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Growth and arrest of topological cycles in small physical networks

The chordless cycle sizes of spatially embedded networks are demonstrated to follow an exponential growth law similar to random graphs if the number of nodes [Formula: see text] is below a critical value [Formula: see text]. For covalent polymer networks, increasing the network size, as measured by...

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Autor principal: Sirk, Timothy W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7355031/
https://www.ncbi.nlm.nih.gov/pubmed/32571907
http://dx.doi.org/10.1073/pnas.2006305117
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author Sirk, Timothy W.
author_facet Sirk, Timothy W.
author_sort Sirk, Timothy W.
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description The chordless cycle sizes of spatially embedded networks are demonstrated to follow an exponential growth law similar to random graphs if the number of nodes [Formula: see text] is below a critical value [Formula: see text]. For covalent polymer networks, increasing the network size, as measured by the number of cross-link nodes, beyond [Formula: see text] results in a crossover to a new regime in which the characteristic size of the chordless cycles [Formula: see text] no longer increases. From this result, the onset and intensity of finite-size effects can be predicted from measurement of [Formula: see text] in large networks. Although such information is largely inaccessible with experiments, the agreement of simulation results from molecular dynamics, Metropolis Monte Carlo, and kinetic Monte Carlo suggests the crossover is a fundamental physical feature which is insensitive to the details of the network generation. These results show random graphs as a promising model to capture structural differences in confined physical networks.
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spelling pubmed-73550312020-07-24 Growth and arrest of topological cycles in small physical networks Sirk, Timothy W. Proc Natl Acad Sci U S A Physical Sciences The chordless cycle sizes of spatially embedded networks are demonstrated to follow an exponential growth law similar to random graphs if the number of nodes [Formula: see text] is below a critical value [Formula: see text]. For covalent polymer networks, increasing the network size, as measured by the number of cross-link nodes, beyond [Formula: see text] results in a crossover to a new regime in which the characteristic size of the chordless cycles [Formula: see text] no longer increases. From this result, the onset and intensity of finite-size effects can be predicted from measurement of [Formula: see text] in large networks. Although such information is largely inaccessible with experiments, the agreement of simulation results from molecular dynamics, Metropolis Monte Carlo, and kinetic Monte Carlo suggests the crossover is a fundamental physical feature which is insensitive to the details of the network generation. These results show random graphs as a promising model to capture structural differences in confined physical networks. National Academy of Sciences 2020-07-07 2020-06-22 /pmc/articles/PMC7355031/ /pubmed/32571907 http://dx.doi.org/10.1073/pnas.2006305117 Text en Copyright © 2020 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/ https://creativecommons.org/licenses/by-nc-nd/4.0/This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Physical Sciences
Sirk, Timothy W.
Growth and arrest of topological cycles in small physical networks
title Growth and arrest of topological cycles in small physical networks
title_full Growth and arrest of topological cycles in small physical networks
title_fullStr Growth and arrest of topological cycles in small physical networks
title_full_unstemmed Growth and arrest of topological cycles in small physical networks
title_short Growth and arrest of topological cycles in small physical networks
title_sort growth and arrest of topological cycles in small physical networks
topic Physical Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7355031/
https://www.ncbi.nlm.nih.gov/pubmed/32571907
http://dx.doi.org/10.1073/pnas.2006305117
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