Surface characterization of different surface treatments associations with plasma and bonding analysis of Y-TZP and the veneering ceramic
Bibliographic record
Abstract
OBJECTIVES: To characterize the surface of zirconia (Y-TZP) submitted to different surface treatments (with and without plasma associations) and to evaluate the shear bond strength (SBS) between veneering ceramic (VC) and Y-TZP after different aging methods. METHODS: particle; L: liner; P: plasma; Al + L: airborne + liner; Al + P: airborne + plasma; P + L: plasma + liner. The Y-TZP surface was characterized by SEM, EDS, AFM, surface profilometry, surface-free energy (SFE), and XRD. SBS between Y-TZP and VC was verified after three aging protocols: initial, after hydrothermal aging (autoclave for 5 h), or thermal fatigue (30,000 baths - 5-55 °C). One- (profilometry, SFE) and two-way ANOVA (SBS), and Tukey's HSD test were used. RESULTS: For the plasma groups, a full globular surface coverage was observed (SEM, AFM). Si was found for Al, L, Al + L, and P + L. Roughness was lower for C, P, and Al + P. For SFE, the highest values were found when the liner was applied (>74.59 nm/Nm). The highest monoclinic content was observed for Al + L (6.96%) and Al + P (5.86%). For the initial period, Al and P + L presented the lowest SBS values (<5.85 MPa; P > 0.331). The highest SBS values were found for L, P, and P + L (hydrothermal aging) and for P, L, Al + L, and Al + P (thermal fatigue). SIGNIFICANCE: Changes in Y-TZP topography and the SBS with the VC were found, according to treatments performed. Plasma treatment improved SBS and did not cause phase transformation.
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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) | 0.003 | 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".