Sorption capacities of chitosan/polyethylene oxide (PEO) electrospun nanofibers used to remove ibuprofen in water
Bibliographic record
Abstract
Abstract Pharmaceutical residues coming from urban wastewater were recognized as a major source of pollution for the aquatic environment. Their occurrence in most municipal effluent seems to indicate that conventional wastewater treatments have only a limited ability to remove such substances from sewage. Therefore, the undesired effects caused by these emergent contaminants on the environment force the authorities to consider new measures to treat and recycle contaminated water. In this study, electrospun nanofibers made of chitosan and poly(ethylene oxide) (PEO) were used to remove the anti-inflammatory drug ibuprofen in solution. The electrospinning parameters such as the mixture solution concentration, applied voltage, distance needle-collector, and flow rate were optimized to get the best nanofiber morphology characterized by scanning electron microscopy (SEM). With the use of a high-performance liquid chromatography with ultraviolet diode array detection (HPLC-UV DAD) system, sorption tests were performed by modifying experimental conditions, e.g. pH, concentration of ibuprofen, and temperature of the tested solutions. Langmuir, Freundlich, and Dubinin-Radushkevich (DR) adsorption models were compared for the mathematical description of adsorption equilibria. Kinetic assays showed that the adsorption of chitosan nanofiber followed a pseudo-second-order model. After 20 min of exposure, 25 mg of nanofiber had removed 70% of the initial ibuprofen concentration.
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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".