4D Printing of Smart Polymer Nanocomposites: Integrating Graphene and Acrylate Based Shape Memory Polymers
Abstract
:1. Introduction
2. Materials and Methods
2.1. Preparation of tBA-co-DEGDA Resin
2.2. Resin Characterization
2.3. Addition of Graphene Nanoparticles
3. Results and Discussion
3.1. Characterization of Pristine SLA SMP’s
3.1.1. Effect of Print Parameters on the Tensile Stress
3.1.2. Effect of Print Parameters on the Surface Roughness
3.1.3. Effect of Print Parameters on Shape Memory Effect
3.1.4. Regression Modelling
Multi-Objective Optimization Using Composite Desirability Approach
Verification of Regression Modelling
3.1.5. FTIR Analysis Comparing the Initial Resin and Resin Printed Using Optimal Condition
3.1.6. Differential Scanning Calorimetry (DSC) Characterization
3.1.7. Dynamic Mechanical Analysis (DMA)
3.2. Characterization of a Mixture of SMP and Graphene Nanoparticles
3.2.1. Effect of Addition of Graphene on the Tensile Stress
3.2.2. Effect of Addition of Graphene on the Surface Roughness
3.2.3. Effect of Addition of Graphene on the Shape Memory Effect
3.2.4. Dynamic Mechanical Analysis
4. Conclusions
- The tensile strength is the highest at lower print speeds and higher pulse frequencies, and the highest value of about 16.1 MPa, is observed at 70 mm/s speed and 110 pulses/s pulses frequency.
- An increase in laser power and print speeds improves the surface behaviour of the polymers, and the best surface finish of 0.6461 microns is observed. Excess laser power would result in better curing and a consequent increase in surface behaviour, but it simultaneously poses the danger of breaking the chemical bonds between the tBA-co-DEGDA, and thereby deteriorating the material properties. Moreover, the printed materials become brittle and lose their impact resistance if they are over cured. This would result in them having unfavorable characteristics in their applications. Hence, utmost care should be taken while fixing the 4D printing parameters. The best print parameters are about 90 mm/s speed and 110 pulses/s laser frequency. Shape fixity and recovery time are found to be inversely proportional to each other, and that the maximum of 6.6 s under the pristine condition, and about 85% shape fixity is found, and the shape fixity is found to depend upon the tensile strength of the material.
- It has been found, using regression modelling from a multi-objective plot, that the print speed of 90 mm/s and laser power frequency of 110 pulses/s results in the best possible sample with the best set of mechanical and shape memory properties, for which the FTIR, DSC, and DMA analyses were conducted, which concluded that the glass transition temperature was 49.24 °C, and the viscoelastic nature was discussed.
- With the addition of graphene nanoparticles, an increase in tensile stress, a reduction in surface roughness, and no significant changes in strain fixity were observed, whereas the recovery time increased dramatically. It is concluded that the addition of 0.3% graphene particles results in the best set of properties, and that the properties begin to deteriorate with a further addition of graphene, with the exception of tensile stress.
- The work can be extended to measure the failure analysis for the samples that are built using tBA-co-DEGDA under various print parameters, such as laser speed and laser pulse frequency. Moreover, the sample built using optimized parameters can be infused with carbon nanotubes and then mechanical and material properties could be extensively investigated. Finally, the characteristic variation between graphene-infused and CNT-infused tBA-co-DEGDA could be highlighted.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Sl. No | Factors | Levels | ||
---|---|---|---|---|
Low (−1) | Medium (0) | High (1) | ||
1 | Laser speed | 70 | 80 | 90 |
2 | Laser power frequency | 70 | 90 | 110 |
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Chowdhury, J.; Anirudh, P.V.; Karunakaran, C.; Rajmohan, V.; Mathew, A.T.; Koziol, K.; Alsanie, W.F.; Kannan, C.; Balan, A.S.S.; Thakur, V.K. 4D Printing of Smart Polymer Nanocomposites: Integrating Graphene and Acrylate Based Shape Memory Polymers. Polymers 2021, 13, 3660. https://doi.org/10.3390/polym13213660
Chowdhury J, Anirudh PV, Karunakaran C, Rajmohan V, Mathew AT, Koziol K, Alsanie WF, Kannan C, Balan ASS, Thakur VK. 4D Printing of Smart Polymer Nanocomposites: Integrating Graphene and Acrylate Based Shape Memory Polymers. Polymers. 2021; 13(21):3660. https://doi.org/10.3390/polym13213660
Chicago/Turabian StyleChowdhury, Jaydeep, Premnath Vijay Anirudh, Chandrasekaran Karunakaran, Vasudevan Rajmohan, Arun Tom Mathew, Krzysztof Koziol, Walaa F. Alsanie, Chidambaram Kannan, Arunachalam S. S. Balan, and Vijay Kumar Thakur. 2021. "4D Printing of Smart Polymer Nanocomposites: Integrating Graphene and Acrylate Based Shape Memory Polymers" Polymers 13, no. 21: 3660. https://doi.org/10.3390/polym13213660
APA StyleChowdhury, J., Anirudh, P. V., Karunakaran, C., Rajmohan, V., Mathew, A. T., Koziol, K., Alsanie, W. F., Kannan, C., Balan, A. S. S., & Thakur, V. K. (2021). 4D Printing of Smart Polymer Nanocomposites: Integrating Graphene and Acrylate Based Shape Memory Polymers. Polymers, 13(21), 3660. https://doi.org/10.3390/polym13213660