Phase Morphology and Electrical Properties of Polymer Blend Nanocomposites
Bibliographic record
Abstract
Conductive polymer nanocomposites (CPNs) combine the advantages of a polymeric matrix, such as lightweight, durability, easy process-ability and low cost with the outstanding properties of nanofillers such as high surface area, high electron and thermal transport, etc, for electrical applications. Polymer blends constitute one of the best alternatives to produce novel polymer nanocomposites because we can mix the properties of two or more polymers with the properties of nanofillers. The majority of the polymer blends are immiscible, which means different segregated morphologies are possible. Therefore, the properties of these multiphase polymer materials, greatly depend on the morphology. In this Ph.D. thesis, we used phase–separated, biphasic polymer blends as a strategy to tune the final electrical properties of nanocomposites by manipulation of the phase morphology. We employed two different techniques to produce the conductive polymer blend nanocomposites: 1) incorporation of conductive nanofillers by melt mixing and 2) in-situ formation of electrically 3D conductive networks. For the first approach, we added (MWCNT) into a biphasic polymer matrix by melt mixing. The second approach consisted in the direct creation of highly conductive graphene 3-D network by radiation of the immiscible polymer blend with laser. The latter technique provides a technological alternative to overcome the drawbacks of the conventional melt mixing methods, such as nanofiller agglomeration and nanofiller structure/properties deterioration due to high shear forces and temperature exerted during mixing. Addition of MWCNT in immiscible blends by melt mixing, caused surprising morphological transformations that allowed us to tune the final electrical properties of the nanocomposites from capacitive to dissipative. Therefore, locating the MWCNT in different parts of the blend was selected as the tool to control the blend morphology and the electrical network formation in the system. We suggested that factors such as changes in viscosity and elasticity of the polymer melts due to the selective localization of the nanofiller are key parameters in the morphological changes. In addition, we proposed MWCNT filler migration from one phase to the other as a new mechanism for the morphological changes in the nanocomposite. The effect of different MWCNT migration regimes was also discussed. Since the localization of MWCNT is a determinant parameter in the final properties of the blend nanocomposites, we performed molecular simulations to understand the interactions between the polymeric chains and the MWCNT, and thus to predict the MWCNT localization. On the other hand, considering the immiscibility of the polymer blends, compatibilization is needed in order to take advantage of the properties of each of the components. This was done using block copolymers, which localize at the interphase and improve the phase adhesion. Interestingly, the decrease of domain size and decrease of blend co-continuity provided by the addition of copolymers, generated a positive impact in the electrical conduction of the material. This is the result of the network formed by the copolymer third phase with the MWCNT that interconnected the smaller MWCNT filled domains and micelles. Conventional mixing methods (i.e. solution mixing or melt mixing) used to produce CPNs can often change the nanoparticles structure due to multiple reasons, such as high shear forces, high temperature, and incompatible chemicals. In addition, agglomeration of the nanoparticles is another issue that degrades the performance of the CPNs and leads to inconsistent properties. Thus, we used a novel techniques to fabricate highly conductive polycarbonate (PC)/graphene nanocomposites from PC/polyetherimide (PEI) blends using commercial laser cutter platforms. The laser transforms the PEI phase to a conductive graphene network inside the PC matrix, and nanocomposites with controlled electrical conductivities are obtained with 26 S.m-1 to 400 S.m-1 conductivity, depending on the PEI dosage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".