Effect of zirconia decontamination protocols on bond strength and surface wettability
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effects of human saliva decontamination protocols on bond strength of resin cement to zirconia (Y-PSZ), wettability, and microbial decontamination. MATERIALS AND METHODS: Zirconia plates were sandblasted and divided into (a) not contaminated, (b) contaminated with human saliva and: (c) not cleaned, (d) cleaned with air-water spray, (e) cleaned with 70% ethanol, (f) cleaned with Ivoclean, or (g) cleaned with nonthermal atmospheric plasma (NTAP). The wettability and microbial decontamination of the surfaces were determined after saliva contamination or cleaning. Monobond Plus (Ivoclar Vivadent) was applied after cleaning, followed by Variolink LC (Ivoclar Vivadent). The samples were stored 1 week before shear bond strength (SBS) testing, and data (SBS and wettability) were analyzed by one-way analysis of variance and Tukey test (α = .05). RESULTS: Saliva contamination reduced SBS to zirconia compared to not contaminated. Both Ivoclean and NTAP produced higher SBS compared to not cleaned and were not significantly different from the not contaminated. Ivoclean produced the highest contact angle, and NTAP the lowest. With the exception of using just water-spray, all cleaning protocols decontaminated the specimens. CONCLUSIONS: Both Ivoclean and NTAP overcame the effects of saliva contamination, producing an SBS to zirconia comparable to the positive control. CLINICAL SIGNIFICANCE: Dental ceramics should be cleaned prior to resin cementation to eliminate the effects of human saliva contamination, and Ivoclean and NTAP are considered suitable materials for this purpose.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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".