Effect of chemical and thermochemical treatments on the surface properties of commercially pure titanium
Bibliographic record
Abstract
Chemical and physical methods have been generally used for modifying the surface of titanium implants with the aim of achieving better osseointegration.In this work, an evaluation of the effect of different chemicals and thermochemical treatments on the surface modification of titanium was carried out.Plates of commercially pure titanium were polished and treated with four different methods.Scanning electron microscopy, atomic force microscopy, energy-dispersive X-ray spectroscopy, infrared spectroscopy, X-ray diffraction and drop shape analysis were used to characterize the modified titanium surface.Our results showed that the AEPTTT surface exhibited the highest Ra values followed by AEAT, AEPT, polished Ti and AE samples.In addition, the AEAT and AEPTTT treatments developed the largest surface area, while the AE treatment produced the lowest surface area.Furthermore, the AEAT treatment created the most hydrophilic surface, which is known to positively affect the osseointegration rate of dental implants.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".