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
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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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) | 0.000 | 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) | 0.001 | 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".