Effect of nitrogen doping on medium-amplitude oscillatory shear (MAOS) response of nanotube/polyvinylidene fluoride nanocomposites: Molecular simulations, rheology, and broadband electrical conductivity
Bibliographic record
Abstract
This study sheds light on the effect of nitrogen (N) doping of carbon nanotubes (CNTs) on medium-amplitude oscillatory shear (MAOS) response of CNT/polyvinylidene fluoride (PVDF) nanocomposites within a rheologically percolated concentration regime. Custom-synthesized CNTs without and with nitrogen heteroatom (at a nitrogen atomic percent of 3.85 at. %) were incorporated into a PVDF matrix using a miniature melt-mixer at different concentrations. In both cases, as confirmed by TEM investigations, a nanoscopic state of dispersion in the PVDF matrix was achievable using the applied melt mixing procedure. Our results indicated that N-doped nanocomposites, well below their electrical percolation, form a hybrid, load-bearing network structure where network interconnectivity is driven by N-doped CNT domains and near-surface regions of the PVDF phase. This hybrid network formation behavior combined with N-doped CNTs inferior aspect ratio and their higher susceptibility to breakage and length loss during the melt mixing process have led to delayed electrical percolation. In contrast, localized clustering and contact aggregation in a submicrometer scale was the dominant mode of network formation in the undoped CNT/PVDF nanocomposites. These microstructural inferences were further validated in the frame of molecular simulations and optical microscopy investigations.
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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".