Analyzing conducting rod networks using centrality

Electrical conductance of two-dimensional random percolating networks based on mixtures of nanowires and nanorings: A mean-field approach along with computer simulation

We have studied the electrical conductance of two-dimensional (2D) random percolating networks of zero-width metallic nanowires (a mixture of rings and sticks). We took into account the nanowire resistance per unit length and the junction (nanowire-nanowire contact) resistance. Using a mean-field approximation (MFA) approach, we derived the total electrical conductance of these nanowire-based networks as a function of their geometrical and physical parameters. The MFA predictions have been confirmed by our Monte Carlo (MC) numerical simulations. The MC simulations were focused on the case when the circumferences of the rings and the lengths of the wires were equal. In this case, the electrical conductance of the network was found to be almost insensitive to the relative proportions of the rings and sticks, provided that the wire resistance and the junction resistance were equal. When the junction resistance dominated over the wire resistance, a linear dependency of the electrical conductance of the network on the proportions of the rings and sticks was observed.

Edge betweenness centrality as a failure predictor in network models of structurally disordered materials

Network theoretical measures such as geodesic edge betweenness centrality (GEBC) have been proposed as failure predictors in network models of load-driven materials failure. Edge betweenness centrality ranks which links are significant, based on the fraction of shortest paths that pass through the links between network nodes. We study GEBC as a failure predictor for two-dimensional fuse network models of load transmission in structurally disordered materials. We analyze the evolution of edge betweenness centrality in the run-up to failure and the correlation between GEBC and failure propensity for both hierarchical and non-hierarchical networks exhibiting various degrees of disorder. We observe a non trivial relationship between GEBC and failure propensity, which suggests that the idea of GEBC as a useful failure predictor needs to be strongly qualified.

Metal Oxide Chemiresistors: A Structural and Functional Comparison between Nanowires and Nanoparticles

Metal oxide nanowires have become popular materials in gas sensing, and more generally in the field of electronic and optoelectronic devices. This is thanks to their unique structural and morphological features, namely their single-crystalline structure, their nano-sized diameter and their highly anisotropic shape, i.e., a large length-to-diameter aspect ratio. About twenty years have passed since the first publication proposing their suitability for gas sensors, and a rapidly increasing number of papers addressing the understanding and the exploitation of these materials in chemosensing have been published. Considering the remarkable progress achieved so far, the present paper aims at reviewing these results, emphasizing the comparison with state-of-the-art nanoparticle-based materials. The goal is to highlight, wherever possible, how results may be related to the particular features of one or the other morphology, what is effectively unique to nanowires and what can be obtained by both. Transduction, receptor and utility-factor functions, doping, and the addition of inorganic and organic coatings will be discussed on the basis of the structural and morphological features that have stimulated this field of research since its early stage.

Random nanowire networks: Identification of a current-carrying subset of wires using a modified wall follower algorithm

We mimic random nanowire networks by the homogeneous, isotropic, and random deposition of conductive zero-width sticks onto an insulating substrate. The number density (the number of objects per unit area of the surface) of these sticks is supposed to exceed the percolation threshold, i.e., the system under consideration is a conductor. To identify any current-carrying part (the backbone) of the percolation cluster, we have proposed and implemented a modification of the well-known wall follower algorithm-one type of maze solving algorithm. The advantage of the modified algorithm is its identification of the whole backbone without visiting all the edges. The complexity of the algorithm depends significantly on the structure of the graph and varies from O(sqrt[N_{V}]) to Θ(N_{V}). The algorithm has been applied to backbone identification in networks with different number densities of conducting sticks. We have found that (i) for number densities of sticks above the percolation threshold, the strength of the percolation cluster quickly approaches unity as the number density of the sticks increases; (ii) simultaneously, the percolation cluster becomes identical to its backbone plus simplest dead ends, i.e., edges that are incident to vertices of degree 1. This behavior is consistent with the presented analytical evaluations.