Study of Plasma Sprayed Mullite Coating Using Mullite and a Mixture of Alumina and Silica Powder Particles
Bibliographic record
Abstract
Abstract Plasma sprayed ceramic coatings are widely used for thermal barrier coating applications. Commercially available mullite powder particles and a mixture of mechanically alloyed alumina and silica powder particles were used to deposit mullite ceramic coatings by plasma spraying. Microstructure and morphology of both powder particles as well as coatings were investigated by using scanning electron microscopy (SEM). Phase formation and degree of crystallization of coatings were analyzed and estimated by using X-ray diffraction technique. Differential thermal analysis (DTA) method was used to study the phase transformation of coatings. Results indicated that the porosity level in the coating deposited using mullite as initial powder particles was lower than that deposited using the mixed powder particles. The crystallization degree of the coating deposited using the mixed powder particles are higher than that deposited using mullite powder particles. DTA curves of coatings deposited using the mixed powders have showed some phase transformation due to the crystallization of the retained amorphous phases such as mullite and alumina in the coatings. The degree of crystallization of both as sprayed coatings was significantly increased after post deposition heat treatments.
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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".