Carbon Nanotube/Graphene Nanoribbon/Polyvinylidene Fluoride Hybrid Nanocomposites: Rheological and Dielectric Properties
Bibliographic record
Abstract
Results of the present study demonstrate the potential of graphene nanoribbon to induce giant synergistic effects in the broadband dielectric properties of multiwalled carbon nanotube/graphene nanoribbon/polyvinylidene fluoride (MWCNT/GNR/PVDF) nanocomposites. The nanocomposites were prepared using a melt-mixing technique at various nanofiller total contents and MWCNT/GNR weight ratios. Rheology coupled with AC conductivity measurements of the nanocomposites unearthed highly superior capability of MWCNT to neighbor or interlace compared to GNR; i.e., the MWCNT has higher ability to participate in a percolative network. Broadband dielectric spectroscopy demonstrated superior dielectric properties for MWCNT/GNR/PVDF ternary (hybrid) nanocomposites compared to the MWCNT or GNR binary nanocomposites. For instance, at 1.5 wt % and 1000 Hz, the ternary nanocomposite with an MWCNT/GNR ratio of 3:1 presented a real permittivity and dissipation factor of 41.4 and 0.91, surpassing the binary MWCNT nanocomposite with a real permittivity and dissipation factor of 39.3 and 86.7, respectively. We attribute this synergistic effect to the poor interlacing ability of GNRs, as secondary conductive nanofillers, acting as extra nanoelectrodes. In fact, the role of GNRs as extra nanoelectrodes in conjunction with their poor propensity to bridge MWCNTs led to effective nanocapacitor structures with low energy loss.
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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) | 0.001 | 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".