Process-morphology scaling relations quantify self-organization in capillary densified nanofiber arraysElectronic supplementary information (ESI) available: Additional data analysis details (Fig. S1); model derivation (eqn (S1)-(S9), Fig. S2, S3, and Table S1); and full data-sets (Tables S2 and S3). See DOI: 10.1039/c7cp06869g
Capillary-mediated densification is an inexpensive and versatile approach to tune the application-specific properties and packing morphology of bulk nanofiber (NF) arrays, such as aligned carbon nanotubes. While NF length governs elasto-capillary self-assembly, the geometry of cellular patterns form...
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Main Authors | , , , |
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Format | Journal Article |
Published |
07.02.2018
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Online Access | Get full text |
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Summary: | Capillary-mediated densification is an inexpensive and versatile approach to tune the application-specific properties and packing morphology of bulk nanofiber (NF) arrays, such as aligned carbon nanotubes. While NF length governs elasto-capillary self-assembly, the geometry of cellular patterns formed by capillary densified NFs cannot be precisely predicted by existing theories. This originates from the recently quantified orders of magnitude lower than expected NF array effective axial elastic modulus (
E
), and here we show
via
parametric experimentation and modeling that
E
determines the width, area, and wall thickness of the resulting cellular pattern. Both experiments and models show that further tuning of the cellular pattern is possible by altering the NF-substrate adhesion strength, which could enable the broad use of this facile approach to predictably pattern NF arrays for high value applications.
Model-informed experiments reveal that cellular pattern formation in capillary-densified aligned carbon nanotube arrays is governed not only by their height, but also by substrate adhesion strength. |
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Bibliography: | Electronic supplementary information (ESI) available: Additional data analysis details (Fig. S1); model derivation (eqn (S1)-(S9), Fig. S2, S3, and Table S1); and full data-sets (Tables S2 and S3). See DOI 10.1039/c7cp06869g |
ISSN: | 1463-9076 1463-9084 |
DOI: | 10.1039/c7cp06869g |