Behavior of complexing ligands during coagulationflocculation by ferric chloride: A comparative study between sewage water and an engineered colloidal model
Bibliographic record
Abstract
In this work, a comparative study is conducted on sewage as an example of a natural water system and on an engineered colloidal model. The two water systems were treated by coagulationflocculation with ferric chloride. Affinity of phosphate and sulfate used as complexing ligands to coagulant species was studied within the two systems. Aggregation dynamics were followed with jar tests and laser diffraction. Sulfate and phosphate were determined by using ion chromatography. Atomic absorption spectroscopy (AAS) and transmission electron microscopy coupled with energy dispersive X-ray spectrometry were used for elemental and ionic analysis of P, Fe, S, SO4, and PO4 in supernatants and sediments. Investigation of ligands revealed that phosphate and iron(III) species make strong complexes that enhance aggregation velocity, whereas sulfate is weakly complexed with iron(III) species. It can be released in suspensions without any consequences on aggregation dynamics.Key words: aggregation, coagulant species, complexing ligands, coagulation, flocculation.
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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".