Ferrrate(VI) and freeze-thaw treatment for oxidation of hormones and inactivation of fecal coliforms in sludge
Bibliographic record
Abstract
This study examined the individual and combined effects of potassium ferrate(VI) additions and freeze-thaw conditioning for the treatment and dewatering of wastewater sludge in cold climates, with particular focus on the inactivation of fecal coliforms and oxidation of estrogens, androgens, and progestogens. The first phase of the study evaluated the effects of potassium ferrate(VI) pre-treatment followed by freeze-thaw at -20 °C using a low (0.5 g/L) and high (5.0 g/L) dose of potassium ferrate(VI). The results showed that pre-treatment of anaerobically digested sludge with 5 g/L of potassium ferrate(VI) reduced the concentration of fecal coliforms in the sludge cake to below 100 MPN/g DS. The second phase evaluated the ability of ferrate(VI) to oxidise selected hormones in sludge. Anaerobically digested sludge samples were spiked with 10 different hormones: estrone (E1), 17α-estradiol, 17β-estradiol (E2), estriol (E3), 17α-ethinylestradiol (EE2), equilin, mestranol, testosterone, norethindrone and norgestrel in two groups of low (3-75 ng/mL) and high (12-300 ng/L) concentration ranges of hormones. The samples were treated with either 0.5 or 1.0 g/L of potassium ferrate(VI), and hormone concentrations were measured again after treatment. Potassium ferrate(VI) additions as low as 1.0 g/L reduced the concentration of estrogens in sludge. Potassium ferrate(VI) additions of 0.5 and 1.0 g/L were less effective at reducing the concentrations of androgens and progestogens. Increasing ferrate(VI) dose would likely result in more substantial decreases in the concentrations of fecal coliforms and hormones. The results of this study indicate that the combined use of freeze-thaw and ferrate(VI) has the potential to provide a complete sludge treatment solution in cold regions.
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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".