Scalable weaving of resilient membranes with on-demand superwettability for high-performance nanoemulsion separations
Bibliographic record
Abstract
This study leverages the ancient craft of weaving to prepare membranes that can effectively treat oil/water mixtures, specifically challenging nanoemulsions. Drawing inspiration from the core-shell architecture of spider silk, we have engineered fibers, the fundamental building blocks for weaving membranes, that feature a mechanically robust core for tight weaving, coupled with a CO 2 -responsive shell that allows for on-demand wettability adjustments. Tightly weaving these fibers produces membranes with ideal pores, achieving over 99.6% separation efficiency for nanoemulsions with droplets as small as 20 nm. They offer high flux rates, on-demand self-cleaning, and can switch between sieving oil and water nanodroplets through simple CO 2 /N 2 stimulation. Moreover, weaving can produce sufficiently large membranes (4800 cm 2 ) to assemble a module that exhibits long-term stability and performance, surpassing state-of-the-art technologies for nanoemulsion separations, thus making industrial application a practical reality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".