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Cooling Rate Dependent Ellipsometry Measurements to Determine the Dynamics of Thin Glassy Films

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Tunable Percolation in Semiconducting Binary Polymer Nanoparticle Glasses.

Lawrence A Renna¹, Monojit Bag¹, Timothy S Gehan¹

¹Department of Chemistry, University of Massachusetts Amherst , Amherst, Massachusetts 01003-9303, United States.

The Journal of Physical Chemistry. B

|February 9, 2016

Summary

This summary is machine-generated.

Researchers achieved tunable electrical percolation in semiconducting/insulating polymer nanoparticle glasses by adjusting component ratios. This offers a new method for designing advanced functional materials with controlled electrical properties.

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Area of Science:

Materials Science
Polymer Science
Nanotechnology

Background:

Binary polymer nanoparticle glasses enable the assembly of diverse components with nanoscale and mesoscale control.
These materials are promising for developing advanced functional materials.

Purpose of the Study:

To demonstrate tunable electrical percolation in semiconducting/insulating polymer nanoparticle glasses.
To investigate the relationship between composition and electrical properties.
To develop a predictive model for percolation behavior.

Main Methods:

Varying the relative percentages of equal-sized semiconducting and insulating polymer nanoparticles.
Time-of-flight charge carrier mobility measurements.
Conducting atomic force microscopy.
Resistor network modeling and simulation of binary nanoparticle glasses.

Main Results:

Tunable electrical percolation was achieved by altering nanoparticle ratios.
Percolation thresholds were determined to be approximately 24-30%.
The systems exhibited power law scaling percolation behavior.
A resistor network model accurately reproduced experimental data and predicted trends.

Conclusions:

Binary polymer nanoparticle glasses offer a route to control electrical percolation.
The findings provide a supramolecular toolbox for rational material design using polymer nanoparticles.
The developed model aids in predicting and optimizing material properties.