Fouling of nanofiltration membranes based on polyelectrolyte multilayers: The effect of a zwitterionic final layer
Bibliographic record
Abstract
In this work, we investigate the effect of membrane surface chemistry on fouling in surface water treatment for polyelectrolyte multilayer based nanofiltration (NF) membranes. The polyelectrolyte multilayer approach allows us to prepare three membranes with the same active separation layer, apart from a difference in surface chemistry: nearly uncharged crosslinked Poly(allylamine hydrochloride) (PAH), strongly negative poly(sodium 4-styrene sulfonate) PSS and zwitterionic poly(2-methacryloyloxyethyl phosphorylcholine-co-acrylic acid) (PMPC-co-AA). Initially, we study foulant adsorption for the three different surfaces (on model interfaces), to demonstrate how a different surface chemistry of the top layer affects the subsequent adsorption of five different model foulants (Humic Acids, Alginates, Silica Nanoparticles, negatively and positively charged Proteins). Subsequently, we study fouling of the same model foulants on our polyelectrolyte multilayer based hollow fiber NF membranes with identical surface chemistry to the model surfaces. Our results show that nearly uncharged crosslinked PAH surface generally fouls more than strongly negatively charged PSS surface. While negative BSA adsorbs better on PSS, probably due to charge regulation. Overall, fouling was mainly driven by electrostatic and acid-base interactions, which led, for both PAH and PSS terminated membranes, to flux decline and changes in selectivity. In contrast, we demonstrate through filtration experiments carried out with synthetic and real surface water, that the bio-inspired zwitterionic phosphatidylcholine surface chemistry exhibits excellent fouling resistance and thus stable performance during filtration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".