Congo red filtration by polyacrylonitrile-based copolymer membranes
Bibliographic record
Abstract
In this study, Congo red removal from wastewater by a filtration method was studied via membranes obtained from polyacrylonitrile-co-poly(2-ethylhexylacrylate) copolymers having various monomer ratios and polyacrylonitrile-co-poly(2-ethylhexylacrylate)/polyaniline blends with various polyaniline contents. It was found that the dye rejection value increased with acrylonitrile content in polyacrylonitrile-co-poly(2-ethylhexylacrylate) membranes. Also, blending copolymers with polyaniline enhanced the dye rejection rate. The performance of membranes showed incremental increase with increase in the polyaniline content. Both pH and concentration effects on the dye rejection rate of membranes were evaluated. The performance of polyacrylonitrile-co-poly(2-ethylhexylacrylate) membranes did not change significantly, whereas polyaniline-containing membranes had higher dye rejection rates at acidic pH levels. PAN(92)-co-P2EHA(8)-PANI(15%) demonstrated the highest dye rejection value of 99.7% at pH 3 when the feed concentration was 50 ppm. It also showed good resistance to increase in feed concentration. It had dye rejection values of 97.2% and 88.5% for 100 and 200 ppm feed concentrations, respectively.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".