Characterization of the Surface Properties of Commercially Available Dental Implants Using Scanning Electron Microscopy, Focused Ion Beam, and High‐Resolution Transmission Electron Microscopy
Bibliographic record
Abstract
BACKGROUND: Since osseointegration of the respective implant is claimed by all manufacturing companies, it is obvious that not just one specific surface profile including the chemistry controls bone apposition. PURPOSE: The purpose was to identify and separate out a particular set of surface features of the implant surfaces that can contribute as factors in the osseointegration process. MATERIAL AND METHODS: The surface properties of several commercially available dental implants were extensively studied using profilometry, scanning electron microscopy, and transmission electron microscopy. Ultrathin sections prepared with focused ion beam microscopy (FIB) provided microstructural and chemical data which have not previously been communicated. The implants were the Nobel Biocare TiUnite (Nobel Biocare AB, Göteborg, Sweden), Nobel Biocare Steri-Oss HA-coated (Nobel Biocare AB, Yorba Linda, CA, USA), Astra-Tech OsseoSpeed (Astra Tech AB, Mölndal, Sweden), Straumann SLA (Straumann AG, Waldenburg, Switzerland), and the Brånemark Integration Original Fixture implant (Brånemark Integration, Göteborg, Sweden). RESULTS: It was found that their surface properties had differences. The surfaces were covered with crystalline TiO(2) (both anatase and rutile), amorphous titanium oxide, phosphorus doped amorphous titanium oxide, fluorine, titanium hydride, and hydroxyapatite, respectively. CONCLUSION: This indicates that the provision of osseointegration is not exclusively linked to a particular set of surface features if the implant surface character is a major factor in that process. The studied methodology provides an effective tool to also analyze the interface between implant and surrounding bone. This would be a natural next step in understanding the ultrastructure of the interface between bone and implants.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".