Studying the Effects of Polymer Crystallization and Blending on Conductive Network Formation in CNT-Based Conductive Polymer Composites
Bibliographic record
Abstract
The main objective of this thesis was to reduce the percolation threshold of conductive polymer composites (CPCs) through selective localization of carbon nanotubes (CNTs), taking advantage of (i) polymer-filler interactions and (ii) polymer blending. The first part of this study reports on the effects of polymer-filler interactions, particularly the effects of the conductive fillers on the matrix crystallization behavior, on the evolution of the conductive networks. Experimental results indicated that changes in the crystallization behavior and crystal nucleation of a PP matrix in the presence of carbon nanotubes (CNTs) had substantial effects on the formation or destruction of the conductive networks. Based on our experimental results, promotion of heterogeneous crystal nucleation ability of CNTs in CPCs with semicrystalline matrices resulted in wrapping of the conductive fillers with insulating layers of polymer crystals. Such effects resulted in disruption of conductive network formation in CPCs even at high filler contents. Interestingly, current-voltage characterizations showed that in such CPCs, even at a high CNT content of 10 wt.%, electron tunneling was the dominant electron conduction mechanism, signifying a lack of direct contact between the adjacent CNTs. As the second phase of this study, a novel technique was developed to use polymer blending for controlling the conductive network of flexible CPCs with high mechanical properties. We produced thermoplastic vulcanizate (TPV)- based CPCs, with segregated structure, via the incorporation of fine pre-vulcanized rubber (PVR) particles into maleic anhydride grafted polyethylene (MA-g-PE)/CNT composites. Inclusion of the PVR particles resulted in selective localization of CNTs in the MA-g-PE matrix which significantly reduced the percolation threshold. More importantly, our experimental results confirmed that strong chemical bonds formed between the PVR particles and the MA-g-PE matrix which led to excellent interfacial adhesion between the two phases. This engineered structure increased the CNT-based nanocomposites’ stretchability by over 200%, while their percolation threshold was decreased by 50%. The cyclic electromechanical properties were also improved, suggesting the nanocomposites’ great potential for flexible and stretchable electromechanical applications. Such TPV-based CPCs were further studied for electromagnetic interference (EMI) shielding applications, which require highly conductive polymer composites with flexible and stretchable structures.
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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) | 0.000 | 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".