Porous Films via PE/PEO Cocontinuous Blends
Bibliographic record
Abstract
The design of a porous membrane support layer derived from cocontinuous polymer blends is presented. We investigate the effect of blend composition, shear rate, residence time, and annealing time on the cocontinuous morphology of polyethylene (PE)/poly(ethylene oxide) (PEO) blends. Porous PE sheets were generated by water extraction of PEO and used as a support layer for gas separation membranes. The PE/PEO blends using nonfunctional and maleic anhydride functional PE (PE- g -MA) were mixed in a batch microcompounder and in a pilot plant scale corotating twin-screw extruder. Using PE- g -MA resulted in pore size reduction from 10 to 2 μm and suppression of coarsening of the morphology during further annealing of the blends due to formation of PE–PEO graft copolymers. Equilibrium interfacial tension, estimated by fitting the rheology of droplet blends to the Palierne viscoelastic droplet model, was 3 and 0.4 mN/m for PE/PEO and PE- g -MA/PEO systems, respectively. The specific interfacial area and phase size distribution were calculated from 3D images acquired by laser scanning electron microscopy (LSCM). We prepared gas separation membranes by solvent casting an acetone solution of ionic gel into porous PE sheets and discussed the effect of type of processing, average pore size, pore size distribution, and pore wall functionality on their performance.
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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".