Mercury Removal Using Sulfur-Decorated Chitosan Polymer Nanocomposites: Adsorption Performance and Mechanisms
Bibliographic record
Abstract
In this work, pCh-MWCNTs@Ag-TiO2/S and pCh-MWCNTs@Ag-TiO2 nanocomposites were synthesized through a combined phosphorylation and cross-linked polymerization method. The materials were thoroughly characterized using several analytical techniques, including SEM/EDS, FTIR, TGA, and BET analysis. SEM images revealed that the pCh-MWCNTs@Ag-TiO2/S nanocomposite displayed a smooth, flake-like morphology with spherical, dark greenish particles. EDS analysis confirmed the presence of Si, S, P, and Ag as prominent elements, with Ti, C, and O showing the most intense peaks. The TGA curves indicated significant weight loss between 250–610 °C for pCh-MWCNTs@Ag-TiO2 and 210–630 °C for pCh-MWCNTs@Ag-TiO2/S, corresponding to the decomposition of organic components. FTIR spectra validated the existence of functional groups such as hydroxyl (-OH), carboxyl (-COOH), and carbonyl (-C=O) on the surface of the nanocomposites. Following characterization, the materials were evaluated for their capacity to adsorb Hg2+ at parts-per-billion (ppb) concentrations in contaminated water. Batch adsorption experiments identified optimal conditions for mercury removal. For pCh-MWCNTs@Ag-TiO2, the best performance was observed at pH 4, with an adsorbent dose of 4.0 mg, initial mercury concentration of 16 ppb, and a contact time of 90 min. For pCh-MWCNTs@Ag-TiO2/S, optimal conditions were at pH 6, a dosage of 3.5 mg, the same initial concentration, and a contact time of 100 min. Each parameter was optimized to determine the most effective conditions for Hg2+ removal. The nanocomposites showed high efficiency, achieving more than 95% mercury removal under these conditions. Kinetic studies indicated that the adsorption process followed a pseudo-second-order model, while the equilibrium data aligned best with the Langmuir isotherm, suggesting monolayer adsorption behavior. Overall, this research highlights the effectiveness of sulfur-modified chitosan-based nanocomposites as eco-friendly and efficient adsorbents for the removal of mercury from aqueous systems, offering a promising solution for water purification and environmental protection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.002 | 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 teacher head, 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".