Effect of Consecutive Firings on the Optical and Mechanical Properties of Silicate and Lithium Disilicate Based Glass‐Ceramics
Bibliographic record
Abstract
PURPOSE: To evaluate the effect of multiple firings on the optical and mechanical properties of two dental CAD/CAM glass-ceramics. MATERIALS AND METHODS: A total of 45 specimens of each lithium disilicate-LD (IPS E.max CAD, Ivoclar Vivadent) and zirconia lithium silicate-ZLS (Vita Suprinity, Vita Zahnfabrik) material were prepared in a disk shape. These specimens were divided into three groups according to two factors: "type of ceramic" (LD and ZLS) and "numbers of firings" (Control 2F-two firings, 5F-five firings and 7F-seven firings). The firing cycles were performed according to the manufacturer's recommendations. X-ray diffraction was additionally performed to determine crystalline phases in each group, spectrophotometry was used to determine color and translucency variation, and biaxial flexural strength (BFS) evaluated the mechanical behavior. The data were analyzed individually using two-way ANOVA tests and Tukey's test at α = 0.05. RESULTS: The crystalline phases did not present any change after multiple firings for either of the analyzed materials. Both commercial materials showed a significant difference regarding translucency at 7F (p = <0.01), and ZLS presented a difference in color higher than one (ΔE > 1) at 5F and 7F. Regardless of the number of firings, LD presented a higher BFS compared to ZLS (p = <0.001), and a significant increase in BFS comparing 2F and 7F (p = <0.024). CONCLUSION: The use of multiple firings can significantly alter the color, translucency, and mechanical strength of CAD/CAM ceramics.
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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) | 0.000 | 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) | 0.001 | 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".