Modulating the structures of subnanometric metal clusters at the atomic level is a great synthetic and characterization challenge in catalysis. Here we show how the catalytic properties of subnanometric Pt clusters (0.5-0.6 nm) confined in the sinusoidal 10R channels of purely siliceous MFI zeolite modulate upon introduction of partially reduced Sn species that interact with the noble metal at the metal/support interface. The low mobility of Sn in H2 over an extended period of time (>6 h) even at high temperatures (e.g. 600 ⁰C), which is determined by only a few additional Sn atoms added to the Pt clusters. Such structural features, which are not immediately visible by conventional characterization techniques and can be laid out after combination of in situ EXAFS, HAADF-STEM and CO-IR data, is key to provide one-order of magnitude lower deactivation rate in the propane dehydrogenation reaction while maintaining high intrinsic (initial) catalytic activity.
Pt foil a12 2.763 ± 0
A deep convolutional neural network has been developed to denoise atomic-resolution transmission electron microscope image datasets of nanoparticles acquired using direct electron counting detectors, for applications where the image signal is severely limited by shot noise. The network was applied to a model system of CeO2-supported Pt nanoparticles. We leverage multislice image simulations to generate a large and flexible dataset for training the network. The proposed network outperforms state-of-the-art denoising methods on both simulated and experimental test data. Factors contributing to the performance are identified, including (a) the geometry of the images used during training and (b) the size of the network's receptive field. Through a gradient-based analysis, we investigate the mechanisms learned by the network to denoise experimental images. This shows that the network exploits both extended and local information in the noisy measurements, for example, by adapting its filtering approach when it encounters atomic-level defects at the nanoparticle surface. Extensive analysis has been done to characterize the network's ability to correctly predict the exact atomic structure at the nanoparticle surface. Finally, we develop an approach based on the log-likelihood ratio test that provides a quantitative measure of the agreement between the noisy observation and the atomic-level structure in the network-denoised image.
A Au(0.85 wt %)/YSZ
catalyst was prepared through a deposition-precipitation
method and afterward modified with the addition of a very slight amount
of CeO2 (3.7 wt %) by incipient wetness impregnation. A
prior electron microscopy characterization points out that both catalysts,
Au(0.85 wt %)/YSZ and CeO2(3.7 wt %)/Au(0.85 wt %)/YSZ,
exhibit a similar Au nanoparticle distribution with most particles
below 5 nm. The CO oxidation reaction was tested over these catalysts
in a heating–cooling cycles experiment, which evidenced a much
better stability of the CeO2-modified sample against deactivation
under very harsh temperature conditions. The characterization of the
catalysts after reaction indicates that the sintering effect of the
Au nanoparticles is quite similar in both cases, suggesting the key
role of specific interactions between Au and CeO2 on the
performance of the surface modified catalyst. An in-depth aberration
correction electron microscopy study, combining imaging and analytical
techniques, allowed us to characterize the details of the spatial
distribution and structure at the atomic level of CeO2.
The formation of atomically thin CeO2 layers extending
on the surface of the YSZ crystallites was detected, particularly
in the form of coherent monolayers epitaxially growth on YSZ(111),
which guaranteed an interaction between ceria and the supported metal
phase. Image simulation and density functional theory calculations
carried out further confirm the electron microscopy observations.
A comparison, in terms of stability, to the results observed on a
CeO2-modified Au/TiO2 catalyst of similar composition
reveals both a much better performance of the catalyst supported on
YSZ and neat differences in the nature of the interactions between
CeO2 and the support as well as between Au and CeO2. The structural coherence between CeO2 and the
cubic YSZ support triggers specific interaction mechanisms which differentiate
the behavior of CeO2/Au/YSZ catalysts from that of CeO2/Au/TiO2. The whole set of results evidence not
only the key role played by highly dispersed and ultrathin ceria surface
layers as modifier and stabilizer of the performance of Au-based CO
oxidation catalysts but also how advanced, aberration corrected, electron
microscopy techniques are a requirement to unveil the structure of
such unique nanostructures.
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