High Entropy Oxides as Promising Materials for Thermal Barrier Topcoats: A Review
Bibliographic record
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 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 developed for structural and functional applications, and different types of 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-earth elements. 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 is 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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".