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Parametric Study for Dimeric Anthracene-Based Mechanophore-Embedded Thermoset Polymer Matrix Using Molecular Dynamics

Published online by Cambridge University Press:  15 May 2017

Bonsung Koo ,
Ryan Gunckel ,
Aditi Chattopadhyay  and
Lenore Dai
Bonsung Koo*
Affiliation:
School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ, 85287, U.S.A.
Ryan Gunckel
Affiliation:
School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ, 85287, U.S.A.
Aditi Chattopadhyay
Affiliation:
School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ, 85287, U.S.A.
Lenore Dai
Affiliation:
School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ, 85287, U.S.A.
*
*(Email: [email protected])
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Abstract

This paper presents a parametric study to investigate the effect of relevant design variables on mechanochemical reaction and mechanical properties of a mechanophore-embedded thermoset polymer matrix. Mechanophores emit fluorescence when a specific covalent bond breaks due to external stress, and thus have attracted immense research interest as a damage sensor. Recently, a mechanophore named dimeric 9-anthracene carboxylic acid (Di-AC) was synthesized successfully and incorporated into epoxy-based thermoset polymer matrix to detect damage precursor. However, there is significant potential in modeling the complex mechanochemistry associated with the Di-AC to obtain a better understanding of this mechanophore and its interaction with the host thermoset material. In this study, a hybrid MD simulation methodology is employed to explore this complex mechanochemistry along with the investigation of the effect of design parameters on the mechanophore performance. The hybrid MD simulation method enables the simulation of Di-AC synthesis, epoxy curing, and mechanical loading test; therefore, the experimental process performed can be emulated accurately. Previously, the hybrid MD method showed the capability of capturing experimentally observed phenomena such as early signal detection and yield strength variation between neat epoxy system and epoxy with 5 wt% Di-AC thermoset polymer. In this paper, the effect of curing temperature on mechanophore activation and mechanical properties is investigated. A series of temperatures are used in the curing simulation, which are experimentally achievable. Results show that curing temperature below glass transition temperature maintains early signal detection and yield strength decreases when the curing temperature increases above the glass transition temperature. Good correlation is observed with experimental results.

Keywords

simulationstress/strain relationshippolymer
Type
Articles
Information
MRS Advances , Volume 2 , Issue 48: Characterization, Theory and Modeling , 2017 , pp. 2615 - 2620
Copyright
Copyright © Materials Research Society 2017 

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References

REFERENCES

Davis, D. A., Hamilton, A., Yang, J., Nature, 459(7243) pp. 68–72, (2009).CrossRefGoogle Scholar
Song, Y., Lee, K., Hong, W., J. Mater. Chem., 22(4) pp. 1380–1386, (2012).CrossRefGoogle Scholar
Nofen, E. M., Wickham, J., Koo, B., Mater. Res. Express, 3(3) pp. 035701, (2016).CrossRefGoogle Scholar
Carbas, R., Marques, E., Da Silva, L., J. Adhes., 90(1) pp. 104–119, (2014).CrossRefGoogle Scholar
Bandyopadhyay, A., and Odegard, G. M., J. Appl. Polym. Sci., 128(1) pp. 660–666, (2013).CrossRefGoogle Scholar
Makki, H., Adema, K. N., Peters, E. A., Polym. Degrad. Stab., 105pp. 68–79, (2014).CrossRefGoogle Scholar
Yang, S., Choi, J., and Cho, M., ACS Appl. Mater. Interfaces, 4(9) pp. 4792–4799, (2012).CrossRefGoogle Scholar
Silberstein, M. N., Min, K., Cremar, L. D., J. Appl. Phys., 114(2) pp. 023504, (2013).CrossRefGoogle Scholar
Pearlman, D. A., Case, D. A., Caldwell, J. W., Comput. Phys. Commun., 91(1) pp. 1–41, (1995).CrossRefGoogle Scholar
Kaminski, G. G. A., J. Phys. Chem. B, 105(28) pp. 6474; 6474-6487; 6487, (2001).CrossRefGoogle Scholar
Zhang, J., Koo, B., Liu, Y., Smart Mater. Struct., 24(8) pp. 085022, (2015).CrossRefGoogle Scholar
Plimpton, S., J. Comput. Phys., 117(1) pp. 1–19, (1995).CrossRefGoogle Scholar
Koo, B., Chattopadhyay, A., and Dai, L., Comput. Mater. Sci., 120pp. 135–141, (2016).CrossRefGoogle Scholar
Koo, B., Nofen, E., Chattopadhyay, A., Comput. Mater. Sci., 133pp. 167–174, (2017).CrossRefGoogle Scholar
Zoete, V., Cuendet, M. A., Grosdidier, A., J. Comput. Chem., 32(11) pp. 2359–2368, (2011).CrossRefGoogle Scholar
Singh, S. K., Srinivasan, S. G., Neek-Amal, M., Phys. Rev. B, 87(10) pp. 104114, (2013).CrossRefGoogle Scholar
Koo, B., Liu, Y., Zou, J., Modell. Simul. Mater. Sci. Eng., 22(6) pp. 065018, (2014).CrossRefGoogle Scholar
Berendsen, H. J., Postma, J. P. M., van Gunsteren, W. F., J. Chem. Phys., 81pp. 3684, (1984).CrossRefGoogle Scholar
Hossain, D., Tschopp, M., Ward, D., Polymer, 51(25) pp. 6071–6083, (2010).CrossRefGoogle Scholar
Frankland, S., Harik, V., Odegard, G., Composites Sci. Technol., 63(11) pp. 1655–1661, (2003).CrossRefGoogle Scholar
Min, K., Silberstein, M., and Aluru, N., J. Polym. Sci., Part B: Polym. Phys., 52(6) pp. 444–449, (2014).CrossRefGoogle Scholar
Subramaniyan, A. K., and Sun, C., Int. J. Solids Structures, 45(14) pp. 4340–4346, (2008).CrossRefGoogle Scholar