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Razzaq, M.Y.;  Gonzalez-Gutierrez, J.;  Mertz, G.;  Ruch, D.;  Schmidt, D.F.;  Westermann, S. Multicomponent Shape-Memory Polymers. Encyclopedia. Available online: https://encyclopedia.pub/entry/25980 (accessed on 29 July 2024).
Razzaq MY,  Gonzalez-Gutierrez J,  Mertz G,  Ruch D,  Schmidt DF,  Westermann S. Multicomponent Shape-Memory Polymers. Encyclopedia. Available at: https://encyclopedia.pub/entry/25980. Accessed July 29, 2024.
Razzaq, Muhammad Yasar, Joamin Gonzalez-Gutierrez, Gregory Mertz, David Ruch, Daniel F. Schmidt, Stephan Westermann. "Multicomponent Shape-Memory Polymers" Encyclopedia, https://encyclopedia.pub/entry/25980 (accessed July 29, 2024).
Razzaq, M.Y.,  Gonzalez-Gutierrez, J.,  Mertz, G.,  Ruch, D.,  Schmidt, D.F., & Westermann, S. (2022, August 09). Multicomponent Shape-Memory Polymers. In Encyclopedia. https://encyclopedia.pub/entry/25980
Razzaq, Muhammad Yasar, et al. "Multicomponent Shape-Memory Polymers." Encyclopedia. Web. 09 August, 2022.
Multicomponent Shape-Memory Polymers
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Shape-memory polymers (SMPs)  are often combined with other functional materials. For example, polymers, metals, or other inorganic materials, in the shape of particles, fibers, and laminates, are combined to create multicomponent systems. Since these multicomponent systems are built up from discrete phases consisting of SMPs mixed with other polymers at the macroscopic or molecular level or reinforced with other domains (e.g., inorganic particle), they are particularly suited for the realization of multifunctionality.

shape memory polymers polymer-based composites polymer blends multi-material

1. Introduction

Shape-memory polymers (SMPs), whose ability to recover from a temporary programmed shape to a permanent shape in response to an external stimulus, have drawn increasing attention because of their scientific and technological significance [1][2][3][4]. Typical stimuli to trigger the shape transition in SMPs include heat (i.e., thermo-responsive), light (i.e., photo-responsive), chemicals (i.e., chemo-responsive, e.g., water/moisture, pH change), or mechanical loading (i.e., mechano-responsive). Thermo-responsive SMPs (trSMPs) have been most extensively studied because of their tailorable transition temperature (Tr) and the convenience with which their shape change may be triggered [5][6]. The activation of trSMPs can be carried out by either directly, by increasing the environmental temperature (Te), or indirectly, by Joule heating (i.e., passing electric current), inductive heating (i.e., exposure to an alternating magnetic field: AMF), or photo-thermal effects (i.e., exposure to light) [6][7][8]. At the molecular level, trSMPs consists of net points and a switching domain. The net points can either be covalent crosslinks (SMP networks) or physical intermolecular interactions such as hydrogen bonds or crystallites (thermoplastic SMPs) and act as memory components of the network. On the other hand, the switching domains are flexible polymer chains associated with a thermal transition (glass transition temperature, Tg, or melting transition, Tm) and are responsible for the shape change process. To observe the shape-memory effect (SME), a thermomechanical programming procedure consisting of deformation to a temporary shape at elevated temperature (T > Tr) and subsequent cooling under constraint to a low temperature (T < Tr), is required. The recovery to the original shape takes place by reheating the polymer to an elevated temperature (T > Tr). The quantification of the SME is carried out by determining the shape-recovery (Rr) and shape-fixity ratios (Rf), as commonly described in the literature [9][10][11].
A combination of SMPs and other functional materials is helpful to improve properties and increase relevance in technological applications. Such multicomponent SMPs provide high deformation capability, improved recovery force, faster shape recovery, and better shape fixity/recovery ratios [1][12]. The excellent performance of multicomponent SMPs has found potential utility in the aerospace, biomedical, electronic, and textile industries [13][14]. However, conventional processing techniques are limited in their ability to produce complex structures with precise dimensions useful for the aforementioned fields [15][16]. For this reason, additive manufacturing is being investigated.

