Mesoscopic friction and network morphology control the mechanics and processing of carbon nanotube yarns

Yuezhou Wang, Hao Xu, Grigorii Drozdov, Traian Dumitrică

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Carbon nanotube (CNT) yarns are synthetic nanomaterials of interest for diverse applications. To bring fundamental understanding into the yarn formation process, we perform mesoscopic scale distinct element method (mDEM) simulations for the stretching of CNT networks. The parameters used by mDEM, including the mesoscale friction, are based on full atomistic results. By bridging across the atomistic and mesoscopic length scales, our model predicts accurately the mechanical response of the network over a large deformation range. At small and moderate deformations, the microstructural evolution is dominated by zipping relaxations directed along the applied strain direction. At larger deformations, the occurrence of energetic elasticity promotes yarn densification, by lowering CNT waviness and eliminating squashed pores. Next, by varying the mesoscopic dissipation as well as the network structure, we reveal that the mesoscale friction and film morphology are key factors for the yarn formation: While lack of friction compromises the strain-induced alignment process, phononic and polymeric friction promote CNT alignment by enabling load transfer and directed zipping relaxations, especially in networks containing long and entangled CNTs. Yarns drawn from cellular networks are shown instead to maintain high porosity, even with enhanced polymeric friction.

Original languageEnglish (US)
Pages (from-to)94-104
Number of pages11
StatePublished - Nov 2018

Bibliographical note

Funding Information:
This work was supported by NASA's Space Technology Research , Grant NNX16AE03G and by the I nstitute for Ultra-Strong Composites by Computational Design , Grant NNX17AJ32G . Resources supporting this work were provided by the NASA High End Computing Program through the NASA Advanced Supercomputing Division at Ames Research Center. We thank the Itasca Consulting Group, Grant-in-Aid (UMN) and Faculty Research (MNSU) grants for the PFC3D software support.

Publisher Copyright:
© 2018 Elsevier Ltd


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