Solution Precursor Plasma Spray (SPPS) of Ni-YSZ SOFC Anode Coatings
Bibliographic record
Abstract
Abstract Although the deposition of Ni-YSZ anodes by plasma conventional spray is more successful than other SOFC components, the large NiO and YSZ particles used for the spray process, about 50-150 microns for high porosity coating deposition, reduce the density of three phase sites for electrode reaction. In this paper, the solution precursor plasma spraying (DCSPPS) process, in which solution precursors of the desired resultant materials are fed into a DC plasma jet by atomizing gas, was used to synthesize and deposit porous Ni-YSZ composite anodes. The deposition results show that several process parameters have significant effects on the microstructure and phase composition of the deposited material. The deposits were composed of tower-like, irregularly shaped agglomerates and splats. The sizes of the agglomerates increase with the decrease of the plasma torch power and most are not completely molten during the impact. The amount of splats is proportional to the power and they are much smaller than the agglomerates in volume. After heat treatment to reduce the NiO present in the as deposited coatings, the coatings were found to contain small spherical YSZ particles about 0.5 micrometers in diameter distributed in a continuous Ni matrix. The coatings have 29%-51% porosity depending on the process parameters.
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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".