材料科学
氧烷
透射电子显微镜
同步加速器
纳米颗粒
同步辐射
纳米技术
光学
光谱学
量子力学
物理
作者
Ehrenfried Zschech,Emre Topal,Kristina Kutukova,Jürgen Gluch,Markus Löffler,Stephan Werner,Peter Guttmann,Gerd Schneider,Zhongquan Liao,Janis Timoshenko
出处
期刊:Micron
[Elsevier]
日期:2022-03-30
卷期号:158: 103262-103262
被引量:1
标识
DOI:10.1016/j.micron.2022.103262
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI