Engineering Dual CO<sub>2</sub>‐ and Photothermal‐Responsive Membranes for Switchable Double Emulsion Separation
Bibliographic record
Abstract
Abstract Stimulus‐responsive membranes demonstrate promising applications in switchable oil/water emulsion separations. However, they are unsuitable for the treatment of double emulsions like oil‐in‐water‐in‐oil (O/W/O) and water‐in‐oil‐in‐water (W/O/W) emulsions. For efficient separation of these complicated emulsions, fine control over the wettability, response time, and aperture structure of the membrane is required. Herein, dual‐coated fibers consisting of primary photothermal‐responsive and secondary CO 2 ‐responsive coatings are prepared by two steps. Automated weaving of these fibers produces membranes with photothermal‐ and CO 2 ‐responsive characteristics and narrow pore size distributions. These membranes exhibit fast switching wettability between superhydrophilicity (under CO 2 stimulation) and high hydrophobicity (under near‐infrared stimulation), achieving on‐demand separation of various O/W/O and W/O/W emulsions with separation efficiencies exceeding 99.6%. Two‐dimensional low‐field nuclear magnetic resonance and correlated spectra technique are used to clarify the underlying mechanism of switchable double emulsion separation. The approach can effectively address the challenges associated with the use of stimulus‐responsive membranes for double emulsion separation and facilitate the industrial application of these membranes.
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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) | 0.001 | 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".