2. Multicomponent SMPs

SMPs are sometimes used in isolation as single materials; more often, they are combined with other functional materials. For example, polymers, metals, or other inorganic materials, in the shape of particles, fibers, and laminates, are combined to create multicomponent systems. Since these multicomponent systems are built up from discrete phases consisting of SMPs mixed with other polymers at the macroscopic or molecular level or reinforced with other domains (e.g., inorganic particle), they are particularly suited for the realization of multifunctionality [1][17][18][19][20]. Composites that consist of a polymer matrix with solid filler or reinforcement are common multicomponent systems to enable multiple functions in a single material [4][21]. Active fillers such as iron oxide nanoparticles (IONP), carbon black (CB) and carbon nanotubes (CNT) are incorporated into SMPs using conventional processing techniques [22][23][24]. These fillers introduce new functions such as magneto-sensitivity, electric conductivity, or photosensitivity into SMPs along with thermal and structural functionalities [20][21][25].
Furthermore, when well-dispersed and compatible with the matrix, nanofillers provide much higher interfacial area per unit mass and much lower interparticle distances vs. conventional fillers, resulting in fundamental changes in polymer microstructure and behavior in the resultant nanocomposites. Additionally, high-aspect-ratio nanofillers are less readily damaged than their larger equivalents, providing the potential for much lower percolation thresholds in practice. However, being distinct phases, each constituent in such a multicomponent system remains recognizable and retains its characteristics, and well-defined adhesion levels between these phases are required for the successful realization of new functionalities [22][26]. Other possible arrangements to enable SMP-based multicomponent systems are blends, interpenetrating polymer networks (IPNs), and macroscopic layered architectures [27][28].
In the blending approach, instead of non-polymeric domains, SMPs are physically mixed with other functional polymers with or without chemical bonding. The polymer blend not only alters the thermal and mechanical properties of the base SMP but can also offer new functionalities not found in the original polymers. Here, the compatibility of the blend components is an important criterion in the development of high-performance multifunctional materials. However, most polymer blends are heterogenous systems, as sometimes, the structural differences of the individual polymers are more important than their composition. For instance, polyethylene and polypropylene technically have the same composition, since they are both saturated hydrocarbons with the same C:H ratios, but they do not mix [27][28]. Another approach with similar spatial organization is an IPN, a thermodynamically stable arrangement of multicomponent crosslinked polymeric materials. An IPN is a combination of two or more polymer networks which are not covalently linked to each other and are obtained by two independent crosslinking reactions occurring sequentially or simultaneously to form two different networks. Numerous IPN-based SMPs have been investigated in recent years, and some have exhibited improved properties and new functionalities as well [28].
Among these different techniques, multilayer technology is considered one of the most efficient and practical owing to its simplicity and versatility [29]. SMPs and other functional polymers with desirable functionalities and architectures can be stacked together in a bi-/multilayer polymer system with a simple interface [30]. For example, combinations of SMPs with soft and rigid layers or layers with different coefficients of thermal expansion have been produced to enable novel shape change behavior [31][32]. However, successful integration of different functions depends on the individual layers, layer geometry and the character of the interface between layers [29].
Modern 3DP techniques provide a growing range of possibilities to print multicomponent systems (i.e., composites, blends, IPNs or layered structures) at different dimensional scales [33][34][35]. For example, SMP-based multicomponent systems have been used for the 3DP of responsive mechanical devices, where part geometry has been scaled down to micro- and nanometer levels to form metamaterials [36][37]. However, among the list of widely adopted 3DP techniques, few have been used or customized to be suitable for the 3DP of multicomponent SMPs formulations. These mainly include MEX-based 3DP (e.g., FFF and DIW), VPP (e.g., DLP, SLA, and MPP) and MJT [38]. Here, the selection of a 3D printer is related to its suitability to print either an ink, liquid resin, or filament. Furthermore, the printer should allow for the adjustment of key parameters affecting material deposition and solidification, such as printing speeds, delivery volumes, material, and chamber temperatures [39][40][41]. A schematic demonstration of some of most used 3DP techniques along with their capacity to print different multicomponent SMP formulations is shown in Figure 1.
