Influence of three antiseptic mouthwashes on microtensile bond strength of self-etch adhesive to dentin: Rinsing versus non-rinsing with water
Bibliographic record
Abstract
Objective: The purpose of this study was to examine the effect of several antiseptic mouthwashes on the dentin microtensile bond strength of self-etch adhesive, considering the influence of rinsing with water. Methods: Forty flat dentin surfaces were produced by sectioning sound extracted third molars. They were randomly divided into four main groups: (1) artificial saliva (control); (2) 1 % hydrogen peroxide; (3) 0.2 % povidone-iodine; and (4) 0.12 % chlorhexidine. Each group was tested with and without rinsing with distilled water. Subsequently, a nanofilled resin composite was applied to the bonded dentin using the self-etch adhesive for producing resin-dentin sticks. The specimens were sectioned for the microtensile bond strength assessment. Failure modes were evaluated using a stereomicroscope. The dentin surfaces treated with three mouthwashes were analyzed using an X-ray diffractometer. Results: The rinsing control group exhibited significantly greater microtensile bond strength than the povidone-iodine and chlorhexidine groups. However, it did not differ from the hydrogen peroxide group. The non-rinsing groups displayed no significant differences in bond strength, except for povidone-iodine, which showed lower value than the remaining groups. In all the rinsing groups, noticeably higher microtensile bond strengths were observed when compared to the non-rinsing groups in all types of antiseptic mouthwashes. Conclusion: Antiseptic mouthwashes affected the dentin microtensile bond strength of self-etch adhesive. Rinsing with water significantly enhanced the bond strength. Hydrogen peroxide exhibited the greatest microtensile bond strength in the rinsing group, while chlorhexidine and hydrogen peroxide performed effectively in the non-rinsing group.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".