Application of β-Tricalcium Phosphate in Adhesive Dentin Bonding
Bibliographic record
Abstract
The study aimed at synthesizing β-tricalcium phosphate (β-TCP) nanoparticles and comparing the mechanical properties and dentin interaction of two adhesives: experimental adhesive (EA) and EA with 5 wt.% β-TCP nanoparticles (β-TCP-5%). These filler nanoparticles were synthesized and then characterized with scanning electron microscopy (SEM) and micro-Raman spectroscopy. The β-TCP nanoparticles were incorporated in the adhesives to form two groups: gp-1: EA (control) and gp-2: β-TCP-5%. These adhesives were characterized by SEM, energy-dispersive X-ray (EDX) spectroscopy and were also assessed for their micro-tensile bond strength (μTBS) with (TC) and without thermocycling (NTC). Fourier Transform Infrared (FTIR) spectroscopy was performed to evaluate the degree of conversion (DC) of two adhesives. The β-TCP filler was seen as irregularly shaped agglomerates on SEM. The micro-Raman spectra revealed characteristic peaks associated with β-TCP nanoparticles. Both adhesives presented suitable dentin interaction, which was demonstrated by the formation of resin tags of variable depths. The EDX analysis verified the existence of calcium (Ca) and phosphate (P) for the β-TCP-5% group. The greatest μTBS values were shown by β-TCP-5% group samples when they were non-thermocycled (NTC) (β-TCP-5%-NTC: 34.11 ± 3.46) followed by the thermocycled (TC) samples of the same group (β-TCP-5%-TC: 30.38 ± 3.66), compared with the EA group. Although the DC presented by β-TCP-5% group was comparable to the EA group, it was still lower. The addition of β-TCP nanoparticles in the adhesive improved its μTBS and resulted in a suitable dentin interaction, seen in the form of hybrid layer and resin tag formation. Nonetheless, a decreased DC was observed for the β-TCP-5% adhesive. Future studies probing the effect of different filler concentrations on various properties of the adhesive are warranted.
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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".