Effect of Mechanical Fatigue on the Bond Between Zirconia and Composite Cement.
Bibliographic record
Abstract
PURPOSE: To examine the effect of mechanical fatigue on the bond strength of resin composite cemented to silica-coated yttria-tetragonal zirconia polycrystal ceramic (Y-TZP). MATERIALS AND METHODS: Ten Y-TZP blocks were polished down to 600-grit silicon carbide paper. Specimens were silica coated by airborne-particle abrasion with 30-μm silica-modified Al2O3 particles. Blocks were cleaned in an ultrasonic bath, and a dental adhesive was applied and light cured for 20 s. Pre-cured composite blocks were luted to treated Y-TZP surfaces with a dual-curing resin cement. Half of the samples (n = 5) were subjected to mechanical fatigue before trimming (fatigue group) and the other half tested 24 h after bonding procedures (control group). Forty-five beam-shaped samples with an approximately 1 mm2 cross-sectional area were prepared for each group and tested in microtensile mode at 0.5 mm/min. Fractographic analysis was performed by optical and scanning electron microscopy. Only specimens that failed at the interface area were considered for statistical analysis. Weibull distribution (95% confidence bounds) was used to determine the characteristic strength (σ0 in MPa) and Weibull modulus (m) for each group. Probability of survival was calculated over the range of loads until specimens failed. RESULTS: The control group showed σ0 = 45.91 MPa and m = 7.98, and the fatigue group σ0 = 43.94 MPa and m = 6.44 (p > 0.05). The probability of survival did not differ significantly between groups. CONCLUSIONS: Fatigue did not affect the bond strength between silica-treated Y-TZP intaglio surfaces and composite cement under these experimental conditions.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".