Removing aromatic organic pollutants by cloud point extraction using biodegradable nonionic surfactants: equilibrium constants and diffusion kinetics
Bibliographic record
Abstract
The impacts of inadequate disposal of industrial wastewater cause environmental, economic, social and human health damage. The search for innovative and viable methodologies from a technical and economic standpoint aims to promote suitable management of effluents produced by different industrial activities. The objective of this paper was to study phenol, aromatic alcohol and aromatic amine removal using cloud point extraction based on analysis of extraction capacity, miscibility curves, equilibrium lines, distribution coefficients and mass transfer solubilisation diffusion. The oxo-C10E3P4E2 surfactant produced the best results, exhibiting smaller coacervate volume fractions, low concentration in the diluted phase and efficient extraction (E%) of phenol (81.79%), 1-phenylethanol (67.88%), 2-phenylethanol (65.41%), benzyl alcohol (57.01%), aniline (56.91%), p-toluidine (73.96%) and 2,4-dimethylaniline (84.87%). The ternary phase diagrams revealed a large miscibility area favourable to extraction, with equilibrium lines showing a pronounced slope in favour of the coacervate, indicating high compound concentration factors. Equilibrium isotherms were used to obtain the distribution and solubilisation constants (Log KC/D KS), in the following order: aniline (1.05; 9.15) < p-toluidine (1.35; 20.40) < 2,4-dimethylaniline (1.76; 45.10) and benzyl alcohol (1.07; 8.19) < 2-phenylethanol (1.22; 14.04) < 1-phenylethanol (1.28; 15.23) < phenol (1.59; 36.72). These parameters show a linear correlation with Log KO/W, revealing that micellar solubilisation is governed by the hydrophobic nature of aromatic organic compounds. The evolution of pH demonstrated that the ionised forms of phenol and 2,4-dimethylaniline do not establish interactions (electrostatic, van der Waals, hydrogen bonds) with surfactants. Mass transfer diffusion is governed by hydrophobic compounds, slightly compromising phenol due to its high solubility in water.
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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".