Electrically conductive membranes featuring integrated porous feed spacers for superior antifouling performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 it