Nanotechnological Development and Evaluation of Super-Hydrophobic Titanium Surfaces for Use in Dental Abutments: A Proof of Concept Study
Bibliographic record
Abstract
Evidence shows that biofilm on dental implant abutments can promote inflammation and bone loss and threaten implant success/survival. The interaction of biofilm with the dental implant surface has become crucial in understanding oral health. Biofilm adhesion shows a direct positive correlation with surface hydrophobicity of the upper and lower sections of the dental implant. The aim of this study was (a) to develop a super-hydrophobic titanium (Ti6Al4V) surface by treating/changing titanium surface topography, and (b) to evaluate the initial oral (salivary proteins) pellicle/biofilm formation on the treated/changed titanium surfaces compared with untreated Ti6Al4V surfaces (control). Two types of treatment of titanium (Ti6Al4V) surfaces were compared. In one case the thin film of synthetic polymer applied to the surface was polycaprolactone (PCL) and, in the second case, polystyrene (PS). Untreated samples were used as a control. Our results demonstrate that Ti6Al4V surfaces treated with a thin film of PS have more promise for dental implants because of their greater hydrophobicity compared to PCL and may be a suitable surface for manufacturing abutments because of the potential for a low colonization of pellicle/biofilm.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".