Effect of carbon nanotube on PA6/ECO composites: Morphology development, rheological, and thermal properties
Bibliographic record
Abstract
ABSTRACT Thermoplastic elastomer (TPE) nanocomposites based on polyamide‐6 (PA6)/poly(epichlorohydrin‐ co ‐ethylene oxide) (ECO)/multiwall carbon nanotube (MWCNTs) were prepared by melt compounding process. Different weight ratios of ECO (20, 40, and 60 wt %) and two kinds of functionalized and non‐functionalized MWCNTs were employed to fabricate the nanocomposites. The morphological, rheological, and mechanical properties of MWCNTs‐filled PA6/ECO blends were studied. The scanning electron microscopy of PA6/ECO blends showed that the elastomer particles, ECO, are well‐dispersed within the PA6 matrix. The significant improvement in the dispersibility of the carboxylated carbon nanotubes (COOH‐MWCNTs) compared to that of non‐functionalized MWCNTs (non‐MWCNTs) was confirmed by transmission electron microscopy images. The tensile modulus of samples improved with the addition of both types of MWCNTs. However, the effect of COOH‐MWCNTs was much more pronounced in improving mechanical properties of PA6/ECO TPE nanocomposites. Crystallization results demonstrated that the MWCNTs act as a nucleation agent of the crystallization process resulted in increased crystallization temperature ( T c ) in nanocomposites. Rheological characterization in the linear viscoelastic region showed that complex viscosity and a non‐terminal storage modulus significantly increased with incorporation of both types of MWCNTs particularly at low frequency region. The increase of rheological properties was more pronounced in the presence of carboxylic (COOH) functional groups, in the other words by addition of COOH‐MWCNTs. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135 , 45977.
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 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 | 0.002 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".