Highly Porous Poly(high internal phase emulsion) Membranes with “Open-Cell” Structure and CO<sub>2</sub>-Switchable Wettability Used for Controlled Oil/Water Separation
Bibliographic record
Abstract
Polymer membranes with switchable wettability have promising applications in smart separation. Hereby, we report highly porous poly(styrene- co - N, N -(diethylamino)ethyl methacrylate) (i.e., poly(St- co -DEA)) membranes with “open-cell” structure and CO 2 -switchable wettability prepared from water-in-oil (W/O) high internal phase emulsion (HIPE) templates. The open-cell porous structure facilitates fluid penetration through the membranes. The combination of CO 2 -switchable functionality and porous microstructure enable the membrane with CO 2 -switchable wettability from hydrophobic or superoleophilic to hydrophilic or superoleophobic through CO 2 treatment in an aqueous system. This type of membrane can be used for gravity-driven CO 2 -controlled oil/water separation, in which oil selectively penetrates through the membrane and separates from water. After being treated with CO 2 switching wettability of the membrane, a reversed separation of water and oil can be achieved. Such a wettability switch is fully reversible, and the membrane could be regenerated through simple removal of CO 2 and oil residual through drying. This facile and cost-effective approach represents the development of the first CO 2 -switchable polyHIPE system, which is promising for smart separation in a large volume.
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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 | 0.001 |
| 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".