Study on the effect of the organic acid structure on the rheological behavior and aggregate transformation of a pH-responsive wormlike micelle system
Bibliographic record
Abstract
A worm-like micelle (WLM) system can be obtained by mixing long-chain cationic surfactants and polybasic organic acids in an aqueous solution. However, the effect of different organic acid structures on the rheological behavior of WLM systems has not been researched. Herein, a novel pH-responsive wormlike micelle system (EATA) was constructed by the complexation of N-erucamidopropyl-N,N-dimethylamine (UC22AMPM) and benzene tricarboxylic acid (TA) at a molar ratio of 3 : 1. UC22AMPM/citric acid (EACA) was also prepared to perform a comparison. The rheological behavior, aggregate transformation and thickening mechanism of EATA solutions were investigated by using rheological measurements, cryo-TEM, DLS, surface tension and 1H NMR. The results show that, at low pH, spherical micelles were formed and the EATA solution exhibited a lower viscosity than the EACA system due to the strong hydrophobicity of the phenyl groups of TA molecules, but the viscosity reaches 106 mP s at pH 4.80. Because of the lower pKa value of TA than CA, the viscosity of the EATA system drops sharply with the appearance of precipitates caused by the isoelectric point when the pH is greater than 4.80. In addition, by circularly changing the pH value several times, the wormlike micelles could maintain their original viscoelasticity without being weakened in the slightest.
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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 | 0.000 |
| 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".