Tribological Performance of High-Entropy Coatings (HECs): A Review
Bibliographic record
Abstract
Surface coatings that operate effectively at elevated temperatures provide compatibility with critical service conditions as well as improved tribological performance of the components. High-entropy coatings (HECs), including metallic, ceramics, and composites, have gained attention all over the world and developed rapidly over the past 18 years, due to their excellent mechanical and tribological properties. High-entropy alloys (HEAs) are defined as alloys containing five or more principal elements in equal or close to equal atomic percentage. Owing to the high configurational entropy compared to conventional alloys, HEAs are usually composed of a simple solid solution phase, such as the BCC and FCC phases, instead of complex, brittle intermetallic phases. Several researchers have investigated the mechanical, oxidation, corrosion and wear properties of high-entropy oxides, carbides, borides, and silicates using various coating and testing techniques. More recently, the friction and wear characteristics of high-entropy coatings (HECs) have gained interest within various industrial sectors, mainly due to their favourable mechanical and tribological properties at high temperatures. In this review article, the authors identified the research studies and developments in high-entropy coatings (HECs) fabricated on various substrate materials using different synthesis methods. In addition, the current understanding of the HECs characteristics is critically reviewed, including the fabrication routes of targets/feedstock, synthesis methods utilized in various research studies, microstructural and tribological behaviour from room temperature to 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".