High-Entropy Alloy Coatings Deposited by Thermal Spraying: A Review of Strengthening Mechanisms, Performance Assessments and Perspectives on Future Applications
Bibliographic record
Abstract
Thermal spray deposition techniques have been well-established, owing to their flexibility in addressing degradation due to wear and corrosion issues faced due to extreme environmental conditions. With the adoption of these techniques, a broad spectrum of industries is experiencing continuous improvement in resolving these issues. To increase industrial-level implementation, state-of-the-art advanced materials are required. High-entropy alloys (HEAs) have recently gained considerable attention within the scientific community as advanced materials, mainly due to their exceptional properties and desirable microstructural features. Unlike traditional material systems, high-entropy alloys are composed of multi-component elements (at least five elements) with equimolar or nearly equimolar concentrations. This allows for a stable microstructure that is associated with high configurational entropy. This review article provides a critical assessment of different strengthening mechanisms observed in various high-entropy alloys developed by means of deposition techniques. The wear, corrosion, and oxidation responses of these alloys are reviewed in detail and correlated to microstructural and mechanical properties and behavior. In addition, the review focused on material design principles for developing next-generation HEAs that can significantly benefit the aerospace, marine, oil and gas, nuclear sector, etc. Despite having shown exceptional mechanical properties, the article describes the need to further evaluate the tribological behavior of these HEAs in order to show proof-of-concept perspectives for several industrial applications in extreme environments.
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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) | 0.002 | 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".