Triple Phase Boundary Density Research Articles

Microstructural changes in nickel‐ceria fuel electrodes at elevated temperature

AbstractDurability testing of low temperature solid oxide cells is challenging as degradation phenomena related to microstructural changes like nickel‐agglomeration are slow. In the present study, a nickel/gadolinia doped ceria (GDC) fuel electrode with a porous GDC‐interlayer towards the zirconia electrolyte was investigated. The electrode, designed for operating temperatures of 600°C, was tested at an elevated temperature of 900°C for up to 1100 h to accelerate aging. Contrary to every expectation, the electrodes showed continuous improvement in electrochemical performance. Impedance spectroscopy, the distribution of relaxation times analysis, scanning electron microscope and transmission electron microscope were applied to correlate electrochemical and microstructural changes. Structural analysis showed a significant Ni agglomeration accompanied by a decrease in triple phase boundary density. Furthermore, a minor particle growth in the GDC‐phase decreased the volume‐specific double phase boundary GDC/pore. Considering these microstructural changes, the decrease in active reaction sites should have increased the polarization resistance, but a decrease of about 32% was observed. The discrepancy between polarization resistance improvement and microstructural degradation might be attributed to an activation of the GDC‐surfaces in the electrode and the porous GDC‐interlayer.

Quantitative estimation of triple phase boundaries in solid oxide fuel cell electrodes via artificial neural network

Virtual solid oxide fuel cell (SOFC) electrode microstructures composed of pore, electrolyte and catalyst phases with various particle sizes and volume fractions are reconstructed to design high-performance electrodes by investigating the role of microstructural properties on the electrodes and thereby the cell performance. The active TPB (triple phase boundary) densities in these microstructures are numerically measured and the data are used to train numerous artificial neural networks established with different model parameters and learning methods. Based on the results of 10,000 trainings of each model, the network that employs a backpropagation method of Bayesian regulation and has 2 hidden layers with 15 neurons is found to be the best one. It is then used to simulate new cases, whose parameters are in the range of those used in training. Further validation of the best network is also performed by considering a few randomly selected cases. The simulation results providing active TPB densities quantitatively are discussed regarding the microstructural properties. The overall results reveal that active TPBs can be increased by reducing the particle size of the phases and volume fraction of any phase should be selected according to the particle size to improve the number of active TPBs.

Microstructural Analysis of Solid Oxide Electrochemical Cells via 3D Reconstruction Using a FIB-SEM Dual Beam System

The 3D reconstruction based on tomography technology enables quantitative and qualitative microstructural analysis of complex multiphase oxide structures. This powerful approach is widely investigated in diverse areas, in particular, gaining more importance in solid oxide electrochemical cells (SOCs) fields. SOCs are promising energy conversion devices with high efficiency, however, they have complex and porous/dense multilayered microstructures, which are closely related to the electrochemical reaction in the electrodes, thus, one of the major factors determining overall output performance of SOCs. Therefore, it is necessary to quantify the microstructural parameters of the cell. A focused ion beam-scanning electron microscope (FIB-SEM) dual beam system is one well-established method to obtain tomographic images to reconstruct 3D microstructures. It has an appropriate scale of tenth of nm to μm-level with high spatial resolution to represent the microstructural characteristics of the SOC electrodes. This presentation is intended to introduce our progress on 3D reconstruction techniques to quantitatively analyse SOCs, obtaining microstructural features such as particle size, connectivity, tortuosity, contact area, and triple phase boundary density. These in-depth analyses are helpful in extensively understanding electrochemical behavior in SOC electrodes.

