Adhesive Bond Integrity of Experimental Zinc Oxide Nanoparticles Incorporated Dentin Adhesive: An SEM, EDX, μTBS, and Rheometric Analysis
Bibliographic record
Abstract
Objective. Our study is aimed at preparing an experimental adhesive (EA) and assessing the influence of adding 5-10 wt.% concentrations of zinc oxide (ZnO) nanoparticles on the adhesive’s mechanical properties. Methods. Field emission scanning electron microscopy (FESEM) and energy dispersive X-ray (EDX) spectroscopy were employed to investigate the morphology and elemental distribution of the filler nanoparticles. To examine the adhesive properties, microtensile bond strength (μTBS) testing, an investigation of the rheological properties, degree of conversion (DC), and analysis of the interface between the adhesive and dentin were carried out. Results. The SEM micrographs of ZnO nanoparticles demonstrated spherical agglomerates. The EDX plotting confirmed the incidence of Zn and oxygen (O) in the ZnO nanoparticles. The highest μTBS was observed for nonthermocycled (NTC) 5 wt.% ZnO group ( 32.11 ± 3.60 MPa), followed by the NTC-10 wt.% ZnO group ( 30.04 ± 3.24 MPa). Most of the failures observed were adhesive in nature. A gradual reduction in the viscosity was observed at higher angular frequencies, and the addition of 5 and 10 wt.% ZnO to the composition of the EA lowered its viscosity. The 5 wt.% ZnO group demonstrated suitable dentin interaction by showing the formation of resin tags, while for the 10 wt.% ZnO group, compromised resin tag formation was detected. DC was significantly higher in the 0% ZnO (EA) group. Conclusion. The reinforcement of the EA with 5 and 10 wt.% concentrations of ZnO nanoparticles produced an improvement in the adhesive’s μTBS. However, a reduced viscosity was observed for both nanoparticle-reinforced adhesives, and a negotiated dentin interaction was seen for 10 wt.% ZnO adhesive group. Further research exploring the influence of more filler concentrations on diverse adhesive properties is recommended.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".