Effect of the Sample Preparation and Light-curing Unit on the Microhardness and Degree of Conversion of Bulk-fill Resin-based Composite Restorations
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of the sample preparation and light-curing units (LCUs) on the Knoop hardness (KH, N/mm2) and degree of conversion (DC, %) of bulk-fill resin-based composite restorations. METHODS: Two molds were made using human molar teeth embedded in acrylic resin. One was a conventional tooth mold where the molar received a mesio-occluso-distal (MOD) preparation. In the other, the tooth was sectioned in three slices (buccal, middle, and lingual). The center slice received a MOD preparation similar to the conventional mold. Both tooth molds were placed in the second mandibular molar position in a Dentoform with a 44-mm interincisal opening. Restorations were made using Opus Bulk Fill (FGM) high viscosity bulk-fill resin-based composite (RBC) and light cured using two different lights: VALO Cordless (Ultradent) and Bluephase G2 (Ivoclar Vivadent). The RBC was placed in one increment that was light-cured for a total of 80 seconds (40 seconds at the occluso-mesial and occluso-distal locations). The RBC specimens were then prepared as follows: EmbPol - tooth mold specimen was embedded in polystyrene resin and polished before testing; Pol - tooth mold specimen was not embedded, but was polished before testing; NotPol - sectioned tooth mold, specimen not embedded nor polished before testing. The KH was measured in different depths and regions of the specimens, and the DC was measured using Raman spectroscopy. RESULTS: The results were analyzed using a 2-way analysis of variance (ANOVA) or repeated measures followed by the Tukey post-hoc test (α=0.05). The preparation method (p<0.001), depth of restoration (p<0.001), and the interaction between method and depth (p=0.003) all influenced the KH values. Preparation method (p<0.001), tooth region (p<0.001), and the interaction between method and tooth region (p=0.002) all influenced DC values. The KH values were reduced significantly from the top to the bottom of the restorations and also at the proximal box when compared with the occlusal region. This outcome was most significant in the proximal boxes. The NotPol method was the most effective method to detect the effect of differences in KH or DC within the restoration. A lower DC and KH were found at the gingival regions of the proximal boxes of the restorations. When the KH and DC values were compared, there were no significant differences between the LCUs (KH p=0.4 and DC p=0.317). CONCLUSION: Preparation methods that embedded the samples in polystyrene resin and polished the specimens reduced the differences between the KH and DC values obtained by different preparation techniques. The NotPol method was better able to detect differences produced by light activation in deeper areas.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".