Conditional GAN for Generation of 3D SOFC Electrode Microstructure Dataset

Synthetic three-dimensional porous structures of solid oxide fuel cell anode are generated using the deep convolutional conditional generative adversarial neural network (DCCGAN). The developed network consists of a generator that produces an artificial structure dataset from random numbers, so-called latent variables, and a discriminator that judges whether the input structure dataset is real or fake. The generator and discriminator are alternately trained to improve their performance in an adversarial manner so that the generator can eventually create realistic structures indistinguishable from the real ones. The training process is performed using the real structure dataset of conventional Ni-YSZ anodes with different solid volume ratios, i.e., Ni: YSZ = 70:30, 50:50, and 30:70. In this study, additional training is performed for the generator to control the volume fraction of the generated structures. First, the values of the volume fractions are input to the generator alongside the latent variables. Then, the volume fraction loss is calculated from the difference between the volume fractions of the generated structure and the input values. Finally, the volume fraction loss is used to train the generator in addition to the ordinal training process. The trained network is validated by comparing the generated structures with the real structures in terms of microstructural parameters such as volume fraction, surface area density, and triple-phase boundary density. This comparison is conducted not only for the structures used for the training but also those that are not used for the training, i.e., a solid volume ratio of Ni: YSZ = 40:60 and 60:40. It is found from the results that the volume fractions of the generated structures are in good agreement with the specified input values to the generator. Also, other microstructural parameters quantified from the artificial microstructures are similar to those of the real structures. In addition, since the developed network does not contain fully-connected layers, and is hence categorized as a fully-convoluted network, structures with arbitrary sizes can be generated using the same generator by changing the dimension of the latent variables. The generated structures are confirmed to be free from digital artifacts such as noise, disconnectivity, and periodicity in structures.

Ni-YSZ Electrode Stability in Solid Oxide Electrolysis Cells Operated in 90-98% Steam

Ni-YSZ electrode-supported SOECs were tested at 750 and 800oC using extremely high steam concentrations, 90, 95, and 98%. The performance was compared to SOECs tested in 50% steam with 50% hydrogen. Multiple cells were tested simultaneously with several repeats, all cells configured into a single furnace for each test to ensure a reasonable side-by-side comparison and results reproducibility. The Ni/YSZ electrodes was reduced by hydrogen in situ at each operating temperature and then 50-98% steam was added. Cells were tested at a fixed current or voltage for 1000-3000 hours. The electrochemical impedance data was periodically collected followed by the DRT analyses to identify any changes in polarization losses in time. Minimal to no changes in cell performance in time was observed when compared cell operation in 90+% steam vs 50% steam. Slightly higher degradation was seen for cells tested at 800oC vs those tested at 750oC during the initial 500 hours. After the initial break-in period, the degradation rates for all cells in the following >1000 hours remained the same.A separate set of tests was performed to characterize break-in period and to understand how the initial Ni thermal aging at different temperatures affects the time to a pseudo steady-state, i.e., if the Ni aging/conditioning could be used to accelerate the initial degradation and shorten break-in. Symmetrical cells with two Ni-YSZ electrodes on each side of 10 micron YSZ electrolyte were also tested.The extensive SEM/EDS analyses were performed on all cells to quantify the Ni particle size and describe any microstructural changes. The 2-dimensional SEM images were reconstructed into the 3D microstructures to calculate the triple phase boundary density, species transport paths tortuosity, and particle/pore size distribution.

Conditional generative adversarial network for generation of three-dimensional porous structure of solid oxide fuel cell anodes with controlled volume fractions

A structure generation model based on a generative adversarial network (GAN) is developed to synthesize artificial porous microstructures of solid oxide fuel cell (SOFC) anodes. Different from the conventional framework of GANs, additional training is performed for the generator to control statistical parameters, namely, volume fractions, of the generated structures. The developed model is validated by comparing the synthesized structures with the real electrode microstructures obtained by three-dimensional microscopy analysis. Microstructural parameters, such as volume fraction, specific surface area, and triple-phase boundary density, are used for the comparison in addition to the visual observation. The effect of the input vector size for the generator and the definition of the loss on the ability to generate realistic structures and control the volume fractions of the structures is investigated. The developed model successfully generates realistic anode microstructures with accurately controlled volume fractions, even for compositions not included in the training datasets. It is also found that the balance between the losses influences the accuracy of the volume fraction control and diversity of the generated structures. The GAN model developed is expected to be helpful in constructing a digital twin of electrode fabrication and evaluation processes.

