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

Meffert, Matthias; Wankmüller, Florian; Störmer, Heike; Weber, André; Lupetin, Piero; Ivers‐Tiffée, Ellen; Gerthsen, D.

doi:10.1002/fuce.202000080

Cited by 11 publications

(6 citation statements)

References 36 publications

(46 reference statements)

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“…Further information about the equipment can be found in Ref. 31. Three different cell layers become visible at this SEM image, namely the 3YSZ electrolyte 1 (cf.…”

Section: Methodsmentioning

confidence: 99%

Deconvolution of Gas Diffusion Polarization in Ni/Gadolinium-Doped Ceria Fuel Electrodes

Grosselindemann¹,

Russner²,

Dierickx³

et al. 2021

J. Electrochem. Soc.

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The deconvolution of physicochemical processes in impedance spectra of SOCs with nickel/ceria fuel electrodes is challenging as gas diffusion strongly overlaps with the electrochemical processes at fuel and air electrode. To overcome this issue, symmetrical cells were applied and the gas diffusion process at the fuel electrode was quantified by altering the inert component (nitrogen / helium) in a ternary fuel gas mixture. An effective gas transport parameter considering microstructural and geometrical features was derived, enabling a precise quantification of polarization resistances related to gas diffusion and hydrogen electrooxidation. The obtained values were applied to parameterize a dc cell model. The model validation in fuel cell and electrolyzer mode showed an excellent agreement between measured and simulated current/voltage characteristics over a wide range of technically meaningful gas compositions and operating temperatures.

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“…Further information about the equipment can be found in Ref. 31. Three different cell layers become visible at this SEM image, namely the 3YSZ electrolyte 1 (cf.…”

Section: Methodsmentioning

confidence: 99%

Deconvolution of Gas Diffusion Polarization in Ni/Gadolinium-Doped Ceria Fuel Electrodes

Grosselindemann¹,

Russner²,

Dierickx³

et al. 2021

J. Electrochem. Soc.

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“…Microstructures and related parameters of Ni-YSZ cermet fuel electrodes, YSZ based electrolytes as well as air electrodes were investigated. (4,(7)(8)(9)(10) Computed Tomography X-ray computed tomography enables the non-destructive investigation of a sample. Depending on the materials included in the sample, volumes in an order of magnitude of 10 6 μm 3 can be investigated.…”

Section: Focused Ion Beam Tomographymentioning

confidence: 99%

Three-Dimensional Microstructural Analysis for the Characterization of Failure Mechanisms of a Novel SOEC

Wehner¹,

Lenser²,

Dzięcioł³

et al. 2023

ECS Trans.

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A novel fuel electrode-supported electrolysis cell has been developed as promising candidate for hydrogen production at intermediate temperatures. It contains a Ni-GDC electrode functional layer, a three-layer GDC-YSZ-GDC electrolyte and an LSC air electrode. Although this cell enables improved performance compared to state-of-the-art YSZ electrolyte-based cells, especially at lower operation temperatures, 700 °C and below, and electrolysis currents around 1.2-1.7 A cm-2 a voltage drop and subsequently crack formation in the electrolyte was observed in initial studies. In the current work, FIB/SEM and X-ray computed tomography are used for microstructural investigations. Based on calculated microstructural parameters the suitability of the methods for an assessment of the failure mechanisms are discussed.

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“…Different phases can be distinguished by brightness level in the gray-scale 2D crosssectional images obtained by FIB-SEM, typically in backscattered electron detector (BSED) mode. However, because of the image artifacts, such as inhomogeneous brightness, uneven cross-section, etc., the differentiation between different phases in the raw FIB-SEM images is not straightforward (5). Thus, precise phase segmentation is necessary before quantitative analysis and 3D reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Phase Segmentation of Ni/YSZ Anode for Solid Oxide Fuel Cells by Deep Learning

Wang,

Matsui,

Hosomizo

et al. 2023

ECS Trans.

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A deep learning network is implemented for the phase segmentation of Ni/YSZ anode of solid oxide fuel cells (SOFCs), as manual segmentation of focused ion beam-scanning electron microscopy (FIB-SEM) images is time-consuming. Segmentation is performed on two samples with different volume ratios of Ni to YSZ. The mean intersection over union (mIoU) reaches 0.9363, indicating good agreement between the predicted images and the manually segmented images. Furthermore, the impact of image augmentation on segmentation accuracy is investigated, and it is found that augmentation is necessary when the number of images in the training set is small or the training and testing sets come from different samples. Furthermore, mixing a sixth of manually segmented images from a new sample into the training set can significantly improve the accuracy of automated segmentation for the new sample. This strategy reduces the number of images requiring manual segmentation and improves processing efficiency.

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

Cited by 11 publications

References 36 publications

Deconvolution of Gas Diffusion Polarization in Ni/Gadolinium-Doped Ceria Fuel Electrodes

Deconvolution of Gas Diffusion Polarization in Ni/Gadolinium-Doped Ceria Fuel Electrodes

Three-Dimensional Microstructural Analysis for the Characterization of Failure Mechanisms of a Novel SOEC

Phase Segmentation of Ni/YSZ Anode for Solid Oxide Fuel Cells by Deep Learning

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