Recycling of Poly (Methyl Methacrylate) Waste Sheets to Synthesize Catalyst-Free Bi- Functional Cation Exchange Resin for Sequestering of Toxic Pollutant
Bibliographic record
Abstract
Abstract A simple and green method for the recovery of methyl methacrylate (MMA) has been developed by recycling waste sheets of poly (methyl methacrylate) without using any catalyst. The liquefaction yield was 97.33% while the purity of recovered MMA was up to 92%. The recovered monomer utilized for the synthesis of methylmethacrylate-copolymer-divinylbenzene at 20% cross-linking in the presence of different porogens (methyl isobutylketone, cyclohexane, toluene, and cyclohexanone) by suspension polymerization. The same copolymer was also synthesized but with different cross-linking percentages such as 4, 10, 15, 20, 25, 30, and 40% in the presence of a single porogen (methyl isobutylketone). Different copolymers having pore volumes in the range of 0.120 to 0.682 mL/g were obtained. The pendant –COOCH3 groups within the polymeric chain were converted to –COOH groups via acid hydrolysis, accompanied by the attachment of –SO3H groups to phenyl rings. FTIR, UV-Vis spectrometry and HPLC techniques were used for qualitative and quantitative analyses of–COOH and –SO3H groups. The obtained results were compared with copolymers and resin synthesized using commercial MMA, displaying similar behavior. The resin derived from the recovered monomer was utilized for Co2+ ion removal at various pH levels, demonstrating excellent performance.
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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 | 0.001 |
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".