Shear bond strength between different materials bonded with two resin cements
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to compare the shear bond strength between Ni-Cr alloy specimens bonded to air-abraded Ni-Cr, bur-abraded Ni-Cr, etched ceramic and etched enamel substrates using the resin cements RelyX ARC or Enforce. MATERIALS AND METHODS: Ni-Cr specimens were made and sandblasted with Al(2)O(3) airborne-particles. Disc-shaped patterns were made for each of the four experimental substrates: Ni-Cr treated with Al(2)O(3) airborne-particles, Ni-Cr treated with diamond bur abrasion, etched enamel and etched ceramic. RESULTS: Significant differences in shear bond strength were found between the different materials and luting agents evaluated. The Ni-Cr alloy cylinders bonded to Ni-Cr surfaces sandblasted with 50 μm Al(2)O(3) particles and bonded with Enforce achieved the highest bond strength when compared with other substrates (28.9 MPa, p < 0.05). Bur-abraded metal discs had lowest values, regardless the cement used (2.9 and 6.9 MPa for RelyX and Enforce, respectively). Etched enamel and etched ceramic had similar shear bond strengths within cement groups and performed better when RelyX was used. CONCLUSIONS: Bonding Ni-Cr to Ni-Cr and ceramic may result in similar and higher bond strength when compared to Ni-Cr/enamel bonding. For metal/metal bonding, higher shear bond strength was achieved with resin cement Enforce, and for metal/ceramic and metal/enamel bonding, RelyX had higher results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".