Chitosan Biocomposites with Variable Cross-Linking and Copper-Doping for Enhanced Phosphate Removal
Bibliographic record
Abstract
The fabrication of chitosan (CH) biocomposite beads with variable copper (Cu2+) ion doping was achieved with a glutaraldehyde cross-linker (CL) through three distinct methods: (1) formation of CH beads was followed by imbibition of Cu(II) ions (CH-b-Cu) without CL; (2) cross-linking of the CH beads, followed by imbibition of Cu(II) ions (CH-b-CL-Cu); and (3) cross-linking of pristine CH, followed by bead formation with Cu(II) imbibing onto the beads (CH-CL-b-Cu). The biocomposites (CH-b-Cu, CH-b-CL-Cu, and CH-CL-b-Cu) were characterized via spectroscopy (FTIR, 13C solid NMR, XPS), SEM, TGA, equilibrium solvent swelling methods, and phosphate adsorption isotherms. The results reveal variable cross-linking and Cu(II) doping of the CH beads, in accordance with the step-wise design strategy. CH-CL-b-Cu exhibited the greatest pillaring of chitosan fibrils with greater cross-linking, along with low Cu(II) loading, reduced solvent swelling, and attenuated uptake of phosphate dianions. Equilibrium and kinetic uptake results at pH 8.5 and 295 K reveal that the non-CL Cu-imbibed beads (CH-b-Cu) display the highest affinity for phosphate (Qm = 133 ± 45 mg/g), in agreement with the highest loading of Cu(II) and enhanced water swelling. Regeneration studies demonstrated the sustainability and cost-effectiveness of Cu-imbibed chitosan beads for controlled phosphate removal, whilst maintaining over 80% regenerability across several adsorption–desorption cycles. This study offers a facile synthetic approach for controlled Cu2+ ion doping onto chitosan-based beads, enabling tailored phosphate oxyanion uptake from aqueous media by employing a sustainable polysaccharide biocomposite adsorbent for water remediation by mitigation of eutrophication.
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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 | 0.000 |
| 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) | 0.000 | 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".