Color Stability, Translucency, and Wettability of a Lithium Disilicate Dental Ceramics Submitted to Different Surface Treatments
Bibliographic record
Abstract
Aim and objective: This study aimed to evaluate the color stability, translucency, and wettability of a lithium disilicate dental ceramic.Materials and methods: Forty specimens (6 × 1 mm) of lithium disilicate dental ceramic were fabricated.Initial color and translucency readings were measured using a spectrophotometer.Then, the specimens were randomly separated into four groups (n = 10), according to the different types of surface treatment (control: control group; HF + S: hydrofluoric acid gel and silane; Al + HF + S: Al 2 O 3 , hydrofluoric acid gel, and silane; Al + S: Al 2 O 3 and silane) and new color and translucency readings were done.The wettability was analyzed using the sessile drop method in all specimens, and the results were statistically analyzed using one-way ANOVA and the Tukey test (p < 0.05). Results:The results showed higher color and translucency changes to the groups treated (HF + S, Al + HF + S, and Al + S), different (p < 0.05) from the control group, and without significance between them.All groups demonstrated different wettability (p < 0.05), lower for the control group.Conclusion: It is possible to conclude that the surface treatment can influence the color, translucency, and wettability of lithium disilicate dental 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 | 0.001 | 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".