Enhancing Shear Bond Strength in Lithium Silicate Glass Ceramics: Surface Treatment Optimization for Reseating Protocols
Bibliographic record
Abstract
The rapid evolution of lithium silicate-based glass ceramics in the field of dental ceramics has led to the availability of different compositions in the market. This in vitro study was conducted to assess an effective protocol for recementing de-bonded lithium silicate-based glass ceramics by evaluating the shear bond strength of three reseating methods. The study included IPS e.max® CAD, Vita Suprinity®, Celtra Duo®, and n!ce as lithium-based glass ceramics. The samples underwent a series of preparation steps, including embedding in acrylic resin, hand polishing, etching with 5% hydrofluoric acid, and application of universal primer and adhesive as per manufacturer instructions. Subsequently, adhesive resin cement was applied to the ceramic tablets, and shear bond strength was assessed using a standardized method. The findings revealed that no single method demonstrated significantly superior results compared to the others. However, it was observed that etching with 5% hydrofluoric acid for 20 s yielded favorable outcomes in terms of time efficiency and standardized results. Additionally, it was noted that although sandblasting increased surface area, it did not enhance bond strength due to unfavorable surface disturbance. In conclusion, the study suggests that etching with 5% hydrofluoric acid for 20 s is a favorable protocol for reseating de-bonded lithium disilicate-based glass ceramics, offering both time efficiency and consistent results for clinicians.
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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 | 0.001 |
| 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".