Hydroxyapatite nanoparticles coating on Ti-6Al-4V substrate using plasma spray method
Bibliographic record
Abstract
In this research, hydroxyapatite powder particles were coated on a Ti-6Al-4V alloy substrate by the plasma spraying method to create bone implants by benefitting from titanium's mechanical properties and hydroxyapatite's biological properties. Hydroxyapatite prepared by the plasma spraying method suffers from decomposition, formation of other calcium phosphate phases, weak adhesion to the substrate, and microcrack formation in the coating due to residual stresses initiated by the high temperature of the coating process. To improve the hydroxyapatite coating properties, the Ti-6Al-4V alloy was preheated, and then hydroxyapatite coating took place. The results showed that residual stress in the interface decreased, and adhesion improved by preheating the substrate. However, forming an amorphous phase on coating was observed, resulting in a higher dissolution rate in the biological environment and weak mechanical properties compared to crystalline hydroxyapatite. Experimental results showed that heat treatment after the coating process decreased the amount of this amorphous phase and heightened the crystallinity of the coating by up to more than 60%.
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.
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".