Melt Mixing of Polycarbonate with Multi‐Walled Carbon Nanotubes in Miniature Mixers
Bibliographic record
Abstract
Abstract Summary: MWNT mixtures with PC were prepared in three different miniature mixers at 265 °C and 50 rpm for 6 min by the master batch dilution method. One mixer is a 4.5 cm3 DACA microcompounder (DACA Instruments) consisting of two conical co‐rotating screws with a bypass, allowing the material to circulate for defined periods. The other two miniature mixers are custom‐built in our lab: the 2.2 cm3 APAM and the 3 cm3 MBM. The volume resistivity for the nanocomposites obtained from the APAM and the MBM showed a similar trend for different MWNT compositions. The electrical percolation concentration for the nanocomposites prepared in the APAM and the MBM is between 0.50 wt.‐% (or 0.34 vol.‐%) and 0.75 wt.‐% (or 0.52 vol.‐%) MWNT, and it is between 0.75 wt.‐% (or 0.52 vol.‐%) and 1.00 wt.‐% (or 0.69 vol.‐%) for the DACA microcompounder. Rheological characterization indicates that the microstructure of PC/MWNT composites prepared from the miniature mixers changes at a concentration of 0.38 wt.‐% for the APAM and the MBM and 0.50 wt.‐% for the DACA where an interconnected network is formed. TEM micrographs show that there are some small aggregates in the nanocomposites obtained from the APAM, fewer aggregates from the MBM, and least from the DACA. AFM analysis suggests that the length of nanotubes is reduced from 0.57 µm to 0.38–0.42 µm after they were melt mixed in the three mixers. Effect of MWNT content on volume resistivity of PC/MWNT obtained from different microcompounders. magnified image Effect of MWNT content on volume resistivity of PC/MWNT obtained from different microcompounders.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".