Diffusion in Solutions of Micelles. What Does Dynamic Light Scattering Measure?
Bibliographic record
Abstract
Dynamic light scattering (DLS) is routinely used to measure the diffusion of surfactant micelles. Theory and experiment suggest, however, that the microscopic concentration fluctuations monitored by DLS obey the same mutual diffusion equations that describe the decay of macroscopic concentration gradients. According to this interpretation, DLS provides mutual diffusion coefficients for the total surfactant components, including contributions from micelles, free surfactant monomers, and counterions. An attempt is made to decide the correct interpretation of DLS measurements by comparing DLS diffusion coefficients ( D DLS ) with mutual diffusion coefficients measured by macroscopic gradient techniques for binary aqueous solutions of ionic and zwitterionic surfactants. Possible contributions to D DLS from free surfactant monomers are investigated by extending DLS measurements into the critical micelle (cmc) region where substantial portions of the surfactants diffuse as free monomers. The widely held assumption that D DLS is the micelle diffusion coefficient is tested by comparing D DLS with micelle diffusion coefficients measured unambiguously by NMR or Taylor dispersion techniques for solubilized trimethylsilane or decanol tracers. D DLS is found to decrease sharply as the surfactant concentration is raised through the cmc, in agreement with the steep drop in the mutual diffusion coefficient caused by the association of free surfactant monomers. Above the cmc, D DLS and the micelle and mutual diffusion coefficients are nearly identical for the zwitterionic surfactants. For the ionic surfactants, D DLS and the mutual diffusion coefficients are several times larger than the micelle diffusion coefficients as a result of the diffusion of charged micelles with relatively mobile counterions to maintain electroneutrality. The results suggest that DLS diffusion coefficients are surfactant mutual diffusion coefficients.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".