Effects of Different Temperatures and Storage Time on the Degree of Conversion and Microhardness of Resin-based Composites
Bibliographic record
Abstract
OBJECTIVE: Dental materials are often made at room temperature, whereas clinically they are made in the mouth. This study evaluated the effects of temperature on the degree of conversion (DC) and Knoop microhardness (KHN). MATERIALS AND METHODS: Two types of resin-based composites (RBCs) were light-cured using a light-emitting diode (LED) light-curing unit. The resin specimens were centered on an Attenuated Total Reflectance Fourier transform infrared (FT-IR) plate heated to 23°C or 33°C. The DC of the resin was calculated after 120 seconds, the specimens were removed, and the KHN was tested at the bottom of the specimens both immediately, after 24 hours, and after 7 days storage in distilled water in complete darkness at 37°C. The effects of different temperatures on the DC and KHN with their storage time were compared by analysis of variance and Fisher's protected least significant difference post hoc multiple comparison tests (p < 0.05). RESULTS: Increasing the temperature had a significant and positive effect on the DC and KHN for immediate values of the RBCs. Greater conversion and hardness occurred when the curing temperature was increased from 23°C to 33°C. The KHN increased significantly after 24 hours of storage. There was a linear relationship between DC and KHN (R(2) = 0.86) within the range of DC and KHN studied. CONCLUSION: The physical properties of dental materials can be expected to be better when made in the mouth than when they are made in a laboratory at room temperature.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".