Boosting water flux and dye removal: Advanced composite membranes incorporating functionalized AC-PAA for wastewater treatment
Bibliographic record
Abstract
• Polyacrylic acid brushes were grafted onto activated carbon (AC) via surface-initiated atom transfer radical polymerization. • Polyether sulfone composite membranes were prepared using Polyacrylic acid-grafted AC via the phase inversion method. • The composite membranes demonstrated improved dye rejection and antifouling performance compared to pristine PES membranes. • Response Surface Methodology (RSM) was employed to predict the optimal parameters for dye removal performance. This study addresses the challenge of enhancing dye removal and antifouling properties in wastewater treatment by developing a composite membrane incorporating poly(acrylic acid)-functionalized activated carbon (AC-PAA) into a polyethersulfone (PES) matrix. The activated carbon was functionalized using surface-initiated atom transfer radical polymerization (SI-ATRP), followed by hydrolysis to introduce hydrophilic poly(acrylic acid) chains. The AC-PAA composite was characterized using Fourier transform infrared spectroscopy, thermogravimetric analysis, transmission electron microscopy, and energy-dispersive X-ray analysis, confirming successful grafting and functionalization. Compared to pristine PES, the addition of 0.5 wt% AC-PAA led to significantly enhanced water flux (54 L/m 2 h vs. 30 L/m 2 h) and superior dye removal, achieving 63 % for methyl orange and 67 % for methylene blue at alkaline pH. Poly(acrylic acid) was selected for its carboxyl groups, which enhance adsorption capacity and antifouling characteristics. In addition to effective dye removal, the composite membranes were antifouling, with a flux recovery ratio of 72 %. Response surface methodology optimized parameters, confirming highest performance at pH 11 and 6 bar. AC-PAA functionalized membranes are an efficient solution in wastewater treatment, increasing dye removal and antifouling capacity versus current membrane technologies.
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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 | 0.000 | 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 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".