The Push-Out Bond Strength, Surface Roughness, and Antimicrobial Properties of Endodontic Bioceramic Sealers Supplemented with Silver Nanoparticles
Bibliographic record
Abstract
This study evaluated push-out bond test (POBT), surface roughness, and antimicrobial properties against Enterococcus faecalis of bioceramic sealers supplemented with silver nanoparticles (AgNPs). The sealers tested were CeraSeal®, EndoSequence® BC SealerTM, and Bio-C® Sealer. The POBT was measured with a Universal Testing Machine, and the type of failure was evaluated with a stereomicroscope. The roughness average (Sa) and peak–valley height (Sy) values were evaluated by atomic force microscopy. The bacterial growth inhibition was evaluated using a disk diffusion test, and antimicrobial activity was determined with the plate microdilution method. The POBT showed no significant difference between sealers with and those without NPs in cervical and apical thirds (p > 0.05). In the middle third, the adhesion force was significant for Endosequence BC Sealer® (p < 0.05). The results showed that the Sa and Sy parameters, when AgNPs were added, did not show a statistically significant difference compared to the groups without nanoparticles (p > 0.05). All tested sealers showed bacterial growth inhibition, but no significant difference was found. Their efficacy, in descending order of antibacterial activity when AgNPs were added, is as follows: EndoSequence® BC SealerTM > Bio-C® Sealer > CeraSeal®. The incorporation of AgNPs into bioceramics improves antimicrobial activity without affecting mechanical properties.
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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 | 0.001 | 0.001 |
| 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 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".