Effect of artificial aging on optical properties and crystalline structure of high‐translucency zirconia
Bibliographic record
Abstract
PURPOSE: To investigate the effect of different in vitro aging protocols on the optical properties and crystalline structure of high-translucency (HT) zirconia. MATERIALS AND METHODS: Thirty-six specimens of HT and extra-high translucency (XT) zirconia were divided into three groups: control (CO)-no treatment; hydrothermal aging (HA)-autoclave aging for 12.5 h at 134°C, 2 bar; clinically related aging (CRA)-aging in the chewing simulator for 1.2 million cycles, followed by 50,000 thermocycles (5-55°C) and immersion in HCl (pH 1.2) for 15 h. Optical properties, crystalline structure, and surface roughness were analyzed and compared using analysis of variance (5% significance level). RESULTS: There was no statistically significant effect of aging on translucency (p = 0.10), but CRA promoted the development of a high contrast ratio (p = 0.03). Aging did not cause significant color changes for HT (p = 0.65) or XT (p = 0.36). The proportion of monoclinic crystals increased to 40% for HT-zirconia after HA and 5% after CRA. No monoclinic crystals were detected for XT groups. There was no effect of aging on surface roughness (p = 0.77). CONCLUSIONS: Although hydrothermal aging has been widely used to verify zirconia crystalline stability, it did not generate an effect similar to clinically related aging on the optical properties and crystalline structure of zirconia. HA affected the crystalline structure of HT-zirconia, and CRA compromised the optical properties of XT zirconia.
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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".