Shear Bond Strength of Resin Cement Bonded to Alumina Ceramic after Treatment by Aluminum Oxide Sandblasting or Silica Coating
Bibliographic record
Abstract
PURPOSE: To evaluate the shear bond strength and bond durability between a dual-cured resin cement (RC) and a high alumina ceramic (In-Ceram Alumina), subjected to two surface treatments. MATERIALS AND METHODS: Forty disc-shaped specimens (sp) (4-mm diameter, 5-mm thick) were fabricated from In-Ceram Alumina and divided into two groups (n = 20) in accordance with surface treatment: (1) sandblasting by aluminum oxide particles (50 μm Al(2) O(3) ) (SB) and (2) silica coating (30 μm SiO(x) ) using the CoJet system (SC). After the 40 sp were bonded to the dual-cured RC, they were stored in distilled water at 37°C for 24 hours. After this period, the sp from each group were divided into two conditions of storage (n = 10): (a) 24 h-shear bond test 24 hours after cementation; (b) Aging-thermocycling (TC) (12,000 times, 5 to 55°C) and water storage (150 days). The shear test was performed in a universal test machine (1 mm/min). RESULTS: ANOVA and Tukey (5%) tests noted no statistically significant difference in the bond strength values between the two surface treatments (p = 0.7897). The bond strengths (MPa) for both surface treatments reduced significantly after aging (SB-24: 8.2 ± 4.6; SB-Aging: 3.7 ± 2.5; SC-24: 8.6 ± 2.2; SC-Aging: 3.5 ± 3.1). CONCLUSION: Surface conditioning using airborne particle abrasion with either 50 μm alumina or 30 μm silica particles exhibited similar bond strength values and decreased after long-term TC and water storage for both methods.
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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) | 0.000 | 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".