Adsorption of Pharmaceutical Contaminants from Aqueous Solutions Using N,O-Carboxymethyl Chitosan/Polyethylene Oxide (PEO) Electrospun Nanofibers
Bibliographic record
Abstract
Residues of pharmaceutical and direct metabolites discharged into the aquatic environment have become a challenge for wastewater treatment facilities due to their increase in concentration and their different physicochemical properties. These emerging contaminants are daily detected in surface water and wastewater discharged by municipalities. To remediate the contaminated water, various methods are currently used including primary, secondary, and tertiary advanced treatments. However, some economic and environmental limitations have forced the scientific community to develop alternative disinfection processes to purify wastewater. As such, the adsorption strategy represents a “green” low-cost and effective solution to remove pollutants from water. In this study, a nanomaterial made of N,O-carboxymethyl chitosan (N,O-CMCS) was prepared using chitosan (CS) and monochloroacetic acid under various conditions. N,O-CMCS electrospun was synthetized with the copolymer polyethylene oxide (PEO) to create nanofiber membranes showing a better specificity toward diversified contaminants depending on the pH of medium. The developed adsorbent was used to remove fluoxetine (FLX) from aqueous solutions. The new nanomaterial was characterised using FTIR, NMR, and SEM techniques. Sorption batch tests were carried out using high-performance liquid chromatography and ultraviolet diode array detector (HPLC-UV DAD) under controlled pH experimental conditions to determine the contaminant removal capacity of the nanomaterial. The promising adsorption results obtained with N,O-CMCS/PEO nanofibers are among the best ones obtained so far in comparison to other commercial and synthetized adsorbents tested for FLX’s adsorption. Kinetic experiments were also performed to investigate effects of contact times on the FLX adsorption. Experimental results were fitted to both common kinetic models pseudo-first and second order. The latter kinetic model described the best the sorption on surface. It revealed a possible chemisorption mechanism with electrostatic bounding for N,O-CMCS/PEO nanofibers.
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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) | 0.000 | 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 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".