Regenerable Magnetic Iron Oxide Incorporated Chitosan Beads for Sulfate ( <scp> SO <sub>4</sub> <sup>2</sup> </scp> <sup>−</sup> ) Removal From Wastewater: Isotherms, Kinetics and Mechanism
Bibliographic record
Abstract
ABSTRACT Sulfate contamination removal from mine waste and industrial water is a primary environmental concern, with high concentrations leading to disease. This research focuses on the preparation of iron oxide‐incorporated chitosan beads (IOICBs) for the effective adsorption removal of sulfate. The IOICBs were synthesized by a chemical co‐precipitation approach and characterized by Scanning Electron Microscopy, x‐ray Diffraction, Brunauer–Emmett–Teller analysis, Fourier Transform Infrared Spectroscopy, and energy‐dispersive x‐ray mapping for structural, chemical, and morphological properties. The sulfate adsorption on IOICBs was investigated in batch mode studies using UV–Vis spectroscopy. The effect of several parameters, such as adsorbent dosage, pH, initial concentration, and contact time, on the sorption capacity of the IOICBs was studied. The IOICBs have a high efficacy for sulfate removal from contaminated water at pH 2 with a maximum sorption capacity of 147.7 mg/g at room temperature. The mechanism suggested that the presence of acidic NH 4 + functional groups on the surface of chitosan facilitates the chemisorption of the sulfate ions. The adsorption equilibrium is in good agreement with the Langmuir Isotherm ( R 2 = 0.997), and the kinetics analysis suggests the adsorption is a pseudo‐second‐order process ( R 2 = 0.992), confirming the chemisorption of sulfate species on IOICBs. After sulfate adsorption, the IOICBs were regenerated in 0.1 M NaOH solution, reused for multiple sorption/desorption cycles, and the reusability remained at over 83% after four consecutive cycles. Thus, IOICBs are potential absorbents for removing industrial pollutants such as sulfate ions from 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) | 0.000 | 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".