménez-Benítez, Antonio Jesús, Gontard, Lionel Cervera, Hungría, Ana Belén, Calvino, José Juan, López-Haro, Miguel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10538501/
https://www.ncbi.nlm.nih.gov/pubmed/37780410
http://dx.doi.org/10.1021/acs.chemmater.3c01163
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author Marqueses-Rodríguez, José
Manzorro, Ramón
Grzonka, Justyna
Jiménez-Benítez, Antonio Jesús
Gontard, Lionel Cervera
Hungría, Ana Belén
Calvino, José Juan
López-Haro, Miguel
author_facet Marqueses-Rodríguez, José
Manzorro, Ramón
Grzonka, Justyna
Jiménez-Benítez, Antonio Jesús
Gontard, Lionel Cervera
Hungría, Ana Belén
Calvino, José Juan
López-Haro, Miguel
author_sort Marqueses-Rodríguez, José
collection PubMed
description [Image: see text] 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., CeO(2)) 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 CeO(2), a very high density (7.22 g/cm(3)) oxide involving a quite high-atomic-number element, Ce (Z = 58).
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spelling pubmed-105385012023-09-29 Quantitative 3D Characterization of Functionally Relevant Parameters in Heavy-Oxide-Supported 4d Metal Nanocatalysts Marqueses-Rodríguez, José Manzorro, Ramón Grzonka, Justyna Jiménez-Benítez, Antonio Jesús Gontard, Lionel Cervera Hungría, Ana Belén Calvino, José Juan López-Haro, Miguel Chem Mater [Image: see text] 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., CeO(2)) 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 CeO(2), a very high density (7.22 g/cm(3)) oxide involving a quite high-atomic-number element, Ce (Z = 58). American Chemical Society 2023-09-14 /pmc/articles/PMC10538501/ /pubmed/37780410 http://dx.doi.org/10.1021/acs.chemmater.3c01163 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Marqueses-Rodríguez, José
Manzorro, Ramón
Grzonka, Justyna
Jiménez-Benítez, Antonio Jesús
Gontard, Lionel Cervera
Hungría, Ana Belén
Calvino, José Juan
López-Haro, Miguel
Quantitative 3D Characterization of Functionally Relevant Parameters in Heavy-Oxide-Supported 4d Metal Nanocatalysts
title Quantitative 3D Characterization of Functionally Relevant Parameters in Heavy-Oxide-Supported 4d Metal Nanocatalysts
title_full Quantitative 3D Characterization of Functionally Relevant Parameters in Heavy-Oxide-Supported 4d Metal Nanocatalysts
title_fullStr Quantitative 3D Characterization of Functionally Relevant Parameters in Heavy-Oxide-Supported 4d Metal Nanocatalysts
title_full_unstemmed Quantitative 3D Characterization of Functionally Relevant Parameters in Heavy-Oxide-Supported 4d Metal Nanocatalysts
title_short Quantitative 3D Characterization of Functionally Relevant Parameters in Heavy-Oxide-Supported 4d Metal Nanocatalysts
title_sort quantitative 3d characterization of functionally relevant parameters in heavy-oxide-supported 4d metal nanocatalysts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10538501/
https://www.ncbi.nlm.nih.gov/pubmed/37780410
http://dx.doi.org/10.1021/acs.chemmater.3c01163
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