Processing Strategies for Tailoring Ceramic-Based Nanostructured Thermal Spray Coatings
Bibliographic record
Abstract
Abstract New and more demanding applications and higher performance requirements are creating the need for a greater degree of sophistication in engineering coating structures. The use of nanostructured feedstocks provides the possibility of tailoring the structure of thermal spray ceramic-based coatings at the nanoscale. In the present study, it has been found that such an approach can produce coatings with enhanced mechanical, thermal and bioperformance characteristics. It has been shown that the internal structure and external size of agglomerates as well as the spray conditions employed for deposition play a key role in determining the nature and extent of zones of nanostructured material produced in coatings. The characteristics (such as porosity and bonding) of these zones can have an important effect on the coating performance. This approach has been used to tailor Al2O3-TiO2, ZrO2-Y2O3, WC-Co, TiO2, and hydroxyapatite coatings targeted for use as abradables, for TBCs, against wear and on orthopedic implants.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 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".