Fundamental study of crystallization, orientation, and electrical conductivity of electrospun PET/carbon nanotube nanofibers
Bibliographic record
Abstract
Abstract The morphology, structure, and properties of polyethylene terephthalate (PET)/Carbon Nanotubes (CNT) conductive nanoweb were studied in this article. Nanocomposite nanofibers were obtained through electrospinning of PET solutions in trifluoroacetic acid (TFA)/dichloromethane (DCM) containing different concentrations and types of CNTs. Electrical conductivity measurements on nanofiber mats showed an electrical percolation threshold around 2 wt % multi‐wall carbon nanotubes (MWCNT). The morphological analysis results showed smoother nanofibers with less bead structures development when using a rotating drum collector especially at high concentrations of CNTs. From crystallographic measurements, a higher degree of crystallinity was observed with increasing CNT concentrations above electrical percolation. Spectroscopy results showed that both PET and CNT orientation increased with the level of alignment of the nanofibers when the nanotube concentration was below the electrical percolation threshold; while the orientation factor was reduced for aligned nanofibers with higher content in CNT. Considerable enhancement in mechanical properties, especially tensile modulus, was found in aligned nanofibers; at least six times higher than the modulus of random nanofibers at concentrations below percolation. The effect of alignment on the mechanical properties was less important at higher concentrations of CNTs, above the percolation threshold. © 2010 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 48: 2052–2064, 2010
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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".