Osseointegration of Ti–6Al–4V Alloy Implants with a Titanium Nitride Coating Produced by a PIRAC Nitriding Technique: A Long-Term Time Course Study in the Rat
Bibliographic record
Abstract
This study examined bone tissue responses to Ti-6Al-4V alloy implants with a hard TiN coating applied by an original powder immersion reaction-assisted coating (PIRAC) nitriding method. Progression of implant fixation in the distal epiphysis and within the medullary cavity of the rat femur was evaluated between 3 days and 6 months postimplantation by scanning electron microscopy, oxytetracycline incorporation, and histochemistry. After 6 months, successful osseointegration was achieved in both epiphyseal and diaphyseal sites. Throughout, implant portions located within the epiphysis remained in close contact with bone trabeculae that gradually engulfed the implant forming a bone collar continuous with the trabecular network of the epiphysis. In the diaphysis, woven bone was first formed within the marrow cavity around the implant and later was replaced by a shell of compact bone around the implant. In general, higher osseointegration rates were measured for TiN-coated versus the uncoated implants, both in the epiphysis and in the diaphysis. In conclusion, our findings indicate an excellent long-term biocompatibility of TiN coatings applied by the PIRAC nitriding technique and superior osteoinductive ability in comparison with uncoated Ti-6Al-4V alloy. Such coatings can, therefore, be considered for improving the corrosion and wear resistance of titanium-based 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".