Flexural properties of experimental nanofiber reinforced composite are affected by resin composition and nanofiber/resin ratio
Bibliographic record
Abstract
OBJECTIVES: To evaluate the influence of different resin blends concentrations and nanofibers mass ratio on flexural properties of experimental Poliacrylonitrile (PAN) nanofibers reinforced composites. MATERIALS AND METHODS: Poliacrylonitrile (PAN) nanofibers mats were produced by electrospinning and characterized by tensile testing and scanning electron microscopy (SEM). Experimental resin-fiber composite beams were manufactured by infiltrating PAN nanofiber mats with varied concentrations of BisGMA-TEGDMA resin blends (BisGMA/TEGDMA: 30/70, 50/50 and 70/30weight%). The mass ratio of fiber to resin varied from 0% to 8%. Beams were cured and stored in water at 37°C. Flexural strength (FS), flexural modulus (FM) and work of fracture (WF) were evaluated by three-point bending test after 24h storage. RESULTS: The tensile properties of the PAN nanofibers indicated an anisotropic behavior being always higher when tested in a direction perpendicular to the rotation of the collector drum. Except for WF, the other flexural properties (FS and FM) were always higher as the ratio of BisGMA to TEGDMA increased in the neat resin beams. The addition of different ratios of PAN fibers did not affect FS and FM of the composite beams as compared to neat resin beams (p>0.05). However, the addition of fibers significantly increased the WF of the composite beams, and this was more evident for the blends with higher TEGDMA ratios (p<0.05). SIGNIFICANCE: The inclusion of PAN nanofibers into resin blends did not negatively affect the properties of the composite and resulted in an increase in toughness that is a desirable property for a candidate material for prosthodontics application.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".