Degree of conversion and mechanical properties studies of UDMA based materials for producing dental posts
Bibliographic record
Abstract
Abstract Nanocomposites are a relatively new material in producing fiber re‐enforced dental posts. The mechanical properties of nanocomposites, which strongly associate with the resin matrix, nanoparticles, and the interface between inorganic fillers and organic matrix, play an important role in determining the quality of dental posts. This work was to investigate the effect of degree of conversion (DC) and silanization of fillers on the mechanical properties of nanocomposties. Experimental Urethane dimethacrylate (UDMA) based dental composites containing unsilanized and silanized SiO 2 filler and various amount of triethylene glycol dimethacrylate (TEGDMA) were prepared at the first step. The DC of composites at different ratios of UDMA/TEGDMA, cure temperature and cure time was measured by Fourier transform infrared spectroscopy. The results showed that DC increases with the increase of TEGDMA content in resin matrix. Both increase of the cure temperature and cure time can cause the increase of DC. The incorporation of fillers, either silanized, or unsilanized filler, caused the decrease of DC. However, composites reinforced with silanized silica showed relatively lower DC, and DC decreased with the increase of silanized filler content. The effect of incorporation of fillers on the mechanical properties was investigated. Silanized silica can effectively improved the flexural strength and flexural modulus of material, and these properties increased with the increase of silica content. Thermomechanical analysis (DMA) provided the similar results to the static property measurements. SEM images of fracture surfaces of specimens from flexural testing revealed the surface morphology is strongly related to the quality of interface between inorganic fillers and organic matrix. POLYM. COMPOS., 2008. © 2008 Society of Plastics Engineers
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".