Generate Realistic Solid Oxide Cell Microstructures – Fast and Reliable
GeoApp: Solid Oxide Electrode Generation
Solid oxide cell (SOC) technology is key to future energy systems and offers a pathway to efficient, sustainable energy conversion — enabling efficient power generation with solid oxide fuel cells (SOFC mode) and renewable energy storage with solid oxide electrolyzer cells (SOEC mode). But experimental research is costly and slow, and material degradation and lifetime challenges continue to slow down industrial adoption.
The GeoApp Solid Oxide Electrode Generation for creating microstructures helps you overcome these barriers. Using advanced stochastic geometric modeling, the tool creates realistic three-phase microstructures (2 solids + 1 pore phase) that mimic electrode materials such as Ni-YSZ or Ni-CGO anodes and offers an advanced alternative to particle-packing methods.
Unlike traditional approaches, GRF-based modeling:
- Produces continuous phase networks that reflect real sintered microstructures
- Allows precise control over solid volume fractions and wetting behavior
- Enables large-scale parametric studies with realistic variability
- Control characteristics of the produced microstructure by varying the correlation functions used in the generation process
The result: physically meaningful, customizable 3D structures ready for virtual testing, image analysis, and multiphysics simulations.
Moreover, such structures cannot only be useful for SOC, but also for designing other parts in a fuel cell with similar microstructure, such as the catalyst layers (CL) of proton exchange membrane (PEM) fuel cells and electrolyzers with realistic distribution of the carbon phase with Pt catalyst, the ionomer, and the pore space.
Whether you’re developing next-generation SOC electrodes or optimizing proven systems like Ni-YSZ, this tool empowers you to:
- Rapidly generate digital twins of real materials
- Virtually test performance impacts of microstructure variations
- Extract design guidelines for longer-lasting, higher-performing SOC devices
Our tip: Cloud-based simulations as a driver for SOC innovation
With GeoDict Cloud and Massive Simultaneous Cloud Computing (MSCC), hundreds of virtual structures can be studied in parallel, drastically reducing time-to-insight.
Start generating SOC microstructures today and accelerate your materials discovery journey.
Marmet et al., Stochastic microstructure modeling of SOC electrodes based on a pluri-Gaussian method, Energy Adv., 2023, 2, 1942-1967, DOI 10.1039/D3YA00332A
Marmet et. al.: Standardized microstructure characterization of SOC electrodes as a key element for Digital Materials Design, Energy Advances, 2023, volume 2, issue 7, pages 980-1013, DOI 10.1039/D3YA00132F
Holzer et. al.: Tortuosity and Microstructure Effects in Porous Media, 2023, Springer Series in Material Sciences, Volume 333, DOI 10.1007/978-3-031-30477-4
3D Tomography Data of an Ni-YSZ Electrode
- Example: Holzer et al (2020)*
- Continuous phases due to sintering, no more powder
particles geometries visible - Modeling with Gaussian Random Fields (GRFs)
*L. Holzer, et al. (2020), https://doi.org/10.5281/zenodo.4056538
Structure Generation based on Gaussian Random Fields
- Based on: Moussaoui et al (2020)*
- Stochastic geometric modeling for realistic three-phase
microstructures (2 solids + 1 pore phase) - Thresholding and phase control based on user-defined
volume fractions - Wetting behavior tuning through contact angle parameters
*H. Moussaoui et al. (2018), https://doi.org/10.1016/j.commatsci.2017.11.015
Different Correlation Functions
- Reference case with Gaussian Random Fields (GRFs)
used for both solid phases - Replacing one GRF with a Laplacian based correlation
function - Replacing both GRFs with a Laplacian based correlation
function
- Independent Gaussian Random Fields to build statistically robust morphologies
- Thresholding and phase control based on user-defined volume fractions
- Wetting behavior tuning through contact angle parameters
- Accelerate R&D – explore large design spaces without costly experiments
- Develop next-gen SOC materials or optimize established electrode systems
- Digital Materials Design (DMD) – virtually screen and optimize electrode concepts
- Realistic modeling – capture electrode properties like conductivity, pore transport, and three-phase boundary length
- Cost efficiency – reduce lab time, materials, and trial-and-error
- Standardized microstructure generation – reproducible, automated workflows
- Scalable exploration – run hundreds of simulations in parallel with cloud-based workflows
- Direct integration – feed structures into existing simulation pipelines, into image analysis or multiphysics models with ease
GeoDict Online User Guide
Following modules are often used in combination with the "Solid Oxide Electrode Generation" GeoApp:
| Import & Image Processing | ||||
| Image Analysis | MatDict | PoroDict | ||
| Material Modeling | ||||
| Simulation & Prediction | BatteryDict | ConductoDict | DiffuDict | FlowDict |
| Interfaces |