Bibliographic record
Abstract
Abstract Mixing is the most important operation in polymer processing. Uniformity of the molecular weight, degree of entanglement, temperature, and composition, is the prerequisite for good performance. Mixing of molten polymers is always within the laminar (as opposed to turbulent) flow regime. This paper presents an overview of the effect of flow type on mixing. The distributive mixing is best modeled considering either laminar or chaotic mixing. Here the system is ”passive“, i.e. the rheological properties are identical and the interface is ”invisible“. Furthermore, the effects of flow and morphology are ”decoupled“. By contrast, the dispersive mixing is best modeled by means of microrheology, complemented by coalescence. Both models – the laminar mixing and the microrheology – show the unambiguous superiority of the extensional flows for dispersive and distributive mixing, in particular, when the viscosity ratio of two principal components is large. However this superiority is balanced by practical difficulties in generating a flow that may ascertain a high level of extension. Results of simulation and model experiments on drop deformabilty in converging and diverging (c-d) flow were used to develop a simple and inexpensive mixer capable of generating a high level of extension that lead to enhanced mixing capability. In consequence, an extensional flow mixer (EFM) was designed. In this device, a liquid mixture is repeatedly exposed to extensional flow fields and semi-quiescent zones. The c-d channels are of progressively increasing intensity. To reduce the pressure drop, and to prevent blockage, slit restrictions are used. The EFM (as well as its newer, dynamic version – DEFM) is fully adjustable, general-purpose mixers. Several its versions have been used for: polymer blending, incorporation of elastomers into resins, and dispersion of high viscosity polymers or ”gel particles“. For most mixing applications a singe-screw extruder equipped with either EFM or DEFM performed at least as well as a co-rotating, inter-meshing twin-screw extruder, with high efficiency screw configuration.
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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) | 0.002 | 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".