Selective removal of anionic contaminants via porous polyethylene glycol -templated chitosan aerogel beads
Bibliographic record
Abstract
Persistent anionic pollutants, such as pharmaceuticals and dyes, are increasingly detected in water systems, driving demand for sustainable sorbents with readily accessible functional groups. Although chitosan contains abundant amino sites, its dense structure limits their effective utilization. In this work, chitosan aerogel beads were prepared using poly(ethylene glycol) (PEG) as a removable template and epichlorohydrin as a hydroxyl-reactive crosslinker to create lightweight sorbents with improved active-site accessibility. The effects of PEG molecular weight (1.5–20 kDa) and loading (0–1.0 w/w PEG-to-chitosan mass ratio) on bead morphology, porosity, and mechanical strength were systematically evaluated. PEG templating generated interconnected mesoporous networks, with amino groups preserved through epichlorohydrin crosslinking. Beads prepared with PEG 6 kDa at a 0.75 mass ratio exhibited the most favorable properties, including a surface area of 188.8 m 2 /g, pore volume of 0.0975 cm 3 /g, and uniform pores. Ibuprofen uptake was rapid (<45 min) and reached equilibrium after 8 h, following pseudo-second-order kinetics and monolayer adsorption described by the Langmuir model, with a maximum capacity of 70.9 mg/g. Response surface methodology identified optimal conditions at pH 6, a dosage of 1.5 g/L, and an initial concentration of 10 mg/L. The contribution of electrostatic ion-pairing interactions to the adsorption process was confirmed by XPS analysis. The optimized aerogel also showed strong selectivity toward anionic dyes (Methyl Orange, Orange G, Rose Bengal, Eosin B; >97 % removal) and negligible uptake of Crystal Violet, underscoring the influence of surface charge. These findings establish PEG-templated, epichlorohydrin-crosslinked chitosan aerogels as promising sorbents for removing anionic contaminants from water.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".