The Influence of a Commercial Few-Layer Graphene on Electrical Conductivity, Mechanical Reinforcement and Photodegradation Resistance of Polyolefin Blends
Bibliographic record
Abstract
This work demonstrates the potentials of a commercially available few-layer graphene (FLG) in enhancing the electro-dissipative properties, mechanical strength, and UV protection of polyolefin blend composites; interesting features of electronic packaging materials. Polyethylene (PE)/ polypropylene (PP)/ FLG blend composites were prepared following two steps. Firstly, different concentrations of FLG were mixed with either the PE or PP phases. Subsequently, in the second step, this pre-mixture was melt-blended with the other phase of the blend. FLG-filled composites were characterized in terms of electrical conductivity, morphological evolution upon shear-induced deformation, mechanical properties, and UV stability of polyolefin blend composites. Premixing of FLG with the PP phase has been observed to be a better mixing strategy to attain higher electrical conductivity in PE/PP/FLG blend composite. This observation is attributed to the influential effect of FLG migration from a thermodynamically less favourable PP phase to a favourable PE phase via the PE/PP interface. Interestingly, the addition of 4 wt.% (~2 vol.%) and 5 wt.% (~2.5 vol.%) of FLG increased an electrical conductivity of ~10 orders of magnitude in PE/PP—60/40 (1.87 × 10−5 S/cm) and PE/PP—20/80 (1.25 × 10−5 S/cm) blends, respectively. Furthermore, shear-induced deformation did not alter the electrical conductivity of the FLG-filled composite, indicating that the conductive FLG network within the composite is resilient to such deformation. In addition, 1 wt.% FLG was observed to be sufficient to retain the original mechanical properties in UV-exposed polyolefin composites. FLG exhibited pronounced UV stabilizing effects, particularly in PE-rich blends, mitigating surface cracking and preserving ductility.
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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".