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  4. Multi-scale microscopy study of 3D morphology and structure of MoNi4/MoO2@Ni electrocatalytic systems for fast water dissociation
 
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2022
Journal Article
Title

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

Abstract
The 3D morphology of hierarchically structured electrocatalytic systems is determined based on multi-scale X-ray 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 × 1 µm2 and a length of 10-20 µm 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.
Author(s)
Zschech, Ehrenfried
deepXscan
Topal, Emre  
Fraunhofer-Institut für Keramische Technologien und Systeme IKTS  
Kutukova, Kristina  
Fraunhofer-Institut für Keramische Technologien und Systeme IKTS  
Gluch, Jürgen  
Fraunhofer-Institut für Keramische Technologien und Systeme IKTS  
Löffler, Markus W.  
TU Dresden  
Werner, Stephan  
Guttmann, Peter  
Helmholtz-Zentrum Berlin
Schneider, Gerd  
Helmholtz-Zentrum Berlin
Liao, Zhongquan  
Fraunhofer-Institut für Keramische Technologien und Systeme IKTS  
Timosenko, Janis  
Fritz-Haber-Institut der Max-Planck-Gesellschaft
Journal
Micron  
DOI
10.1016/j.micron.2022.103262
Language
English
Fraunhofer-Institut für Keramische Technologien und Systeme IKTS  
Keyword(s)
  • Convolutional neural network

  • crystalline structure

  • electrocatalyst

  • morphology

  • NEXAFS

  • TEM

  • X-ray computed tomography

  • X-ray microscopy

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