Evolution of morphology and electrical properties under controlled flow in polypropylene/polystyrene co-continuous blends containing interfacially localized carbonaceous nanoparticles
Bibliographic record
Abstract
This study investigates the evolution of morphology and electrical properties of polypropylene (PP)/polystyrene (PS) blend nanocomposites under controlled steady shear flow. These nanocomposites contain either few-layer graphene (FLG) or a mixture of FLG and multi-walled carbon nanotubes (MWCNT), prepared via a conventional melt-mixing. Composites were created by premixing FLG or FLG/MWCNT with either PP [PP/PS/FLG or PP/PS/(FLG+MWCNT)] or PS [PS/PP/FLG or PS/PP/(FLG+MWCNT)] at a PP/PS ratio inducing co-continuous morphology. Results showed a significant reduction in the percolation threshold (PT) for PS/PP/FLG composites, with an 81% decrease compared to PS/FLG. When FLG was premixed with PS, PT required only 2 wt. % FLG, compared to 5.9 wt. % in PP/PS/FLG. Steady shear deformation disrupted the electrical network in both PP/PS/FLG and PS/PP/FLG composites. However, the PS/PP/FLG composites exhibited greater stability in electrical conductivity at lower FLG concentrations (above 3 wt. %) compared to the PP/PS/FLG composites (above 6 wt. %). The applied shear did not affect the co-continuous morphology of the blend-based composites containing 1 wt. % or more of FLG. Additionally, the synergistic effects of the hybrid FLG/MWCNT mixture on the electrical conductivity and rheological properties of both PP/PS/(FLG+MWCNT) and PS/PP/(FLG+MWCNT) composites were evaluated. The incorporation of MWCNT into both PP/PS/FLG and PS/PP/FLG composites significantly enhanced the formation of a hybrid electrical network structure, leading to a further reduction in the percolation threshold concentration of FLG. Specifically, in PP/PS/FLG composites, PT decreased from 5.9 to 1–3 wt. % of FLG, while in PS/PP/FLG composites, PT dropped from 2 to 1 wt. % of FLG.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".