High Entropy Alloy (HEA) Bond Coats for Thermal Barrier Coatings (TBCs)—A Review
Bibliographic record
Abstract
Abstract Due to the aggressive operation conditions of turbine hot sections, protective coatings are required to provide oxidation and hot corrosion resistance for superalloy components. Thermal barrier coatings (TBCs) are comprised of a ceramic top coat and a metallic bond coat (BC) and are typically used as thermal protection systems against these aggressive environments. Conventional BC materials are MCrAlX, with M being metals or alloys (e.g., Ni, Co or NiCo) and X being reactive elements such as Y, Hf, Ta, Si. Due to their strength, thermal stability, and oxidation resistance, high-entropy alloys (HEAs) have presented promise for use as BC materials in hightemperature applications. Owing to its cocktail effect, optimally chosen HEAs could help to enhance the hot corrosion resistance of BCs by forming a more continuous, dense, and uniform thermally grown oxide (TGO). Furthermore, HEAs could help to control the diffusion between the bonding layer and substrate in elevated temperature environments. This paper will discuss the thermodynamic, mechanical, and microstructural behaviour of HEAs. Furthermore, the selection and usage of HEAs as BCs will be explored and compared to conventional BCs in TBC systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".