Fuel Cell Interconnecting Coatings Produced by Different Thermal Spray Techniques
Bibliographic record
Abstract
Doped LaCrO3 ceramic material is commonly used to produce interconnect coatings for solid oxide fuel cells (SOFCs). Three different thermal spray methods are used in this work to deposit La0.9Sr0.1CrO3 interconnect coatings: high velocity oxy-fuel (HVOF) using a modified nozzle and two different atmospheric plasma spray (APS) torches. One of them is a commercial torch that uses Ar/H2 as plasma forming gases and the other is a new torch design that uses gas mixtures based in CO2. The spray parameters of each torch were set by studying the in-flight temperature and velocity of the particles as a function of the stand-off distance using a substitute powder (ZrO2-TiO2-Al2O3 composite) with similar physical properties to La0.9Sr0.1CrO3. The process parameters that produced coatings with the lowest porosity were employed to deposit La0.9Sr0.1CrO3 coatings on zirconium oxide substrates. Scanning electron microscopy (SEM) and X-ray diffraction analysis techniques were used to characterize the coatings produced by the three different torches. The microstructure features and crystalline phases present in the coatings are explained in terms of the process parameters and correlated with preliminary measurements of electrical resistivity of the as-sprayed coatings. In some cases, post-deposition heat treatments are studied in order to decrease the electrical resistivity.
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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 | 0.001 |
| 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) | 0.002 | 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".