Investigation of sustainable waste-derived adsorbents for arsenic and copper in water treatment
Bibliographic record
Abstract
Arsenic contamination in drinking water is a global issue, affecting millions and posing serious health risks such as cancer and neurological disorders. This is particularly critical in communities relying on groundwater. This research explores using mussel shells and agricultural waste, including date seeds and orange peels, as sustainable adsorbents for removing arsenic and copper from water. Nanoparticles were incorporated into calcined mussel shell powder and biochars derived from these biomass wastes to enhance adsorption capacity. The adsorbents were characterized to evaluate their surface properties, and adsorption mechanisms were studied to understand their effectiveness. Batch experiments were conducted to examine the effects of pH, adsorbent dosage, initial metal concentrations, and contact time. These experiments identified optimal conditions for maximizing removal efficiency. Statistical methods were used to optimize the adsorption processes. The results showed that modified mussel shells have high arsenic adsorption potential, while TiO₂-modified orange peel biochar performed well as a low-cost option for copper removal. Both biochars also demonstrated strong copper adsorption performance. Kinetic and isotherm models helped describe the rate and equilibrium behavior of arsenic and copper adsorption. Thermodynamic analysis indicated that the adsorption processes were spontaneous and endothermic. The mechanisms of arsenic removal by mussel shells were further studied, considering ionic strength, surface charge, and functional groups using various analytical techniques. Beyond batch tests, column studies were performed using mussel shells in point-of-use (POU) filtration systems. These tests assessed exhaustion capacity, the influence of co-existing ions, and reusability. Breakthrough curve analysis showed how initial arsenic concentration, flow rate, and adsorbent mass affected performance. Modified mussel shells demonstrated superior arsenic removal in POU cartridges compared to commercial activated carbon. This study offers a sustainable and cost-effective approach to arsenic removal, particularly for remote communities with limited access to centralized water treatment, by repurposing mussel shells and agricultural waste as effective adsorbents.
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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 | 0.001 |
| 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".