Viscosity Model for Nanoparticulate Suspensions Based on Surface Interactions

Area of Science:

• Materials Science
• Chemical Engineering
• Rheology

Background:

• Nanoparticulate suspensions exhibit complex rheological behavior influenced by particle interactions and fluid dynamics.
• Accurate viscosity modeling is crucial for processing and application of these materials.

Purpose of the Study:

• To develop a widely mechanistic model for predicting the rheological behavior of nanoparticulate suspensions.
• To accurately model viscosity across a broad range of conditions, including solids content, shear rate, temperature, and particle size.

Main Methods:

• A hybrid modeling approach combining mechanistic relationships with heuristic expressions.
• Utilized a genetic algorithm for model development and selection of optimal parameters.
• Integrated the new model with existing physical models to capture various viscosity-influencing phenomena.

Main Results:

• The developed model effectively depicts the rheological behavior based on surface interaction forces and drag forces.
• The model accurately predicts viscosity for epoxy resin filled with boehmite nanoparticles.
• The model demonstrates applicability over a wide range of solids contents (up to 20 wt.%), shear rates, temperatures, and particle sizes.

Conclusions:

• The mechanistic model provides a robust framework for understanding and predicting nanoparticulate suspension rheology.
• The model's ability to extrapolate beyond calibration data offers significant predictive power with minimal parameters.
• This approach facilitates the design and optimization of processes involving nanoparticulate materials.