Fracture Toughness of Resin Based Composites, Impregnated with Silver Nanoparticles and Bioactive Glass
Bibliographic record
Abstract
OBJECTIVE: To investigate the effect, on fracture toughness (KIc), of impregnating silver nanoparticles (AgNPs) and bioactive glass (BAG) in resin based composites (RBCs). METHODOLOGY: During the period from August 2016 to May 2018, this study was performed at Peshawar Dental College (Pakistan) and Montreal University (Canada), using; a commercial RBC and experimental RBCs with or without BAG content (5-15wt%), and AgNPs (0.009%). Standardized specimens (n=6) were made in each of five RBCs (G1-G5). AgNPs were synthesized and characterized by uv-vis spectroscopy. With universal testing machine, the KIc for RBCs specimens was computed. SEM and dynamic light scattering (DLS) was used to assess the size and form of the prepared silica. One-way ANOVA and Tukey post hoc test were used for data analyses. RESULTS: KIc values varied both within and between RBCs groups. The commercial RBC had highest mean KIc (G1=1.03+0.24). Mean KIc values for the experimental RBCs were; G2=0.69±0.14, G3=0.9±0.13, G4=0.9±0.14 and G5=0.69±0.13. The only RBC groups that had statistically significant variations between their mean KIc values were; G1-G2 (p=0.017) & G1-G5 (p=0.017). SEM and DLS analysis of synthesized silica particles having round shape and sizes of 0.9-1µm. Uv-vis spectroscopy of AgNPs showed round shape with size up to 20nm. CONCLUSION: The KIc of the experimental RBCs with BAG (5-10 wt%) and AgNPs (0.009%) was not significantly different than the commercial RBC. KEYWORDS: Resin based composites, RBCs, Silver nanoparticles, AgNPs, Bioactive glass, BAG, Re-mineralizing resin based composite, Fracture toughness, KIc HOW TO CITE: Hanif A, Ghani F. Fracture toughness of resin based composites, impregnated with silver nanoparticles and bioactive glass. J Pak Dent Assoc 2020;29(4):179-184.
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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".