Silica infiltration as a strategy to overcome zirconia degradation
Bibliographic record
Abstract
Abstract The excellent clinical performance of yttria-partially stabilized zirconias (Y-SZs) makes them promising materials for indirect restorations. However, the Y-SZ phase stability is a concern, and infiltrating Y-SZs with a silica nanofilm may delay their degradation processes. In this study, we analyzed stabilities of silica-infiltrated zirconia surfaces after exposure to artificial aging (AA). Four zirconia materials with different translucencies (n = 40) were used, including low translucency 3 mol% Y-SZ (3Y-LT, Ceramill ZI, Amann Girrbach); high translucency 4 mol% Y-SZ (4Y-HT, Ceramill Zolid); and two high translucency 5 mol% Y-SZs (5Y-HT, Lava Esthetic, 3M and 5Y-SHT, Ceramill Zolid, FX white). Sintered specimens were exposed to 40 cycles of silica (SiO2) through room temperature atomic layer deposition (RT-ALD) using tetramethoxysilane (TMOS) and ammonium hydroxide (NH4OH). AA was applied for 15 h in an autoclave (134°C, 2 bar pressure). Stabilities of zirconia-silica surfaces were characterized in terms of hardness and Young's modulus using nanoindentation techniques and crystalline contents using x-ray diffraction (XRD) analyses. Silica deposition was also characterized by X-ray photoelectron spectroscopy (XPS). There was a significant effect of the interaction of materials and surface treatments on the hardness and Young's modulus values of zirconia-silica surfaces (p < 0.001). Silica deposition on zirconia surfaces improved the material resistance to degradation by AA.
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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 | 0.000 |
| 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".