Maximizing the wetting resistance of fluorine-free omniphobic membranes for hypersaline wastewater desalination
Bibliographic record
Abstract
Membrane distillation (MD) equipped with omniphobic (non-wetting) membranes has found a niche in water reclamation from hypersaline industrial wastewater. Here, we examined the efficacy of non-fluorinated materials as surface coating agents for omniphobic MD membrane fabrication, and identified necessary mechanisms to attain a maximized wetting resistance using fluorine-free materials. We first prepared MD membranes with different surface chemistries using a series of linear alkylsilanes and polydimethylsiloxane (PDMS) as representative fluorine-free, low surface energy materials. Membranes modified with a longer chain alkylsilane exhibited a lower surface energy and demonstrated a greater wetting resistance in direct contact MD experiments using feedwaters of various surface tensions. Despite the nearly identical surface energy measured for the longest alkylsilane and PDMS, PDMS-modified membrane exhibited an extended antiwetting performance as compared to the membrane treated with the longest alkylsilane. To elucidate the source of the distinctive wetting resistance, we examined the nucleation and condensation kinetics on the surfaces with the different surface chemistries via environmental scanning electron microscopy. Our analysis suggests that the membranes treated with long chain alkylsilanes contain surface defects (i.e., hydrophilic regions) whereas the high mobility of the PDMS effectively minimizes the defect exposure, slowing down the condensation and subsequent surface wetting.
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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) | 0.000 | 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".