Advanced ceramics and coatings for erosion‐related applications in mineral and oil and gas production: A technical review
Bibliographic record
Abstract
Abstract The applications of advanced ceramics, composites and coatings in mineral, mining, fuel production, and processing are reviewed. The materials include oxide and non‐oxide ceramics (specifically SiC‐based), ceramic–ceramic, and ceramic–metal composites, coatings on metallic components where functional application properties can be achieved. Some principles of materials selection, specifically for erosion wear and corrosion applications, and manufacturing are considered. The examples of the successful development and processing of ceramics, coatings, and composites with manageable structures and phase compositions, in the erosion‐related applications, particularly conducted by the author, are discussed and reviewed. Specifically, industrially employed types of ceramics and processing routes were focused on the considered applications. Particular demands for advanced materials with high reliability and complex shapes or for protective coatings on complex shape steel components and long tubing with inner surface protection require novel and optimized processing. The factors affecting erosion and erosion–corrosion resistance and the paths for the erosion resistance enhancement of ceramic and coating materials are considered. Ceramic components design, technology, and installation features are reviewed.
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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 | 0.003 | 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) | 0.004 | 0.002 |
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".