Enhancing the Optimized HEA Bond Coating in TBC Systems via HVAF Technique
Bibliographic record
Abstract
Abstract To achieve higher engine combustion efficiency while reducing emissions, it is necessary to address the challenges posed by elevated operating temperatures. High Entropy Alloys (HEAs) have emerged as promising materials for this purpose, offering exceptional properties at high temperatures, including synergistic effects and excellent resistance to oxidation and corrosion. In this study, a FeCoNiCrAl HEA was investigated as a bond coat material due to its excellent balance of strength and ductility, coupled with outstanding oxidation resistance. It was deposited using HVAF M3 and i7 guns equipped with different nozzles/powder injectors and pressures. Notably, this research marks the first study of the i7 gun globally for the HEA bond coat, coupled with the optimization of HVAF parameters for both i7 and M3 guns. Characterization of both powder and as-sprayed samples was carried out using X-ray Diffraction (XRD), Energy-dispersive X-ray spectroscopy (EDS), and Field Emission Scanning Electron Microscopy (FESEM) techniques. The results revealed the formation of a dense and homogeneous microstructure. Additionally, isothermal oxidation tests were conducted to analyze the behavior of the thermally grown oxide. After 50 hours at 1000 °C, a dense, uniform, and thin alumina TGO layer was observed to have formed. These tests revealed that FeCoNiCrAl HEA exhibits significant potential to enhance oxidation resistance at high temperatures.
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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".