Employing Nitrogen Doping as Innovative Technique to Improve Broadband Dielectric Properties of Carbon Nanotube/Polymer Nanocomposites
Bibliographic record
Abstract
Undoped carbon nanotubes (CNTs) and N‐CNTs are synthesized by chemical vapor deposition using Fe catalyst, and then melt‐mixed in an APAM mixer with polyvinylidene fluoride (PVDF) to prepare the nanocomposites. The morphology, crystallinity, aspect ratio, nitrogen content, and nitrogen bonding type of CNTs, and the broadband dielectric properties of undoped CNT/PVDF and N‐CNT/PVDF nanocomposites are analyzed. The results show that while undoped CNTs present a crystalline structure with open channels, doping with nitrogen results in CNTs with a bamboo‐like configuration, inferior crystallinity, smaller length, and larger diameter. The N‐CNT/PVDF nanocomposites, thus, have a higher percolation threshold (≈ 3.5 wt%) compared to that of the undoped CNT/PVDF nanocomposites (≈ 0.5 wt%). Comparison of the broadband dielectric properties of the generated nanocomposites reveals that nitrogen doping improved the dielectric properties in the insulative region. This is ascribed to the role of nitrogen atoms and their sequent defects in the nanotubes, which act as scattering centers and provide additional polarization sites. For instance, 1.0 wt% N‐CNT/PVDF nanocomposites exhibit a real permittivity of ε′ = 22 and a dissipation factor of tan δ = 0.03 at 1 kHz, a combination superior to that of 0.5 wt% undoped CNT/PVDF nanocomposite with ε′ = 11.2 and tan δ = 3.8, and 1.0 wt% undoped CNT/PVDF nanocomposites with ε′ = 40 and tan δ = 1.4 × 105. image
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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".