Effect of Microstructure on Oxidation Resistance and TGO Formation in FeCoNiCrAl HEA Coatings Deposited by Low-Temperature HVAF Spraying
Bibliographic record
Abstract
The effects of microstructure, density, and porosity of a FeCoNiCrAl high-entropy alloy (HEA) coating, fabricated using an internal diameter high-velocity air fuel (ID-HVAF) torch (model: i7 ID), on the isothermal oxidation behavior were investigated. This study pioneers the use of the ID-HVAF i7 ID system for HEA bond coat manufacturing, achieving a highly dense microstructure because of its low-operating spray temperature technique. To elucidate these effects, the microstructure and chemistry of the coating, the growth of the thermally grown oxides (TGOs), the phase transformation of alumina, and the oxidation rate were investigated at different temperatures. After 50 h at 1000 °C, 1100 °C, and 1150 °C, a dense, uniform, and thin alumina TGO layer (1.8 μm) was observed. The results demonstrate that the oxidation resistance of the HEA coating is enhanced because of the dense microstructure achieved via HVAF-i7, characterized by low porosity and uniform phase distribution, which contribute to improved barrier properties against oxygen diffusion. The growth of the TGO layer is controlled, resulting in a dense and continuous TGO layer. However, with increasing temperature and time, the alumina TGO layer becomes spalled, which is attributed to the absence of reactive elements. Overall, this study reveals that the FeCoNiCrAl HEA exhibits significant potential for enhancing 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) | 0.000 | 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 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".