Understanding the Interaction Mechanism between Elemental Selenium and Ferric Hydroxide in Wastewater Treatment
Bibliographic record
Abstract
The conversion of selenium oxyanions to elemental selenium (Se0) of low solubility and bioavailability is an effective industrial approach for selenium management in wastewater treatment. The generated Se0 particles require further treatment with coagulants such as ferric ions to facilitate the precipitation of Se0 particles. In this work, the settling of Se0 particles in simulated wastewater was investigated with amorphous ferric hydroxide (Fe(OH)3) as a coagulant prepared from hydrolysis of ferric chloride. The effects of ferric ion dosage, water composition, and pH on the Se0 removal percentage were investigated. The surface properties, i.e., ζ potential and morphology, were characterized, which influenced the interactions between Se0 and Fe(OH)3. The forces acting between Se0 and Fe(OH)3 surfaces were directly measured, for the first time, using atomic force microscopy (AFM). The water composition and pH had a significant effect on the adhesion force. In simulated wastewater, the adhesion force generally increased with pH, suggesting that the adsorption of Ca2+ and Mg2+ on Fe(OH)3 surface increased with pH, which enhanced the adhesion. Interestingly, long-range pull-off forces and sawtooth patterns were observed on the retraction force–separation curves, which were attributed to the stretching of Fe(OH)3 particle aggregates or chains during separation. Bulk settling tests showed that the best Se0 removal performance of Fe(OH)3 was found to be around pH 8, which was because the largest amount of Fe(OH)3 precipitates was found around this pH. The results indicate that the Fe(OH)3 solubility as well as the related intermolecular and surface forces play the predominant role in determining the Se0 removal performance of the ferric coagulant. This work, for the first time, revealed the interaction mechanisms of Se0 particle and amorphous Fe(OH)3, providing useful insights on the performance of ferric coagulants on Se0 particle removal from wastewater under various solution conditions. The experimental approach used in this work can be readily extended to other water treatment systems and processes such as polymer flocculation.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".