The Effect of Electrode Morphology on Solid Oxide Fuel Cell Performance
Bibliographic record
Abstract
The morphology of the active electrode in a solid oxide fuel cell has a strong impact on both the transport properties and the active triple phase boundary length and distribution. The performance of the electrode emerges from the interaction of these two parameters and is impacted strongly by the size and shape of the starting powders used to make the ionically and electronically conducting phases of the electrode as well as by the manufacturing method used and the sintering process post manufacture. This paper presents a numerical study over a range of electrode morphologies chosen to represent different manufacturing processes and presents comparisons of geometric characteristics, transport properties and electrode performance. Three different morphologies are generated from three different starting powders, spheres representing non aggregated powders, agglomerates of spheres and high aspect ratio cylinders modelling splats formed in plasma sprays. Of particular interest in this study is the effect of anisotropy of the base particles on the resulting electrode. For each base particle shape multiple stochastic realizations of densely packed particle systems are generated using a collective rearrangement algorithm. The structures are then analysed geometrically with methods from spatial statistics using our own Java-based software, and meshed, solved and analysed with respect to transport and performance using a custom modified version of the open source computational fluid dynamics package, openFOAM. Detailed comparisons are presented in terms of tortuosity of paths through each phase of the material, constrictivity and chord lengths in different directions, directional transport properties in each of the three phases, distribution of the triple phase boundary lines and electrode performance
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".