A Noncovalent Compatibilization Approach to Improve the Filler Dispersion and Properties of Polyethylene/Graphene Composites
Bibliographic record
Abstract
Graphene was prepared by low temperature vacuum-assisted thermal exfoliation of graphite oxide. The resulting thermally reduced graphene oxide (TRGO) had a specific surface area of 586 m(2)/g and consisted of a mixture of single-layered and multilayered graphene. The TRGO was added to maleated linear low-density polyethylene LLDPE and to its derivatives with pyridine aromatic groups by melt compounding. The LLDPE/TRGO composites exhibited very low electrical percolation thresholds, between 0.5 and 0.9 vol %, depending on the matrix viscosity and the type of functional groups. The dispersion of the TRGO in the compatibilized composites was improved significantly, due to enhanced noncovalent interactions between the aromatic moieties grafted onto the polymer matrix and the filler. Better dispersion resulted in a slight increase in the rheological and electrical percolation thresholds, and to significant improvements in mechanical properties and thermal conductivity, compared to the noncompatibilized composites. The presence of high surface area nanoplatelets within the polymer also resulted in a substantially improved thermal stability. Compared to their counterparts containing multiwalled carbon nanotubes, LLDPE/TRGO composites had lower percolation thresholds. Therefore, lower amounts of TRGO were sufficient to impart electrical conductivity and modulus improvements, without compromising the ductility of the composites.
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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) | 0.001 | 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 | 0.001 |
| 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".