Selective Solubilization of Polycyclic Aromatic Hydrocarbons from Multicomponent Nonaqueous-Phase Liquids into Nonionic Surfactant Micelles
Bibliographic record
Abstract
This research investigates the equilibrium solubilization behavior of naphthalene and phenanthrene from multicomponent nonaqueous-phase liquids (NAPLs) by five different polyoxyethylene nonionic surfactants. The overall goal of the study was to achieve an improved understanding of surfactant-aided dissolution of polycyclic aromatic hydrocarbons (PAHs) from multicomponent NAPLs in the context of surfactant-enhanced remediation of contaminated sites. The extent of solubilization of the PAHs in the surfactant micelles increased linearly with the PAH mole fraction in the NAPL. The solubilization extent and micelle-water equilibrium partition coefficient of the PAHs increased with the size of the polar shell region of the micelles rather than the size of the hydrophobic core of the micelle. The presence of both PAHs in the shell region of the micelles was confirmed by 1H NMR analysis. This is an important observation because it is commonly assumed that in multi-solute systems the solutes with relatively greater hydrophobicity are solubilized only in the micellar core. A comparison of the 1H NMR spectra of pure surfactant solutions and solutions contacted with various NAPLs demonstrated that the distribution of PAHs between the shell and the core changed with the concentration of PAHs in the micelles and in the NAPL. Competitive solubilization of the PAHs was observed when both PAHs were present in the NAPL. For example, in surfactant solutions of Brij 35 and Tween 80, the solubilization of phenanthrene was decreased in the presence of naphthalene as compared to systems that contained phenanthrene as the only solute. In contrast, with micellar solutions of Tergitol NP-10 and Triton X-100, phenanthrene solubilization was enhanced in the presence of naphthalene. The activity coefficients of the PAHs in the micellar phase were generally found to increase with PAH concentrations in the micelle.
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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".