Mesoscale characterization of local property distributions in heterogeneous electrodes

Hsu, Tim; Epting, William K.; Mahbub, Rubayyat; Nuhfer, Noel T.; Bhattacharya, Sudip; Lei, Yinkai; Miller, Herbert M.; Ohodnicki, Paul R.; Gerdes, Kirk; Abernathy, Harry; Hackett, Gregory; Rollett, Anthony D.; Graef, Marc De; Litster, Shawn; Salvador, Paul A.

doi:10.1016/j.jpowsour.2018.03.025

Cited by 41 publications

(61 citation statements)

References 60 publications

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“…borders surrounded by a thin layer of binder (white phase), and the phases seem distributed in the same proportion in the real and generated images. In the case of the SOFC, no structuring element is clearly defined; however, the shapes of the white and grey phases of the generated set show the typical shape of the real data which is particular for the sintering process in the experimental generation of these materials 44 .…”

Section: Resultsmentioning

confidence: 99%

“…This material is also made by mixing an ink, but this time it is deposited onto a ceramic substrate and then sintered at high temperature to bind the components together. Details of the sample preparation, imaging, reconstruction and segmentation approaches used can found in 34 for the cathode and 44 for the anode. The specifications of both datasets are shown in Table 1.…”

Section: Introductionmentioning

confidence: 99%

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Pores for thought: generative adversarial networks for stochastic reconstruction of 3D multi-phase electrode microstructures with periodic boundaries

Mosser²,

et al. 2020

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The generation of multiphase porous electrode microstructures is a critical step in the optimisation of electrochemical energy storage devices. This work implements a deep convolutional generative adversarial network (DC-GAN) for generating realistic nphase microstructural data. The same network architecture is successfully applied to two very different three-phase microstructures: A lithium-ion battery cathode and a solid oxide fuel cell anode. A comparison between the real and synthetic data is performed in terms of the morphological properties (volume fraction, specific surface area, triple-phase boundary) and transport properties (relative diffusivity), as well as the two-point correlation function. The results show excellent agreement between datasets and they are also visually indistinguishable. By modifying the input to the generator, we show that it is possible to generate microstructure with periodic boundaries in all three directions. This has the potential to significantly reduce the simulated volume required to be considered "representative" and therefore massively reduce the computational cost of the electrochemical simulations necessary to predict the performance of a particular microstructure during optimisation.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pores for thought: generative adversarial networks for stochastic reconstruction of 3D multi-phase electrode microstructures with periodic boundaries

Mosser²,

et al. 2020

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“…This material comprises sintered grains with characteristic length scales on the order of 0.5 lm and exhibits substantial variability in volume fractions over the length scale of 15 lm (or 30 times the characteristic length scale). 25,26 The extent and connectivity of triplephase boundaries (TPB), where YSZ, Ni, and pore phases meet in a line junction, governs the performance of the material. The TPB network topology is complex and interconnected, necessitating a 3D microstructural representation for accurate performance modeling.…”

Section: Experimental Microstructure Data Setmentioning

confidence: 99%

“…1. The original microstructure image data were experimentally captured using a Xe plasma focused ion beam combined with scanning electron microscope (Xe PFIB-SEM) 25,26 and represents the largest 3D volume of SOFC material characterized to date. Although the microstructure consists of three phases, we train our GAN model with the microstructure data in grayscale (SEM signal) format, without prior segmentation, to emphasize and test the generality of the GAN framework.…”

Section: Introductionmentioning

confidence: 99%

Microstructure Generation via Generative Adversarial Network for Heterogeneous, Topologically Complex 3D Materials

Hsu

Epting

Kim

et al. 2020

JOM

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Using a large-scale, experimentally captured 3D microstructure data set, we implement the generative adversarial network (GAN) framework to learn and generate 3D microstructures of solid oxide fuel cell electrodes. The generated microstructures are visually, statistically, and topologically realistic, with distributions of microstructural parameters, including volume fraction, particle size, surface area, tortuosity, and triple-phase boundary density, being highly similar to those of the original microstructure. These results are compared and contrasted with those from an established, grain-based generation algorithm (DREAM.3D). Importantly, simulations of electrochemical performance, using a locally resolved finite element model, demonstrate that the GAN-generated microstructures closely match the performance distribution of the original, while DREAM.3D leads to significant differences. The ability of the generative machine learning model to recreate microstructures with high fidelity suggests that the essence of complex microstructures may be captured and represented in a compact and manipulatable form.

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“…Chen et al have simulated the influence of mesoand macro-pores on reagent concentration and catalyst utilization by editing an artificially-generated pore network [20]. Hsu et al have compared real and synthetic microstructures to investigate the origin of inhomogeneity in heterogeneous electrodes [21]. In our previous study, we have constructed a finite-volume method-based, three-dimensional, non-continuous model of transport phenomena in a solid oxide fuel cell anode in order to discuss the influence of heterogeneity on electrode performance [18].…”

Section: Introductionmentioning

confidence: 99%

A Three-Dimensional Numerical Assessment of Heterogeneity Impact on a Solid Oxide Fuel Cell’s Anode Performance

et al. 2018

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In this research, a fully three-dimensional, multiphase, microstructure-scale heterogeneous (non-continuous) electrode, Solid Oxide Fuel Cell (SOFC) stack model is implemented in order to assess the impact of homogeneity disturbance in an SOFC anode. The Butler–Volmer model is combined with recent empirical relations for conductivity and aspects of the Maxwell–Boltzmann kinetic theory describing the transport of mass within the porous medium. Methods for the localized quantification of electrode morphology parameters (such as triple phase boundary length) are implemented. The exchange current distribution in the electrode, the partial pressures and the electric potential fields for each phase are computed numerically. In order to simulate heterogeneity, transfer barriers of varying placement and size are added to an otherwise homogeneous, virtual microstructure based on data from FIB-SEM tomography. The results are compared to a model based on the continuous electrode theory, and the points of discrepancy are highlighted.

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Mesoscale characterization of local property distributions in heterogeneous electrodes

Cited by 41 publications

References 60 publications

Pores for thought: generative adversarial networks for stochastic reconstruction of 3D multi-phase electrode microstructures with periodic boundaries

Pores for thought: generative adversarial networks for stochastic reconstruction of 3D multi-phase electrode microstructures with periodic boundaries

Microstructure Generation via Generative Adversarial Network for Heterogeneous, Topologically Complex 3D Materials

A Three-Dimensional Numerical Assessment of Heterogeneity Impact on a Solid Oxide Fuel Cell’s Anode Performance

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