Multi-scale microscopy study of 3D morphology and structure of MoNi 4 /MoO 2 @Ni electrocatalytic systems for fast water dissociation

• Published in: Micron (Oxford, England : 1993) Vol. 158; p. 103262
• Main Authors: Zschech, Ehrenfried, Topal, Emre, Kutukova, Kristina, Gluch, Jürgen, Löffler, Markus, Werner, Stephan, Guttmann, Peter, Schneider, Gerd, Liao, Zhongquan, Timoshenko, Janis
• Format: Journal Article
• Language: English
• Published: England 01-07-2022
• Subjects: Electrocatalyst; NEXAFS; X-ray computed tomography; X-ray microscopy; Convolutional neural network; Morphology; TEM; Crystalline structure
• ISSN: 1878-4291

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 - MoNi
electrocatalysts anchored on MoO
cuboids aligned on Ni foam (MoNi
/MoO
@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 MoO
and MoNi
. Multi-scale XCT provides 3D information of the hierarchical morphology of the MoNi
/MoO
@Ni material system nondestructively: Micro-XCT images clearly resolve the Ni foam and the attached needle-like MoO
micro cuboids. Laboratory nano-XCT shows that the MoO
micro cuboids with a rectangular cross-section of 0.5 × 1 µm
and a length of 10-20 µm are vertically arranged on the Ni foam. MoNi
nanoparticles with a size of 20-100 nm, positioned on single MoO
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.