Dispersion Characteristics and Properties of Poly(methyl methacrylate)/Multi-Walled Carbon Nanotubes Nanocomposites
Bibliographic record
Abstract
The preparation, characterization, and properties of poly(methyl methacrylate) (PMMA)/multi-walled carbon nanotubes (MWCNTs) nanocomposites are described. Nanocomposites have been prepared by melt-blending in a batch mixer. Both unmodified and surface modified MWCNTs have been used for the nanocomposites preparation. Using both unmodified and modified MWCNTs, the effect of surface modification in nanocomposites is investigated by focusing on three major aspects: dispersion characteristics, mechanical properties, and electrical conductivity measurements. Dispersion of the MWCNTs in the PMMA matrix is examined by scanning and transmission electron microscopy that revealed a homogeneous distribution-dispersion of MWCNTs in the PMMA matrix for both unmodified and modified MWCNTs. Thermomechanical behavior is studied by dynamic mechanical analyzer and results showed a substantial improvement in the mechanical properties of PMMA in conjunction to an increase in the elastic behavior. The tensile properties of neat PMMA moderately improved after nanocomposites preparation with both modified and unmodified MWCNTs, however, electrical conductivity of neat PMMA significantly improved after nanocomposites preparation with 2 wt% unmodified MWCNTs. For example, the through plane conductivity increased from 3.6 x 10(-12) S x cm(-1) for neat PMMA to 3.6 x 10(-9) S x cm(-1) for nanocomposite. The various property measurements have been conducted and results have shown that, in overall, surface modifications have very little or no effect on final properties of neat PMMA.
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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 | 0.001 |
| 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".