Adhesive bond integrity of Y‐TZP post with calcium fluoride infiltrated resin dentin adhesive: An SEM, EDX, FTIR and micro‐Raman study
Bibliographic record
Abstract
The study aimed to synthesize calcium fluoride (CaF2) nanoparticles and compare the push‐out bond strength and viscosity of experimental adhesive (EA) and EA with 5 wt.% CaF2 (CAF‐5%). The CaF2 nanoparticles were synthesized and then characterized with scanning electron microscopy (SEM)‐energy‐dispersive X‐ray spectroscopy (EDX), Fourier transform infrared (FTIR), and micro‐Raman spectroscopies. CaF2 nanoparticles were incorporated in the adhesives to yield two groups; gp‐1: EA‐CAF‐0% (control) and gp‐2: CAF‐5%. Canals of 20 teeth (N = 20) were prepared, and then yttria‐stabilized tetragonal zirconia polycrystalline (Y‐TZP) ceramic posts were cemented. Adhesives were assessed for push‐out bond strength and rheology. CaF2 filler was seen as irregularly shaped agglomerates on SEM. The EDX analysis demonstrated the presence of calcium and fluoride for the CAF‐5% group. The FTIR indicated characteristic bands for CaF2 containing materials. The micro‐Raman spectra of CaF2 nanoparticles demonstrated the presence of CaF2 by showing strong bands at 840 cm−1, 1380 cm‐1, and 1400 cm−1 for fluorine and 950 cm−1 and 1080 cm−1 for calcium ions. The highest push‐out bond strength values were obtained for CAF‐5% group samples (cervical: 9.67 ± 1.46 MPa, apical: 8.66 ± 1.24 MPa), and both adhesives revealed adhesive‐dentin interfacial fractures. The CAF‐5% adhesive also revealed comparable rheological properties with the controls. The addition of CaF2 nanoparticles in the adhesive improved its push‐out bond strength to Y‐TZP post and root dentin, although CAF‐5% showed reduced viscosity on rheological assessment (compared with the controls).
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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 | 0.001 | 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".