Fatigue Life of Bioactive Titanium Dental Implants Treated by Means of Grit‐Blasting and Thermo‐Chemical Treatment
Bibliographic record
Abstract
OBJECTIVE: This study focuses on the fatigue behavior of titanium dental implants as-received, with a grit-blasted surface and with a new bioactive surface treatment (2Steps). BACKGROUND: The 2Step process consists of (1) an initial grit-blasting process to produce a micro-rough surface, followed by (2) a combined thermo-chemical treatment that produces a potentially bioactive surface, that is, that can form an apatitic layer when exposed to biomimetic conditions in vitro. The 2Step treatment produced micro-rough and apatitic coating implants. METHODS: Residual stresses were determined by means of X-ray diffraction. The fatigue tests were carried out at 37°C on 500 dental implants, and the S-N curve was determined. The fatigue-crack nucleation for the different treatments was analyzed. RESULTS: The fatigue tests show that the grit-blasting process improves the fatigue life. This is a consequence of the layer of compressive residual stresses that the treatment generates in titanium surfaces. Dental implants that had its surfaced prepared with the 2Step procedure (grit-blasting and thermo-chemical treatment) had its fatigue life decreased by 10% due to the incorporation of oxygen to the surface and the relaxation of the compressive residual stress produced by the heat treatment. CONCLUSIONS: Thermo-chemical treatment is an excellent compromise between the improvement of bioactive and mechanical long-life behaviors.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".