Obtaining and evaluation of polyethylene nanocomposites with graphene nanoplatelets through in‐situ ethylene polymerization
Bibliographic record
Abstract
Abstract In‐situ polymerization of ethylene with graphene has been previously studied; however, the reported nanocomposites exhibit poor filler dispersion, even at low graphene loadings. In the present work, well dispersed and homogeneous graphene nanoplatelets (GNP)‐high density polyethylene (HDPE) nanocomposites were obtained in up to 12.5% weight content of graphene through an in‐situ polymerization of ethylene in graphene suspensions. The composites' potential as masterbatches was evaluated by melt mixing them with commercial HDPE. The obtained materials exhibited important physical‐mechanical properties increases when compared with those of commercial HDPE. To our knowledge, our materials' GNP concentration (≤0.82 wt.%) is the lowest reported, with the composites showing a 27% increase in their maximum tensile strength and a 41% increase in their flexural modulus when compared with blank HDPE. In addition, Raman spectroscopy and scanning electron microscope (SEM) analyses give evidence of the higher exfoliation degree of GNP obtained in the in‐situ ethylene polymerizations.
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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".