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Record W2178475670 · doi:10.1002/cjce.22393

Impact of sonication on activated sludge properties and consequences on PAH partitioning

2015· article· en· W2178475670 on OpenAlexvenueno aff
I. Mozo, Nicolas Lesage, Mathieu Spérandio, Yolaine Bessière

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsSonicationChemistryNaphthaleneAqueous solutionPyrenePartition coefficientActivated sludgeAqueous two-phase systemFraction (chemistry)ChromatographyOrganic chemistrySewage treatmentEnvironmental engineering

Abstract

fetched live from OpenAlex

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/kg TS0 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 K G = 2.8–3.0 after 40 000 kJ/kg TS0 was applied). PAH partition is governed by molecule hydrophobicity (log Kow) for raw sludge and by sonication intensity for sonicated sludge.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.152

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.230
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2015
Admission routes1
Has abstractyes

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