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
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
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.
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 | 0.001 | 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