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

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

扫码查看
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.

MorphologyCrystalline structureX-ray microscopyX-ray computed tomographyNEXAFSTEMElectrocatalystConvolutional neural networkELECTRONIC-PROPERTIESHYDROGENMOLYBDENUMEVOLUTION

Zschech, Ehrenfried、Topal, Emre、Kutukova, Kristina、Gluch, Jurgen、Loeffler, Markus、Werner, Stephan、Guttmann, Peter、Schneider, Gerd、Liao, Zhongquan、Timoshenko, Janis

展开 >

deepXscan GmbH

Fraunhofer Inst Ceram Technol & Syst

Tech Univ Dresden

Helmholtz Zent Berlin

Fritz Haber Inst Max Planck Soc

展开 >

2022

Micron

Micron

EISCI
ISSN:0968-4328
年,卷(期):2022.158
  • 21