Advanced ceramics and coatings for erosion‐related applications in mineral and oil and gas production: A technical review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it