Insight into mechanism of boosted oxygen reduction reaction in mixed-conducting composite cathode of solid oxide fuel cell via a novel open-source pore-scale model

Composite engineering is one of the most practical strategies for mitigating thermo-mechanical instability in state-of-the-art cobalt-based mixed ionic-electronic conducting electrodes for solid oxide cells. Nevertheless, the mechanism underlying improved performance and durability of composite electrodes is not substantially examined and understood. Pore-scale modeling can effectively bridge the gap between material property, microstructure discovery, and performance evaluation. However, most of the previous pore-scale models are built on in-house codes or commercial software that is closed-source to the public, limiting customization and community involvement. Here we report for the first time a microstructure-resolved 3D pore-scale model based on an open-source Lattice Boltzmann library, implementing a well-validated electrochemical module that considers gas, ion and electron transport, as well as electrochemical reaction at triple-phase-boundary (TPB) and double-phase-boundary (DPB). The pure La0.6Sr0.4Co0.2Fe0.8O3-δ (LSCF) and 50/50 vol% composite LSCF/GDC (Gd-doped ceria) cathodes are reconstructed using focused ion beam-scanning electron microscopy and then are investigated under various current densities, temperatures, oxygen concentrations, and thicknesses. Results reveal the advantages of boosted TPB density and ionic conductivity outweigh the disadvantages of lower DPB density and electronic conductivity for the composite cathode. Furthermore, the composite cathode performs better at low temperatures and low oxygen concentrations due to the presence of more macropores, the stable bulk ion conductivity of GDC, and higher TPB density. Local current distribution at active sites indicates a thickness of 20 to 40 μm is favorable for reduced overpotential and moderate reaction homogeneity.

Silver and Samaria-Doped Ceria (Ag-SDC) Cermet Cathode for Low-Temperature Solid Oxide Fuel Cells

This study demonstrated a silver (Ag) and samarium-doped ceria (SDC) mixed ceramic and metal composite (i.e., cermet) as a cathode for low-temperature solid oxide fuel cells (LT-SOFCs). Introducing the Ag-SDC cermet cathode for LT-SOFCs revealed that the ratio between Ag and SDC, which is a crucial factor for catalytic reactions, can be tuned by the co-sputtering process, resulting in enhanced triple phase boundary (TPB) density in the nanostructure. Ag-SDC cermet not only successfully performed as a cathode to increase the performance of LT-SOFCs by decreasing polarization resistance but also exceeded the catalytic activity of platinum (Pt) due to the improved oxygen reduction reaction (ORR). It was also found that less than half of Ag content was effective to increase TPB density, preventing oxidation of the Ag surface as well.

Elucidating the performance benefits enabled by YSZ/Ni-YSZ bilayer thin films in a porous anode-supported cell architecture.

Increasing the performance and improving the stability of solid oxide cells are critical requirements for advancing this technology toward commercial applications. In this study, a systematic comparison of anode-supported cells utilizing thin films with those utilizing conventional screen-printed yttria-stabilized zirconia (YSZ) is performed. High-resolution secondary ion mass spectrometry (SIMS) imaging is used to visualize, for the first time, the extent of Ni diffusion into screen-printed microcrystalline YSZ electrolytes of approximately 2-3 μm thickness, due to the high temperature (typically >1300 °C) used in the conventional sintering process. As an alternative approach, dense YSZ thin films and Ni(O)-YSZ nanocomposite layers are prepared using pulsed laser deposition (PLD) at a relatively low temperature of 750 °C. YSZ thin films exhibit densely packed nanocrystalline grains and a remarkable suppression of Ni diffusion, which are further associated with some reduction in the ohmic resistance of the cell, especially in the low temperature regime. Moreover, the use of a Ni-YSZ nanocomposite layer resulted in improved contact at the YSZ/anode interface as well as a higher density of triple phase boundaries due to the nanoscale Ni and YSZ grains being homogeneously distributed throughout the structure. The cells utilizing the YSZ/Ni-YSZ bilayer thin films show excellent performance in fuel cell operation and good durability in short-term operation up to 65 hours. These results provide insights into ways to improve the electrochemical performance of SOCs by utilizing innovative thin film structures in conjunction with commercially viable porous anode-supported cells.

