Substrate adhesion evolves non-monotonically with processing time in millimeter-scale aligned carbon nanotube arrays
Name
d0nr05469k.pdf
Description
Published version
Size
3.95 MB
Format
Adobe PDF
Checksum (MD5)
0af4601431bc6bc427fb0cafe939afc8
Author(s)
Kaiser, Ashley L
Lidston, Dale L
Peterson, Sophie C
Acauan, Luiz H
Steiner, Stephen A
Guzman de Villoria, Roberto
Vanderhout, Amy R
Stein, Itai Y
Wardle, Brian L
Date Issued
2021
Journal
Nanoscale
Publisher
Royal Society of Chemistry (RSC)
Version
Final published version
Abstract
© 2021 The Royal Society of Chemistry. The advantageous intrinsic and scale-dependent properties of aligned nanofibers (NFs) and their assembly into 3D architectures motivate their use as dry adhesives and shape-engineerable materials. While controlling NF-substrate adhesion is critical for scaled manufacturing and application-specific performance, current understanding of how this property evolves with processing conditions is limited. In this report, we introduce substrate adhesion predictive capabilities by using an exemplary array of NFs, aligned carbon nanotubes (CNTs), studied as a function of their processing. Substrate adhesion is found to scale non-monotonically with process time in a hydrocarbon environment and is investigated via the tensile pull-off of mm-scale CNT arrays from their growth substrate. CNT synthesis follows two regimes: Mode I ('Growth') and Mode II ('Post-Growth'), separated by growth termination. Within 10 minutes of post-growth, experiments and modeling indicate an order-of-magnitude increase in CNT array-substrate adhesion strength (∼40 to 285 kPa) and effective elastic array modulus (∼6 to 47 MPa), and a two-orders-of-magnitude increase in the single CNT-substrate adhesion force (∼0.190 to 12.3 nN) and work of adhesion (∼0.07 to 1.5 J m-2), where the iron catalyst is found to remain on the substrate. Growth number decay in Mode I and carbon accumulation in Mode II contribute to the mechanical response, which may imply a change in the deformation mechanism. Predictive capabilities of the model are assessed for previously studied NF arrays, suggesting that the current framework can enable the future design and manufacture of high-value NF array applications.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1039/d0nr05469k
