Your browser doesn't support javascript.
loading
Finding the Optimal Nets for Self-Folding Kirigami.
Araújo, N A M; da Costa, R A; Dorogovtsev, S N; Mendes, J F F.
Afiliação
  • Araújo NAM; Departamento de Física, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal.
  • da Costa RA; Centro de Física Teórica e Computacional, Universidade de Lisboa, 1749-016 Lisboa, Portugal.
  • Dorogovtsev SN; Department of Physics and I3N, University of Aveiro, 3810-193 Aveiro, Portugal.
  • Mendes JFF; Department of Physics and I3N, University of Aveiro, 3810-193 Aveiro, Portugal.
Phys Rev Lett ; 120(18): 188001, 2018 May 04.
Article em En | MEDLINE | ID: mdl-29775357
ABSTRACT
Three-dimensional shells can be synthesized from the spontaneous self-folding of two-dimensional templates of interconnected panels, called nets. However, some nets are more likely to self-fold into the desired shell under random movements. The optimal nets are the ones that maximize the number of vertex connections, i.e., vertices that have only two of its faces cut away from each other in the net. Previous methods for finding such nets are based on random search, and thus, they do not guarantee the optimal solution. Here, we propose a deterministic procedure. We map the connectivity of the shell into a shell graph, where the nodes and links of the graph represent the vertices and edges of the shell, respectively. Identifying the nets that maximize the number of vertex connections corresponds to finding the set of maximum leaf spanning trees of the shell graph. This method allows us not only to design the self-assembly of much larger shell structures but also to apply additional design criteria, as a complete catalog of the maximum leaf spanning trees is obtained.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Phys Rev Lett Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Portugal

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Phys Rev Lett Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Portugal