Carbon Nanotube Migration in a Compatibilized Blend System, Leading to Kinetically Induced Enhancement in Electrical Conductivity and Mechanical Properties
Bibliographic record
Abstract
Kinetic factors that facilitate carbon nanotube (CNT) migration in a polymer blend from a high-density polyethylene (HDPE) phase to a poly (p-phenylene ether) (PPE) phase were studied, with the objective to induce CNT migration and localization at the interface. Herein, a CNT filler was pre-localized in an HDPE polymer and then blended with PPE at different blend compositions of 20:80, 40:60, 60:40, and 80:20 of PPE/HDPE at a constant filler concentration of 1 wt%. The level of CNT migration was studied at different mixing times of 5 and 10 min. The electrical conductivity initially increased by 2-3 orders of magnitude, with an increase in the PPE content up to 40%, and then it decreased significantly by up to 12 orders of magnitude at high PPE content up to 100%. We determined that the extent of migration was related to the difference in the melt viscosity between the constituent polymers. A triblock copolymer styrene-ethylene/butylene-styrene (SEBS) was used to improve the blend miscibility, and 2 wt% copolymer was found to be the optimum concentration for the electrical properties for the two blend compositions of 20:80 and 80:20 of PPE/HDPE, at a constant filler concentration of 1 wt%. The introduction of the SEBS triblock copolymer significantly increased the conductivity almost by almost four orders of magnitude for PPE/HDPE/80:20 composites with 1 wt% CNT and 2 wt% SEBS compared to the uncompatibilized blend nanocomposite. The mechanical strength of the compatibilized blend nanocomposites was found to be higher than the unfilled compatibilized blend (i.e., without CNT), uncompatibilized blend nanocomposites, and the pristine blend, illustrating the synergistic effect of adding nanofillers and a compatibilizer. SEM and TEM microstructures were used to interpret the structure-property relationships of these polymer blend nanocomposites.
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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) | 0.000 | 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 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".