Quantitative Evaluation of Supported Catalysts Key Properties from Electron Tomography Studies: Assessing Accuracy Using Material-Realistic 3D-Models

Abstract

Electron Tomography (ET) reconstructions can be analysed, via segmentation techniques, to obtain quantitative, 3D-information about individual nanoparticles in supported catalysts. This includes values of parameters out of reach for any other technique, like their volume and surface, which are required to determine the dispersion of the supported particle system or the specific surface area of the support; two figures that play a major role in the performance of this type of catalysts.However, both the experimental conditions during the acquisition of the tilt series and the limited fidelity of the reconstruction and segmentation algorithms, restrict the quality of the ET results and introduce an undefined amount of error both in the qualitative features of the reconstructions and in all the quantitative parameters measured from them.Here, a method based on the use of well-defined 3D geometrical models (phantoms), with morphological features closely resembling those observed in experimental images of an Au/CeO2 catalyst, has been devised to provide a precise estimation of the accuracy of the reconstructions. Using this approach, the influence of noise and the number of projections on the errors of reconstructions obtained using a Total Variation Minimization in 3D (TVM-3D) algorithm have been determined. Likewise, the benefits of using smart denoising techniques based on Undecimated Wavelet Transforms (UWT) have been also evaluated.The results clearly reveal a large impact of usual noise levels on both the quality of the reconstructions and nanometrological measurement errors. Quantitative clues about the key role of UWT to largely compensate them are also provided.

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

“…In a recent study of gold nanoparticles supported on ceria cubes, sphericity has been related to the extent of truncation (α) of a sphere along its diameter. 16 In this work, the truncation of the sphere was parametrized in such a way that no truncation corresponded to α = 0, truncation to a hemisphere to α = 1, and total truncation to α = 2. With this parametrization, the truncation value of each representative cluster was determined; Figure 7 f.…”

“…These estimates exhibited excellent agreement with values obtained through macroscopic techniques such as N 2 physisorption isotherms, X-ray fluorescence, and 2D STEM microscopy-based metal particle size distributions. Bouziane et al further advanced the tomographic analysis of this type of catalyst by quantitatively evaluating the expected accuracy of these measurements using material-realistic 3D models …”

“…Bouziane et al further advanced the tomographic analysis of this type of catalyst by quantitatively evaluating the expected accuracy of these measurements using material-realistic 3D models. 16 The higher value of the atomic number of Au (Z = 79) with respect to Ce (Z = 58) allows for easy differentiation of Au nanoparticles from CeO 2 in HAADF-STEM images. However, for catalysts based on lighter metals like Pd (Z = 46), visualizing small Pd nanoparticles supported on CeO 2 , even in 2D HAADF-STEM projections, is challenging.…”

“…Bouziane et al further advanced the tomographic analysis of this type of catalyst by quantitatively evaluating the expected accuracy of these measurements using material-realistic 3D models. 16 …”