(Digital Presentation) Synthesizing Electrode Microstructures with Predefined Spatial Gradients By Conditional Generative Adversarial Networks

Recent progress of manufacturing techniques in the field of solid oxide fuel cells (SOFCs) enables fabrications of complex multi-sized gradient microstructures with the spatially varied properties. Methods using additive manufacturing, layer-by-layer deposition, tape casting, nano-imprint, pulse laser deposition and laser engraving, etc. provide possibilities to fabricate 3D structures with flexible design. Moreover, the spatially optimized structures can provide better mechanical and thermal properties, enhance diffusion and improve electrochemical reaction kinetics. In particular, multi-layered electrode designs are attracting increasing interest.The layered electrode design and optimization require effective methods to fabricate synthetic microstructures with property gradients. The generative adversarial network (GAN) proves its usabilities in fabricating artificial microstructures belonging to the same class as the training data [1] and reconstructing 3D structures from 2D image [2], etc. The applications primarily focus on the structures with uniform spatial properties identical to the training sample. The Wasserstein GAN was recently applied for the fast inverse design of two-phase homogenous microstructures with user-defined properties [3]. In this study, an approach using conditional GAN (C-GAN) is proposed to flexibly generate multi-phase structures with predefined gradients.The proposed method is tested on the microstructures of porous nickel-gadolinium doped ceria (Ni-GDC) SOFC anodes. The C-GAN training dataset consists of real Ni-GDC microstructures with varied Ni and GDC compositions (GDC share in the range of 30 – 70 vol%) and with varied porosity [4]. In total, 10 different samples were prepared and sintered at 1350 oC in air atmosphere and further reduced in the H2 flow at 800 oC. The fabricated electrodes had significantly different microstructural properties and electrochemical performance. The samples were infiltrated with epoxy resin to enable clear phase recognition, polished to expose cross-section and characterized by the scanning electron microscope (SEM). The SEM images were segmented prior to the C-GAN training.The C-GAN was trained with patches of 256 x 256 pixels which were randomly extracted from SEM images of each sample. The sample with Ni : GDC = 50 : 50 vol% was excluded from the training data and used only for testing. The generator network input consists of 4 variables: random noise vector, Ni share in composite, porosity and particle size (Fig. 1A). The particle size is controlled by the magnification of the training images. The Ni volume fraction and porosity are calculated separately for each patch during the iterative training process.The primary version of the trained C-GAN network generates homogenous artificial microstructures with the patch size of 256 x 256 pixels identical to the training data. The fabricated microstructures (Fig. 1B) show excellent visual and good statistical agreements with the real samples. It is possible to reproduce not only the volumetric fractions, but also the surface area density and triple phase boundary density of the real Ni-GDC electrodes (Fig. 1C). The C-GAN can fabricate not only the microstructures belonging to the training dataset, but also samples with other compositions.Although the C-GAN generator was trained based on patches with 256 × 256 pixels, it can be used for the fabrication of larger structures by increasing the size of the generator input. By adjusting the generator input matrix consisting of the Ni share, porosity and particle size, various microstructure patterns can be fabricated including linear gradients (Fig. 1D) and layered structures (Fig. 1E). Those structures have a significance for the SOFC anodes design as layered designs are conventionally incorporated in SOFC electrodes, e.g. support and active anode layers. In addition, it was shown that the graded structures have superior performance compared with the isotropic anodes [5].The proposed framework provides a convenient tool for generating realistic microstructures with wide range of predefined properties. Further, it can be coupled with the existing simulation tools to evaluate the wide range of graded and layered microstructural designs.[1] A. Gayon-Lombardo, L. Mosser, N. P. Brandon, and S. J. Cooper, npj Comput. Mater., 6, 1–11 (2020).[2] A. Sciazko, Y. Komatsu, and N. Shikazono, ECS Trans., 103, 1363–1373 (2021).[3] X. Lee, et al., Nature Computational Science, 1, 229, (2021).[4] Y. Komatsu, A. Sciazko, and N. Shikazono, J. Power Sources, 485, 229317 (2021).[5] Z. Yan, et al., ECS Trans., 91, 2055, (2019).Fig. 1. A) Schema of C-GAN network, B) synthetic homogenous microstructures fabricated by C-GAN, C) TPB dependence on the Ni and pore volume fractions, D) synthetic structure with predefined gradient and E) synthetic layered structure. Figure 1

