Year: 2019
Type: article
Abstract: Metal wire networks rely on percolation paths for electrical conduction, and by suitably introducing break-make junctions on a flexible platform, a network can serve as a resistive strain sensor. Several experimental designs have been proposed using networks consisting of silver nanowires, carbon nanotubes and metal meshes with high sensitivities. However, there is limited theoretical understanding; previous studies have taken the numerical approach and only consider rearrangement of nanowires with strain, while the critical break-make property of the sensor observed experimentally has been ignored. Herein, we propose a generic geometrical based model and study distortion, including the break-make aspect, and change in electrical percolation of the network, on applying strain. The result shows that when a given strain is applied, wire segments below a critical angle with respect to the applied strain direction end up breaking, leading to increased resistance of the network. The percolation shows interesting attributes; the calculated resistance increases linearly for lower strains and superlinearly for higher strains, consistent with the experimental findings. In practical situations, the strain direction is not always in the direction of measurement, hence, strain value and its direction both are incorporated into the treatment. The study reveals interesting anisotropic conduction features; higher strain sensitivity paralel to the strain, while broader strain range perpendicular to it. The direct microscopic images of metal networks are also analyzed to obtain resistance-strain characteristics and identification of current percolation pathways. The findings will be important for electrical percolation, particularly in predicting characteristics and improvising metal network-based strain sensors.
Source: IEEE Sensors Journal
Author: Ankush Kumar
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2943380691