Enhanced Properties of Aramid/Salinized MWCNT Composites Through Surface Treatment: A Combined Chemical and Microscopic Approach
Bibliographic record
Abstract
MWCNTs are made of many layers of graphene cylinders with hexagonal arrangements on walls and mainly sp2 hybrid bonds. They exhibit structural defects that allow functionalization to enhance compatibility with polymers, improving properties like dispersion, conductivity, and mechanical strength. This study aimed to reinforce an aramid matrix with uniformly dispersed, functionalized multi-walled carbon nanotubes (MWCNTs), establishing strong interfacial bonds and achieving improved thermo-mechanical properties. MWCNTs were oxidized and functionalized using amino-silane agents (APrTES and APhTMS) via a sol-gel process. The modified nanotubes were incorporated into aramid matrices to fabricate composite films. Characterization included Raman, FTIR, UV-Vis, XPS, and microscopy (SEM, TEM, AFM). FTIR spectra confirmed successful functionalization, with Si-O-Si and amide bonds appearing at 1192 cm− 1 and 533.38 eV, respectively. Raman analysis revealed ID/IG ratios increasing from 1.05 (pristine) to 1.18 (oxidized), reflecting defect introduction, and then decreasing to 1.06 after silanization, indicating partial defect healing. SEM and AFM demonstrated uniform dispersion and reduced surface roughness in composites with aromatic silanes. Mechanical tests revealed improved tensile strength and thermal stability with 7.5 wt.% silanized MWCNTs. The results demonstrated the potential of silanized MWCNTs to enhance aramid composite properties through strong interfacial bonding and improved dispersion. This work provides a pathway for developing advanced materials with superior performance in structural and thermal applications.
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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".