Polyampholytic Hydrogels from Chitosan Macromonomers with Aryl-Mono and Di-Sulfonated Groups: An Approach to the Removal of Copper Ions and Ciprofloxacin in Aqueous Solutions
Bibliographic record
Abstract
Functional hydrogels have significant potential for applications in the pharmaceutical, agricultural, and environmental sectors. This study focuses on the synthesis of polyampholytic hydrogels through free radical polymerization using functionalized chitosans. The chitosan was modified with mono and disulfonic groups at different temperatures (25 °C and 60 °C) and reaction times (1, 8, 24 h), followed by further modification with glycidyl methacrylate to introduce vinyl groups into the polymers structure. The modified polymers were analyzed using proton nuclear magnetic resonance, Fourier transform infrared, scanning electron spectroscopy, thermogravimetric analysis, and solubility tests. Specifically, 0.74 mmol/g and 1.58 mmol/g of the primary amine groups available in the chitosan chain (out of a total of 4.93 mmol/g) were substituted with mono- and disulfonic groups, respectively. Following treatment with glycidyl methacrylate, 3.39 mmol/g and 2.21 mmol/g of the remaining primary amine groups in the mono- and disulfonic polymers, respectively, were substituted. The hydrogels obtained by the modified polymers at optimal conditions of 1 h and 25 °C, were characterized by the techniques already mentioned in addition to rheological tests, and water absorption studies across different pHs. The hydrogels demonstrated potential for environmental remediation, particularly in adsorptions of ciprofloxacin (CPX) and copper (Cu2+) from aqueous solutions at pH 7, achieving adsorption efficiencies of 24–25% for CPX and 83% for Cu2+. The results suggest that the synthesized hydrogels could provide an eco-friendly and efficient solution to challenges in wastewater treatment.
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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 | 0.000 | 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".