Ceramic coatings for extreme environments and energy systems: A review
Bibliographic record
Abstract
This paper provides a comprehensive review of wear-resistant ceramic coatings used in extreme environments, such as oil and gas operations, thermal barrier coatings, energy, and industrial applications. It explores various material classes, including oxides, carbides, nitrides, and borides, focusing on their thermal stability, mechanical strength, and resistance to oxidation and wear. The study discusses different deposition techniques, including chemical vapor deposition (CVD), physical vapor deposition (PVD), and plasma spraying, highlighting their advantages and challenges. Key challenges, including brittleness, adhesion issues, and high-temperature oxidation, were explained in detail, along with emerging solutions like high-entropy ceramics, self-healing materials, and computational modeling. The integration of smart monitoring systems and advanced fabrication methods is demonstrated as a promising way for optimizing the durability and performance of ceramic coatings. This review also aims to bridge the existing knowledge gaps, offering insights into the latest advancements and future directions in the development of high-performance ceramic coatings for 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".