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Flash NanoPrecipitation for the Encapsulation of Hydrophobic and Hydrophilic Compounds in Polymeric Nanoparticles
Published on: January 7, 2019
Continuum percolation of polydisperse nanofillers
Ronald H J Otten1, Paul van der Schoot
1Theory of Polymers and Soft Matter and Eindhoven Polymer Laboratories, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands. r.h.j.otten@tue.nl
Physical Review Letters
|April 7, 2010
Summary
A new percolation theory explains electrical conduction in carbon-nanotube composites. It details how particle variations and nonconductive materials influence conductivity onset, crucial for material science applications.
Area of Science:
- Materials Science
- Physics
- Chemistry
Background:
- Percolation theory is vital for understanding composite material properties.
- Anisotropic particle mixtures present challenges for existing theories.
- Carbon-nanotube composites exhibit variable electrical conduction behavior.
Purpose of the Study:
- To develop a tractable generalized connectedness percolation theory for anisotropic particle mixtures.
- To explain variations in electrical conduction onset in carbon-nanotube composites.
- To model the impact of nonconductive species on percolation thresholds.
Main Methods:
- Generalized connectedness percolation theory.
- Application to anisotropic particle mixtures, including carbon-nanotube composites.
- Analysis of sensitivity to particle polydispersity (length and diameter).
Main Results:
- The theory is applicable to complex mixtures with potentially infinite components.
- Percolation threshold is sensitive to particle length and diameter polydispersity.
- Nonconductive species proportionally increase the percolation threshold based on abundance.
Conclusions:
- The developed theory provides a robust framework for predicting electrical properties of complex composites.
- Understanding polydispersity effects is key to controlling composite conductivity.
- The presence of insulating components significantly impacts the conductive network formation.
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