Arsenic removal from aqueous solutions by adsorption on magnetite nanoparticles
Bibliographic record
Abstract
Abstract Magnetite nanoparticles were used to treat arsenic‐contaminated water. Because of their large surface area, these particles have an affinity for heavy metals by adsorbing them from a liquid phase. The results of the study showed that the maximum arsenic adsorption occurred at pH 2, with a value of approximately 3.70 mg/g for both As(III) and As(V) when the initial concentration of both arsenic species was maintained at 2 mg/L. The study showed that, apart from pH, the removal of arsenic from contaminated water also depends on the contact time, the initial concentration of arsenic, the phosphate concentration in the water and the adsorbent concentration. The results suggest that arsenic adsorption involved the formation of weak arsenic–iron oxide complexes at the magnetite surface. At a fixed adsorbent (magnetite nanoparticles) concentration of 0.4 g/L, percent arsenic removal decreased with increasing phosphate concentration. Magnetite nanoparticles removed <50% of arsenic from water containing >6 mg/L phosphate. In this case, an optimum design for achieving high arsenic removal by magnetite nanoparticles may be required.
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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) | 0.001 | 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".