Removal of arsenic(III) from aqueous solution by concrete‐based adsorbents
Bibliographic record
Abstract
The present paper investigates the adsorption of arsenic(III) (As(III)) onto 2 concrete‐based low‐cost materials, i.e., Aerocrete and Vermiculite impregnated by ferric oxyhydroxide. Adsorption experiments were performed to study the effect of initial pH, initial concentration of As(III), contact time, and ions usually present in water. No significant effect of the initial pH on the adsorption of As(III) by Aerocrete and Vermiculite was observed at the pH range of 4–8. The As(III) removal efficiency decreased at a high initial pH (i.e., 10). The Langmuir isotherm showed that the maximum As(III) adsorption capacity of Aerocrete and Vermiculite is 15.15 and 13.51 mg/g, respectively, which is higher than that observed using titanium dioxide (i.e., 3.52 mg/g), at pH 7 and 24 ±1 °C. A pseudo‐second order kinetic model fitted well the experimentally obtained kinetic data. This suggests that chemisorption most probably controls the adsorption of As(III) on Aeroctere and Vermiculite. Significantly, As(III) (1 mg/L) could be removed almost completely by both Aeroctere and Vermiculite (1 g/L) in 30 and 60 min, respectively at pH 7 and 24 ±1 °C. Importantly, Ca2+, Mg2+, Na+, HCO3−, SO42−, and Cl− ions had no significant effect on the adsorption of As(III) on Aeroctere and Vermiculite. The results showed that the proposed concrete‐based adsorbents have the potential to remove As(III) 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) | 0.001 | 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) | 0.000 | 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".