Carbon Nanotubes Embedded in Nanofibrillated EPDM Rubber as Thermally and Electronically Conducting Polypropylene Nanocomposites for Flexible Electrostatic Discharging
Bibliographic record
Abstract
Herein, we propose a hybrid approach for optimizing the carbon nanotube (CNT) dispersion in polypropylene (PP) nanocomposites based on both chemical functionalization and physical confinement. Our approach relies on a two-step scheme where CNTs are first functionalized and dispersed in an ethylene-propylene-diene-monomer (EPDM) rubber phase via solution mixing, followed by a second step where the CNT-reinforced EPDM phase is melt-mixed with PP and taken through the in situ fibrillation process. Morphological characterization supported by rheological analysis show that the CNTs are successfully confined and dispersed within an interconnected network of nanosized rubbery EPDM fibrils, distributed throughout the PP matrix. In addition to reducing the electrical and thermal percolation thresholds from approximately 1.5 to 0.25 wt %, this unique morphology brings significant improvement in the crystallization behavior of the PP nanocomposites, resulting in a more uniform crystallization behavior with both increased percent crystallinity and increased crystallization temperature compared to conventional PP/CNT nanocomposites. This morphology brings also significant improvement in the mechanical properties, raising both the tensile toughness and ductility by three times compared to conventional PP/CNT nanocomposites. All in all, our innovative morphology strikes an excellent balance between high electrical/thermal conductivity and high toughness and ductility presenting them as promising for flexible antistatic packaging and electrostatic dischargers.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".