Multifunctional PVDF Membrane Coated with ZnO-Ag Nanocomposites for Wastewater Treatment and Fouling Mitigation: Factorial and Mechanism Analyses
Bibliographic record
Abstract
In this study, a multifunctional poly(vinylidene fluoride) (PVDF) membrane was developed through chemical binding with ZnO-Ag nanocomposites to increase wastewater treatment efficiency. The unique characteristics of ZnO-Ag nanocomposites endowed the membrane with high surface hydrophilicity, organic/bio fouling resistance, and photocatalytic antibacterial activity. The significantly decreased water contact angle and increased under-water oil contact angle suggested improved surface hydrophilicity and organic fouling resistance. Through factorial analysis, it was found that the antibacterial activity of the multifunctional membrane could be significantly improved under visible light condition and with ZnO-Ag nanocomposites which obtained under higher Ag concentration and sintering temperature. The increase of Ag composition of ZnO-Ag nanocomposites on modified membrane surface significantly improved the membrane antibacterial activity but had little effect on membrane hydrophilicity. In addition, the photocatalytic antibacterial activity of ZnO-Ag nanocomposites could further improve the membrane biofouling resistance through simple exposure to visible light. The effects of different Ag chemical states on the performances of ZnO-Ag nanocomposites and the corresponding modified membranes were studied, and the relevant mechanism of antibacterial activity under both dark and light conditions was discussed. Filtration experiments with secondary wastewater effluent as feed solution indicated that the developed membrane exhibited one order of magnitude larger permeate flux compared to the pristine PVDF membrane, while maintaining comparable bacteria rejection rates during the filtration process.
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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 | 0.001 |
| 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 | 0.001 | 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".