Carbon Nanotube versus Graphene Nanoribbon: Impact of Nanofiller Geometry on Electromagnetic Interference Shielding of Polyvinylidene Fluoride Nanocomposites
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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