Poly(methyl methacrylate)/Graphene Oxide Nanocomposites by a Precipitation Polymerization Process and Their Dielectric and Rheological Characterization
Bibliographic record
Abstract
We report a method for achieving controlled dispersion of graphene oxide (GO) in poly(methyl methacrylate) (PMMA) via the precipitation polymerization process in a water/methanol mixture. GO acts as a surfactant and adsorbs on the interface between polymerized PMMA particles and solvent mixture. Scanning electron and transmission electron microscopy confirmed that the precipitate consists of polymer particles (<1 μm) surrounded by the GO sheets. Compression molding of the precipitate yields a polymer nanocomposite with the GO organized into a regularly spaced 3D network which percolates at 0.2 wt % GO. Simple thermal reduction of the GO sheets dispersed in PMMA at relatively low temperature (210 °C) achieved electrical conductivity higher than 10 –2 S/m at 0.4 wt % of GO. Parallel dielectric and rheological characterization demonstrated that the thermal reduction is a quite fast process without significant degradation of the polymer. The study should open up new opportunities in the design of GO-based polymer nanocomposites.
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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) | 0.000 | 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 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".