Elucidating the Trade-off between Membrane Wetting Resistance and Water Vapor Flux in Membrane Distillation
Bibliographic record
Abstract
Membrane distillation (MD) has been receiving considerable attention as a promising technology for desalinating industrial wastewaters. While hydrophobic membranes are essential for the process, increasing membrane surface hydrophobicity generally leads to the reduction of water vapor flux. In this study, we investigate the mechanisms responsible for this trade-off relation in MD. We prepared hydrophobic membranes with different degrees of wetting resistance through coating quartz fiber membranes with a series of alkylsilane molecules while preserving the fiber structures. A trade-off between wetting resistance and water vapor flux was observed in direct-contact MD experiments, with the least-wetting-resistant membrane exhibiting twice as high vapor flux as the most wetting-resistant membrane. Electrochemical impedance analysis, combined with fluorescence microscopy, elucidated that a lower wetting resistance (still water-repelling) allows deeper penetration of the liquid-air interfaces into the membrane, resulting in an increased interfacial area and therefore a larger evaporative vapor flux. Finally, we performed osmotic distillation experiments employing anodized alumina membranes that possess straight nanopores with different degrees of wetting resistance, observed no trade-off, and substantiated this proposed mechanism. Our study provides a guideline to tailor the membrane surface wettability to ensure stable MD operations while maximizing the water recovery rate.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".