Modeling of interfacial diffusion in adjacent flows of polymer films
Bibliographic record
Abstract
Adjacent flow of two polymeric fluids occurs in many industrial processes. Under these processes, entangled polymer chains usually undergo extensional flow and shear flow deformation fields, rendering orientation and stretching within polymer chains. In the present paper, the chain stretching ratio and interfacial diffusion in a symmetric bilayer film in the isothermal adjacent flows in a coextrusion process are modeled using the double constraint release with chain stretching model. Extension-dominant and shear-dominant flows are considered separately for ease of the modeling process. Also, the impact of entanglement density on the reptation relaxation is investigated to determine the entanglement density variation and its effect on the stretching ratios and interfacial diffusion. Our findings show that extension-dominant and shear-dominant deformation fields have different impacts on polymer chain stretching, affecting polymer interfacial chain diffusion. Our findings show that while shear flow with the strength of 30 s−1 increases the stretching ratio by 28%, extensional flow with the same strength increases the stretching ratio by 60% of the maximum stretching ratio for polystyrene chains with an average molecular weight of 200 km/mol at 175 °C. Our results show that initial entanglement density is effective on the chain stretching only in the transition step before chains reach equilibriums. This study highlights the impact of flow conditions and chain configuration in polymers on engineering the diffusion of polymer chains at the interface of layered configurations.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".