Eco‐Friendly Hollow Fiber Nanofiltration Membranes for Efficient Water Desalination
Bibliographic record
Abstract
ABSTRACT Water scarcity is a critical global issue requiring sustainable and efficient desalination. This study introduces a novel eco‐friendly hollow fiber nanofiltration membrane for water desalination, employing a wet spinning method to create membranes from polyether sulfone (PES), polyethylene glycol (PEG), and polyvinylpyrrolidone (PVP). The prepared membrane demonstrated significant improvements in pure water permeability (PWP) and salt rejection rates, achieving up to 94% rejection. Scanning Electron Microscopy (SEM) analysis revealed a unique asymmetric structure characterized by small macro‐voids towards the outer edge of the membrane. Increasing the dope extrusion rate (DER) from 2.0 to 2.5 cm 3 /min notably enhanced molecular orientation, optimizing rejection rates. Additionally, increasing the pH elevation from 5.5 to 6.5 further improved rejection due to altered membrane surface charges. Long‐term and thermal stability assessments confirmed robust membrane performance over a temperature range of 10°C–70°C, indicating its viability for diverse applications. This research significantly contributes to overcoming existing knowledge gaps, particularly regarding the effects of the dope extrusion rate (DER), and advances practical, sustainable desalination technology.
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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".