Mechanism of nanofiltration fouling in high hardness and dissolved organic carbon surface water based on chemical characterization of foulant
Bibliographic record
Abstract
Nanofiltration (NF) membranes purifying high hardness and dissolved organic carbon (DOC) surface water are prone to much more severe fouling than those supplied by waters of better quality. We conducted analyses on an ultrafiltration/nanofiltration pilot plant supplied by high hardness and DOC water to investigate the NF foulant chemical composition and fouling mechanism. NF foulant was primarily organic (97%). Analyses of the physically (PRF) and chemically (CRF) removable foulant components unveiled their different compositions. CRF, i.e., the fouling substances directly adsorbed on the nanofilter surface and pores, mainly comprised hydrophobic and low molecular weight & building blocks of humic substances. PRF, i.e., the gel-layer formed by the substances continuous deposition, contained more compounds with hydrophilic character and a wide range of molecular weights, from low molecular weight & building blocks (<1 kDa) to humic substances (1 – 20 kDa) and biopolymers (>20 kDa), showing a more uniform distribution of those features. Calcium and magnesium may have promoted the bridging of the organics with the NF membrane, and between the organics, strengthening the foulant structure. The presence of a humic- and EPS-conditioning film may have interfered with or contributed to bacterial adhesion, respectively, forming biofilm patches. • High DOC and hardness water produces a NF gel-like foulant with some bacteria • Hydrophobic low molecular weight humic substances adsorb first on the NF membrane • Gel-layer forms next from varied molecular weight hydrophilic & hydrophobic compounds • Biopolymers favor bacterial attachment while humic substances interfere with it • Bacteria may form biofilm patches directly attached to the NF membrane surface
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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".