Quantum transport in graphene nanoribbon networks: complexity reduction by a network decimation algorithm

We study electronic quantum transport (QT) in graphene nanoribbon (GNR) networks on mesoscopic length scales. We focus on zigzag GNRs and investigate the conductance properties of statistical networks. To this end we use a density-functional-based tight-binding model to determine the electronic structure and QT theory to calculate electronic transport properties. We then introduce a new efficient network decimation algorithm that reduces the complexity in generic three-dimensional GNR networks. We compare our results to semi-classical calculations based on the nodal analysis (NA) approach and discuss the dependence of the conductance on network density and network size. We show that a NA model cannot reproduce the QT results nor their dependence on model parameters well. Thus, solving the quantum network by our efficient approach is mandatory for accurate modelling the electron transport through GNR networks.

Automated summary

This study focuses on the electronic quantum transport in graphene nanoribbon networks on mesoscopic length scales, particularly on zigzag graphene nanoribbons. The conductance properties of statistical networks are investigated using a density-functional-based tight-binding model for determining the electronic structure and quantum transport theory for calculating electronic transport properties. A new efficient network decimation algorithm is introduced to reduce complexity in generic three-dimensional graphene nanoribbon networks. The results are compared with semi-classical calculations based on the nodal analysis approach and the dependence of conductance on network density and size is discussed. The findings highlight the necessity of solving the quantum network using the efficient approach for accurately modeling electron transport through graphene nanoribbon networks.