Strongly modulated transmissions in gapped armchair graphene nanoribbons with sidearm or on-site gate voltage

We propose two schemes of field-effect transistor based on gapped armchair graphene nanoribbons connected to metal leads, by introducing sidearms or on-site gate voltages. We make use of the band gap to reach excellent switch-off character. By introducing one sidearm or on-site gate to the graphene nanoribbon, conduction peaks appear inside the gap regime. By further applying two sidearms or on-site gates, these peaks are broadened to conduction plateaus with a wide energy window, thanks to the resonance from the dual structure. The position of the conduction windows inside the gap can be fully controlled by the length of the sidearms or the on-site gate voltages, which allows "on" and "off" operations for a specific energy window inside the gap regime. The high robustness of both the switch-off character and the conduction windows is demonstrated and shows the feasibility of the proposed dual structures for real applications.Comment: 6 pages, 6 figure

A Parameterized Centrality Metric for Network Analysis

A variety of metrics have been proposed to measure the relative importance of nodes in a network. One of these, alpha-centrality [Bonacich, 2001], measures the number of attenuated paths that exist between nodes. We introduce a normalized version of this metric and use it to study network structure, specifically, to rank nodes and find community structure of the network. Specifically, we extend the modularity-maximization method [Newman and Girvan, 2004] for community detection to use this metric as the measure of node connectivity. Normalized alpha-centrality is a powerful tool for network analysis, since it contains a tunable parameter that sets the length scale of interactions. By studying how rankings and discovered communities change when this parameter is varied allows us to identify locally and globally important nodes and structures. We apply the proposed method to several benchmark networks and show that it leads to better insight into network structure than alternative methods.Comment: 11 pages, submitted to Physical Review