Micellar Solubilization of Naphthalene and Phenanthrene from Nonaqueous-Phase Liquids
Bibliographic record
Abstract
The equilibrium partitioning of the polycyclic aromatic hydrocarbon (PAH) compounds naphthalene and phenanthrene, from nonaqueous-phase liquids (NAPLs) into micellar solutions of five different nonionic polyethoxylated surfactants, is evaluated in this study. A series of synthesized NAPLs, comprised of naphthalene and/or phenanthrene dissolved in hexadecane at varying concentrations, were equilibrated with surfactant solutions in well-mixed batch systems. It was observed that the extent of micellar partitioning of PAH compounds increases linearly with their relative abundance in the NAPLs. A theoretical liquid-liquid partitioning framework that describes PAH equilibrium partitioning between the NAPL, aqueous, and the liquid-like micellar phases is presented. Although the maximum solubilization capacity of micelles is generally higher for naphthalene as compared to phenanthrene, results indicate that with certain NAPLs phenanthrene may be solubilized to a similar extent as naphthalene, even when equal mole fractions of the compounds are present in the NAPLs. Selective solubilization of naphthalene over phenanthrene into micellar solutions of Brij 35 was observed in systems where naphthalene and phenanthrene were both present. The extent of micellar partitioning of phenanthrene was decreased by approximately 18% in the presence of naphthalene, while naphthalene partitioning was unaffected by the presence of phenanthrene.
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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".