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Updated: Dec 4, 2025

Assembly and Characterization of Polyelectrolyte Complex Micelles
Published on: March 2, 2020
Overcharging of polyelectrolyte complexes: an entropic phenomenon
Mohsen Ghasemi1, Sean Friedowitz, Ronald G Larson
1Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan 48109, USA. rlarson@umich.edu.
Complex coacervation overcharging, where a polyelectrolyte complex coacervate absorbs excess polymers, is explained by ion entropy and polymer binding. This phenomenon depends on salt concentration, ion-pairing, and chain hydrophobicity.
Area of Science:
- Polymer Science
- Physical Chemistry
- Materials Science
Background:
- Complex coacervation involves polyelectrolyte complex coacervates (PECs) formed by oppositely charged polymers.
- Overcharging occurs when PECs absorb a significant excess of one polyelectrolyte type.
- Existing explanations for overcharging lack consensus.
Purpose of the Study:
- To predict and explain overcharging in complex coacervation using a new thermodynamic model.
- To elucidate the driving forces behind overcharging, resolving competing theories.
- To compare theoretical predictions with experimental data for specific polyelectrolyte systems.
Main Methods:
- Development of a thermodynamic model incorporating reversible ion binding and electrostatic correlations.
- Analysis of the contributions of counterion translational entropy and polyelectrolyte binding entropy.
- Experimental comparison using multilayers of PDADMA and PSS in KBr and NaCl solutions.
Main Results:
- Overcharging is driven by both counterion entropy and polyelectrolyte binding entropy.
- The extent of overcharging shows non-monotonic behavior with salt concentration.
- Overcharging increases with ion-pairing strength and chain hydrophobicity.
Conclusions:
- The developed thermodynamic model successfully predicts overcharging in complex coacervation.
- The study resolves competing explanations for overcharging by highlighting dual entropic contributions.
- Good qualitative agreement between model predictions and experimental multilayer data validates the approach.
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