Application of Oxygen Ion Implantation to Titanium Surfaces: Effects on Surface Characteristics, Corrosion Resistance, and Bone Response
Bibliographic record
Abstract
BACKGROUND: The surface oxide layer of titanium plays a decisive role in determining biocompatibility. However, there are some reports demonstrating that the natural oxide film may not be sufficiently protective in the aggressive biologic environment. PURPOSE: The goal of this study was to examine the effectiveness of a thick oxide layer on corrosion resistance in vitro and the bone formation around titanium implants in vivo. MATERIALS AND METHODS: A plasma source ion implantation (PSII) method was used to increase the thickness of the surface oxide layer. Several instruments were employed to confirm the surface properties before and after the surface modification. Potentiodynamic polarization measurements in a phosphate-buffered saline (PBS) solution were carried out to investigate corrosion resistance in vitro. Bone formation around this surface-modified specimen was examined in a rabbit model and assessed in histomorphometry. RESULTS: Improved corrosion resistance was demonstrated by the potentiodynamic polarization measurements. Light microscopic histomorphometry showed that all implants were in contact with bone and had some proportion of bone within the threads at 4 weeks; however, there were no significant differences compared with as-machined controls. CONCLUSIONS: The results indicate that in spite of improved corrosion resistance in vitro, a thick oxide layer fabricated with the PSII method does not influence early bone formation around titanium implants in vivo.
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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".