Wettability's influence on <scp>MOF</scp> membranes for oil/water separation: A review
Bibliographic record
Abstract
Abstract The efficient separation of oil and water is crucial for environmental protection and resource management. Due to tunable structures and functionalities, metal–organic frameworks (MOFs) have emerged as promising membrane‐based oil/water separation materials. This review explores the significant influence of wettability on the performance of MOF membranes in this application. We examine three primary categories: hydrophilic/oleophobic, hydrophobic/lipophilic, and switchable wettability MOF membranes, detailing their synthesis, separation mechanisms, and performance characteristics. The analysis reveals that while all three types can achieve high separation efficiencies, hydrophilic/oleophobic membranes often exhibit superior flux. A key challenge is membrane fouling, and strategies for regeneration and self‐cleaning, particularly those leveraging switchable wettability, are discussed. This review highlights wettability's importance in dictating MOF membranes' performance for oil/water separation. It identifies key areas for future research to enhance membrane stability, scalability, and effectiveness in addressing real‐world water purification challenges.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".