Use of mussel shells for removal of arsenic from water: Kinetics and equilibrium experimental investigation
Why this work is in the frame
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Bibliographic record
Abstract
• A sustainable solution for arsenic-contaminated water in resource-limited areas • Local waste, mussel shells, was transformed into a valuable adsorbent • XRD, BET, SEM, XPS and FTIR characterized the adsorbent • High arsenic removal efficiency: 94.9% of As(III) and 98.5% of As(V) • Pseudo-second-order kinetics, endothermic, and spontaneous arsenic adsorption 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 ( C i ), adsorbent dose ( A d ), and contact time ( t c ) using response surface methodology (RSM) to maximize the adsorption efficiency. The optimal conditions for As(III) removal were pH 6.4, C i = 57.9 mg L −1 , A d = 3.4 g L −1 , and t c = 4.4 h, achieving a removal efficiency of 94.9%. For As(V) removal, the optimal conditions were pH 5.7, C i = 59.9 mg L −1 , A d = 2.7 g L −1 , and t c = 4.9 h, achieving a removal efficiency of 98.5%. Kinetic studies revealed that pseudo-second-order models (PSO) 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 IO-CMS homogenous surface (As(III): q max = 28.74, R 2 = 0.87; As(V): q max = 31.54, R 2 = 0.98). The adsorption process for As(III) and As(V) was spontaneous and endothermic. This work highlights the potential of CMS potential as an environmentally acceptable and affordable adsorbent for removing arsenic from water sources.
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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 | 0.000 |
| 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 it