Extensional flow mixer for polymer nanocomposites
Bibliographic record
Abstract
Abstract The extensional flow mixer (EFM) has been used in industry, for e.g., homogenization of reactor products, polymer blending, incorporation of plasticizer, etc. Recently, several laboratories attempted to use EFM for dispersing organoclay in a molten polymer. Thus, usually EFM was mounted on a twin‐screw extruder equipped with a gear pump. The use of EFM resulted in improved dispersion and performance—more significant in polyamide or thermoplastic polyester—and marginal in a polyolefin or polystyrene. Recently, to improve EFM efficiency, the commercial EFM‐3 was modified by redesigning the convergent–divergent plates that engender the extensional flow. The two mixers, EFM‐3 and the new EFM‐N, were evaluated using a single‐screw extruder. Two systems were examined: (1) polyamide‐6 (PA‐6) with Cloisite®‐15A (C15A) and (2) polypropylene with maleated‐PP and C15A. The compounded samples were injection‐molded, and then tested for the degree of dispersion and mechanical performance. The results showed superiority of EFM‐N. Compounding PA‐6 with C15A in a single‐screw extruder with EFM‐N exfoliated the organoclay, producing polymeric nanocomposites with high performance, comparable or better than that of a commercial nanocomposite produced by polycondensation of ϵ‐caprolactam in the presence of clay, preintercalated with reactive cations. POLYM. ENG. SCI. 46:1040–1050, 2006. © 2006 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 | 0.001 | 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) | 0.002 | 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".