The chemical surface evaluation of black and white porous titanium granules and different commercial dental implants with energy‐dispersive x‐ray spectroscopy analysis
Bibliographic record
Abstract
BACKGROUND: The chemical surface structure of the porous titanium grafts has not been found to study in the literature on the similarity of chemical surfaces of different commercial dental implants. PURPOSE: The purpose of this study is to investigate the chemical composition and surface energies of white (WPTG) and black porous titanium granules (PTG) by energy dispersive x-ray spectrometry (EDX) analysis to compare with different commercial dental implant surface. MATERIALS AND METHODS: The surface chemical compositions of six commercially available dental implants with different surface structures, PTG and WPTG were examined by EDX analysis. Surface analyzes were performed on the apical, middle, and coronal parts of each implant and on the top, flank, and valley regions on each side. Surface analyzes of dental implants were evaluated at ×200 and ×2000 magnifications. The EDX evaluation of PTG grafts were evaluated at ×250, ×2000, ×5000, and ×50 000 magnifications. RESULTS: PTG grafts showed elements of Na (8.88 ± 9.98%), Cl (2.44 ± 1.96%), and Al (0.99 ± 0.37%) as well as Ti (90.06 ± 11.34%) molecule at ×5000 magnification. In WPTG, Ti (%34.55 ± 6.41%) and O (%65.44 ± 6.42%) molecules were detected. CONCLUSIONS: It has been found that PTG surface was not made of pure titanium, it has different chemical molecules at larger magnifications. Cell culture and experimental studies are needed to establish a relationship between the different commercial implants and the surface structure of the titanium granules.
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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 | 0.000 |
| 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 | 0.000 | 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".