Nanoleakage for Self-Adhesive Resin Cements used in Bonding CAD/CAD Ceramic Material to Dentin
Bibliographic record
Abstract
OBJECTIVES: To determine nanoleakage of CAD/CAM ceramic blocks bonded to dentin with self-adhesive resin cement. METHODS: Eighteen sound extracted human molars were sterilized and sectioned into 3 mm-thick dentin sections. Trilux Cerec Vitablocks (Vita) were also sectioned into 3 mm sections, surface-treated using 5% hydrofluoric acid-etchant, and then coated with silane primer (Vita). Trilux and dentin sections were cemented together by means of three resin cements: Rely-X Unicem (3M/ESPE), BisCem (Bisco), and Calibra (Dentsply), according to manufacturers' recommendations. Calibra was used in conjunction with Prime/Bond-NT adhesive (Dentsply), while the other two are self-adhesive. The bonded specimens were stored for 24h in distilled water at 37°C. Specimens were vertically sectioned into 1 mm-thick slabs, yielding up to six per specimen. Two central slabs were randomly chosen from each specimen making up the cement groups (n=12). Each group was subdivided into two subgroups (n=6), a control and a thermocycled subgroup (5-55°C) for 500 cycles. Slabs were coated with nail polish up to 1 mm from the interface, immersed in a 50% silver nitrate solution for 24h, and tested for nanoleakage using Quanta Environmental SEM and EDAX. Data were statistically analyzed using two-way ANOVA and Tukey's post-hoc tests. RESULTS: Rely-X Unicem and Calibra groups demonstrated no significant difference in the percentage of silver penetration, while the BisCem group revealed a significantly higher percentage (P≤.05). Thermocycling (500 cycles) did not have a statistically significant effect on the percentage of silver penetration (P>.05). CONCLUSIONS: One self-adhesive-resin cement demonstrated a similar sealing ability when compared with a standard resin cement. Thermo-cycling did not significantly increase dye penetration under the test conditions.
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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 | 0.001 | 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".