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Modeling ionic hydrogels swelling: characterization of the non-steady state
Tamar Traitel1, Joseph Kost, Smadar A Lapidot
1Department of Chemical Engineering, Ben-Gurion University of the Negev, POB 653, Beer-Sheva 84105, Israel.
Biotechnology and Bioengineering
|August 12, 2003
Summary
This study developed a mathematical model to understand the dynamic swelling of pH-sensitive ionic hydrogels. The model reveals that proton and water entry rates differ, aiding in the design of controlled release systems.
Area of Science:
- Materials Science
- Chemical Engineering
- Biomedical Engineering
Background:
- Ionic hydrogels are stimuli-responsive materials used in controlled release systems.
- Existing research often focuses on equilibrium swelling, neglecting the significant dynamic processes.
- pH-sensitive hydrogels, like poly(2-hydroxyethyl methacrylate-co-N, N-dimethylaminoethyl methacrylate) (poly(HEMA-co-DMAEMA)), are crucial for applications such as glucose-sensitive insulin delivery.
Purpose of the Study:
- To develop a mathematical compartmental model for understanding non-steady state swelling in pH-sensitive ionic hydrogels.
- To analyze the kinetic parameters governing the swelling behavior of poly(HEMA-co-DMAEMA).
- To facilitate the design and specification of stimuli-responsive hydrogel systems.
Main Methods:
- Development of a mathematical compartmental model using SAAM II software.
- Integration of experimental data with kinetic analysis.
- Investigation of factors influencing water uptake, including pH, amine group concentration, and crosslinking density.
Main Results:
- The rate of proton entry differs from the rate of water entry in poly(HEMA-co-DMAEMA) hydrogels.
- The water transport coefficient is dependent on external pH, incorporated amine groups, and polymer crosslinking density.
- Swelling equilibrium is achieved when all amine groups within the hydrogel become protonated.
Conclusions:
- A kinetic model can accurately predict the dynamic swelling behavior of pH-sensitive hydrogels.
- The model demonstrates predictive capability for both interpolated and extrapolated data, aiding future experimental design.
- Combining experimental and computational approaches is essential for overcoming complexities in hydrogel research and development.