Competitive biosorption of Ag(I) and Cu (II) by tripolyphosphate crosslinked chitosan beads
Bibliographic record
Abstract
In this study, alkalization of chitosan before crosslinking was applied in to enhance the adsorption capacity of the modified chitosan. Competitive adsorption of Ag (I) and Cu (II) from bimetallic solutions was studied using the newly synthesized tripolyphosphate crosslinked chitosan beads. Results indicated that alkalization before crosslinking helps to protect free-amine groups from crosslinking and hence increases the uptake capacity and selectively of the synthesized beads towards Ag (I). The maximum uptakes of Ag (I) and Cu (II) were 82.9 and 15.5 mg/g respectively at room temperature with an initial concentration of each metal being 2.0 mmol/L and the sorbent dosage of 1.0 g L⁻¹. Langmuir isotherm and pseudo-second order kinetic model provide better descriptions of adsorption isotherm and kinetics of metal ions on sorbent surfaces. Analyses from FT-IR and XPS confirmed that free amine, hydroxyl and P₃O₁₀⁵⁻ groups are involved in metal binding with amine and hydroxyl groups more selective to Ag (I). Then, continuous adsorption with the newly synthesized chitosan beads was simulated using the lumped kinetic model. According to the parameters obtained in the batch adsorption, the overall mass-transfer coefficient (Kf), and axial dispersion coefficient (DL) were determined using the empirical correlations. The value of Kf for Ag (I) is in the range of 5.028×10⁻⁵ s⁻1 to 8.389×10⁻⁵ s⁻¹, the value of Kf for Cu (II) was in the range of 8.000×10⁻⁵ s⁻¹ to 1.283×10⁻⁴ s⁻¹; The range of axial dispersion coefficient for Ag (I) and Cu (II) were both varying from 1.806×10⁻⁴ cm²/s to 1.778×10⁻⁴ cm²/s. Results from the breakthrough and elution profiles indicated that decreasing the flow rate, sample concentration and injection time, or increasing the bed length could enhance the separation of the two metal ions. Besides, it was found that concentration overload by increasing the sample concentration is more effective to improve the separation of two metal ions in fixed-bed column than volume overload by increasing the injection time. In conclusion, the newly synthesized chitosan-based biosorbents showed great selective adsorption for Ag (I) in bimetallic solutions, and the simulation studies provided good potential in industrial applications to recover precious metal ions from water or wastewater.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".