The effect of five kinds of surface treatment agents on the bond strength to various ceramics with thermocycle aging
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Bibliographic record
Abstract
This study evaluated the effects of ceramic surface treatment agents on shear bond strengths to ceramic materials with and without thermocycling. Ceramic plates were prepared from feldspathic ceramic; AAA, lithium disilicate ceramic material; IPS e.max Press, zirconia ceramic; Lava. Ceramic surfaces were pretreated with one of five surface treatment agents (Clearfil PhotoBond mixed with Porcelainbond activator (PB), Clearfil SE One mixed with Porcelainbond activator (SO), Ceramic Primer (CP), Universal Primer (UP), Scotchbond Universal (SU)), and then a resin cement (Clapearl DC) was filled. After 0, 5,000, and 10,000 thermocycles, micro-shear bond strengths between ceramic-cement interfaces were determined. SU exhibited significantly lower initial bond strength to AAA and e.max than PB, SO, CP, and UP. For Lava, PB, SO, CP and SU exhibited higher initial bond strengths than UP. Thermocycles reduced bond strengths to all the ceramic materials with any surface treatment.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 it