Cobalt Catalyst Grown Carbon Nanotube/Poly(Vinylidene Fluoride) Nanocomposites: Effect of Synthesis Temperature on Morphology, Electrical Conductivity and Electromagnetic Interference Shielding
Bibliographic record
Abstract
Abstract A chemical vapor deposition technique was executed to synthesize multi‐walled carbon nanotubes (CNTs) at different temperatures (550, 650, 750 and 850 °C) using cobalt catalyst. Different concentrations of the synthesized CNTs were melt mixed with a polyvinylidene fluoride (PVDF) matrix, and then compression molded. The nanocomposites containing CNT 650 had significantly lower electrical percolation threshold (0.3 wt.%) and higher electromagnetic interference shielding effectiveness compared to their counterparts. We investigated the underlying reasons by means of various characterization techniques, where the most significant results are as follows. Thermogravimetric analysis demonstrated that the synthesis temperature of 650 °C leads to superior carbon purity and CNT quality. We found that 650 °C is the optimum temperature providing sufficient energy for the synthesis with minimum catalyst sintering. Employing light microscopy and transmission electron microscopy, it was realized that CNT 650 had better micro‐dispersion and nano‐dispersion states within the polymer matrix than the other CNTs. Different characterization methods established that the superior electrical properties of nanocomposites containing CNT 650 are attributable to a combination of high carbon purity, high aspect ratio, and high crystallinity of CNT 650 along with optimum dispersion state of CNT 650 within the PVDF matrix.
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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".