Plasma Spray Deposition of Hydroxyapatite Coatings from Sol Precursors
Bibliographic record
Abstract
Plasma spray deposition of hydroxyapatite (HA) coatings is a well established commercial process. When deposited on metallic substrates, these coatings have been shown to promote bone fixation and osteconductivity. A concern with current coatings is the formation of relatively large debris particles during resorption. The size of the debris is related to the particle size of the powder injected into the plasma during the deposition process. The use of solution precursors or dispersions of fine particle size powders as the feedstock for plasma spraying has been shown to produce submicron/nanocrystalline structured coatings from relatively inexpensive precursors. Nanocrystalline HA coatings may improve the resorption of the coating in the body, avoiding the irritant effect of large particles which may be seen in current thermal sprayed HA coatings. We have explored the use of sols prepared from several different precursors as the feedstock for the plasma spray deposition of HA coatings on Ti6Al4V substrates, employing statistical design of experiments to establish optimal deposition conditions. We report on the formation and the characteristics of the coatings as a function of the deposition parameters. The presence of different Ca-P crystalline and amorphous phases was assessed by X-ray diffraction analysis. The coating microstructure was characterized by scanning/transmission electron microscopy. The suitability of the technique to coat biomedical implants is discussed.
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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) | 0.000 | 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".