Three-dimensional X-ray imaging and quantitative analysis of solid oxide cells

被引:0
|
作者
Chiu, Wilson K. S. [1 ,2 ]
De Angelis, Salvatore [1 ]
Jorgensen, Peter Stanley [1 ]
Kuhn, Luise Theil [1 ]
机构
[1] Tech Univ Denmark, Dept Energy Convers & Storage, DK-2800 Lyngby, Denmark
[2] Univ Connecticut, Sch Mech Aerosp & Mfg Engn, Storrs, CT 06269 USA
关键词
Solid oxide cells; X-ray imaging; Synchrotron; degradation; TRIPLE-PHASE-BOUNDARY; 3-D MICROSTRUCTURAL NETWORKS; DRIVEN SOFC DEGRADATION; FUEL-CELL; COMPUTED-TOMOGRAPHY; INVERSE PROBLEMS; ELECTRODE MICROSTRUCTURE; NEURAL-NETWORK; 3D; NICKEL;
D O I
10.1016/j.mattod.2024.08.016
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The article presents an overview on the progress of X-ray imaging of solid oxide cells (SOC) during the past decade. X-ray imaging has enabled significant advances in solid oxide cells. Laboratory-based X-ray sources allowed researchers to investigate the electrode porosity, different material phases, and its crystallography and grain boundaries. Synchrotron-based X-ray sources enable a more detailed understanding of the chemistry under in situ and operando conditions due to the significantly brighter source. Recent breakthroughs using synchrotron X-ray sources have allowed researchers to understand SOC performance and degradation at unprecedented spatial, chemical and temporal resolution using novel absorption contrast and XANES tomography, ptychographic and holographic X-ray tomography and 3-D X-ray diffraction imaging. Three-dimensional images have been used to advance numerical modeling and simulations, e.g., phase field models, lumped element models, and artificial structure generation. Machine learning and deep neural network algorithms are being explored for automated image segmentation. X-ray imaging has also been used to advance the creation of hierarchical electrode structures. Even though the theory and methods for X-ray imaging and analysis now exist, most studies still don't take full advantage of this. Typical studies only use direct interpretation of images. As structures get more complicated, e.g., hierarchical structures, the quantitative interpretation of images will be needed to correlate structure to performance.
引用
收藏
页码:481 / 496
页数:16
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