Post-fatigue fracture load, stress concentration and mechanical properties of feldspathic, leucite- and lithium disilicate-reinforced glass ceramics
Bibliographic record
Abstract
Objective: To evaluate the mechanical properties of different CAD/CAM ceramic systems and the post-fatigue fracture and stress distribution when used as cemented crowns. Materials and methods: Sixty (60) CAD/CAM monolithic crowns were milled using three different ceramic materials (FD - Feldspathic [Vita Mark II]), LE - Leucite-based ceramic [IPS Empress CAD] and LD - Lithium Disilicate [IPS e.max CAD]) and adhesively cemented on resin composite dyes. Specimens were stored in distillated water (37 °C) for 7 days. After, half of the crowns were submitted to immediate fracture load test while the other half was submitted to fatigue cycling. The average cement layer of approximately 80 μm was assessed using scanning electron microscopy (SEM). The average thickness was used in the three-dimensional (3D) Finite Element Analysis (FEA). For each ceramic material, the density, Poisson ratio, shear modulus, Young modulus, fracture toughness, and true hardness were assessed (n = 3). The data was used to assess the Maximum Principal Stress throughout 3D-FEA according to each material during load to fail and post-fatigue. Data were submitted to two-way ANOVA and Tukey test (α = 0.05). Results: LD showed the highest compression load, density, shear modulus, Young modulus, fracture toughness and true hardness values. While LE presented the lowest mechanical properties values. There is no difference in the Poisson ratio between the evaluated ceramics. Conclusion: LD was susceptible to aging process but presented stronger physicomechanical properties, showing the highest post-fatigue fracture load and highest stress magnitude.
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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 | 0.000 |
| 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 | 0.000 | 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".