Figure 1. Schematic of commonly used 3DP techniques including their capacity to print different multicomponent SMP architectures: (a) fused filament fabrication (FFF); (b) direct ink writing (DIW); (c) digital light processing (DLP); (d) material jetting (MJT).
MEX printers are commonly used to print SMP particulate composites or blends. Additionally, by using print heads with multiple nozzles, MEX systems can simultaneously print several materials (i.e., layered structures), for example, multi-color objects or complex objects with dissolvable support structures [42][43]. Nevertheless, there are some limitations in MEX printing, as the printed parts have mechanical anisotropy in the z-direction (vertical direction) and produce parts with stepped structures at their surfaces [39][44]. Furthermore, a lack of strong inter-layer bonding can lead to mechanical failure of the printed object, particularly after repeated shape changing cycles [16][45]. Nevertheless, the printing parameters and the material characteristics have a significant effect on the fatigue behavior of the printed objects [46].
VPP, on the other hand, is limited to the printing of low-viscosity (<10 Pa s) photopolymers that tend to have poor aging characteristics and limited thermal stability but can provide objects with good resolution and surface finish [47]. The VPP of high-viscosity resins is challenging, as the vertical detachment of the crosslinked layer from vat film can occur. Nevertheless, there are some reports about the heat-assisted VPP of highly viscous photopolymers, as heating can reduce the viscosity [48]. VPP techniques have also been used to print multicomponent SMP resins with electric or magnetic functionalities [49][50]. Here, careful control over the ratio of the reinforcement and matrix is required as high amount of reinforcement can absorb and scatter the light, resulting in partially crosslinked systems with poor mechanical properties [51]. Nonetheless, by controlling the exposure time or by using multiple vat systems with different photopolymers, multi-material printing can also be performed. However, changing materials during printing significantly slows the printing process since a wiping/cleaning step is needed to avoid cross-contamination [52].
Compared to VPP, MJT printing has an intrinsic capability to print multiple materials simultaneously, given the ease with which multiple ink reservoirs and multiple nozzles may be implemented as in a conventional color ink jet printer [53][54]. The multi-material printing of a variety of SMP resins via the MJT method is reported [55][56]. Such multi-material systems provide novel shape memory functionalities along with tailorable thermal and mechanical properties of the printed objects.
Anisotropic polymer chain orientation, complex geometries that resemble natural structures, and combinations of different materials in different layouts bring more flexibility to accomplish complex actuation not possible with other shaping methods such as injection molding, extrusion, and thermoforming. One possible disadvantage of AM with respect to other shaping technologies is that AM is prone to small defects that can change the mechanical, optical, thermal, and electrical properties of the produced objects.
In sum, there have been significant efforts in the scientific community to fabricate SMP based multicomponent systems with novel functions via a variety of approaches.

References

  1. Behl, M.; Razzaq, M.Y.; Lendlein, A. Multifunctional Shape-Memory Polymers. Adv. Mater. 2010, 22, 3388–3410.
  2. Xia, Y.; He, Y.; Zhang, F.; Liu, Y.; Leng, J. A Review of Shape Memory Polymers and Composites: Mechanisms, Materials, and Applications. Adv. Mater. 2020, 33, 2000713.
  3. Wang, W.; Liu, Y.; Leng, J. Recent developments in shape memory polymer nanocomposites: Actuation methods and mechanisms. Coord. Chem. Rev. 2016, 320–321, 38–52.
  4. Gunes, I.S.; Jana, S.C. Shape memory polymers and their nanocomposites: A review of science and technology of new multifunctional materials. J. Nanosci. Nanotechnol. 2008, 8, 1616–1637.
