Investigation of a postprocessing method to tailor the mechanical properties of carbon nanotube/polyamide fibers
Bibliographic record
Abstract
ABSTRACT The incorporation of carbon nanotubes to thermoplastic fibers can potentially improve mechanical, thermal and electrical properties. In this article, a methodology to tailor the mechanical properties of carbon nanotube/nylon fibers is presented. Multiwalled nanotubes (MWNT) were combined to polyamide 12 through melt compounding and twin‐screw extrusion. Pellets containing between 0 and 5.0 wt % MWNT were extruded and subsequently melt spun with a capillary rheometer to produce filaments. To further promote the alignment of the polymer chains and MWNTs, postdrawing parameters were systematically investigated: temperature, drawing speed and elongation. The best improvements in terms of elastic modulus and yield strength were measured at 140°C and 500% elongation, whereas drawing speed was shown to have a negligible effect. It was confirmed through electron microscopy and X‐ray diffraction that these enhancements were mainly induced by the alignment of the polymer chains along the fibers' axis. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 130: 4375–4382, 2013
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 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".