Electrically conductive membranes featuring integrated porous feed spacers for superior antifouling performance
Bibliographic record
Abstract
Surface patterning is a promising anti-fouling strategy, yet its integration with conductive polymers remains underexplored. This study investigates electrically conductive, surface-patterned membranes with integrated porous feed spacers using polyaniline (PANI) as a conductive additive in polyethersulfone (PES) membranes. Among tested concentrations (0.25–2.00 wt.%), 1.00 wt.% PANI membrane (PN1) showed the best performance, with electrical conductivity of ≈130.5 ± 2.87 mS·m−1 and pure water flux of 107.2 ± 15.5 L·m−2·h−1 which is around five times that of pristine PES membrane. Under a 4 V electric field, PN1 exhibited lower flux decline (60.6%) and higher flux recovery (FRR 90.1 ± 2.15%). Surface-patterned PN1 membrane (PN1_Patterned) further enhanced performance, achieving a flux of 168.2 ± 20.7 L·m−2·h−1 and reduced fouling (51.6% flux decline) compared to surface-patterned PES membrane (66.7%). PN1_Patterned membrane also showed higher FRR (95.4 ± 1.68%) and stable natural organic matter (NOM) rejection ( > 92.9 ± 1.65%). These results highlight the synergistic benefits of combining conductivity with surface patterning, offering a potential approach for improved membrane performance.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".