Characterization of a Reverse Micellar System by <sup>1</sup>H NMR
Bibliographic record
Abstract
The (1)H NMR spectrum of IgepalCO520 in ternary mixtures containing water and cyclohexane shows a complex dependence on water content. This is in part because of rapid exchange between surfactant molecules within the micelles and free surfactant dissolved in the continuous phase. The analysis of this two-state system is further complicated by the fact that the chemical shifts of both free and micellar surfactants vary with micelle size. We demonstrate that the relative quantities of free and micellar surfactants can be determined from the NMR spectra if the data are compared within sample sets of constant micelle size but differing global composition. By fixing micelle size, the spectra of both surfactant states remain constant within a given series and only the relative populations of the free and micellar species change with overall composition. This method of analysis allows for the determination of free surfactant concentration as a function of micelle size. Results are presented for the water/IgepalCO520/cyclohexane system and indicate that the free surfactant concentration is far from negligible and strongly dependent on micelle size. The free surfactant concentration increases with decreasing micelle size, reflecting the lower stability of the smaller micelles. Similar behavior can be expected for other reverse micellar systems based on non-ionic surfactants.
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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 | 0.001 |
| 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".