Effect of Mold Type and Diameter on the Depth of Cure of Three Resin-Based Composites
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effects of different mold materials, their diameters, and light-curing units on the mechanical properties of three resin-based composites (RBC). METHODS AND MATERIALS: A conventional nano-filled resin composite (Filtek Supreme Ultra, 3M Oral Care, St Paul, MN, USA) and two bulk-fill composites materials, Tetric Evoceram Bulk fill (Ivoclar Vivadent, Schaan, Liechtenstein) and Aura Bulk Fill (SDI, Bayswater, VIC, Australia), were tested. A total of 240 specimens were fabricated using metal or white semitransparent Delrin molds that were 4 or 10 mm in diameter. The RBCs were light cured for 40 seconds on the high-power setting of either a monowave (DeepCure-S, 3M Oral Care) or polywave (Bluephase G2, Ivoclar Vivadent) light-emitting diode (LED) curing unit. The depth of cure was determined using a scraping test, according to the 2009 ISO 4049 test method. Data were analyzed using multivariate analysis of variance followed by Tukey multiple comparison test ( p<0.05). RESULTS: In general, when used for 40 seconds, both LED curing lights achieved the same depth of cure ( p=0.157). However, the mold material and its diameter had a significant effect on the depth of cure of all three RBCs ( p<0.0001). CONCLUSION: Curing with either the polywave or monowave LED curing light resulted in the same depth of cure in the composites. The greatest depth of cure was always achieved using the 10-mm-diameter Delrin mold. Of the three RBCs tested, both Tetric Bulk Fill and Aura achieved a 4-mm depth of cure when tested in the 10-mm-diameter metal mold. Tetric Bulk Fill was the most transparent and had the greatest depth of cure, and the conventional composite had the least depth of cure. Very little violet (<420 nm) light penetrated through 6 mm of any of the RBCs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".