Mixed Matrix Membranes Containing Green Synthesized Poly(MBAAm-<i>co</i>-VSPI) Zwitterionic Nanoparticles for the Removal of Reactive Dyes
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Reactive dyes are well known for their color fastness. However, they also display a tendency toward carcinogenicity and mutagenicity. Hence, among the many methods for their removal from dye wastewater, membrane technology appears to be the most viable. In this work, zwitterionic polymeric nanoparticles poly(MBAAm- co -VSPI) were synthesized via precipitation polymerization following a free radical mechanism using N, N ′-methylene bis(acrylamide) (MBAAm) as a cross-linker and laboratory-synthesized 1-vinyl-3-(3-sulfanatopropyl)-1 H -imidazole-3-ium (VSPI) as the monomer. The reaction was carried out in water by utilizing a water-soluble free radical initiator 2,2′-azobis-2-methylpropionamide dichloride (V-50). The resulting nanoparticles were analyzed using FTIR, SEM, EDS, XRD, TGA, zeta potential, DLS, and BET studies. Mixed matrix membranes were fabricated by the incorporation of laboratory-synthesized nanoparticles in the polysulfone (PSf) polymer matrix. Among the series of membranes fabricated, PM-2 showed the highest rejection of the reactive dyes, Reactive Black 5 (RB5 98%) and Reactive Orange 16 (RO16 86%), at 20 ppm concentration along with good pure water permeability of 82.34 L m –2 h –1 bar –1 . Hence, this membrane has potential for the treatment of textile wastewater.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".