Effect of MDP-Based Primers on the Luting Agent Bond to Y-TZP Ceramic and to Dentin
Bibliographic record
Abstract
Purpose. The aim of this study was to evaluate the influence of multimode MDP-based primers and different application protocols on the bond strength of a representative resin cement to an yttrium stabilized tetragonal zirconia (Y-TZP) ceramic.Materials and Methods. The occlusal dentin from 60 human molars was exposed. The teeth and zirconia cylinders (N = 60) (3 mm of diameter; 4 mm of height) were divided into six groups (n = 10) according to the ceramic surface conditioning: (1) air abraded with SiO2particles; (2) Z-Prime Plus; (3) air abraded with SiO2particles + Z-Prime Plus; (4) air abraded with SiO2particles + All-Bond Universal; (5) air abraded with SiO2particles + ScotchBond Universal Adhesive; and (6) untreated zirconia. The luting agent (Duo-Link cement) was applied on the treated dentin surface. Specimens were stored in water (37°C, 24 h) and tested in shear bond strength. Data were statistically analyzed using 2-way ANOVA and Post hoc Tukey tests (α= 0.05).Results. Significant effects of ceramic conditioning were found ( p<0.0001 ). The specimens sandblasted with silica particles followed by the application of Z-Prime Plus or All-Bond Universal presented greater bond strength values. For the untreated zirconia, several specimens failed prematurely prior to testing.Conclusions. Sandblasting with silica particles combined with Z-Prime Plus increased the bond strength.
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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) | 0.001 | 0.001 |
| 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".