SOFC/CeO2 Doped Electrolyte Deposition Using Suspension Plasma Spraying
Bibliographic record
Abstract
Abstract Ceria (CeO2) based electrolytes have been considered for use in solid oxide fuel cells (SOFC) for more than 20 years. There are however some limitations to this usage that this study has tried to address, indeed the study objective has been that of synthesizing and thermal spraying thin layers (50 - 100 µm) of doped CeO2 by the technique of suspension plasma spraying, using radio frequency (RF) plasma technology. Various dopant combinations and concentrations have been selected for this work in order to increase the useful partial oxygen pressure range for satisfactory ionic conductivity development, thereby increasing the anionic conductivity and preventing CeO2 reduction in fuel cell service. Ceria possesses the fluorite crystal structure at low temperatures but does not have enough oxygen vacancies to be a good ionic conductor. In ceria the cerium have 4+ oxidation state within the fluorite structure, and by substituting a certain amount of Ce4+ ions by trivalent dopant ions, oxygen vacancies are induced into the structure. Recent studies have demonstrated that at low temperatures doped ceria seems to be a better electrolyte than doped zirconia. Also, it seems that dopants with ionic radii close to Ce4+ ions give rise to better ionic conductivities. The doped ceria conductivity increases with the dopant concentration because more oxygen vacancies are created, but at higher concentrations vacancy ordering occurs which results in decreased ionic conductivity.
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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 | 0.000 |
| 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) | 0.001 | 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".