Nanofibrous material of n-succinyl chitosan/ polyethylene oxide in the removal of emerging pharmaceuticals from aqueous solution by adsorption/desorption method
Bibliographic record
Abstract
Pharmaceutical metabolites and their residues have been identified as major environmental pollutants in aquatic ecosystems. N-succinyl chitosan (NSCS) was studied as a potential adsorbent for a pharmaceutical residue, fluoxetine, from aqueous media. NSCS was investigated using Fourier transform infrared (FTIR) and 1H-nuclear magnetic residence (NMR) spectroscopies. Scanning electron microscopy (SEM) was used to investigate the formation of nanofibers by electrospinning of these green biomaterial polymer membranes. The mechanisms of FLX adsorption, as well as the effects of pH value in the original solution on adsorption capacities, were investigated using high-performance liquid chromatography (HPLC-UV DAD) under identical adsorption conditions. It was found that NSCS/PEO nanofibers with a diameter of 183 ± 38 nm were more effective to remove pharmaceutical residues from aqueous media than other commercial and modified adsorbents such as activated carbon (AC). FLX adsorption on NSCS/PEO nanofibers was favoured at pH 8.0. Pseudo-first order model was the more adequate to represent the kinetic data, being the maximum adsorption capacities of FLX on NSCS/PEO reached up to 70%. A study of the desorption potential and reusability of the mat was also conducted. According to the results, electrospun NSCS/PEO mats can be desorption and reused up to 4 times without significant loss in adsorption capability.
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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".