The Effect of Different Surface Treatments on the Micromorphology and the Roughness of Four Dental CAD/CAM Lithium Silicate-Based Glass-Ceramics
Bibliographic record
Abstract
Objective: This study aimed to investigate and compare the effect of various surface treatments on the micromorphology and the roughness of four CAD/CAM lithium silicate-based glass-ceramics (LSGC). Method: Eighty specimens of four LDGC materials (IPS e. max® CAD (Ivoclar-Vivadent, Liechtenstein, Schaan), Vita Suprinity® (Vita Zahnfabrik, Bad Säckingen, Germany), Celtra Duo® (Dentsply, Hanau-Wolfgang, Germany) and n!ce (Straumann, Basel, Switzerland)) were used for this study. All specimens were highly polished with 400, 600, 1200 grit silicon carbide paper and then polished with 3 µm and 1 µm polycrystalline diamond suspension liquid with grinding devices. Each group of ceramic was assigned to one of the following three surface treatments (1) sand-blasting (SB) with 50 µm Al2O3 at 70 psi for 10s, (2) hydrofluoric acid etching (HF) with 5% hydrofluoric acid, according to the manufacturer instructions, (3) and a combination of sand-blasting and hydrofluoric acid (SB + HF). All specimens were cleaned with ethanol for 2 min and placed in an ultrasonic unit with distilled water for 15 min. The microstructure was analyzed by scanning electron microscopy (SEM). The surface roughness and topography were evaluated with atomic force microscopy in tapping mode (AFM). Statistical analysis was done using two-way ANOVA and Tukey tests (α = 5%). Results: All surface treatments had a significant effect on LDGC surface roughness compared to the untreated surface (p < 0.05). The sand-blasting treatment had a significantly higher mean surface roughness value for Vita Suprinity and Celtra Duo compared to other surface treatments (p < 0.05). However, there was no significant difference for surface roughness between sand-blasting and sand-blasting + etching for e.max CAD and n!ce. The hydrofluoric acid produced less surface roughness compared to other surface treatments but was able to change the surface structure. (5) Conclusions: The sand-blasting + etching treatment could be a sufficient method to produce surface roughness for all LSGC types.
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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 | 0.001 |
| 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".