Columnar-structured thermal barrier coatings deposited via the water-based suspension plasma spray process
Bibliographic record
Abstract
Abstract Suspension plasma spray (SPS) has been developed as a rapid, facile and cost-effective process to deposit columnar-structured thermal barrier coatings (TBCs). In contrast to the most commonly used ethanol-based suspensions, water-based suspensions have not been used in the SPS process to deposit columnar-structured TBCs due to their high surface tension, although they are much cheaper and safer. In this work, a new water-based SPS process was prepared by adding surfactant to lower the surface tension. The optimum content of dispersant and surfactant added to the suspension was determined via measurements of viscosity, particle size, surface tension, contact angles, and atomized droplet size. Coatings deposited using suspensions with and without surfactant showed typical columnar-structured microstructures and vertically cracked microstructures, respectively. The coatings deposited using suspensions with surfactant also showed evolution from columnar-structured microstructures to mixed microstructures of columns and cracks, and to homogeneous microstructures with the increase in standoff distance. The formation of different coating microstructures was correlated to the size of droplets after aerodynamic breakup and the Stokes number of in-flight particles. The new water-based suspension together with the water-based SPS process show great potential to be a cheap and effective alternative to the ethanol-based SPS process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".