Silica deposition on zirconia via room-temperature atomic layer deposition (RT-ALD): Effect on bond strength to veneering ceramic
Bibliographic record
Abstract
PURPOSE: To develop and to characterize a hybrid interface between yttria-stabilized zirconia (Y-TZP) transformed layer and silica-based nanofilm to enable a better bonding between Y-TZP and a veneering ceramic. MATERIAL AND METHODS: OH). Y-TZP surface wettability and shear bond strength (SBS) between Y-TZP and the veneering ceramic were analyzed for all groups after surface treatments. One-way ANOVA and Tukey's HSD test were used for data analysis (p ≤ 0.05). RESULTS: The highest contact angle was observed for the control group (64.46 ± 6.09 θ), while the lowest values (p < 0.001) were presented after Si20 (29.85 ± 4.23 θ) and Si40 (30.37 ± 5.51 θ) treatments. Hydrothermal treatment (49.3 ± 2.69 θ) and alumina abrasion (45.84 ± 4.12 θ) resulted in intermediate contact angle values. The highest SBS values were observed for Al (16.74 ± 1.68 MPa) and Ht (15.27 ± 2.11 MPa) groups (p < 0.018). Groups Si20 (9.66 ± 1.22 MPa), Si40 (9.33 ± 2.11 MPa), Ht + Si40 (9.37 ± 1.02 MPa) and C (12.54 ± 2.64 MPa) all resulted in similar SBS results (p > 0.998). CONCLUSION: The experimental treatments proposed enhanced surface wettability, but shear bond strength between Y-TZP and veneering ceramic was not improved. Alumina particle-abrasion improved SBS values while a decrease in wettability was observed.
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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".