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Updated: Aug 2, 2026

A Facile and Efficient Approach for the Production of Reversible Disulfide Cross-linked Micelles
Published on: December 23, 2016
The critical micelle condition revisited
1Univ. Connecticut, Groveton, NH 03582.
This study explores micellar size distributions, proposing a thermodynamic shell model. The model successfully predicts a critical micelle condition and broad distributions, aligning with experimental observations.
Area of Science:
- Physical Chemistry
- Colloid Science
- Thermodynamics
Background:
- Understanding micellar size distributions is crucial for explaining the critical micelle condition.
- Existing models like the two-state system or indefinite self-association do not fully align with experimental requirements.
- A need exists for a model that predicts both broad distributions and a clear threshold concentration.
Purpose of the Study:
- To investigate alternative models for micellar size distributions.
- To detail a thermodynamic "shell model" for predicting micelle formation.
- To ensure theoretical models are consistent with the experimentally observed critical micelle condition.
Main Methods:
- Examination of simple theoretical models for micellar self-association.
- In-depth analysis of a thermodynamic "shell model" for micelle distributions.
- Comparison of molar and concentration distribution functions within the shell model framework.
Main Results:
- The "continuous self-association with an upper limit" model is theoretically plausible but lacks experimental backing.
- The proposed "shell model" predicts broad distributions of micellar species, consistent with earlier statistical-mechanical studies.
- The shell model's molar distribution function correctly predicts a minimum concentration at a specific degree of polymerization, unlike other models.
Conclusions:
- The thermodynamic shell model provides a self-consistent framework for micellar size distributions.
- The model successfully predicts the critical micelle condition and the existence of broad distributions.
- The predicted minimum concentration's position shifts with varying overall concentration, offering further predictive power.
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