Effects of Surface Modification of MWCNT on the Mechanical and Electrical Properties of Fluoro Elastomer/MWCNT Nanocomposites
Bibliographic record
Abstract
Surface modification is a good way to improve the surface activity and interfacial strength of multiwalled carbon nanotubes (MWCNTs) when used as fillers in the polymer composites. Among the reported methods for nanotube modification, mixed acid oxidation and plasma treatment is often used by introducing polar groups to the sidewall of MWCNT successfully. The purpose of this study is to evaluate the effect of different surface modification of MWCNT on the mechanical property and electrical conductivity of Fluoro‐elastomer (FE)/MWCNT nanocomposites. MWCNTs were surface modified by mixed oxidation and CF4 plasma treatment and then used to reinforce the fluoro elastomer (FE, a copolymer of trifluorochloroethylene and polyvinylidene fluoride). FE/MWCNT composite films were prepared from mixture solutions of ethylacetate and butylacetate, using untreated CNTs (UCNTs), acid‐modified CNTs (ACNTs), and CF4 plasma‐modified CNT (FCNTs). In each case, MWCNT content was 0.01 wt%, 0.05 wt%, 0.1 wt%, and 0.2 wt% with respect to the polymer. Morphology and mechanical properties were characterized by using scanning electron microscopy (SEM), Raman spectroscopy, as well as dynamic mechanical tests. The SEM results indicated that dispersion of ACNTs and especially FCNTs in FE was better than that of UCNTs. DMA indicated mechanical properties of FCNT composites were improved over ACNT and UCNT filled FE. The resulting electrical properties of the composites ranged from dielectric behavior to bulk conductivities of 10−2 Sm−1 and were found to depend strongly on the surface modification methods of MWCNTs.
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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".