High Entropy Oxides as Promising Materials for Thermal Barrier Topcoats—A Review
Bibliographic record
Abstract
Abstract Multi-layered thermal barrier coatings (TBCs) are deposited on gas-turbine metallic components to protect them against high temperatures, oxidation, and corrosion. However, TBCs have limited working temperatures and lifetimes due to their material properties. Several approaches are being tested to increase TBC topcoats' phase stability and properties. Increasing entropy to stabilize phases is a concept introduced in 2004 and required decreasing the Gibbs free energy. Many high-entropy ceramics are being developed for structural and functional applications, and high-entropy oxides (HEOs) are promising TBC ceramics due to their unique characteristics. HEOs are single-phase solid solutions that contain five or more cations, usually a mixture of transition metals and rare earths. Due to the cocktail effect, the final material has a different behavior from its constituents, making it a viable method to improve the properties of traditional materials. Generally, high entropy materials are characterized by three additional phenomena: sluggish diffusion, severe lattice distortion, and high entropy. A review of possible improvements in the lifetime of TBC topcoats using different HEOs in terms of their composition, properties, and stability are presented here. Different HEOs are then examined and various thermophysical properties, high-temperature stability, and sintering resistance are discussed.
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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 | 0.001 | 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 | 0.001 |
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 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".