Numerical study on TPB density and percolation properties of microstructure reconstruction of nickel/yttria stabilized zirconia cermet anode based on discrete element method

Microstructure is essential for electrochemistry and transport in Ni/YSZ cermet anodes. Targeting the complexity and irregularity of porous networks, the percolation theory is widely used to model electrode microstructures in representative element (RE) scale based on an ideal prototype of particle units. With the pros of simplicity and generality, however, the idealized particle model deviates somehow the true state of particle clusters. Modeling connections between a lot of particles, the discrete element method (DEM) provides a more realistic state, meanwhile brings high computation cost. This paper provides a combined approach which starts from a DEM-based model for compaction and evolution between particle arrays, then analyzes the statistical properties, e.g. density of triple phase boundary (TPB) and percolations of particles. With consideration of multiple-particle overlaps, the DEM-fitted relationship of percolation probability vs. coordination number presents a smaller percolation threshold and a slower growing rate before reaching a full percolation, compared with some published formulae based on the percolation theory. Simulations at different temperatures are employed to study the effects of sintering. This work has significance of understanding the evolution and optimization of the electrode microstructure, not limited to Ni/YSZ system, and is helpful for more accurate solid oxide fuel cell modeling.

Computational fluid dynamics modeling of anode-supported solid oxide fuel cells using triple-phase boundary-based kinetics

Fuel oxidation in the solid oxide fuel cell occurs at the triple-phase boundary where electronic, ionic, and gas phases simultaneously interact. A quantitative knowledge of the triple-phase boundary density is therefore important in analyzing the fuel cell performance as well as designing the electrode structures and materials. In this work, the triple-phase boundary-based kinetics, developed from the patterned anode experiments are used in a computational fluid dynamics model to assess the performance of anode-supported nickel-yttria stabilized zirconia cells. The simulation results suggested that the effective triple-phase boundary density required to carry out the electrochemical oxidation reactions is several orders of magnitude lower when compared with the physical triple-phase boundary density of similar cermet anodes. The anode concentration gradients are found to be larger near the anode/electrolyte interface compared to that of fuel channel that is ascribed to the electrochemical reactions taking place in the anode active region and mass transport resistance of the microporous structure. The cell voltage decreased rapidly at high current density due to fuel starvation and subsequent drop of the exchange-current density. Furthermore, the effects of triple-phase boundary density and operating temperature on the cell performance are also studied and discussed.

Quantifying Percolated Triple Phase Boundary Density and Its Effects on Anodic Polarization in Ni-Infiltrated Ni/YSZ SOFC Anodes

Increasing the density of percolated triple phase boundaries (TPBs) by infiltrating nanoscale electrocatalysts can improve the performance of solid oxide fuel cell (SOFC) anodes. However, the complex microstructure of these infiltrated nanocatalysts creates challenges in quantifying their role in anode performance improvements. In this research, scanning electron microscopy of fractured cross-sections of a Ni-nanocatalyst infiltrated anodic symmetric cell along with three-dimensional (3-D) reconstruction of the same anode have been used to quantify the changes in percolated TPB densities due to infiltration. This change in percolated TPB density has been compared to the improvement in anode activation polarization resistance measured by electrochemical impedance spectroscopy (EIS). It was found that increased TPB densities only partially accounted for the measured performance improvement. Distribution of relaxation times (DRT) analyses showed that a reduction in the time constants of the catalytic processes in the anode also play a role, suggesting that the added nanoscale percolated TPB boundaries are more electrochemically active as compared to the cermet TPB boundaries.

