Dentin Bond Integrity of Filled and Unfilled Resin Adhesive Enhanced with Silica Nanoparticles—An SEM, EDX, Micro-Raman, FTIR and Micro-Tensile Bond Strength Study
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Bibliographic record
Abstract
The objective of this study was to synthesize and assess unfilled and filled (silica nanoparticles) dentin adhesive polymer. Methods encompassing scanning electron microscopy (SEM)—namely, energy dispersive X-ray spectroscopy (EDX), micro-tensile bond strength (µTBS) test, Fourier transform infrared (FTIR), and micro-Raman spectroscopy—were utilized to investigate Si particles’ shape and incorporation, dentin bond toughness, degree of conversion (DC), and adhesive–dentin interaction. The Si particles were incorporated in the experimental adhesive (EA) at 0, 5, 10, and 15 wt. % to yield Si-EA-0% (negative control group), Si-EA-5%, Si-EA-10%, and Si-EA-15% groups, respectively. Teeth were set to form bonded samples using adhesives in four groups for µTBS testing, with and without aging. Si particles were spherical shaped and resin tags having standard penetrations were detected on SEM micrographs. The EDX analysis confirmed the occurrence of Si in the adhesive groups (maximum in the Si-EA-15% group). Micro-Raman spectroscopy revealed the presence of characteristic peaks at 638, 802, and 1300 cm−1 for the Si particles. The µTBS test revealed the highest mean values for Si-EA-15% followed by Si-EA-10%. The greatest DC was appreciated for the control group trailed by the Si-EA-5% group. The addition of Si particles of 15 and 10 wt. % in dentin adhesive showed improved bond strength. The addition of 15 wt. % resulted in a bond strength that was superior to all other groups. The Si-EA-15% group demonstrated acceptable DC, suitable dentin interaction, and resin tag formation.
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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 | 0.000 | 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 it