Interface Strengthening of PS/aPA Polymer Blend Nanocomposites via In Situ Compatibilization: Enhancement of Electrical and Rheological Properties
Bibliographic record
Abstract
The process of strengthening interfaces in polymer blend nanocomposites (PBNs) has been studied extensively, however a corresponding significant enhancement in the electrical and rheological properties is not always achieved. In this work, we exploit the chemical reaction between polystyrene maleic anhydride and the amine group in nylon (polyamide) to achieve an in-situ compatibilization during melt processing. Herein, nanocomposites were made by systematically adding polystyrene maleic anhydride (PSMA) at different compositions (1–10 vol%) in a two-step mixing sequence to a Polystyrene (PS)/Polyamide (aPA) blend with constant composition ratio of 25:75 (PS + PSMA:aPA) and 1.5 vol% carbon nanotube (CNT) loading. The order of addition of the individual components was varied in two-step mixing procedure to investigate the effect of mixing order on morphology and consequently, on the final properties. The electrical and rheological properties of these multiphase nanocomposite materials were investigated. The optical microscope images show that for PS/aPA systems, CNTs preferred the matrix phase aPA, which is the thermodynamically favorable phase according to the wettability parameter calculated using Young’s equation. However, aPA’s great affinity for CNT adversely influenced the electrical properties of our blend. Adding PSMA to PS/aPA changed the structure of the droplet phase significantly. At 1.5 vol% CNT, a more regular and even distribution of the droplet domains was observed, and this produced a better framework to create more CNT networks in the matrix, resulting in a higher conductivity. For example, with only 1.5 vol% CNT in the PBN, at 3 vol% PSMA, the conductivity was 7.4 × 10−2 S/m, which was three and a half orders of magnitude higher than that seen for non-reactive PS/aPA/CNT PBN. The mechanism for the enhanced conductive network formation is delineated and the improved rheological properties due to the interfacial reaction is presented.
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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".