Use Co- and Ni-Free Air Electrode in Solid Oxide Cell Manufacturing for Electrolysis and Fuel Cell Applications

The production of durable and high performance Solid Oxide Cells (SOC) units for electrolysis (SOE) and fuel cell (SOFC) applications, using cost efficient raw materials with low recycling cost is one of the main challenges for SOC developers to reach a large scale industrialization. Nickel and cobalt, state-of-the-art compounds in the manufacturing of SOC electrodes, require costly safety management during the manufacturing and recycling processes due to their carcinogenic properties. Cobalt, also recognized since 2017 as Critical Raw Materials (CRM) for the EU community, presents also a high economical risk for European SOC manufacturers, regarding its supply (55% of the world’s cobalt being sourced from Central Africa). Use of Co- and Ni- free electrode materials would provide a significant reduction of use of CRM in European SOC manufacturing and offers cell and stack developers the benefit of a more environmental-friendly and lower cost manufacturing process, and the potential of using recycled materials from previous manufacturing.Within the European project NewSOC, TNO develops Co- and Ni-free air electrode materials based on the (La, Ca, Sr)FeO3 perovskite class of materials for SOE and SOFC applications. The electrochemical performances of different Co-free compounds were measured and compared to the state-of-the-art electrode materials (e.g. La0.6Sr0.4Co0.2Fe0.8O3- d) to achieve the performance targets as defined within the NewSOC project: Area Specific Resistance (ASR) < 0.5 ohm.cm2 for Low-Temperature operations at 650°COperations at high current densities (1 A/cm2) for SOFC and SOE operationsLow degradation rates under SOFC and SOE operations and SOFC/SOE cycles (< 1%/kh)Stability upon thermal cycling (< 0.2 mV%/cycles) and redox stability The removal of nickel and cobalt in the oxygen electrode tends to increase the overpotential of the air electrode, with ASR values = 0.6-0.9 ohm.cm2 and maximum current density < 1 A/cm2 for operation at 650°C. Taking this drawback into account, TNO addresses the performance optimization of the Co- and Ni-free air electrode compounds by developing a novel air electrode architectures based on Co-free air electrode materials. The envisaged electrode architecture aims to enhance the triple phase boundary density at the Co- and Ni- free air electrode/electrolyte interface by creating a gradient composite electrode on top of a patterned porous barrier layer. This novel concept is also envisaged to improve the electrode performance and prevent electrode delamination during SOC operations. A higher triple phase boundary density contributes to lower the local partial oxygen pressure between electrolyte and oxygen electrode, known as critical degradation phenomena of the oxygen electrode at high current density under electrolysis conditions. During the conference, the last development of the novel Co- and Ni- free air electrode design developed at TNO will be presented. Figure 1

Leveling Transient and Spatial Gradients in Planar Solid Oxide Cells Using Spatially Varied Microstructures: A Numerical Study

Sharp spatial and transient gradients of overpotential, current density, gas species and temperature within planar solid oxide cells (SOCs) can lead to increased degradation rates, decreased active area utilization and high thermal stresses. Such issues will lead to decreased cell efficiency and functional lifetime while increasing the risk of sudden cell failure due to cracking or delamination. Spatial gradients occur in part due to the interconnect geometry. Commonly, the electrochemical reaction rate within SOC electrodes is disproportionately higher near the inlet regions of the fuel or steam channels and decreases towards the outlets as the concentrations of fuel or steam decreases for fuel cell and electrolysis modes, respectively. Transient gradients occur on short time scales due to rapid fluctuations in loading during ramp up or ramp down and for reversible operation when the current direction is reversed. It might be possible to greatly reduce both transient and spatial gradients by controlling the reaction rate distribution within SOC electrodes through the strategic manipulation of the electrode active layer microstructures. Advances in additive manufacturing techniques now facilitate the design of engineered electrodes with precisely controlled microstructure properties such as pore size, porosity and volume ratios of active layer materials (i.e. Ni or YSZ). The purpose of the present study is to explore the impact and possible benefits of applying functionally graded or regionally altered microstructural properties in the active layer of planar SOCs using a previously developed transient 3D multiphysics performance model of commercial sized planar SOCs. Percolation theory is used to estimate properties such as triple phase boundary (TPB) density and tortuosities from the applied variations of particle sizes, volume fractions and pore former properties as shown in Figure 1(a). It is shown that indeed both spatial and transient gradients can be significantly decreased by engineering gradients in composition and microstructure. For example, under the extreme conditions of ramping up from 0 to 0.75 A/cm2 current density and 75% H2 utilization in fuel cell mode, the multiphysics performance model predicts a nearly 300K temperature increase in approximately 100s for the baseline case as shown in Figure 1(d). Applying linear gradients of porosity and Ni/YSZ ratios such that TPB density increases from the fuel inlet to outlet significantly decreases this temperature gradient by 25-100K. However, there may be a tradeoff in terms of overall performance with decreasing steady state cell voltage corresponding with decreased temperature gradients. This is partly associated with the decreased temperature itself as the reaction kinetics are assumed to increase with increasing temperature resulting in decreased overpotential at the TPBs. Additional impacts of these and other functionally graded microstructures include decreased current density gradients and more uniform oxygen reduction in the oxygen electrode compared to the baseline case. Results indicate that functionally graded electrode microstructures could be designed based on the desired operating conditions to reduce degradation and thermal stresses within cell and stack assemblies. Such electrode designs have the potential to significantly reduce cell failure rate and increase cell lifetime particularly in transient loading and/or high current density applications. Figure 1

