The Structure of Sub‐nm Platinum Clusters at Elevated Temperatures

Abstract

Little is known about metallic clusters consisting merely of a dozen of atoms or even less, despite of their importance in catalysis and crystal nucleation. Scanning transmission electron microscopy (STEM) provides direct atomic structure information but has inherently suffered from limited time resolution. We employ fast dynamic STEM combined with a spatio‐temporal image denoising algorithm to explore the structure and stability of Pt clusters on carbon, which represents a highly relevant catalysis system. At room temperature, dynamic amorphous 2D structures are found, while above ≈300 °C, the clusters transform into a crystalline state. Our experimental and theoretical data reveal an unexpected strong trend of the crystalline clusters to exhibit the face‐centered cubic, bulk structure of Pt with cuboidal geometries being most prominent.

Exploring the materials space in the smallest particle size range: from heterogeneous catalysis to electrocatalysis and photocatalysis

“…22 Fast dynamic STEM combined with a spatio-temporal image denoising algorithm was employed to unveil the dynamic structure of subnanometer Pt clusters anchored to a carbon support. 23 The analysis unexpectedly revealed for 30-50 atom Pt clusters a transition from amorphous-like quasi-2D arrangements at room temperature to ordered crystal-like (fcc or hcp) motifs.…”

Atomic mechanisms of gold nanoparticle growth in ionic liquids studied by in situ scanning transmission electron microscopy

“…Due to the high magnification necessary for dynamic atomic resolution imaging, the dose rates were in the range of 2-8.5*10 ^4 e -A -2 s -1 , similar to dose rates previously reported in similar experiments. 31,48 As [Bu 4 N][Cl] is very beam-resistant compared to other imidazolium-based ionic liquids and aqueous solutions, [42][43][44][45] it is expected to withstand electron irradiation of the applied dose. Indeed, the dose rates and total dose used in our experiments did not lead to any visible degradation of the ionic liquid.…”

Multi-scale Convolutional Neural Networks for Inverse Problems

“…Moreover, STEMs can produce millions of images in a few hours at a speed of 100 fps, and this amount of data can require months to process for a conventional algorithm. It is therefore important to predict desired images from noisy observations very quickly without loss of information [39]. Due to complex unknown environmental variables of specific STEM setups, real-world STEM image denoising is too complex for a single monolithic denoising algorithm.…”