Preparation and characterization of vinylidene chloride‐<i>co</i>‐vinyl chloride copolymer/fluorinated synthetic mica nanocomposites I thermal and mechanical properties studies
Bibliographic record
Abstract
Abstract Poly(vinylidene chloride‐co‐vinylchloride)/organically modified fluorinated synthetic mica (MEE) (VDC‐VC/MEE) nanocomposites were prepared by melt blending of VDC‐VC copolymer with MEE, in the presence of dioctyl phthalate (DOP) which acted as a plasticizer and a cointercalating agent. The nanostructure, thermal, and dynamic mechanical properties of the VDC‐VC/MEE nanocomposites were studied by wide angle X‐ray diffractometer (WAXD), scanning electron microscope (SEM), transmission electron microscope (TEM), thermogravimetric analyzer (TGA), and dynamic mechanical analyzer (DMA). It was found that partially intercalated and partially exfoliated structures coexisted in VDC‐VC/MEE nanocomposities. Below 8 wt % MEE content, the intercalation effect of nanocomposites decreased with increasing the MEE content. Under a nitrogen atmosphere, VDC‐VC/MEE nanocomposites exhibited a single step thermal degradation behavior. The nanostructure of VDC‐VC/MEE can effectively prevent volatile gases from being released, and thus enhances its thermal stability. The thermal stability of VDC‐VC/MEE nanocomposites is strongly related to the morphology of nanocomposites and the degraded composites structure. DMA revealed a significant improvement in the storage modulus within the testing temperature range. The increase in storage modulus depends on the MEE content, which is attributed to the dispersed phase morphology. The glass transition temperature of VDC‐VC/MEE nanocomposites is affected by the chain mobility in the nanocomposites rather than the aggregative morphology. © 2008 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 46: 1214–1225, 2008
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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".