Influence of preheating temperature on splat morphology of spray deposited yttria-stabilized zirconia and lanthanum magnesium hexaaluminate in thermal barrier coatings
Bibliographic record
Abstract
The performance of thermal barrier coatings (TBCs) depends upon the morphology of individual splats and how a single particle flattens. A splat is a single unit cell of thermal barrier coatings. Its properties significantly influence the overall performance of the coating. The transition temperature of the substrate affects the splat morphology and also influences the adhesion strength of the applied coating. This study investigates the effect of substrate preheating temperature on splat morphology and the critical transition temperature for yttria-stabilized zirconia (8YSZ) and lanthanum magnesium hexaaluminate (LaMgAl11O19, LaMA) powders deposited via atmospheric plasma spray (APS). Using scanning electron microscopy (SEM), a critical transition temperature of 400°C was identified for both materials. At this temperature, disc-shaped splats with improved adhesion formed, while irregular shapes were observed below 400°C, and disordered morphologies appeared above it. Notably, at 400°C, 8YSZ splats exhibited surface cracks, whereas LaMA splats remained crack-free, highlighting differences in their thermo-mechanical properties. These findings emphasize the importance of optimizing preheating temperature to achieve desirable splat morphology and enhance TBC 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 | 0.000 |
| 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".