3D quantitative characterization of metal catalysts supported on heavy oxides by High Angle Annular Dark Field (HAADF) STEM electron tomography (ET) is a very challenging task. Noble metal nanoparticles (Au, Ru) supported on ceria or ceria mixed oxides illustrates the case. The difference, very small in some cases, between the atomic number (Z) of metal and support (ZRu=44, ZAu=79, ZCe=58) complicates the discrimination of the small particles. This makes that each step, acquisition, alignment, reconstruction and segmentation, has to be carefully optimized not only to improve the visualization of the nanoparticles but also to extract relevant quantitative results, e.g. metal loading, specific surface area, or particle size distribution. In this work, we have combined advanced image processing algorithms, based on undecimated wavelets transform (UWT) , to improve the contrast and denoising the tilt series projections, with new reconstruction algorithms, based on compressed sensing (CS) , to study a series of catalysts with a high potential in different processes related to the production of hydrogen, as CeO2 Nanorods, Au/CeO2 Nanocubes or Ru/Ce2Z2O8 . The results obtained by ET have been compared to those determined by macroscopic characterization techniques as Inductively Coupled Plasma (ICP) or Brunauer, Emmett and Teller (BET) isotherm method.
HAADF-STEM ET has been carried out using a FEI TITAN3 THEMIS 60-300 operated at 200kV recently installed at Cadiz University. Data collections were obtained by tilting the specimen about a single axis perpendicular to the electron beam. Series of projections were acquired between -70º and +70° either every 2° or 5º. The images series were aligned using Inspect3D and TomoJ and reconstructed using the ASTRA Toolbox implemented in Matlab . In the particular case of CS, Total Variation Minimization (TVM) was carried by using the TVAL3 solver .
Figure 1 shows the 3D-rendered voxel of a catalyst consisting of Au nanoparticles supported on CeO2 Nanocubes after reconstructing the raw tilt series by SIRT (Figure 1a) and TVM (Figure 1b) and after denoising the tilt series projections by UWT and reconstructed by SIRT (Figure 1c) and TVM (Figure1d). Note how in the case of UWT-TVM reconstruction an important improvement in terms of morphology of the support and visualization of nanoparticles is obtained. The 3D rendered surface of a similar sample and the 3D quantifications of relevant properties as metal loading, specific surface area and average particle size are shown in Figure 2. It is important to point out how the values are quite similar to those determined by macroscopic characterization techniques. These results indicate that this combination of techniques allows determining nanostructural features representative of the catalysts at macroscopic level.
 T. Printemps et al. Ultramicroscopy 160 (2016) 23-24
 B. Goris et al. Ultramicroscopy 113 (2012) 120-130
 W. van Aarle et al. Ultramicroscopy 157 (2015) 35-47
 Authors acknowledge funding from MINECO/FEDER (MAT2013-40823R and CSD09-00013). Financial resources from the European Union Seventh Framework Programme under Grant Agreement 312483 – ESTEEM2 (Integrated Infrastructure Initiative – I3) is also acknowledged.
To cite this abstract:Miguel Lopez-Haro, Miguel Tinoco, Ana Belen Hungria , Jose Juan Calvino; Quantitative Electron Tomography Study of Metal Catalysts Supported on Heavy Oxides Combining Image De-noising and Compressed Sensing Techniques. The 16th European Microscopy Congress, Lyon, France. https://emc-proceedings.com/abstract/quantitative-electron-tomography-study-of-metal-catalysts-supported-on-heavy-oxides-combining-image-de-noising-and-compressed-sensing-techniques/. Accessed: October 31, 2020
EMC Abstracts - https://emc-proceedings.com/abstract/quantitative-electron-tomography-study-of-metal-catalysts-supported-on-heavy-oxides-combining-image-de-noising-and-compressed-sensing-techniques/