  5. Behl, M.; Lendlein, A. Shape-memory polymers. Mater. Today 2007, 10, 20–28.
  6. Meng, Q.; Hu, J. A review of shape memory polymer composites and blends. Compos. Part A Appl. Sci. Manuf. 2009, 40, 1661–1672.
  7. Lendlein, A.; Kelch, S. Shape-memory polymers. Angew. Chem. Int. Ed. Engl. 2002, 41, 2035–2057.
  8. Madbouly, S.A.; Lendlein, A. Shape-memory polymer composites. Shape-Mem. Polym. 2010, 226, 41–95.
  9. Zhao, Q.; Qi, H.J.; Xie, T. Recent progress in shape memory polymer: New behavior, enabling materials, and mechanistic understanding. Prog. Polym. Sci. 2015, 49–50, 79–120.
  10. Meng, H.; Li, G. A review of stimuli-responsive shape memory polymer composites. Polymer 2013, 54, 2199–2221.
  11. Sauter, T.; Heuchel, M.; Kratz, K.; Lendlein, A. Quantifying the Shape-Memory Effect of Polymers by Cyclic Thermomechanical Tests. Polym. Rev. 2013, 53, 6–40.
  12. Lendlein, A.; Langer, R. Biodegradable, Elastic Shape-Memory Polymers for Potential Biomedical Applications. Science 2002, 296, 1673–1676.
  13. Mather, P.T.; Luo, X.; Rousseau, I.A. Shape memory polymer research. Annu. Rev. Mater. Res. 2009, 39, 445–471.
  14. Lei, M.; Chen, Z.; Lu, H.; Yu, K. Recent progress in shape memory polymer composites: Methods, properties, applications and prospects. Nanotechnol. Rev. 2019, 8, 327–351.
  15. Barletta, M.; Gisario, A.; Mehrpouya, M. 4D printing of shape memory polylactic acid (PLA) components: Investigating the role of the operational parameters in fused deposition modelling (FDM). J. Manuf. Process. 2020, 61, 473–480.
  16. Falahati, M.; Ahmadvand, P.; Safaee, S.; Chang, Y.-C.; Lyu, Z.; Chen, R.; Li, L.; Lin, Y. Smart polymers and nanocomposites for 3D and 4D printing. Mater. Today 2020, 40, 215–245.
  17. Razzaq, M.Y.; Behl, M.; Kratz, K.; Lendlein, A. Multifunctional Hybrid Nanocomposites with Magnetically Controlled Reversible Shape-Memory Effect. Adv. Mater. 2013, 25, 5730–5733.
  18. Platzer, N. Multicomponent Polymer Systems. In Applied Polymer Science; ACS Symposium Series; American Chemical Society: Washington, DC, USA, 1985; Volume 285, pp. 219–237.
  19. Miles, I.S.; Rostami, S. Multicomponent Polymer Systems; Longman Scientific & Technical: London, UK, 1992.
  20. Ze, Q.; Kuang, X.; Wu, S.; Wong, J.; Montgomery, S.M.; Zhang, R.; Kovitz, J.M.; Yang, F.; Qi, H.J.; Zhao, R. Magnetic Shape Memory Polymers with Integrated Multifunctional Shape Manipulation. Adv. Mater. 2019, 32, e1906657.
  21. Leng, J.; Du, S. Shape-Memory Polymers and Multifunctional Composites; CRC Press: Boca Raton, FL, USA, 2010.
  22. Salimon, A.; Senatov, F.; Kalyaev, V.; Korsunsky, A. Shape memory polymer blends and composites for 3D and 4D printing applications. In 3D and 4D Printing of Polymer Nanocomposite Materials; Elsevier: Amsterdam, The Netherlands, 2020; pp. 161–189.
  23. Razzaq, M.Y.; Anhalt, M.; Frormann, L.; Weidenfeller, B. Thermal, electrical and magnetic studies of magnetite filled polyurethane shape memory polymers. Mater. Sci. Eng. A 2007, 444, 227–235.
