Enhancing electrical properties of <scp>MWCNT</scp> s in immiscible blends of poly(methyl methacrylate) and styrene‐acrylonitrile copolymer by selective localization
Bibliographic record
Abstract
Multiwalled carbon nanotubes (MWCNTs) were introduced into poly(methyl methacrylate) (PMMA) and styrene‐acrylonitrile copolymer (SAN) blends by melt mixing in an asymmetric miniature mixer (APAM). A composition of 70 wt% of PMMA and 30 wt% of SAN was mixed to create a co‐continuous morphology. Transmission electron microscopy images of ultra‐microtomed samples (70 nm in thickness) showed selective localization of MWCNTs inside the percolated SAN phase. The occurrence of the double percolation phenomenon resulted in lower electrical percolation thresholds of PMMA/SAN/MWCNT blends molded at high temperatures. Dielectric spectroscopy indicated a higher electrical permittivity for samples that were compression molded at 260°C. Due to the higher affinity of MWCNTs to SAN, there was a migration of MWCNTs into the SAN phase during the melt processing. Conductivity measurements revealed a significant decrease in electrical percolation threshold (0.4 wt%) for PMMA70/SAN30 blends compared with MWCNT‐filled SAN and MWCNT‐filled PMMA (ca. 0.8 wt%). POLYM. COMPOS., 37:1523–1530, 2016. © 2014 Society of Plastics Engineers
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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".