Super-Resolved in-Operando Observation of SOFC Pattern Electrodes

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

Enhanced Long-Term Durability of Solid Oxide Electrolysis Cells by Microstructure Tailoring of the Air Electrode

High-temperature solid oxide electrolysis cells (SOECs) provide greater efficiency and may potentially provide future technology to meet the enormous energy storage needs generated by the surge in the availability of intermittent solar and wind electricity. However, in implementing this SOEC technology, the high polarization losses and long-term degradation of the SOEC electrodes are the bottlenecks. In this research, we tailored the microstructure of SOEC air electrode to improve electrochemical efficiency and increase the stability of the air electrode. The optimization of the microstructure culminated in a 30% improvement in SOEC electrochemical performance at 800 °C for H2O conversion and considerably improved long-term stability for >1000 hours. The Post-test SEM and TEM investigations suggested that delamination of the air electrode was prevented due to effective microstructure tailoring, and resistive interfaces forming Sr diffusion were suppressed inside the barrier layer and electrolyte. Due to the increased triple phase boundary density, porosity at the interface, and greater active surface area of the electrode, the buildup of the oxygen partial pressure over the electrolyte/barrier layer/air-electrode was decreased.

Selection of Highlights of the European Project Next Generation Solid Oxide Fuel Cell and Electrolysis Technology – NewSOC