  24. Leng, J.; Lau, A.K.-T. Multifunctional Polymer Nanocomposites; CRC Press: Boca Raton, FL, USA, 2010.
  25. He, Y.-J.; Shao, Y.-W.; Xiao, Y.-Y.; Yang, J.-H.; Qi, X.-D.; Wang, Y. Multifunctional Phase Change Composites Based on Elastic MXene/Silver Nanowire Sponges for Excellent Thermal/Solar/Electric Energy Storage, Shape Memory, and Adjustable Electromagnetic Interference Shielding Functions. ACS Appl. Mater. Interfaces 2022, 14, 6057–6070.
  26. Curtis, P. Multifunctional polymer composites. Adv. Perform. Mater. 1996, 3, 279–293.
  27. Kulshreshtha, A.K.; Vasile, C. Handbook of Polymer Blends and Composites; iSmithers Rapra Publishing: Akron, OH, USA, 2002; Volume 1.
  28. Lipatov, Y.S. Polymer blends and interpenetrating polymer networks at the interface with solids. Prog. Polym. Sci. 2002, 27, 1721–1801.
  29. Kuila, B.K.; Formanek, P.; Stamm, M. Multilayer polymer thin films for fabrication of ordered multifunctional polymer nanocomposites. Nanoscale 2013, 5, 10849–10852.
  30. Zheng, Y.; Dong, R.; Shen, J.; Guo, S. Tunable Shape Memory Performances via Multilayer Assembly of Thermoplastic Polyurethane and Polycaprolactone. ACS Appl. Mater. Interfaces 2016, 8, 1371–1380.
  31. Sadasivuni, K.K.; Deshmukh, K.; Al-Maadeed, M.A.S. 3D and 4D Printing of Polymer Nanocomposite Materials: Processes, Applications, and Challenges; Elsevier: Amsterdam, The Netherlands, 2019.
  32. Roudbarian, N.; Baniasadi, M.; Nayyeri, P.; Ansari, M.; Hedayati, R.; Baghani, M. Enhancing shape memory properties of multi-layered and multi-material polymer composites in 4D printing. Smart Mater. Struct. 2021, 30, 105006.
  33. Joshi, S.; Rawat, K.; Karunakaran, C.; Rajamohan, V.; Mathew, A.T.; Koziol, K.; Thakur, V.K.; Balan, A. 4D printing of materials for the future: Opportunities and challenges. Appl. Mater. Today 2019, 18, 100490.
  34. Goo, B.; Hong, C.-H.; Park, K. 4D printing using anisotropic thermal deformation of 3D-printed thermoplastic parts. Mater. Des. 2020, 188, 108485.
  35. 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.
  36. Cao, L.; Wang, L.; Zhou, C.; Chunhua, L.; Fang, L.; Ni, Y.; Lu, C.; Xu, Z. Surface Structures, Particles, and Fibers of Shape-Memory Polymers at Micro-/Nanoscale. Adv. Polym. Technol. 2020, 2020, 7639724.
  37. Lee, W.L.; Low, H.Y. Geometry- and Length Scale-Dependent Deformation and Recovery on Micro- and Nanopatterned Shape Memory Polymer Surfaces. Sci. Rep. 2016, 6, 23686.
  38. Rafiee, M.; Farahani, R.D.; Therriault, D. Multi-material 3D and 4D printing: A survey. Adv. Sci. 2020, 7, 1902307.
  39. Keneth, E.S.; Lieberman, R.; Rednor, M.; Scalet, G.; Auricchio, F.; Magdassi, S. Multi-Material 3D Printed Shape Memory Polymer with Tunable Melting and Glass Transition Temperature Activated by Heat or Light. Polymers 2020, 12, 710.
  40. Ready, S.; Whiting, G.; Ng, T.N. Multi-material 3D printing. In Proceedings of the NIP & Digital Fabrication Conference, Philadelphia, PA, USA, 7–11 September 2014; pp. 120–123.
