Carbon Nanotube Conductive Networks through the Double Percolation Concept in Polymer Systems
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Bibliographic record
Abstract
Abstract We investigated the electrical conductivity and percolation behavior of binary and ternary nanocomposites based on multiwalled carbon nanotubes (MWCNs) using polypropylene (PP) and a blend of PP with cyclic butylene terephthalate (CBT). The nanocomposites were prepared by diluting a commercial 20 %wtMWCNT PP masterbatch using optimized melt-mixing conditions. The concentration of carbon nanotubes in the diluted PP samples was as low as 0.5 % and as high as 15 % in weight. For the PP/CBT blend CBT concentration was varied up to 40 %wt while the loading of CNT was from 0 to 5 %wt. SEM and TEM techniques were used to examine the quality of the dispersion and the formation of nanotube networks within the polymer matrix. TEM and Raman spectroscopy results showed that for the diluted PP/MWCNT composites the nanotubes are well aligned in samples obtained the microinjection molding process, although the level of alignment is less with crystalline PP than in an amorphous matrix such as polycarbonate (PC). FTIR and XRD results revealed that the orientation of both polymer chains and crystals decreased with the incorporation of nanotubes into PP. The electrical conductivity was also significantly altered by the nanotube alignment in a PP matrix, as was previously observed for PC/MWCNT composites; the conductivity decreased and the percolation threshold rose in highly sheared samples; however, the presence of a crystalline phase improved the conductivity even for high shear conditions through the phenomenon of double percolation threshold. This last concept refers to the requirement that the filler-rich phase be continuous and conductive and not to the existence of two percolation thresholds at two different CNT concentrations. The electrical conductivity of PP/CBT blends was also improved through a double percolation that is the basic requirement for the conductivity of the ternary nanocomposites.
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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 | 0.000 | 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 | 0.001 | 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 it