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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 platinum clusters (0.5–0.6 nm) confined in the sinusoidal 10R channels of purely siliceous MFI zeolite are modulated upon introduction of partially reduced tin species that interact with the noble metal at the metal/support interface. The platinum–tin clusters are stable in H 2 over an extended period of time (>6 h), even at high temperatures (for example, 600 °C), which is determined by only a few additional tin atoms added to the platinum clusters. The structural features of platinum–tin clusters, which are not immediately visible by conventional characterization techniques but can be established after combination of in situ extended X-ray absorption fine structure, high-angle annular dark-field scanning transmission electron microscopy and CO infrared data, are key to providing a one-order of magnitude lower deactivation rate in the propane dehydrogenation reaction while maintaining high intrinsic (initial) catalytic activity. Tuning the structures of subnanometric metal clusters is challenging but can unlock unexpected catalytic properties. Here, the authors show that changing the composition of MFI zeolite-encapsulated PtSn subnanometric clusters by adding just a few tin atoms can lead to a remarkable stability enhancement in propane dehydrogenation.

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.

Spatially resolved in situ transmission electron microscopy (TEM), equipped with direct electron detection systems, is a suitable technique to record information about the atom-scale dynamics with millisecond temporal resolution from materials. However, characterizing dynamics or fluxional behavior requires processing short time exposure images which usually have severely degraded signal-to-noise ratios. The poor signal-to-noise associated with high temporal resolution makes it challenging to determine the position and intensity of atomic columns in materials undergoing structural dynamics. To address this challenge, we propose a noise-robust, processing approach based on blob detection, which has been previously established for identifying objects in images in the community of computer vision. In particular, a blob detection algorithm has been tailored to deal with noisy TEM image series from nanoparticle systems. In the presence of high noise content, our blob detection approach is demonstrated to outperform the results of other algorithms, enabling the determination of atomic column position and its intensity with a higher degree of precision.