Morphology Evolution, Molecular Simulation, Electrical Properties, and Rheology of Carbon Nanotube/Polypropylene/Polystyrene Blend Nanocomposites: Effect of Molecular Interaction between Styrene-Butadiene Block Copolymer and Carbon Nanotube
Bibliographic record
Abstract
This work studied the impact of three types of styrene-butadiene (SB and SBS) block copolymers on the morphology, electrical, and rheological properties of immiscible blends of polypropylene:polystyrene (PP:PS)/multi-walled carbon nanotubes (MWCNT) with a fixed blend ratio of 70:30 vol.%. The addition of block copolymers to PP:PS/MWCNT blend nanocomposites produced a decrease in the droplet size. MWCNTs, known to induce co-continuity in PP:PS blends, did not interfere with the copolymer migration to the interface and, thus, there was morphology refinement upon addition of the copolymers. Interestingly, the addition of the block copolymers decreased the electrical resistivity of the PP:PS/1.0 vol.% MWCNT system by 5 orders of magnitude (i.e., increase in electrical conductivity). This improvement was attributed to PS Droplets-PP-Copolymer-Micelle assemblies, which accumulated MWCNTs, and formed an integrated network for electrical conduction. Molecular simulation and solubility parameters were used to predict the MWCNT localization in the immiscible blend. The simulation results showed that diblock copolymers favorably interact with the nanotubes in comparison to the triblock copolymer, PP, and PS. However, the interaction between the copolymers and PP or PS is stronger than the interaction of the copolymers and MWCNTs. Hence, the addition of copolymer also changed the localization of MWCNT from PS to PS-PP-Micelles-Interface, as observed by TEM images. In addition, in the last step of this work, we investigated the effect of the addition of copolymers on inter- and intra-cycle viscoelastic behavior of the MWCNT incorporated polymer blends. It was found that addition of the copolymers not only affects the linear viscoelasticity (e.g., increase in the value of the storage modulus) but also dramatically impacts the nonlinear viscoelastic behavior under large deformations (e.g., higher distortion of Lissajous-Bowditch plots).].
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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) | 0.000 | 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 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".