Structure and properties of melt‐spun PET/MWCNT nanocomposite fibers
Bibliographic record
Abstract
Abstract Polyethylene terephthalate (PET) melt‐spun fibers were modified with multiwall carbon nanotubes (MWCNT) to obtain conductive microfibers smaller than 90 μm in diameter. Physical properties such as crystallinity and orientation of as‐spun fibers were studied by X‐ray diffraction, Raman spectroscopy, and microscopy techniques at different draw ratios (DR) and MWCNT concentrations. Morphological and orientation analysis of MWCNT after melt‐spinning process showed agglomerates formation and highly oriented CNTs. The study of the orientation of PET crystalline phase in drawn fibers proved that the addition of nanoparticles decreases the orientation of crystalline units inside the fibers. The orientation of MWCNT as well as that of PET chains was studied using Raman spectroscopy at different DR and a high degree of CNT orientation was observed under high DR conditions. Mechanical and electrical properties of as‐spun fibers were also investigated. Our results showed that it was possible to achieve conductive fibers at a MWCNT concentration of 2% w/w, and more conductive fibers using higher DR were also obtained without increasing the MWCNT concentration. Mechanical properties results showed interestingly high value of maximum tensile strain at break (εmax) of nanocomposite fibers, up to three times more than pure PET fibers. POLYM. ENG. SCI., 50:1956–1968, 2010. © 2010 Society of Plastics Engineers
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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".