Integrated emulsion separation and fog collection with functionalized Janus wood membrane for water scarcity solutions
Bibliographic record
Abstract
Global climate change has exacerbated water scarcity, while traditional water treatment technologies are often unsustainable due to high energy consumption and negative environmental impacts, posing an urgent need for a sustainable solution. This study developed a novel wood-based flexible Janus membrane coupled with a spine structure for efficient oil-water emulsion separation and fog harvesting. The Janus wood membrane showed high separation efficiency (> 99.6%), high filtration flux (water-in-oil and oil-in-water emulsions exceeded 810 L/m²·h and 747 L/m²·h, respectively), and good reusability. Additionally, the introduction of spine and conical pores significantly enhanced fog collection efficiency (19.23 kg/m²·h), expanding the application potential of Janus membranes. Moreover, this Janus wood membrane offered excellent mechanical properties, dimensional stability, mildew resistance, and environmental benefits. This study underscored the potential of Janus membranes in water management and liquid separation, providing a sustainable solution to water scarcity.
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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 | 0.000 | 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".