Optimization of the microstructure of porous composite cathodes in solid oxide fuel cells
Bibliographic record
Abstract
Abstract A comprehensive micromodel to predict the electrochemical performance of porous composite LSM‐YSZ cathodes in solid oxide fuel cells (SOFCs) is developed. The random packing sphere model is used to estimate the cathode microstructural properties required for the micromodel. The micromodel developed takes into account the complex interdependency among the mass transport, electron and ion transports, and the electrochemical reaction, and can be used for optimization of the microstructure of porous LSM‐YSZ composite cathodes. It is shown that the electrochemical performance of these cathodes depends on the microstructural variables of the cathode porosity, thickness, particle size ratio, and size and volume fraction of LSM particles. The effect of these microstructural variables on the cathode total resistance, as the objective function to achieve the optimum microstructure for the cathode, is studied through computer simulation. The results indicated that for a LSM‐YSZ cathode operated at the average temperature of 1073.15 K, bulk oxygen partial pressure of 0.21 atm, and total current density of 5000 Am −2 , and constrained to the minimum value of 1 μm for the size of LSM particles and 0.25 for the cathode porosity, the optimum microstructure is obtained at the particle size ratio of unity, LSM particle size of 1 μm and volume fraction of 0.413, porosity of 0.25, and thickness of 60 μm. The cathode total resistance corresponding to the cathode optimized is estimated to be 0.291 Ω cm 2 . © 2011 American Institute of Chemical Engineers AIChE J, 2012
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".