Evaluation of the flexural strength of orthodontic acrylic resin incorporated with propolis nanoparticles: an in vitro study
Bibliographic record
Abstract
Aim: Nanopropolis has become the subject of interest in medicine and dentistry as a natural product due to its outstanding properties, particularly antimicrobial activity. This study aimed at investigating the effect of nanopropolis on flexural strength of polymethyl methacrylate (PMMA). Materials and methods: Three groups of two acrylic resin brands namely Acropars and Triplex containing 0 (control group), 0.5%, and 1% of nanopropolis were prepared in 64×10.0×3.3 mm according to ISO 20795-2 (2013). Fifteen samples were allocated to each concentration. Flexural strength was determined following immersion in water and incubation at 37°C for 50±2 hours using a universal testing machine at a crosshead speed of 5±1 mm/min. Data were analyzed using ANOVA, Tukey HSD, and t-test. P<0.05 was set as statistical significance. Results: Control groups of Acropars and Triplex showed the highest mean flexural strength within their own group which both were higher than the recommended 50 MPa. The mean flexural strength of Triplex incorporated with 0.5 and 1% of nanopropolis was higher than that of Acropars with the same percentage. Conclusions: The mean flexural strength of Triplex remained above the recommended value of 50 MPa after incorporation of both 0.5 and 1% nanopropolis. However, that of Acropars dropped below it.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".