The Influence of a Commercial Few-Layer Graphene on Electrical Conductivity, Mechanical Reinforcement and Photodegradation Resistance of Polyolefin Blends
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 it