Adsorption study of a commonly used antidepressant drug, fluoxetine hydrochloride, onto a crosslinked β-cyclodextrin-carboxymethylcellulose polymer
Bibliographic record
Abstract
A study was carried out by ultraviolet-visible (UV-vis) and Fourier transform infrared (FTIR) spectroscopies to establish the efficiency of adsorption of fluoxetine hydrochloride (FLU), onto a crosslinked β-cyclodextrin-carboxymethylcellulose (β-CD-CMC) polymer. The adsorption was performed in mixtures containing aqueous FLU solution at 20 mg/L and 0.01-0.30 g of the β-CD-CMC polymer, at 25 °C, and atmospheric pressure under stirring. The results have revealed that the adsorption is a rapid process and the polymer possesses a high affinity for FLU with an adsorption capacity of 5.076 mg of FLU/g of polymer. This adsorption may involve the formation of a stable inclusion compound β-CD-CMC/FLU through the penetration of the FLU aromatic ring (A and/or B) into the β-CD cavity, and a physical adsorption with the polymer network. The inclusion compound can be stabilized by the formation of H-bonds between the -CF(3) group of FLU and the 6'-OH group of β-CD, and van der Waals interactions between the FLU aromatic ring and β-CD cavity. The data from a kinetic study have also indicated that the adsorption process was well described by the pseudo-second-order kinetic model, in which the initial adsorption rate and constant were estimated at 1.938 mg/g min and 0.075 g/mg min, respectively. Moreover, the results of adsorption equilibrium fitted the Freundlich isotherm, indicating a multilayer coverage and heterogeneous surface. Together, these results suggest that the adsorption of FLU onto the crosslinked β-CD-CMC polymer could constitute an advantageous technology for removing this commonly used antidepressant drug from wastewater due to the high adsorption capacity of the polymer and non-toxic character of β-CD to humans and environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".