NOM foulant−hypochlorite interactions impact PVDF UF membrane ageing
Bibliographic record
Abstract
• NOM foulants enhance radical formation, accelerating UF membrane ageing. • Hydroxyl radicals are key drivers of membrane degradation during cleaning. • Radical scavengers could mitigate ageing resulting from chemical cleaning. Ultrafiltration (UF) membranes are widely used for drinking water treatment, but their performance deteriorates over time due to chemical cleaning with agents such as hypochlorite, which accelerates membrane ageing. The present study investigates the impact of interactions between model natural organic matter (NOM) components (i.e., bovine serum albumin, sodium alginate and humic acid), used to represent membrane foulants, and hypochlorite, used during chemical cleaning, on the ageing of polyvinylidene fluoride (PVDF) UF membranes. Both soak and cyclic accelerated ageing approaches were used to assess the contribution of model NOM foulants to membrane ageing during chemical cleaning. We quantified ageing based on the extent of changes in membrane physical, chemical, and hydraulic properties when exposed to cumulative doses ranging from 0 to 1,300,000 ppm·h, and explored the formation of radical oxidants from model NOM-hypochlorite interactions. Results demonstrate that model NOM foulants may significantly enhance the generation of radical oxidants during chemical cleaning with hypochlorite, which accelerates membrane ageing. The addition of radical scavengers effectively mitigated these effects, reducing the rate of membrane ageing. The results suggest that incorporating radical scavengers during chemical cleaning with hypochlorite could improve membrane longevity, particularly when considering facilities which treat source waters with limited natural scavengers. The study outcomes provide critical insights regarding the mechanisms of membrane ageing and offers practical strategies for its mitigation, paving the way for more sustainable water treatment operations.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".