Adsorption of Arsenate onto Ferrihydrite from Aqueous Solution: Influence of Media (Sulfate vs Nitrate), Added Gypsum, and pH Alteration
Bibliographic record
Abstract
Mineral processing effluents generated in hydrometallurgical industrial operations are sulfate based; hence it is of interest to investigate the effect sulfate matrix solution ("sulfate media") has on arsenate adsorption onto ferrihydrite. In this work, in particular, the influence of media (SO4(2-) vs NO3-), added gypsum, and pH alteration on the adsorption of arsenate onto ferrihydrite has been studied. The ferrihydrite precipitated from sulfate solution incorporated a significant amount of sulfate ions and showed a much higher adsorption capacityfor arsenate compared to nitrateferrihydrite at pH 3-8 and initial Fe/As molar ratios of 2, 4, and 8. Adsorption of arsenate onto sulfate-ferrihydrite involved ligand exchange with SO4(2-) ions that were found to be more easily exchangeable with increasing pH. Added gypsum to the adsorption system significantly enhanced the uptake of arsenate by ferrihydrite at pH 8. Equilibration treatment at acidic pH and addition of gypsum markedly improved the stability of adsorbed arsenate on ferrihydrite when pH was elevated. Comparison of arsenate adsorption onto ferrihydrite to coprecipitation of arsenate with iron(III) showed the latter process to lead to higher arsenic removal.
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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 | 0.000 |
| 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".