An Innovative Surface Modification Technique for Antifouling Polyamide Nanofiltration Membranes
Bibliographic record
Abstract
In this study, we developed a novel surface coating technique to modify the surface chemistry of thin film composite (TFC) nanofiltration (NF) membranes, aiming to mitigate organic fouling while maintaining the membrane’s permselectivity. We formed a spot-like polyester (PE) coating on top of a polyamide (PA) TFC membrane using mist-based interfacial polymerization. This process involved exposing the membrane surface to tiny droplets carrying different concentrations of sulfonated kraft lignin (SKL, 3, 5, and 7 wt %) and trimesoyl chloride (TMC, 0.2 wt %). The main advantages of this surface coating technique are minimal solvent consumption (less than 0.05 mL/cm 2 ) and precise control over interfacial polymerization. Zeta potential measurements of the coated membranes exhibited enhancements in negative charge compared to the control membrane. This enhancement is attributed to the unreacted carboxyl functional groups of the SKL and TMC monomers, as well as the presence of sulfonate groups (SO 3 ) in the structure of SKL. AFM results showed a notable decrease in membrane surface roughness after polyester coating due to the slower diffusion of SKL to the interface and a milder reaction with TMC. In terms of fouling resistance, the membrane coated with a polyester composed of 7 wt % SKL showed a 90% flux recovery ratio (FRR) during Bovine Serum Albumin (BSA) filtration, showing a 15% improvement compared to the control membrane (PA). PE-coated membranes provided stable separation performance over 40 h of filtration. The sodium chloride rejection and water flux displayed minimal variations, indicating the robustness of the coating layer. The final section of the presented study focuses on assessing the feasibility of scaling up and the cost-effectiveness of the proposed technique. The demonstrated ease of scalability and a notable reduction in chemical consumption establish this method as a viable, environmentally friendly, and sustainable solution for surface modification.
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 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 | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".