Analytical modeling of orientation effects in random nanowire networks

TL;DR

This paper introduces a simplified analytical model that accurately predicts the electrical conductivity of random nanowire networks with arbitrary orientations, offering faster computation and new theoretical insights.

Contribution

It presents the first analytical model capturing the effects of nanowire orientation distributions on network conductivity, replacing Monte Carlo methods with a faster approximation.

Findings

Model accurately predicts conductivity based on orientation.

Provides a computationally efficient alternative to Monte Carlo sampling.

Offers new theoretical understanding of orientation effects.

Abstract

Films made from random nanowire arrays are an attractive choice for electronics requiring flexible transparent conductive films. However, thus far there has been no unified theory for predicting their electrical conductivity. In particular, the effects of orientation distribution on network conductivity remain poorly understood. We present a simplified analytical model for random nanowire network electrical conductivity that is the first to accurately capture the effects of arbitrary nanowire orientation distributions on conductivity. Our model is an upper bound and converges to the true conductivity as nanowire density grows. The model replaces Monte Carlo sampling with an asymptotically faster computation and in practice can be computed much more quickly than standard computational models. The success of our approximation provides novel theoretical insight into how nanowire orientation affects electrical conductivity, illuminating directions for future research.