Carbon Nanotube versus Graphene Nanoribbon: Impact of Nanofiller Geometry on Electromagnetic Interference Shielding of Polyvinylidene Fluoride Nanocomposites
Bibliographic record
Abstract
The similar molecular structure but different geometries of the carbon nanotube (CNT) and graphene nanoribbon (GNR) create a genuine opportunity to assess the impact of nanofiller geometry (tube vs. ribbon) on the electromagnetic interference (EMI) shielding of polymer nanocomposites. In this regard, GNR and its parent CNT were melt mixed with a polyvinylidene fluoride (PVDF) matrix using a miniature melt mixer at various nanofiller loadings, i.e., 0.3, 0.5, 1.0 and 2.0 wt%, and then compression molded. Molecular simulations showed that CNT would have a better interaction with the PVDF matrix in any configuration. Rheological results validated that CNTs feature a far stronger network (mechanical interlocking) than GNRs. Despite lower powder conductivity and a comparable dispersion state, it was interestingly observed that CNT nanocomposites indicated a highly superior electrical conductivity and EMI shielding at higher nanofiller loadings. For instance, at 2.0 wt%, CNT/PVDF nanocomposites showed an electrical conductivity of 0.77 S·m−1 and an EMI shielding effectiveness of 11.60 dB, which are eight orders of magnitude and twofold higher than their GNR counterparts, respectively. This observation was attributed to their superior conductive network formation and the interlocking ability of the tubular nanostructure to the ribbon-like nanostructure, verified by molecular simulations and rheological assays.
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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".