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

Zschech, Ehrenfried; Topal, Emre; Kutukova, Kristina; Gluch, Jürgen; Löffler, Markus; Werner, Stephan; Guttmann, Peter; Schneider, Gerd; Liao, Zhongquan; Timoshenko, Janis

doi:10.1016/j.micron.2022.103262

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

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Timoshenko, Janis
Interface Science, Fritz Haber Institute, Max Planck Society;

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Zschech, E., Topal, E., Kutukova, K., Gluch, J., Löffler, M., Werner, S., et al. (2022). Multi-scale microscopy study of 3D morphology and structure of MoNi₄/MoO₂@Ni electrocatalytic systems for fast water dissociation. Micron, 158: 103262. doi:10.1016/j.micron.2022.103262.

Cite as: https://hdl.handle.net/21.11116/0000-000A-3702-6

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.