Perspectives on Surface Functionalization of Polymeric Membranes with Metal and Metal-Oxide Nanoparticles for Water/Wastewater Treatment
Bibliographic record
Abstract
Membrane filtration technology has been extensively applied in water/wastewater treatment to help address the issue of water shortage, in which polymeric membranes are most commonly used. However, the hydrophobic nature of polymeric membranes would contribute to membrane damage caused by accumulation of organic/inorganic fouling during filtration processes. The strategy of membrane surface functionalization with nanoparticles (NPs) has been investigated and utilized to effectively improve membrane performance. Herein, recent research efforts on surface functionalization of polymeric membranes with a variety of NPs for water/wastewater treatment were concisely reviewed, focusing on metal and metal-oxide NPs. Methods for the immobilization of NPs on membrane surface and their influences on membrane properties and performances were overviewed. Results and contributions achieved in the improvement of membrane performances through surface functionalization with NPs were summarized, and emphasis was given to membrane hydrophilicity, stability, as well as antifouling and antibacterial property. Furthermore, perspectives on the current challenges and future research needs in the development and application of surface functionalized polymeric membranes were discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".