A comparison of LLDPE-based nanocomposites containing multi-walled carbon nanotubes and graphene
Bibliographic record
Abstract
Composites of linear-low density polyethylene (LLDPE) with multi-walled carbon nanotubes (MWCNT) and thermally reduced graphene (TRGO) were produced by melt compounding. The composites were compatibilized by grafting aromatic pyridine groups onto the LLDPE backbone. The aromatic moieties established non-covalent π-π interactions with the carbon nanostructures, thus allowing for efficient dispersion, without compromizing their electrical properties. By using identical matrices, it was possible to investigate the effects of filler geometry on the electrical, mechanical and rheological properties of the composites. The 1-D nature and smaller surface area of the MWCNT facilitated their dispersion within the polymer matrix, whereas the graphene agglomerates appeared to breakup through an erosion mechanism. The resulting mixture of aggregates and individual graphene platelets favored lower electrical and rheological percolation thresholds. However the maximum electrical conductivity achieved in the TRGO/LLDPE was lower by about an order of magnitude compared to the MWCNT/LLDPE composites, probably due to residual oxygen in the graphene's structure. TRGO based composites presented higher moduli at the same filler loadings, while elongations at break were comparable. All composites exhibited time-dependent rheological properties, indicative of their tendency to aggregate. A more pronounced increase in viscoelastic properties was noted in the composites containing TRGO, presumably due to the higher surface area of the graphene platelets, and the presence of larger aggregates.
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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".