A Particle-Based Model for Effective Properties in Infiltrated Solid Oxide Fuel Cell Electrodes
Bibliographic record
Abstract
A modeling framework for the numerical reconstruction of the microstructure of infiltrated electrodes is presented in this study. A particle-based sedimentation algorithm is used to generate the backbone, while a novel packing algorithm is used to randomly infiltrate nanoparticles on the surface of backbone particles. The effective properties, such as the connected triple-phase boundary length, the effective conductivity, the effective diffusivity, are evaluated on the reconstructed electrodes by using geometric analysis, finite volume and random-walk methods, and reported in dimensionless form to provide generality to the results. A parametric study on the effect of the main model and operating parameters is performed. Simulations show that the critical loading (i.e., the percolation threshold) increases as the backbone porosity decreases and the nanoparticle diameter increases. Large triple-phase boundary length, specific surface area and good effective conductivity can be reached by infiltration, without detrimental effects on the effective transport properties in gas phase. Simulations reveal a significant sensitivity to the size and contact angle of infiltrated particles, suggesting that the preparation process of infiltrated electrodes should be properly tailored in order to obtain the optimized structures predicted by the model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".