Subpercent Local Strains Due to the Shapes of Gold Nanorods Revealed by Data-Driven Analysis

Removal of phase residues in electron holography

Electron holography provides quantitative phase information regarding the electromagnetic fields and the morphology of micro- to nano-scale samples. A phase image reconstructed numerically from an electron hologram sometimes includes phase residues, i.e. origins of unremovable phase discontinuities, which make it much more difficult to quantitatively analyze local phase values. We developed a method to remove the residues in a phase image by a combination of patching local areas of a hologram and denoising based on machine learning. The small patches for a hologram, which were generated using the spatial frequency information of the own fringe patterns, were pasted at each residue point by an algorithm based on sparse modeling. After successive phase reconstruction, the phase components with no dependency on the vicinity were filtered out by Gaussian process regression. We determined that the phase discontinuities that appeared around phase residues were removed and the phase distributions of an atomic resolution phase image of a Pt nanoparticle were sufficiently restored.

Atomic-Scale 3D Structure of a Supported Pd Nanoparticle Revealed by Electron Tomography with Convolution Neural Network-Based Image Inpainting

Electron tomography based on scanning transmission electron microscopy (STEM) is used to analyze 3D structures of metal nanoparticles on the atomic scale. However, in the case of supported metal nanoparticle catalysts, the supporting material may interfere with the 3D reconstruction of metal nanoparticles. In this study, a deep learning-based image inpainting method is applied to high-angle annular dark field (HAADF)-STEM images of a supported metal nanoparticle to predict and remove the background image of the support. The inpainting method can separate an 11 nm Pd nanoparticle from the θ-Al2O3 support in HAADF-STEM images of the θ-Al2O3-supported Pd catalyst. 3D reconstruction of the extracted images of the Pd nanoparticle reveals that the Pd nanoparticle adopts a deformed structure of the cuboctahedron model particle, resulting in high index surfaces, which account for the high catalytic activity for methane combustion. Using the xyz coordinate of each Pd atom, the local Pd-Pd bond distance and its variance in a real supported Pd nanoparticle are visualized, showing large strain and disorder at the Pd-Al2O3 interface. The results demonstrate that 3D atomic-scale analysis enables atomic structure-based understanding and design of supported metal catalysts.

Singular behaviour of atomic ordering in Pt-Co nanocubes starting from core-shell configurations

The ordered structure of platinum-cobalt (Pt-Co) alloy nanoparticles has been studied actively because the structure influences their magnetic and catalytic properties. On the Pt-Co alloy's surface, Pt atoms preferentially segregate during annealing to reduce the surface energy. Such surface segregation has been shown to promote the formation of an ordered structure near the surface of Pt-Co thin films. Although this phenomenon seems also useful to control the nanoparticle structure, this has not been observed. Here, we have studied the ordered structure in annealed Pt@Co core-shell nanoparticles using a scanning transmission electron microscope. The nanoparticles were chemically synthesized, and their structural changes after annealing at 600 °C, 700 °C, and 800 °C for 3 h were observed. After being annealed at 600 °C and 800 °C, the particles contained the L1₂-Pt₃Co ordered structure. The structure seems reasonable considering an initial Pt : Co ratio of ∼4 : 1. However, we found that the L1₀-PtCo structure was formed near the nanoparticle surface after annealing at 700 °C. The L1₀-PtCo structure was thought to be formed from the surface segregation of Pt atoms and insufficient diffusion of Pt and Co atoms to mix them in the particle overall.