Membrane fouling control and performance enhancement of ultrafiltration of latex effluent
Bibliographic record
Abstract
Abstract The objective of the present study was to minimize membrane fouling of a latex solution either through improving the membrane surface charge or using a pretreated feed. Hydrophilic polysulphone (PSU) and Ultrafilic flat membranes, with a molecular weight cut‐off (MWCO) of 60 000 and 100 000 dalton, respectively, were implemented under a constant flow rate and cross‐flow mode in ultrafiltration of simulated latex effluent. Hydrophobic polyvinylidene difluoride membrane (PVDF) with a MWCO of 100 000 dalton was also tested. The influence of linear alkyl benzene sulphonate (LAS) on the ionic strength of the simulated latex effluent and the surface charge of latex particles at different LAS concentrations was investigated. LAS was also used, at different concentrations and various treatment times, to ensure the enhancement of the antifouling characteristics of the membrane surface. It was concluded that the LAS‐treated membrane surface is much more favourable than the pH‐changed feed pre‐treatment. As such, the total mass of fouling decreased by 44.00 % and 29.60 % in the cases of treated PVDF membrane surface with LAS at a concentration of 0.0001 g/L, and treated latex feed at pH 11, respectively. Nevertheless, LAS was considered to be an ineffective pre‐treatment for limiting the fouling propensity of latex solutions using hydrophilic membranes even at high concentrations and long treatment times.
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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 | 0.000 |
| 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 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".