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In the present study, Ni-M (M = Fe, Cu) bimetallic fuel electrodes are applied to investigate the effects of transition metal elements on nickel (Ni) migration and Ni coarsening under SOFC and SOEC operations. Ni-Fe, Ni-Cu and pure Ni patterned fuel electrodes are sputtered on YSZ pellets. The electrochemical performance of these Ni-M bimetallic fuel electrodes are lower than pure Ni fuel electrode, while the degradation rate of Ni-Fe fuel electrodes is smaller than the others. The spreading of Ni film on YSZ surface is observed for all samples under anodic polarization, and such Ni migration is suppressed by Fe doping, whereas it was enhanced by Cu doping. The adhesion between the electrode/electrolyte interface is weakened for Ni-Cu and pure Ni fuel electrodes under cathodic polarization, while good adhesion at the interface is maintained for Ni-Fe, which correlates with the smaller performance degradation rate.

The magnetization M, magnetic susceptibility M/H, and specific heat C of TmTi2Al20 single crystal have been measured in an external magnetic field along the crystalline principal directions, [001],...

Solid oxide cells (SOCs), both solid oxide fuel cell (SOFC) and solid oxide electrolysis cell (SOEC), have being attracting more and more attention due to its high energy conversion efficiency and fuel flexibility. However, degradation of fuel electrodes after long-term operation remains as one of the main challenges for their commercial application. In particular, migration and coarsening of nickel (Ni) in Ni - yttria-stabilized zirconia (Ni-YSZ) fuel electrodes have strong impacts on the decreases in both active three-phase boundary (TPB) and Ni network connectivity, which result in performance deterioration. One of the mechanisms proposed as an explanation of Ni migration is Ni transport via volatile Ni(OH)x species, so-called evaporation-condensation mechanism [1]. On the other hand, Jiao and Shikazono [2] proposed a model to explain Ni spreading on YSZ surface based on the variation of Ni-YSZ wettability which is modified by the oxygen adsorption. Nevertheless, further investigations are required to understand the mechanisms of Ni migration. In the present study, Ni-film patterned electrodes which can provide well defined TPB configurations are used to investigate the Ni-YSZ degradation under SOFC, SOEC and sequential SOFC and SOEC operations, i.e. reversible operation (RSOC). The edge of sputtered Ni-film contacting with open YSZ surface can be treated as active TPB during operation. Confocal laser scanning microscopy (CLSM, Keyence, VK-X 1000) is used, which enabled in-operando observation of the real-time local morphological change at active TPB [3]. The in-operando potentiostatic experiment was carried out inside a high temperature chamber which was kept at 800oC with 4% H2O-4% H2-92% N2 as fuel gas and 30% O2-70% N2 as oxidant as shown in Fig. 1(a). The correlations between the Ni morphological changes and cells’ electrochemical performances were investigated. The Ni phase dynamically spread and de-percolated only under the SOFC operation, while such a phenomenon was not observed during the SOEC operation, as shown in Figs. 1(c), (d) and (e). Ni coarsening occurred regardless of the operation modes. The competition between Ni-film spreading and breaking-up finally determined the complicated local morphological change and corresponding cell performance variation. The time variations of active TPB and current density showed good correlation as shown in Fig. 1(b). Real-time in-operando observations revealed different Ni migration under SOFC, SOEC and RSOC operations, which may provide new insights for the understanding of Ni-YSZ fuel electrode degradation. Fig. 1. (a) Schematic illustration of in-operando setup. (b) Correlation between current density and Ni morphological evolution. (c)-(e) CLSM images and 3D surface height contour maps of the yellow-frame-enclosed areas after SOFC, SOEC and RSOC modes, respectively. References [1] M. B. Mogensen et al., Fuel Cells, 17, pp. 434-441 (2017). [2] Z. Jiao, N. Shikazono, Acta Materialia, 135, pp.124-131 (2017). [3] Z. Jiao, N. Shikazono, J. Power Sources, 396, pp.119-123 (2018). Figure 1

Nickel (Ni) is widely used for solid oxide fuel cell (SOFC) anodes due to high catalytic activity and electron conductivity. However, Ni coarsening and depletion are the major causes of electrode degradation during long time operation. In order to investigate the microstructural changes of Ni under actual operating environment, in-operando observations of Ni-pattern electrodes were carried using confocal laser microscope [1,2]. However, low resolution and shallow depth of field limit detailed investigation of the microstructure. On the other hand, high resolution images can be obtained by scanning electron microscope (SEM) or environmental SEM (ESEM), but the sample must be kept inside a vacuum chamber, which makes it extremely difficult to operate the sample cell with electrochemical reaction. Therefore, an automatic method to super-resolve the confocal laser scanning image is desired, which will be a great help to understand the Ni degradation phenomena under operation. Recently, deep learning is applied to image processing in many applications. Convolutional neural network (CNN) is widely used for various vision tasks including image classification and object detection. In particular, an application of generative adversarial network (GAN) [3] based on CNN has been successfully incorporated in image synthesis. The GAN consists of two networks, i.e. the generator to fabricate images and the discriminator to identify the output image as real or fake. Implementation of GAN such as image to image translation (pix2pix) [4] is highly expected in SOFC research. In the present study, two machine learning algorithms for improving the laser microscope image quality are investigated. The algorithms are applied to the Ni pattern electrodes deposited on mirror-polished yttrium stabilized zirconia (YSZ) substrate. Sets of low resolution (LR) laser scanning microscope images and high resolution (HR) SEM images of identical positions were collected for the training data. Firstly, the semantic segmentation proxy is proposed for the automatic material detection from the LR laser microscope images. The incorporated algorithms are based on CNN deep neural network. U-net [5] with encoder-decoder network is also conducted for comparison. In addition to visual assessment, phase fractions and triple phase boundary (TPB) density are calculated. The automatically segmented LR laser microscope images showed good agreement with the manually segmented HR SEM images. The estimated TPB density was close to the real value. The images segmented with U-net were better than those with encoder-decoder CNN in fine details, which indicates that U-net is superior in transferring local information. Secondly, a new method for synthesizing SEM-quality image from laser image is proposed. The super-resolution network incorporates pix2pix GAN architecture [4] which enables image to image translation. Figure 1A shows LR laser microscope image, which is an input for the pix2pix GAN. High resolution image generated by GAN is shown in Fig. 1B. Real HR SEM image is presented in Fig. 1C for comparison. Realistic HR images can be synthesized from LR laser image, which reveals that the proposed method is effective for super-resolving patterned electrode microstructures. The semantic segmentation proxy and super-resolution algorithms will enable quantitative evaluation of dynamic changes of SOFC electrodes under in-operando observation by a laser microscope. [1] Z. Jiao and N. Shikazono, J. Power Sources, 396, pp. 119–123 (2018). [2] Y. Suzuki et al., 14th European SOFC and SOE Forum, B1513 (2020). [3] I. Goodfellow et al., Advances in neural information processing systems, 27, pp. 2672-2680 (2014). [4] P. Isola et al., Proceeding of the IEEE on CVPR, pp. 1125-1134 (2017). [5] O. Ronneberger et al., MICCAI, pp. 234-241 (2015). Figure 1