Exploring Zirconia Adhesion: Pre and Postsintering Physical Surface Treatment, Chemical Treatment, and Cement Interactions
Bibliographic record
Abstract
Background: Adhesion to zirconia remains a significant dental challenge. This study is aimed at assessing the bond strength of zirconia based on surface treatment with pre or postsintering sandblasting associated with different chemical treatments and resin cements. Methods: Zirconia blocks were divided into 12 experimental groups based on the surface treatment (presintering sandblasting or postsintering sandblasting/tribochemical abrasion treatment), chemical treatment (none, Single Bond Universal, or Signum Zirconia Bond), and choice of cement (Panavia F or RelyX™ U200). The bond strength was measured by shear tests using a universal testing machine. The fracture analysis was performed using stereomicroscopy. Data were analyzed using three‐way ANOVA and Tukey’s test ( α = 5 % ). Results: Triple and double factor’s interactions were not significant ( p > 0.05). Regarding the surface treatment factor, the bond strength following postsintering sandblasting treatment associated with tribochemical abrasion (9.15 ± 3.62 MPa) was significantly higher than presintering sandblasting treatment (5.24 ± 3.53 MPa). Concerning the chemical treatment factor, bond strengths were ranked as follows: Signum Zirconia Bond > Single Bond Universal > no treatment. The bond strength of the resin cements did not differ among them. Most fractures (67%) were classified as adhesive, and 32% were categorized as mixed fractures. Conclusion: Surface treatment via postsintering sandblasting combined with tribochemical abrasion demonstrated superior efficacy than in presintering sandblasting. Additionally, chemical treatment with zirconia primer increased the bond strength of zirconia irrespective of the surface physical treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".