2021
DOI: 10.1038/s41598-021-85011-0
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Regulating heat conduction of complex networks by distributed nodes masses

Abstract: Developing efficient strategy to regulate heat conduction is a challenging problem, with potential implication in the field of thermal materials. We here focus on a potential thermal material, i.e. complex networks of nanowires and nanotubes, and propose a model where the mass of each node is assigned proportional to its degree with $$m_i\sim k_i^{\alpha }$$ m i ∼ … Show more

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Cited by 6 publications
(3 citation statements)
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“…In recent decades, with the emergence of complex networks, more and more researchers have begun to enter this field and cross-fertilize with their own research directions [1][2][3][4][5][6][7][8] . For example, medical scientists study the correlation between various diseases (or the relationship between the effects of drugs) through complex networks 9,10 .…”
Section: Introductionmentioning
confidence: 99%
“…In recent decades, with the emergence of complex networks, more and more researchers have begun to enter this field and cross-fertilize with their own research directions [1][2][3][4][5][6][7][8] . For example, medical scientists study the correlation between various diseases (or the relationship between the effects of drugs) through complex networks 9,10 .…”
Section: Introductionmentioning
confidence: 99%
“…It is revealed that most of complex networks have the features of small world and heterogeneity, which are the basis for tremendous applications such as in the aspects of communication in internet, synchronization in brain, and epidemic spreading in social networks [4][5][6][7][8] etc. However, little attention has been paid to the aspect of heat conduction in complex networks, except a few works [9][10][11][12][13][14][15]. In fact, this problem is becoming more and more important, due to the fast developing of massive integration methods boost the interconnected intact structure of the nano-network extending from microscale to macroscale, and provide practical applicability in device designs [16,17].…”
Section: Introductionmentioning
confidence: 99%
“…[24][25][26][27][28] Additionally, several other factors influence heat transport in complex networks, including clustering coefficients, average shortest distances, assortativity coefficient, and masses of nodes. [29][30][31][32][33] Given the intricate nature of these networks, accurately predicting thermal conductance remains a great challenge. The complexity of these networks has led most prior studies to consider only a single heat source node and a single sink node within a given random network.…”
mentioning
confidence: 99%