Micro‐modeling of porous composite anodes for solid oxide fuel cells
Bibliographic record
Abstract
Abstract A new anode micromodel for solid oxide fuel cells to predict the electrochemical performance of hydrocarbon‐fuelled porous composite anodes with various microstructures is developed. In this model, the random packing sphere method is used to estimate the anode microstructural properties, and the complex interdependency among the multicomponent mass transport, electron and ion transports, and electrochemical and chemical reactions is taken into account. As a case study, a porous Ni–YSZ composite anode operated with biogas fuel is simulated numerically and distributions of the current density, polarization, and mole fraction and rate of flux of the fuel components along the thickness of the anode are determined. The effect of the anode microstructural variables including the porosity, thickness, particle‐size ratio, and particle size and volume fraction of Ni particles on the anode electrochemical performance is also studied. © 2011 American Institute of Chemical Engineers AIChE J, 58: 1893–1906, 2012
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".