Solid oxide technologies (SOC: Solid oxide fuel cells SOFC & Solid oxide electrolysis SOE) are key enabling technologies for energy systems based on renewable sources and allow for a strong interlinking of sectors electricity, heat, and gas/fuels. SOC can emerge as key players in many concepts, such as fuel/gas to power and heat at small to large scale,energy storage through power to hydrogen/fuel,utilization and upgrading of biogas,balancing of intermittent electricity from renewable sources through load following and reversible operation, andcentral and decentral solutions for electricity and heat production. In a time, where first SOC systems enter demonstration and commercial markets, the NewSOC project focusses on next generations. It aims at significantly improving performance, durability, and cost competitiveness of solid oxide cells & stacks compared to state-of-the-art (SoA). In order to achieve these goals, NewSOC investigates twelve innovative concepts in the following areas: (i) structural optimization and innovative architectures based on SoA materials, (ii) alternative materials, which allow for overcoming inherent challenges of SoA, (iii) innovative manufacturing to reduce critical raw materials and reduction of environmental footprint at improved performance & lifetime. The NewSOC unifies competences of 16 strong research and industry players. First scientific highlights were achieved despite the challenging working conditions under the European wide covid-19 restrictions in the first year of the project. The presentation will provide a selection of these highlights.One focus area is the development of novel electrode materials, where high performance, impurity tolerance and stability are the primary targets. Nanostructured fuel electrodes based on doped SrTiO3 perovskites such as LaSrFeNiTiO3 are being developed as backbones. Subsequent, wet infiltration of Ni:GDC ensures high electro catalytic activity and ionic conductivity, while retaining the advantages of Ni-metal free fuel electrodes outlined above. Furthermore, a class of doped lanthanum chromites (La0.75Sr0.25CrxM1-xO3-δ, M=Fe, Mn, Ni) is developed as Ni-metal free fuel electrodes, to overcome the challenges faced by the SoA Ni-cermets. The perovskite structured electrodes, combined with highly conductive electrolytes have high performance, low ASR and flexibility in various operating modes (SOEC, rSOC). The most attractive feature of this class of electro catalysts is that they retain their properties (oxidative state, conductivity) in reducing and oxidizing environments at SOC operating temperatures, even in absence of a reducing agent, i.e. H2.Another approach is to modify SoA electrodes. Commercial (SoA) Ni/GDC was modified with iron by deposition – precipitation (D.P.), leading to enhanced performance as functional SOE fuel electrode. The promoting effect depends on the wt.% Fe content, where D.P. of quite a small amount of iron, through the formation of a Ni-Fe alloy, caused a 3-fold enhancement compared to Ni/GDC. Interestingly, the 0.5-Fe-Ni/GDC electrode performed similarly well like the noble-metal modified 3Au-0.3Mo-Ni/GDC.Materials compositions and structuring go hand in hand for tailoring cell properties. Under this focus, novel air electrode architectures for SOFC & SOE applications based on Co- and Ni-free materials with the (La, Ca, Sr)FeO3 perovskite class of materials are developed. The typically increased overpotential of Co- and Ni-free air electrodes is a challenge, which is tackled by introduction of a patterned porous barrier layer and addition of a composite layer at the electrode/electrolyte interface to enhance the triple phase boundary density between electrolyte and the Co-free air electrode. In another approach, the microstructure of SoA Ni/YSZ and LSCF/GDC electrodes was assessed to identify the best tradeoff between the cell performances, the durability and the robustness by a methodology coupling of manufacturing, characterization, and modeling. As a result, the performance of the LSCF/GDC composite electrode was improved, with different porosities, graded electrodes and composites. The role of the Ni/YSZ microstructure on the Ni migration during operation was investigated, with focus on finer and more homogeneous electrodes.Manufacturing and deposition methods contribute significantly to performance & lifetime, but also costs and environmental impact of SOC production. Thus, alternative approaches providing cells & stacks with required specifications are part of the NewSOC project. Progress on sputter deposited GDC buffer layers has been obtained in view of their implementation in the fabrication process of commercial Solid Oxide Fuel Cells.

Super-Resolved in-Operando Observation of SOFC Pattern Electrodes

In order to investigate Nickel (Ni) movements during operation, in-operando observation of Ni-pattern electrode is conducted with confocal laser microscope. In-operando observations have limitation of quantitative analysis due to the low resolution of the laser microscope. In this study, two machine learning algorithms for super resolving laser microscope images are proposed. Firstly, semantic segmentation algorithm based on convolutional neural network (CNN) is introduced for automatic material identification. U-net architecture is incorporated and compared with simple encoder-decoder network. The phase fractions and triple phase boundary (TPB) density estimated from the automatically segmented image agreed well with the ground truth data. The proposed U-net can detect fine features of the microstructure. Secondly, super-resolution generative adversarial network (GAN) is proposed. The developed network applies pix2pix GAN architecture and aims to synthesize SEM-quality image from low resolution laser image. The generated images achieved very high resolution equivalent to SEM images.

Quantitative Characterization of the Microstructure-Property Relationships in Ni and MIEC Nanocatalyst-Infiltrated Ni/YSZ Anodes

The performance of solid oxide fuel cell (SOFC) anodes can be improved by infiltration of nanoscale electrocatalysts, which increases triple phase boundary (TPB) density, but only when the added TPBs are active. Quantitative characterization of the microstructure of infiltrated nanocatalysts remains a critical challenge in understanding their role in improved performance. This paper explores the relationship between electrochemical performance and microstructure for Ni/YSZ anodes infiltrated with Ni, gadolinium-doped-ceria (GDC), and Ni/GDC nanocatalysts using a combination of electrochemical impedance spectroscopy (EIS), distribution of relaxation times (DRT) analysis, scanning electron microscopy (SEM) microstructural characterization, and three-dimensional (3-D) anode reconstruction. Estimated polarization resistance improvements as a result of nickel nanoparticle infiltration are discussed.