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  Cornell University

MAE Publications and Papers

Sibley School of Mechanical and Aerospace Engineering

New article: Computational Investigation of Shear Driven Mechanophore Activation at Interfaces

Article:  Manivannan, MS; Silberstein, MN; “Computational Investigation of Shear Driven Mechanophore Activation at Interfaces”, Extreme Mechanics Letters, 8: 6-12


Abstract:  Self-healing and self-reporting interfaces can alleviate damage related costs for both coatings and composites. In this paper we propose to utilize molecular units termed mechanophores, that undergo productive chemical transformations in response to force, to augment interfaces with self-healing and self-reporting functionality. We investigate the critical parameters for mechanophore-functionalized interfaces by building two computational models: a kinematic model with rigid non-interacting walls forming the interface and a molecular dynamics model with substrates of Lennard-Jones particles. In both the models the mechanophore is idealized as a coarse grained two-bead system governed by a double-well potential that emulates a force directed chemical reaction. Subjecting the interface models to shear, our study reveals that mechanophores progressively activate as interfacial displacement increases starting with the mechanophores aligned along the direction of shear. Subsequently we find that activation happens only if the bonds attaching the mechanophores to the substrates are strong enough to transmit the force necessary to activate the mechanophore. When substrates forming the interface are adhesive, the transmission of shear is delayed, in turn delaying activation of mechanophores. Adhesion-based changes in the conformation of the interface alter the extent of mechanophore activation. These results are a first step towards designing mechanophore-enabled self-reporting and self-healing composites and coatings. (C) 2015 Elsevier Ltd. All rights reserved.

Funding Acknowledgement:  National Science Foundation (NSF) [DMR 1307354, ACI-1053575]

Funding Text:  This material is based upon work supported by the National Science Foundation (NSF) under grant no. DMR 1307354 and used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant no. ACI-1053575. We thank Dr. Jaewoo Kim of the Korea Institute of Science and Technology for useful discussions on this project.


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