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With its unique redox properties, ceria is an oxide with a range of applications, including automotive catalytic converters, which consist of platinum-group metal nanoparticles on ceria-containing supports. In this work, the 3 D architecture of a ceria-based material synthesized by the widely employed glycine-nitrate solution combustion method is revealed for the first time. Together with N2 adsorption volumetry, scanning transmission electron microscopy (STEM) and scanning electron microscopy (SEM), STEM tomography provides a comprehensive picture of the multimodal porous network of a pre-reduced Pt-CeO2 catalyst, from the nanometer to the micrometer scale. This material consists of ceria nanocrystallites forming 3 D aggregates and puzzle-like 2 D walls separating large roundish mesopores and macropores. The small voids between imperfectly assembled crystallites give rise to some microporosity. In addition, it is demonstrated that a significant proportion of platinum nanoparticles (3-4 nm) are not located at the ceria surface following the one-step synthesis process, about half of them are buried within ceria. This result is valid for another metal (Rh) and another fuel (oxalyl dihydrazide), and has important implications for heterogeneous catalysis. [ABSTRACT FROM AUTHOR]

Bulk nanoporous silicon prepared by top-down method form Li-ion batteries was investigated combining different conventional technique such as nitrogen physisorption and high resolution electron microscopy with electron tomography. It was found that the Si nanorods are forming porous aggregates with a half of the volume of the particle occupied by pores. The nanorods are preferentially oriented along the main axis of the aggregate. The porosity and the lack of compaction between the aggregates provide space for the Si expansion during the lithiation process. It was found that the Si nanorods mainly expose the {111} family plane as an external faces. The size distributions of the porous and solid phases in a granule were found to be similar. The pores represent 50% of the total volume of an aggregate. The shape orientation of the particles was quantified and it was found to exhibit a narrow distribution. [ABSTRACT FROM AUTHOR]

Fast tomography in Environmental Transmission Electron Microscopy (ETEM) is of a great interest for in situ experiments where it allows to observe 3D real-time evolution of nanomaterials under operating conditions. In this context, we are working on speeding up the acquisition step to a few seconds mainly with applications on nanocatalysts. In order to accomplish such rapid acquisitions of the required tilt series of projections, a modern 4K high-speed camera is used, that can capture up to 100 images per second in a 2K binning mode. However, due to the fast rotation of the sample during the tilt procedure, noise and blur effects may occur in many projections which in turn would lead to poor quality reconstructions. Blurred projections make classical reconstruction algorithms inappropriate and require the use of prior information. In this work, a regularized algebraic reconstruction algorithm named SIRT-FISTA-TV is proposed. The performance of this algorithm using blurred data is studied by means of a numerical blur introduced into simulated images series to mimic possible mechanical instabilities/drifts during fast acquisitions. We also present reconstruction results from noisy data to show the robustness of the algorithm to noise. Finally, we show reconstructions with experimental datasets and we demonstrate the interest of fast tomography with an ultra-fast acquisition performed under environmental conditions, i.e. gas and temperature, in the ETEM. Compared to classically used SIRT and SART approaches, our proposed SIRT-FISTA-TV reconstruction algorithm provides higher quality tomograms allowing easier segmentation of the reconstructed volume for a better final processing and analysis. [ABSTRACT FROM AUTHOR]