Arsenic Removal from Aqueous Solution by Iron Oxide-Coated Biomass: Common Ion Effects and Thermodynamic Analysis
Bibliographic record
Abstract
A batch study showed that the presence of anions (sulfate, chloride, and nitrate) in solution did not affect the adsorption process of both As(V) and As(III) by iron oxide-coated A. niger biomass. It was found that the presence of Ca2+, Fe2+, and Mg2+ ions at a concentration of 200 mg/L in solution could increase the removal efficiency of As(V) by 86.5%, 95.4%, and 65.8%, respectively. Similarly, the presence of Ca2+, Fe2+, and Mg2+ ions at a concentration of 200 mg/L in solution could increase the removal efficiency of As(III) by 39.3%, 97%, and 8.4%, respectively. The batch adsorption-desorption study showed that the reactions between the arsenic species and the iron oxide-coated A. niger biomass were reversible. Desorption of As(V) and As(III) at neutral pH was approximately 15%. As(V) desorbed more than As(III) in acidic (pH 1.33) and alkaline (pH 12.56) solutions. At a pH of 1.33, 67% of the adsorbed As(V) desorbed, and the percentage of desorbed As(III) was only 47.1% in the same condition. At a solution pH of 12.56, 73.4% of the As(V), and 43.7% of As(III) desorbed. The thermodynamic study showed the spontaneous nature of the sorption of arsenic on IOCB. The high value of the heat of adsorption {ΔH ≈ − 133 kJ/mol for As(V), and 88.9 k/mol for As(III)} indicated that the mechanism of arsenic sorption was chemisorption.
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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) | 0.000 | 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".