Chitosan Hydrogels Crosslinked With Glutaraldehyde for Potential Toxic Elements Removal: Batch and Purification Device Analysis
Bibliographic record
Abstract
ABSTRACT The presence of toxic metal ions in contaminated wastewater raises significant concerns for human health. Three‐dimensional porous systems like hydrogels are being explored for contaminant removal. Still, there is a lack of understanding of their production parameters and performance. Therefore, the present work investigates the production parameters of chitosan hydrogels using the design of experiments methodology. Hydrogels were developed by solubilizing chitosan in acetic acid solution and cross‐linked using glutaraldehyde, varying its proportions. The formulation containing 2% chitosan and 1% glutaraldehyde has water absorption superior to 300% and potential for metal removal, particularly copper and chromium. Crosslinking was validated by FTIR analysis, and the obtained hydrogel presented a highly porous structure. Kinetic studies showed a better fit to a pseudo‐second order, and the Langmuir isotherm presented the best fit, showing sorption capacities of 0.422 and 1.143 mmol g −1 for copper and chromium, respectively. Filtration tests demonstrated that a 0.25 mL/min flow rate provided the best performance, with an initial fast removal that stabilizes. In addition, the weight and design used directly influence the adsorption properties. Results show that chitosan‐based hydrogels can potentially remove metal contaminants. A filtering system is a feasible alternative for developing a low‐cost, efficient, and environmentally friendly system.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".