An Effective Property Model for Infiltrated Electrodes in Solid Oxide Fuel Cells
Bibliographic record
Abstract
An effective property model for infiltrated electrodes is reported that predicts the dependence of effective electronic conductivity and active TPB length on experimentally controllable and measurable parameters. The model uses results from percolation theory and geometric arguments to compute the properties of Ni-infiltrated anodes of solid oxide fuel cells. While the predicted electronic conductivity is comparable to that for a typical composite Ni anode, the predicted effective TPB length is approximately two orders of magnitude higher for a Ni infiltrated anode with a Ni volume fraction of less than 10%. The predictions of the developed model are compared and validated against three independent experimental datasets. Parametric studies using this model suggest that decreasing the particle sizes of the infiltrated film and the substrate, as well as the substrate porosity will increase the active TPB length. While decreasing substrate particle size also increases the effective electronic conductivity of the electrode, decreasing substrate porosity has the opposite effect. Finally, a methodology is presented to quantitatively relate an experimentally observed degradation in effective electronic conductivity of infiltrated electrodes to a reduction in active TPB length as a function of time.
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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 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".