Optimization of Material Contrast for Efficient FIB‐SEM Tomography of Solid Oxide Fuel Cells

• Published in: Fuel cells (Weinheim an der Bergstrasse, Germany) Vol. 20; no. 5; pp. 580 - 591
• Main Authors: Meffert, M., Wankmüller, F., Störmer, H., Weber, A., Lupetin, P., Ivers‐Tiffée, E., Gerthsen, D.
• Format: Journal Article
• Language: English
• Published: Weinheim Wiley Subscription Services, Inc 01-10-2020
• Subjects: Backscattered Electron; Cermets; Detectors; Electrolytic cells; Electron microscopes; Error analysis; FIB‐SEM Tomography; Image acquisition; Image reconstruction; Image segmentation; Ion beams; Material Contrast; Optimization; Parameters; Scanning electron microscopy; Secondary Electron; Solid Oxide Fuel Cell; Solid oxide fuel cells; Tomography; Yttrium oxide; Zirconium dioxide
• ISSN: 1615-6846; 1615-6854
• DOI: 10.1002/fuce.202000080

Focused ion beam (FIB) – scanning electron microscopy (SEM) serial sectioning tomography has become an important tool for three‐dimensional microstructure reconstruction of solid oxide fuel cells (SOFC) to obtain an understanding of fabrication‐related effects and SOFC performance. By sequential FIB milling and SEM imaging a stack of cross‐section images across all functional SOFC layers was generated covering a large volume of 3.5·104 μm3. One crucial step is image segmentation where regions with different image intensities are assigned to different material phases within the SOFC. To analyze all relevant SOFC materials, it was up to now mandatory to acquire several images by scanning the same region with different imaging parameters because sufficient material contrast could otherwise not be achieved. In this work we obtained high‐contast SEM images from a single scan to reconstract all functional SOFC layers consisting of a Ni/Y2O3‐doped ZrO2 (YDZ) cermet anode, YDZ electrolyte and (La,Sr)MnO3/YDZ cathode. This was possible by using different, simultaneous read‐out detectors installed in a state‐of‐the‐art scanning electron microscope. In addition, we used a deterministic approach for the optimization of imaging parameters by employing Monte Carlo simulations rather than trial‐and‐error tests. We also studied the effect of detection geometry, detecting angle range and detector type.