Characterization of the Surface Properties of Commercially Available Dental Implants Using Scanning Electron Microscopy, Focused Ion Beam, and High‐Resolution Transmission Electron Microscopy
Why this work is in the frame
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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.
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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) | 0.000 | 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 | 0.001 |
| 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