Associations between a Pyrene-Labeled Hydrophobically Modified Alkali Swellable Emulsion Copolymer and Sodium Dodecyl Sulfate Probed by Fluorescence, Surface Tension, and Viscometry
Bibliographic record
Abstract
A hydrophobically modified alkali swellable emulsion copolymer labeled with pyrene (Py-HASE) was studied by fluorescence, surface tensiometry, and viscometry at concentrations ranging from 0.01 g/L (lower than the overlap concentration, C* = 2.4 g/L) to 10 g/L (above C* ) in the presence of the anionic surfactant sodium dodecyl sulfate (SDS). The results obtained by the three techniques lead to the conclusion that binding of SDS onto Py-HASE proceeds in four different stages which are separated by three SDS transition concentrations. The values of the SDS transition concentrations are little affected by the Py-HASE concentration at low Py-HASE concentration but are shifted to higher SDS concentrations at high Py-HASE concentration. The average number of pyrenes per mixed micelle could be determined from the analysis of the fluorescence decays which established a maximum average capacity of pyrenes per mixed micelle of 2.4 ± 0.5 independent of the polymer concentration. For a Py-HASE concentration of 6.0 g/L, the average number of pyrenes per mixed micelle was found to take the optimal value of 2.0 at the SDS concentration where the solution viscosity peaked. This study constitutes the first example where fluorescence experiments are being used to rationalize the spike in viscosity exhibited by an associative thickener solution upon addition of a surfactant.
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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 | 0.000 |
| 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 | 0.001 |
| 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".