Functionalized Glutathione on Chitosan-Genipin Cross-Linked Beads Used for the Removal of Trace Metals from Water
Bibliographic record
Abstract
Functionalized glutathione on chitosan-genipin cross-linked beads (CS-GG) was synthesized and tested as an adsorbent for the removal of Fe(II) and Cu(II) from aqueous solution. The beads were characterized by several techniques, including Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), CNS elementary analysis, scanning electron microscopy (SEM), and atomic force microscopy (AFM). The effect of several parameters such as the pH, the temperature, and the contact time was tested to optimize the condition for the adsorption reaction. The beads were incubated in aqueous solutions contaminated with different concentrations of Fe(II) and Cu(II) (under the range concentration from 10 to 400 mg·L−1), and the adsorption capacity was evaluated by inductively coupled plasma optical emission spectrometry (ICP-OES). The adsorption equilibrium was reached after 120 min of incubation under optimal pH 5 for Fe(II) and after 180 min under optimal pH 6 for Cu(II). According to theLangmuirisotherm, the maximum adsorption capacities (qmax) for Fe(II) and Cu(II) were 208 mg·g−1and 217 mg·g−1, respectively. Our results showed that the adsorption efficiency of both metals on CS-GG beads was correlated with the degree of temperature. In addition, the adsorption reaction was spontaneous and endothermic, indicated by the positive values of ΔG0and ΔH0. Therefore, the present study demonstrated that the new synthesized CS-GG beads had a strong adsorption capacity for Fe(II) and Cu(II) and were efficient to remove these trace metals from aqueous solution.
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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 | 0.001 | 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".