Carbon Nanotube Migration in Melt-Compounded PEO/PE Blends and Its Impact on Electrical and Rheological Properties
Bibliographic record
Abstract
In this work, the effects of MWCNT concentration and mixing time on the migration of multi-walled carbon nanotubes (MWCNTs) within polyethylene oxide (PEO)/polyethylene (PE) blends are studied. Two-step mixing used to pre-localize MWCNTs within the PE phase and subsequently to observe their migration into the thermodynamically favored PEO phase. SEM micrographs show that many MWCNTs migrated into PEO. PEO/PE 40:60 polymer blend nanocomposites with 3 vol% MWCNTs mixed for short durations exhibited exceptional electromagnetic interference shielding effectiveness (EMI SE) and electrical conductivity (14.1 dB and 22.1 S/m, respectively), with properties dropping significantly at higher mixing times, suggesting the disruption of percolated MWCNT networks within the PE phase. PE grafted with maleic anhydride (PEMA) was introduced as a compatibilizer to arrest the migration of MWCNTs by creating a barrier at the PEO/PE interface. For the compatibilized system, EMI SE and electrical conductivity measurements showed a peak in electrical properties at 5 min of mixing (15.6 dB and 68.7 S/m), higher than those found for uncompatibilized systems. These improvements suggest that compatibilization can be effective at halting MWCNT migration. Although utilizing differences in thermodynamic affinity to draw MWCNTs toward the polymer/polymer interface of polymer blend systems can be an effective way to achieve interfacial localization, an excessively low viscosity of the destination phase may play a major role in reducing the entrapment of MWCNTs at the interface.
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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".