  41. Lopes, L.; Silva, A.; Carneiro, O. Multi-material 3D printing: The relevance of materials affinity on the boundary interface performance. Addit. Manuf. 2018, 23, 45–52.
  42. Chen, D.; Liu, Q.; Geng, P.; Tang, S.; Zhang, J.; Wen, S.; Zhou, Y.; Yan, C.; Han, Z.; Shi, Y. A 4D printing strategy and integrated design for programmable electroactive shape-color double-responsive bionic functions. Compos. Sci. Technol. 2021, 208, 108746.
  43. Garces, I.T.; Ayranci, C. Advances in additive manufacturing of shape memory polymer composites. Rapid Prototyp. J. 2021, 27, 379–398.
  44. Singh, S.; Singh, G.; Prakash, C.; Ramakrishna, S. Current status and future directions of fused filament fabrication. J. Manuf. Process. 2020, 55, 288–306.
  45. Valvez, S.; Reis, P.; Susmel, L.; Berto, F. Fused Filament Fabrication-4D-Printed Shape Memory Polymers: A Review. Polymers 2021, 13, 701.
  46. He, F.; Khan, M. Effects of Printing Parameters on the Fatigue Behaviour of 3D-Printed ABS under Dynamic Thermo-Mechanical Loads. Polymers 2021, 13, 2362.
  47. Gibson, I.; Rosen, D.; Stucker, B. Vat photopolymerization processes. In Additive Manufacturing Technologies; Springer: Berlin, Germany, 2015; pp. 63–106.
  48. Elomaa, L.; Teixeira, S.; Hakala, R.; Korhonen, H.; Grijpma, D.W.; Seppälä, J.V. Preparation of poly(ε-caprolactone)-based tissue engineering scaffolds by stereolithography. Acta Biomater. 2011, 7, 3850–3856.
  49. Cortés, A.; Cosola, A.; Sangermano, M.; Campo, M.; Prolongo, S.G.; Pirri, C.F.; Jiménez-Suárez, A.; Chiappone, A. DLP 4D-Printing of Remotely, Modularly, and Selectively Controllable Shape Memory Polymer Nanocomposites Embedding Carbon Nanotubes. Adv. Funct. Mater. 2021, 31, 2106774.
  50. Al Rashid, A.; Ahmed, W.; Khalid, M.Y.; Koç, M. Vat photopolymerization of polymers and polymer composites: Processes and applications. Addit. Manuf. 2021, 47, 102279.
  51. Malas, A.; Isakov, D.; Couling, K.; Gibbons, G.J. Fabrication of High Permittivity Resin Composite for Vat Photopolymerization 3D Printing: Morphology, Thermal, Dynamic Mechanical and Dielectric Properties. Materials 2019, 12, 3818.
  52. Zhang, F.; Zhu, L.; Li, Z.; Wang, S.; Shi, J.; Tang, W.; Li, N.; Yang, J. The recent development of vat photopolymerization: A review. Addit. Manuf. 2021, 48, 102423.
  53. Kamble, P.P.; Chavan, S.; Hodgir, R.; Gote, G.; Karunakaran, K. Multi-jet ice 3D printing. Rapid Prototyp. J. 2021, 28, 989–1004.
  54. Udroiu, R.; Braga, I.C. Polyjet technology applications for rapid tooling. In Proceedings of the MATEC Web of Conferences, Sibiu, Romania, 7–9 June 2017; p. 03011.
  55. Mao, Y.; Yu, K.; Isakov, M.S.; Wu, J.; Dunn, M.L.; Qi, H.J. Sequential Self-Folding Structures by 3D Printed Digital Shape Memory Polymers. Sci. Rep. 2015, 5, 13616.
  56. Ge, Q.; Dunn, C.K.; Qi, H.J.; Dunn, M.L. Active origami by 4D printing. Smart Mater. Struct. 2014, 23, 094007.
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