Effect of the Number of Applications of Acetone-based Adhesives on Microtensile Bond Strength and the Hybrid Layer
Bibliographic record
Abstract
PURPOSE: The current study was carried out to evaluate the effect of doubling the adhesive layers of three acetone-based adhesives on the microtensile bond strength and ultra morphological characterization of the resin dentin interface using SEM. MATERIALS AND METHODS: A total of 27 caries-free human molars were used. Superficial flat dentin surfaces were obtained by wet grinding the buccal surfaces. Three adhesive systems Prime & Bond NT (G1), XENO IV (GII) and G BOND (GIII) were used according to three different protocols: (A) according to the manufacturer's instructions, (B) doubling the adhesive layers and light curing and (C): doubling the adhesive layers with intermediate curing between each layer. Resin composite buildups were made using TPH Spectrum resin composite on the bonded surfaces in 1 mm light cured increments for 40 seconds each. The bonded teeth were sectioned to obtain sticks for microtensile testing. The testing was conducted using a universal testing machine at a crosshead speed of 1 mm/minute. The microtensile bond strength means and standard deviations were calculated and the data were statistically analyzed using Two-way Analysis of Variance (ANOVA) and Tukey's post-hoc tests (p < or = 0.05). Ultra morphological characterization of the resin-dentin interface and representative fractured dentin specimens were examined using SEM. RESULTS: When applied according to the manufacturer's instructions, Prime & Bond NT (total etch adhesive) showed significantly high microtensile bond strength with a mean value +/- SD of 35 +/- 12.7 MPa followed by XENO IV (21.2 +/- 9.4 MPa), while G BOND presented a significantly lower mean value (10.9 +/- 2.9 MPa). Doubling the adhesive layers significantly reduced the mean strength of the total etch adhesive system; in contrast, it significantly increased the bond strength of both self-etch adhesives. The relatively thicker adhesive layer was seen with the total-etch adhesive when the application was doubled, while the hybrid layer appeared thicker with self-etch adhesives. CONCLUSIONS: Doubling the adhesive layer applications significantly improved the bond strength of the two self-etch adhesives (XENO IV and G BOND); however, it had a negative effect on the bond strength of the total-etch adhesive (Prime & Bond NT).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".