Treatment of textile dye effluents using a new polyacrylonitrile nanofiltration membrane developed by <scp>UV</scp> photo grafting
Bibliographic record
Abstract
Abstract The purpose of this research work is to develop a new nanofiltration membrane based on polyacrylonitrile (PAN) by ultraviolet photografting using acrylic acid as a vinyl monomer. This work represents a novel lead for applying new resources in activating PAN‐based membranes for photopolymerization reaction. Compared to traditional techniques that could only use photoactive membrane substrates, this method is an efficient procedure that shows a new perspective. Acid red 114, an azo dye, was used as photoinitiator in membrane surface modification process. The influence of different parameters on the grafting process such as irradiation time and monomer concentration on the performances and the characteristics of the membranes were examined. The grafted membrane in optimal condition was also evaluated for the removal of different salt solutions and different anionic direct dyes with the aim of reusing the dye and water in the process. Characterization of the modified membrane is performed by AFM, SEM, contact angle, and zeta potential. The retention of Na 2 SO 4 , MgSO 4 , NaCl, and CaCl 2 salts was obtained as 76%, 63.2%, 29.4%, 23.3%, and the retention of coloured pollutants DB15, DR16, DO26, and DY12 was acquired as 96.3%, 92.5%, 91.3%, and 85%, respectively. Distilled water permeability of photo‐grafted membrane was 13 L · h −1 · m −2 · bar −1 .
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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".