Aspects of the melt spinning of fibres from carbon nanotube- nylon nanocomposites
Bibliographic record
Abstract
Carbon nanotubes (CNT) have experienced growing popularity in the past two decades.The field of advanced composite materials has developed quite an interest in using them for high performance applications, such as into thermoplastic fibres.However, a review of the literature showed that there was a lack of understanding between the rheological behavior of CNT-filled polymer and the processability of such materials through melt spinning.In this work, a methodology to incorporate carbon nanotubes (CNT) into thermoplastic fibres and to relate mechanical properties, fibre quality and viscosity was developed.Multi-walled nanotubes (MWNT) were combined into a polyamide 12 (PA12) matrix through melt compounding and twin-screw extrusion.Pellets containing 0 wt%, 0.5 wt%, 1.0 wt%, 2.0 wt%, 5.0 wt% and 10.0 wt% MWNT were produced.Their rheological behaviour was investigated and spinnability and processability criteria were developed based on the loss factor (tan ) and the relative viscosity.They both predicted that masterbatches containing more than 2.0 wt% CNT would be unsuitable for the production of high quality MWNT/PA12 fibres.The pellets were subsequently melt spun with a capillary rheometer at winding speeds of 41 m/min and 152 m/min.The tensile properties of as-spun filaments were measured with a micro-tensile testing machine.The results showed that the maximum Young's modulus was reached between 0 wt% and 1.0 wt% CNT, exhibiting an increase of 17%.Morphological observations revealed that there was a link between the decrease of elastic modulus and loss of surface quality for filaments containing more than 1.0 wt% MWNT.To further improve the fibres' mechanical properties, post-drawing parameters were systematically investigated: temperature, drawing speed and elongation.The best improvements in terms of elastic modulus and tensile stress were measured for the following post-drawing conditions: 140 o C and 500% elongation, regardless of drawing speed.The elastic modulus (E) and tensile stress ( 0 ) values of MWNT/PA12 fibres were improved by at least 300% after post-drawing.Compared to pure PA12 fibres post-drawn under the same conditions, E increased by up to 45% and 0 by up to 62%, for fibres containing 5.0 wt% MWNT.It was confirmed through electron microscopy comparées aux fibres pures de PA12 étirées selon les mêmes conditions.Il a été confirmé par microscopie électronique et diffraction aux rayons X que ces gains ont été causés par la distribution plus uniforme des nanotubes, l'amélioration du fini de surface et l'alignement des chaînes de polymère le long des fibres, contrôlés par la temperature et l'élongation.Le contrôle des propriétés mécaniques avec les paramètres de post-étirement démontre que cette méthode s'avère fort prometteuse pour confectionner des fibres selon des applications spécifiques.v ACKNOWLEDGEMENTS Je voudrais tout d'abord offrir mes remerciements les plus sincères à mes superviseurs, Professeur Pascal Hubert et Dr David Trudel-Boucher.Leurs précieux conseils et leurs perspectives de recherche différentes m'ont amenée à élargir mes horizons dans ma façon d'approcher les problèmes.Leur porte était toujours ouverte et leur attitude positive face à la recherche scientifique était grandement bienvenue lorsque les choses n'allaient pas comme prévu, tel qu'il est souvent le cas avec les nanocomposites.J'aimerais également remercier les personnes suivantes pour leur aide et assistance durant mes travaux à l'IMI:
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".