Impacts of Hydrophilic Membrane Additives on the Ultrafiltration of River Water
Bibliographic record
Abstract
One of the most serious disadvantages of membrane applications in water treatment is the decreasing water permeation rate with time, which is often called fouling. This study investigates surface modification of polyethersulfone (PES) ultrafiltration membranes as a fouling reduction strategy for drinking water treatment applications. Surface modification was achieved through the addition of three different tailor-made hydrophilic surface modifying macromolecules (LSMM200, LSMM400, and LSMM600). Flat sheet membranes were prepared via a single-step casting procedure; their surface hydrophilicity was quantified via contact angle measurements. The incorporation of hydrophilic additives produced slightly more hydrophilic membranes (contact angle reduction of up to 8°) and improved membrane performance compared with the PES membrane without blending. In the treatment of highly colored river water, LSMM400- and LSMM600-modified membranes achieved up to 32% higher final fluxes. Surface modification resulted in significantly decreased flux reductions and natural organic matter accumulation. Dissolved organic carbon removals were approximately 70% for all the membranes studied. No clear correlation between membrane hydrophilicity and fouling reduction was observed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.000 |
| 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".