An NMR study of macromolecular aggregation in a model polymer-surfactant solution
Bibliographic record
Abstract
A model complex-forming nonionic polymer-anionic surfactant system in aqueous solution has been studied at different surfactant concentrations. Using pulsed-field-gradient diffusion NMR spectroscopy, we obtain the self-diffusion coefficients of poly(ethylene glycol) (PEO) and sodium dodecyl sulfate (SDS) simultaneously and as a function of SDS concentration. In addition, we obtain NMR relaxation rates and chemical shifts as a function of SDS concentration. Within the context of a simple model, our experimental results yield the onset of aggregation of SDS on PEO chains (CAC=3.5 mM), a crossover concentration (C(2)=60 mM) which signals a sharp change in relaxation behavior, as well as an increase in free surfactant concentration and a critical concentration (C(m)=145 mM) which signals a distinct change in diffusion behavior and a crossover to a solution containing free micelles. C(m) also marks the concentration above which obstruction effects are definitely important. In addition, we obtain the concentration of SDS in monomeric form and in the form of free micelles, as well as the average number of SDS molecules in a PEO-SDS aggregate (N(Aggr)). Taken together, our results suggests continuous changes in the aggregation phenomenon over much of the concentration but with three distinct concentrations that signal changes in the nature of the aggregates.
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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) | 0.000 | 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".