Comparison of mechanical properties of five commercial dental core build-up materials.
Bibliographic record
Abstract
OBJECTIVE: This study aimed to evaluate and compare the mechanical properties of five commercial core materials using fracture toughness (FT), Knoop hardness number (KHN), diametral tensile strength (DTS), and dynamic elastic moduli (DEM). METHODS: Composite material specimens were produced (Rock Core, CosmeCore, ParaCore, MultiCore Flow, and Filtek Supreme Plus). The FT test (n = 15) was performed using notchless triangular prism (NTP) specimens. FT was determined using an Instron testing machine. KHN (n = 3) was evaluated using three indentations applied on each specimen. DTS test (n = 15) was measured using an Instron testing machine. The density. of the specimens (n = 3) was determined by water displacement method. Dynamic Young's, shear moduli, and Poisson's ratio (n = 3) were measured by an ultrasonic method. Statistical analysis was conducted using ANOVA and a Tukey B rank order test (P = 0.05). RESULTS: Rock Core presented the lowest FT values. Filtek Supreme Plus and CosmeCore exhibited significantly higher KHN values than the rest of the materials. CosmeCore had the highest DTS value, which was statistically significant only compared to Rock Core. For DEM, Filtek Supreme Plus exhibited significantly higher Young's and shear moduli than the rest of the materials (P < 0.05). CONCLUSIONS: Results demonstrated significant differences in the FT, KHN, and DTS values of the core build-up materials tested. According to the elastic behavior of the core composite materials, Rock Core had the lowest Young's values.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".