Solubilization of Aromatic Hydrocarbons in Ethylene Oxide‐Propylene Oxide Triblock Micelles: Location of Solubilizate and its Effect on Micelle Size from 2D NMR and Scattering Techniques
Bibliographic record
Abstract
Abstract The solubilization of benzene and toluene in micellar solutions and the effects on the micellization and micelle size of ethylene oxide‐propylene oxide triblock copolymers were investigated by dynamic light scattering (DLS), small angle neutron scattering (SANS), and 2D NMR spectroscopy. The copolymeric surfactants have the same size as the middle hydrophobic polypropylene oxide block (Mol. Wt. 3250) and varying polyethylene oxide end blocks (30, 40 and 50%). The solubilization and the properties of the micelles in the presence of the solubilizates were investigated; the results reveal that the more hydrophobic copolymer showed better solubilization. The cloud points of the copolymers decreased in the presence of oils; the depression in the cloud point is due to the formation of an electron donor–acceptor complex. DLS shows that the effect of benzene is dominated at high oil concentration. SANS data show that the micelles remain spherical in shape and that the micellar core size does not change with higher benzene concentration; observed changes in the low scattering vector region could be because of some small amount of benzene clusters formed at higher benzene concentration. Finally, the locus of solubilization of the oils in the copolymer micelles was determined via 2D NMR experiments. In all cases, significant nuclear Overhauser effect spectroscopy (NOESY) cross peaks were observed that appeared to correlate well with the expected loci of these solubilizates in micelles. Hence, the noninvasive NOESY technique provides important information on the location of the aromatic solubilizates in these copolymer micelles that depends on the structure of the oils.
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 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".