Impact of sonication on activated sludge properties and consequences on PAH partitioning
Bibliographic record
Abstract
Abstract Sonication is an efficient sludge disintegration technique that can be used for reducing the excess sludge produced in water treatment. The effects of activated sludge sonication on its physicochemical properties and their consequences on the partitioning of hydrophobic polycyclic aromatic hydrocarbons (PAH) are reported. Ultrasound treatment led to an increase in dissolved and colloidal matter in the aqueous phase, with a predominance of proteins compared to the initial supernatant. This transfer of DCM was found to be directly correlated to the energy applied, and resulted in proportional transfer of PAHs from the particulate phase to the aqueous fraction. The PAH aqueous fraction, initially ranging from 0.012 g/g (pyrene) to 0.19 g/g (naphthalene), reached between 0.25–0.37 g/g when a specific energy of 40 000 kJ/kgTS0 was applied. For the raw sludge, the logarithm of the equilibrium constant varied between 3.0–4.3, depending on the hydrophobicity of the molecule, but when sonication was applied, the affinity for particles decreased significantly, resulting in a narrow distribution (log KG = 2.8–3.0 after 40 000 kJ/kgTS0 was applied). PAH partition is governed by molecule hydrophobicity (log Kow) for raw sludge and by sonication intensity for sonicated sludge.
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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 | 0.001 |
| 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".