Improving the Clinical Performance of Dental Implants Through Advanced Surface Treatments: The Case of Ti and ZrO2 Coatings
Bibliographic record
Abstract
This review summarizes the development of surface treatments applied to dental implants with the aim of improving their clinical performance. It covers the advancement of various techniques, from the conventional to the more advanced ones. Among the recent advancements, surface texturing has enabled atomic and structural modifications of implant surfaces at the micro- and nanoscales, improving tissue–material interactions. Acid etching and atomic layer deposition applied onto implant surfaces results in optimized osseointegration by stimulating the deposition and proliferation of osteoblasts and fibroblasts. The atomic layer deposition of TiO2, ZnO, ZrO2, and CaCO3 has proven effective in improving osseointegration and tackling corrosion. Corrosion is still an important issue, whereby metals released from titanium implants and their associated degradation products cause local and systemic side effects, leaving a wide avenue for future research. The development of hybrid dental implants is envisaged through new materials and technologies, such as additive manufacturing, which may play a critical role in the fabrication of patient-specific implants with tailored nano-topography capable of enhancing such properties as antibacterial activity and osseointegration.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".