A review of nanostructured surfaces and materials for dental implants: surface coating, patterning and functionalization for improved performance
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
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.
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 | 0.002 | 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