4.4 Article

Multi-scale microscopy study of 3D morphology and structure of MoNi4/ MoO2@Ni electrocatalytic systems for fast water dissociation

Journal

MICRON
Volume 158, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.micron.2022.103262

Keywords

Morphology; Crystalline structure; X-ray microscopy; X-ray computed tomography; NEXAFS; TEM; Electrocatalyst; Convolutional neural network

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Funding

  1. Deutsche Forschungsgemeinschaft via the cluster of excellence EXC1056 Center for Advancing Electronics Dresden (cfaed)

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This study utilized multi-scale X-ray computed tomography (XCT) and transmission electron microscopy (TEM) to reveal the 3D morphology and crystalline structure of hierarchically structured electrocatalytic systems, providing important insights for the design of electrocatalysts in the future.
The 3D morphology of hierarchically structured electrocatalytic systems is determined based on multi-scale Xray computed tomography (XCT), and the crystalline structure of electrocatalyst nanoparticles is characterized using transmission electron microscopy (TEM), supported by X-ray diffraction (XRD) and spatially resolved near edge X-ray absorption fine structure (NEXAFS) studies. The high electrocatalytic efficiency for hydrogen evolution reaction (HER) of a novel transition-metal-based material system - MoNi4 electrocatalysts anchored on MoO2 cuboids aligned on Ni foam (MoNi4/MoO2@Ni) - is based on advantageous crystalline structures and chemical bonding. High-resolution TEM images and selected-area electron diffraction patterns are used to determine the crystalline structures of MoO2 and MoNi4. Multi-scale XCT provides 3D information of the hierarchical morphology of the MoNi4/MoO2@Ni material system nondestructively: Micro-XCT images clearly resolve the Ni foam and the attached needle-like MoO2 micro cuboids. Laboratory nano-XCT shows that the MoO2 micro cuboids with a rectangular cross-section of 0.5 x 1 mu m(2) and a length of 10-20 mu m are vertically arranged on the Ni foam. MoNi4 nanoparticles with a size of 20-100 nm, positioned on single MoO2 cuboids, were imaged using synchrotron radiation nano-XCT. The application of a deep convolutional neural network (CNN) significantly improves the reconstruction quality of the acquired data.

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