Electro‐Conductive Silver‐Coated Polyamide‐Imide Membranes for Sustainable Water Treatment
Bibliographic record
Abstract
Abstract This study focuses on developing and evaluating electro‐conductive polyamide‐imide (PAI) ultrafiltration membranes with a stable metallic coating that can tackle the dual challenges of dye removal and membrane fouling in wastewater treatment applications. The Ag‐coated PAI membranes exhibit high electrical conductivity (exceeding 5.6 × 10 4 S cm −1 ), enabling the use of an applied electric potential to enhance dye removal efficiency and mitigate membrane fouling via electrochemical mechanisms. The electrochemical impedance spectroscopy (EIS) test confirm the high conductivity of the membranes. Meanwhile, linear sweep voltammetry (LSV) revealed the presence of the oxygen reduction reaction (ORR) and the hydrogen evolution reaction (HER) on the membrane surface. These findings provide valuable insights into the electrochemical potential for fabricating electro‐conductive membranes (ECMs). The Ag‐PAI membranes demonstrate remarkable dye rejection, with rates reaching 97% for reactive red 120 (RR120) and 90% for reactive black (RB) at an applied voltage of 7 V, while maintaining a consistent permeate flux of ≈100 LMH. The membranes also show significantly improved resistance to organic fouling, with the flux recovery ratio (FRR) increasing from 49.14% for pristine PAI to 80.41%, representing a 31% enhancement. The enhanced antifouling performance is attributed to gas bubble formation during voltage application, which disrupted the accumulation of the fouling cake layer. Together, these mechanisms effectively enhance the overall performance of the membrane.
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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 | 0.001 | 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".