Assessment of Bond Integrity, Durability, and Degree of Conversion of a Calcium Fluoride Reinforced Dentin Adhesive
Bibliographic record
Abstract
Our study aimed to synthesize and compare the mechanical properties and dentin interaction of two adhesives; experimental adhesive (EA) and EA containing 5 wt.% calcium fluoride (CaF2) nano-crystals (CaF2 adhesive-CAFA). CaF2 nano-crystals were synthesized by reacting two solutions (containing calcium and fluoride) in a glass chamber using a heated air system. The EA was produced using a mix of monomers, photo-initiators, camphorquinone, and electron initiators. The synthesized CaF2 nano-crystals were centrifuged to guarantee that inside the adhesive there is homogenized dispersion of the filler particles. Their integration in the EA yielded two groups; Gp-1: EA (without CaF2, control) and Gp-2: (5 wt.% CaF2 containing adhesive, CAFA). Sixty teeth were prepared and set to form bonded specimens using the two adhesives. The CaF2 nano-crystals were irregularly shaped with an average particle size of 30–200 nm. The highest μTBS values were obtained for CAFA-non-thermocycled (NTC) samples (32.63 ± 3.15), followed by EA-NTC (31.80 ± 3.75) specimens. On thermocycling (TC), both adhesive groups presented lower μTBS values (CAFA-TC: 29.47 ± 3.33 and EA-TC: 24.04 ± 3.69). Hybrid layer (HL) formation and resin tags of varying depths were perceived for both adhesive groups. The EDX analysis demonstrated the presence of carbon (C), silica (Si), calcium (Ca), and fluoride (F) for CAFA group. Micro-Raman spectroscopy revealed distinct peaks for CaF2 nano-crystals. The CAFA group presented the greatest DC. The addition of CaF2 nano-crystals in the adhesive caused improved bond μTBS and DC. The incorporation also demonstrated suitable dentin interaction, depicted by appropriate HL and resin tag development.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".