Microstructural and Mechanical Characterization of CAD/CAM Materials for Monolithic Dental Restorations
Bibliographic record
Abstract
PURPOSE: To determine and compare the microstructure, flexural strength, flexural modulus, fracture strength, and microhardness of four types of computer-aided design/computer-aided manufacturing (CAD/CAM) materials for monolithic dental restorations. MATERIALS AND METHODS: A lithium disilicate (LD; IPS e.max CAD), a zirconia-reinforced lithium silicate (ZLS; VITA Suprinity), a hybrid high-performance polymer (HPP) composite resin (GC Cerasmart), and a hybrid polymer-infiltrated ceramic network (PICN) material (VITA Enamic) were used to manufacture monolithic ceramic posterior crowns (n = 10) that were adhesively cemented on resin-based composite dies and loaded until fracture. In addition, 40 rectangular bars (n = 10) were milled and polished for three-point flexural strength testing. Microhardness (Vickers indentation), as well as quantitative (energy dispersive spectroscopy) and qualitative (scanning electron microscopy) structural analysis were conducted on fracture surfaces. Data were analyzed by one-way ANOVA and Tukey HSD post-hoc test (p = 0.05). RESULTS: Mechanical testing results showed that the material type has a significant effect on the fracture strength (p < 0.0001) of the monolithic crowns with ZLS and LD presenting significantly higher fracture strength than the PICN and HPP hybrid materials. LD showed the highest flexural strength (p < 0.0001) followed by ZLS, HPP, and PICN, respectively. The lowest flexural modulus and hardness were presented by HPP whereas ZLS had the highest flexural modulus and hardness. The LD presented the highest modulus of resilience and the PICN the lowest. CONCLUSIONS: All CAD/CAM crown materials exhibited high values of fracture and flexural resistance, making them suitable materials for posterior full-crown restorations. Glass-ceramics suffered more from catastrophic and nonreparable fracture patterns, whereas minimal chipping and type II fracture patterns were more common in hybrid materials. The combination of more flexibility, less stiffness, and increased softness with satisfactory flexural and fracture strength values observed in PICN and HPP makes these two hybrid materials suitable choices for chairside monolithic crown fabrication.
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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 | 0.001 | 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".