Enhancement of Adhesion between Resin Coating Materials and Resin Cements
Bibliographic record
Abstract
Resin coating technique is a unique method that improves the dentin bond strength of resin cements in indirect restorations. However, the weak link of a specimen bonded using the resin coating technique was reported to be the bonded interface between the resin coating material and resin cement. The purpose of this study, therefore, was to enhance the bonding performance between a resin coating material and a resin cement. Two light-cured flowable composites, Protect Liner F and Clearfil Flow FX, were used as coating materials, and two dual-cure composite materials, Panavia F 2.0 and Clearfil DC Core Automix, were used as resin cements. The ultimate tensile strength of each material and the microtensile bond strengths of the bonded specimens of resin coating material and resin cement were measured using a crosshead speed of 1.0 mm/min. Three-way ANOVA (p=0.05) revealed that the highest microtensile bond strength was obtained using a combination of Clearfil Flow FX and Clearfil DC Core Automix, and when the surface of the coating material was treated with ED Primer II. It was strongly suggested that materials with a higher ultimate tensile strength, when used in both resin coating and cementation, could enhance the bond strength between the two.
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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 | 0.000 |
| 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 | 0.000 | 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".