14,110 research outputs found
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
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