A granular chitosan adsorbent modified with Cu(II) for effective sulfate groundwater remediation
Bibliographic record
Abstract
Sustainable treatment of sulfate contaminated groundwater is a challenging global water security issue that significantly impacts human and ecosystem health. Herein, the utility of a recently developed granular chitosan-Cu(II) biocomposite adsorbent for the adsorption of sulfate from environmental groundwater systems is reported. Several types of groundwater samples (Wells-1, -2, -3 and -4) were investigated for sulfate removal via a fixed-bed column, which was characterized by kinetic adsorption parameters in laboratory and groundwater samples. The lowest exhaustion time for CP-Cu was observed for Well 3 due to the high sulfate concentration of 6772 mg/L. In turn, the adsorption capacity under dynamic conditions for Well 3 was the highest (153 mg/g) compared to the other groundwater samples. Modeling of the experimental sulfate removal under dynamic conditions was achieved using the Thomas, Yoon-Nelson, and Adam-Bohart models. The best-fit results showed that the Thomas and Yoon-Nelson model described the breakthrough curves favourably, as compared with the Adam-Bohart model. The prediction of the sulfate adsorption capacities by the Thomas model are in close agreement with the experimental results. This study contributes to the field of surface and interfacial processes through the adsorption of sulfate for a unique type of granular modified chitosan-Cu(II) bioadsorbent system to afford sustainable and efficacious treatment of environmental groundwater samples.
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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) | 0.000 | 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".