Effect of Water Storage on the Flexural Strength of Four Self-etching Adhesive Resin Cements and on the Dentin-titanium Shear Bond Strength Mediated by Them
Bibliographic record
Abstract
AIM: The aim of this study was to evaluate the effect of water storage on the flexural strength (σf) of four self-etching adhesive resin cements (SEARC) and on the dentin-titanium shear bond strength (SBS) mediated by them. MATERIALS AND METHODS: The selected SEARC were Rely X Unicem, G-Cem, Maxcem, and SmartCem2. For each material, 50 bars (2×2×25 mm) were made and stored in water at 37°C for 1 hour, 1 day, 7 days, 30 days, and 60 days before σf was determined via a three-point bend test. Titanium cylinders were bonded to freshly exposed human dentin surfaces using the selected cements. Fifty samples were obtained for each SEARC and were stored in water at 37°C for 1 hour, 1 day, 7 days, 30 days, and 60 days before SBS was determined. The results were statistically analyzed using two-way analysis of variance followed by Scheffé multiple means comparisons (α=0.05). Pearson's correlation coefficient between σf and SBS was determined. RESULTS: Significantly different σf and SBS values were obtained for the four cements. With regards to the effect of water storage, the σf of all materials increased during the first 7 days, was not significantly different between materials by 30 days, and then remained relatively constant or decreased for SmartCem2; SBS was not affected by water storage, with the exception of Maxcem, where a significant drop in SBS was detected after 1 day and no deterioration thereafter. No correlation was found between σf and SBS. CONCLUSIONS: Under the experimental conditions of this study, 60 days of water storage negatively affected the σf of SmartCem2 but did not negatively affect the SEARC-mediated dentin-titanium SBS (Maxcem showed a significant drop in SBS after 1 day but no deterioration thereafter). The dentin-titanium adherence afforded by Rely X and G-Cem was significantly higher than that of Maxcem and SmartCem2.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".