Bibliographic record
Abstract
In a solid oxide fuel cell (SOFC), the cathode processes account for a majority of the overall electrochemical losses. A composite cathode comprising a mixture of ion-conducting electrolyte and electron-conducting electro-catalyst can help minimize cathode losses provided microstructural parameters such as particle-size, composition, and porosity are optimized. The cost of composite cathode research can be greatly reduced by incorporating mathematical models into the development cycle. Incorporated with reliable experimental data, it is possible to conduct a parametric study using a model and the predicted results can be used as guides for component design. Many electrode models treat the cathode process simplistically by considering only the charge-transfer reaction for low overpotentials or the gas-diffusion at high overpotentials. Further, in these models an average property of the cathode internal microstructure is assumed. This paper will outline the development of a 1-dimensional SOFC composite cathode micro-model and the experimental procedures for obtaining accurate parameter estimates. The micro-model considers the details of the cathode microstructure such as porosity, composition and particlesize of the ionic and electronic phases, and their interrelationship to the charge-transfer reaction and mass transport processes. The micro-model will be validated against experimental data to determine its usefulness for performance prediction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".