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Predictive Model for the Electrical Transport within Nanowire Networks


Forró, Csaba; Demkó, László; Weydert, Serge; Vörös, János; Tybrandt, Klas (2018). Predictive Model for the Electrical Transport within Nanowire Networks. ACS Nano, 12(11):11080-11087.

Abstract

Thin networks of high aspect ratio conductive nanowires can combine high electrical conductivity with excellent optical transparency, which has led to a widespread use of nanowires in transparent electrodes, transistors, sensors, and flexible and stretchable conductors. Although the material and application aspects of conductive nanowire films have been thoroughly explored, there is still no model which can relate fundamental physical quantities, like wire resistance, contact resistance, and nanowire density, to the sheet resistance of the film. Here, we derive an analytical model for the electrical conduction within nanowire networks based on an analysis of the parallel resistor network. The model captures the transport characteristics and fits a wide range of experimental data, allowing for the determination of physical parameters and performance-limiting factors, in sharp contrast to the commonly employed percolation theory. The model thus constitutes a useful tool with predictive power for the evaluation and optimization of nanowire networks in various applications.

Abstract

Thin networks of high aspect ratio conductive nanowires can combine high electrical conductivity with excellent optical transparency, which has led to a widespread use of nanowires in transparent electrodes, transistors, sensors, and flexible and stretchable conductors. Although the material and application aspects of conductive nanowire films have been thoroughly explored, there is still no model which can relate fundamental physical quantities, like wire resistance, contact resistance, and nanowire density, to the sheet resistance of the film. Here, we derive an analytical model for the electrical conduction within nanowire networks based on an analysis of the parallel resistor network. The model captures the transport characteristics and fits a wide range of experimental data, allowing for the determination of physical parameters and performance-limiting factors, in sharp contrast to the commonly employed percolation theory. The model thus constitutes a useful tool with predictive power for the evaluation and optimization of nanowire networks in various applications.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Institute of Biomedical Engineering
Dewey Decimal Classification:170 Ethics
610 Medicine & health
Uncontrolled Keywords:General Engineering, General Physics and Astronomy, General Materials Science
Language:English
Date:27 November 2018
Deposited On:05 Mar 2019 16:16
Last Modified:25 Sep 2019 00:23
Publisher:American Chemical Society (ACS)
ISSN:1936-0851
OA Status:Closed
Publisher DOI:https://doi.org/10.1021/acsnano.8b05406
PubMed ID:30398851

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