A review of nanostructured surfaces and materials for dental implants: surface coating, patterning and functionalization for improved performance
Bibliographic record
Abstract
The emerging field of nanostructured implants has enormous scope in the areas of medical science and dental implants. Surface nanofeatures provide significant potential solutions to medical problems by the introduction of better biomaterials, improved implant design, and surface engineering techniques such as coating, patterning, functionalization and molecular grafting at the nanoscale. This review is of an interdisciplinary nature, addressing the history and development of dental implants and the emerging area of nanotechnology in dental implants. After a brief introduction to nanotechnology in dental implants and the main classes of dental implants, an overview of different types of nanomaterials (i.e. metals, metal oxides, ceramics, polymers and hydrides) used in dental implant together with their unique properties, the influence of elemental compositions, and surface morphologies and possible applications are presented from a chemical point of view. In the core of this review, the dental implant materials, physical and chemical fabrication techniques and the role of nanotechnology in achieving ideal dental implants have been discussed. Finally, the critical parameters in dental implant design and available data on the current dental implant surfaces that use nanotopography in clinical dentistry have been discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".