Microstructural Modeling and Effective Properties of Infiltrated SOFC Electrodes
Bibliographic record
Abstract
A modeling framework for the microstructural modeling of infiltrated SOFC electrodes is presented. The model numerically reconstructs infiltrated electrodes through a sedimentation algorithm for the backbone generation and a novel Monte Carlo packing algorithm for the random infiltration. Effective properties are evaluated by means of Monte Carlo geometric analysis and finite volume method as a function of the loading and of the particle size of infiltrated particles. Infiltration into ion-conducting and composite backbones is analyzed in this study. Simulations show that the infiltration can lead to an increase in TPB density of about two orders of magnitude if compared with conventional composite electrodes. In addition, infiltration into monocomponent backbones can lead to a TPB density about twice the TPB achievable when infiltrating composite backbones. On the other hand, a critical loading of nanoparticles must be reached in monocomponent backbones while in a composite backbone the infiltration is always beneficial.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".