Use of mussel shells for removal of arsenic from water: Kinetics and equilibrium experimental investigation
Bibliographic record
Abstract
This study investigated the potential of calcined mussel shells (CMS) as an adsorbent for removing arsenic (As(III) and As(V)) from water using a comprehensive approach incorporating optimization, kinetics, and equilibrium studies. It assessed the impacts of pH, initial arsenic concentration (Ci), adsorbent dose (Ad), and contact time (tc) using response surface methodology (RSM) to maximize the adsorption efficiency. The optimal conditions for As(III) removal were pH of 6.4, Ci = 57.9 mg L−1, Ad = 3.4 g L−1, and tc = 4.4 h, achieving a removal efficiency of 94.9%. For As(V) removal, the optimal conditions were pH of 5.7, Ci = 59.9 mg L−1, Ad = 2.7 g L−1, and tc = 4.9 h, achieving a removal efficiency of 98.5%. Kinetic studies revealed that pseudo-second-order (PSO) model best described As(III) and As(V) adsorption. According to equilibrium isotherm studies, the Langmuir model provided a more accurate representation of the adsorption behavior, indicating monolayer adsorption on the iron oxide–modified calcined mussel shells (IO-CMS) homogenous surface (As(III): qmax = 28.74, R2 = 0.87; As(V): qmax = 31.54, R2 = 0.98). The adsorption process for As(III) and As(V) was spontaneous and endothermic. This work highlights the potential of CMS as an environmentally acceptable and affordable adsorbent for removing arsenic from water.
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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".