Thiol‐Ene Click Reaction in Constructing Liquid Separation Membranes for Water Treatment
Bibliographic record
Abstract
In the evolving landscape of water treatment, membrane technology has ascended to an instrumental role, underscored by its unmatched efficacy and ubiquity. Diverse synthesis and modification techniques are employed to fabricate state-of-the-art liquid separation membranes. Click reactions, distinguished by their rapid kinetics, minimal byproduct generation, and simple reaction condition, emerge as a potent paradigm for devising eco-functional materials. While the metal-free thiol-ene click reaction is acknowledged as a viable approach for membrane material innovation, a systematic elucidation of its applicability in liquid separation membrane development remains conspicuously absent. This review elucidates the pre-functionalization strategies of substrate materials tailored for thiol-ene reactions, notably highlighting thiolation and introducing unsaturated moieties. The consequential implications of thiol-ene reactions on membrane properties-including trade-off effect, surface wettability, and antifouling property-are discussed. The application of thiol-ene reaction in fabricating various liquid separation membranes for different water treatment processes, including wastewater treatment, oil/water separation, and ion separation, are reviewed. Finally, the prospects of thiol-ene reaction in designing novel liquid separation membrane, including pre-functionalization, products prediction, and solute-solute separation membrane, are proposed. This review endeavors to furnish invaluable insights, paving the way for expanding the horizons of thiol-ene reaction application in liquid separation membrane fabrication.
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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 | 0.000 | 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 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".