Investigation of Chaotic Mixing for MWCNT/Polymer Composites
Bibliographic record
Abstract
A chaotic mixer is developed and optimized in order to overcome challenges associated with mixing polymers with high aspect ratio nano‐particulates. The chaotic mixing system utilizes two cylindrical rotors to uniformly mix multi‐walled carbon nanotubes (MWCNTs) with a thermoplastic. Results of the electrical conductivity and electromagnetic interference (EMI) shielding effectiveness of the chaotic mixed nanocomposites were higher than ones mixed via a commercial HAAKE mixer. MWCNTs’ length was investigated and it was observed that the MWCNTs in chaotic mixed nanocomposites are longer compared to HAAKE mixer. To investigate the effects of MWCNTs’ length on the electrical properties, a 3D electrical model based on random walk method was developed and examined. Obtained results suggest that the chaotic mixer has a higher potential for mixing nano particulates into thermoplastics without breaking the nanotubes and improved electrical properties, compared to other types of melt mixing techniques.
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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) | 0.000 | 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".