Salt Effects on Solute Exchange in Sodium Dodecyl Sulfate Micelles
Bibliographic record
Abstract
We describe the influence of sodium chloride on the rate of solute exchange in aqueous SDS micelles for a water-insoluble solute, a pyrene-containing triglyceride 1. The initially prepared solutions contained a small fraction of micelles containing two molecules of 1 and a large excess of empty micelles. These solutions showed a measurable excimer emission (of intensity I(E)) that was stable for days to weeks in the absence of added salt. Following additions of salt, I(E) decayed exponentially (rate constant, k(obs)) accompanied by an increase in pyrene monomer emission. Values of k(obs) increased strongly with ionic strength (k(obs) similar [Na(+)](4)). There was no contribution of the empty micelle concentration beyond its contribution to the sodium ion concentration. We conclude that the solute exchange involves spontaneous fragmentation of the SDS micelles into two submicelles, each bearing a molecule of 1, which then grow back to normal micelles through condensation of SDS monomers. We propose a model for the fragmentation process in which large amplitude surface fluctuations "pinch off" a subunit that becomes a submicelle. These fluctuations bring sulfate headgroups into close proximity. Fluctuations leading to fission become important only in the presence of sufficient counterion concentration to reduce the electrostatic repulsion between neighboring headgroups.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| 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".