Document Type
Article
Publication Date
6-30-2021
Department
Department of Mechanical Engineering-Engineering Mechanics
Abstract
Resin/reinforcement wetting is a key parameter in the manufacturing of carbon nanotube (CNT)-based composite materials. Determining the contact angle between combinations of liquid resin and reinforcement surfaces is a common method for quantifying wettability. As experimental measurement of contact angle can be difficult when screening multiple high-performance resins with CNT materials such as CNT bundles or yarns, computational approaches are necessary to fa-cilitate CNT composite material design. A molecular dynamics simulation method is developed to predict the contact angle of high-performance polymer resins on CNT surfaces dominated by aromatic carbon, aliphatic carbon, or a mixture thereof (amorphous carbon). Several resin systems are simulated and compared. The results indicate that the monomer chain length, chemical groups on the monomer, and simulation temperature have a significant impact on the predicted contact angle values on the CNT surface. Difunctional epoxy and cyanate ester resins show the overall highest levels of wettability, regardless of the aromatic/aliphatic nature of the CNT material surface. Tetra-functional epoxy demonstrates excellent wettability on aliphatic-dominated surfaces at elevated temperatures. Bismaleimide and benzoxazine resins show intermediate levels of wetting, while typ-ical molecular weights of polyether ether ketone demonstrate poor wetting on the CNT surfaces.
Publication Title
Polymers
Recommended Citation
Bamane, S.,
Gaikwad, P.,
Radue, M.,
Gowtham, S.,
&
Odegard, G. M.
(2021).
Wetting simulations of high-performance polymer resins on carbon surfaces as a function of temperature using molecular dynamics.
Polymers,
13(13).
http://doi.org/10.3390/polym13132162
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/15106
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Publisher’s version of record: https://doi.org/10.3390/polym13132162