Catalyst-Free Thermoset Polyurethane with ... - ACS Publications

Mar 15, 2017 - Figure 1b, it is evident that all the samples show a rubbery ... db (103mol/m3). PU-1. 1:0. 63.1%. 97.2%. 76−134. 16.3 ± 1.0. 1.58. ...
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Catalyst-Free Thermoset Polyurethane with Permanent Shape Reconfigurability and Highly Tunable Triple-Shape Memory Performance Ning Zheng, Jingjing Hou, Yang Xu, Zizheng Fang, Weike Zou, Qian Zhao,* and Tao Xie* State Key Laboratory of Chemical Engineering, College of Chemical and Biological Engineering, Zhejiang University, 38 Zheda Road, Hangzhou 310027, P. R. China S Supporting Information *

ABSTRACT: Thermoset shape memory polymer (SMP) with dynamic covalent bonds in the network is a new class of SMPs for which the permanent shape can be reconfigured via topological rearrangement (plasticity). Catalyzed transcarbamoylation has recently been established as an effective exchange reaction for plasticity in cross-linked polyurethane networks. However, ensuring the plasticity severely constrains the network design which adversely affects the ability to tune other classical shape memory properties for practical applications. Facing this new challenge, we design an amorphous polyurethane system for which the cross-linking density can be adjusted in a wide range. We discovered that the use of an aromatic diisocyanate in the synthesis of the polyurethanes facilitates achieving plasticity without requiring any catalyst. The overall network design leads to tunable recovery stress and shape memory transition temperatures without sacrificing the plasticity. The versatility of our polyurethane SMP is further reflected in its triple-shape memory performance. We anticipate that our tunable polyurethanes will benefit a variety of potential SMP device applications.

S

solid state plasticity carries unique benefits for SMP due to the fusion between shapes and functions.16 A variety of dynamic covalent bonds have been successfully utilized in making dynamic SMP networks with permanent shape reconfigurability including transesterification,17 reversible TAD Chemistry,18 Diels−Alder reaction,19 and transcarbamoylation.20 Despite the rich enabling chemistry, an overlooked important issue is how to tune the classical performance characteristics of SMP (e.g., shape memory transition temperature and recovery stress) required for practical applications. This is not straightforward, as the typical strategies of tuning the SMP, for instance, by increasing the fractions of permanent cross-linkers, can significantly affect the plasticity29 to the extent that plasticity is completely lost.30 Semicrystalline SMP networks typically possess low permanent cross-linking densities due to the high molecular mass between cross-linkers required for crystallization. Whereas the low permanent crosslinking favors achieving the plasticity, it adversely contributes to its low recovery stress. In addition, tuning the shape memory transition temperature (melting temperature) in a wide range is not feasible. In contrast, the tunability of amorphous SMP networks is much better.31,32 In this work, we therefore resort to amorphous networks to achieve tunable performances in a polyurethane SMP system. As for the enabling dynamic

hape memory polymers (SMPs) as smart responsive materials have been studied for decades.1−7 Their ability to fix temporary shapes and recover to permanent shapes has been proven useful for a wide range of applications including aerospace structures, flexible electronics, and biomedical devices. Recent expansion from the classical irreversible dualshape memory effect to the multishape memory8−10 and reversible shape memory effects11−13 has greatly extended the capability of SMPs. Despite the progress, the significance of permanent geometries has largely been neglected. From the standpoint of practical devices, however, the permanent geometric shapes play a critical role in their ultimate functions. Traditionally, the sophistication of permanent shapes is limited by molding and machining techniques. The emergence of 4D printing opens up new opportunities, yet the printing speed and/or diversity of the printable materials remain the bottleneck.14,15 Introducing dynamic covalent linkages in an SMP overcomes this limitation.16−20 The ability of the network topological rearrangement due to the bond exchange leads to permanent shape reconfigurability in its solid state. Such solid-state plasticity allows fabrication of geometrically complex permanent shapes without being limited by molds. This unique and practically important feature distinguishes it from the classical thermoplastic and thermoset SMPs.16 Herein, the underlying network topological rearrangement mechanism is well-known for “covalent adaptable network (CAN)” and “vitrimers”.21−28 However, the permanent shape reconfigurability enabled by © XXXX American Chemical Society

Received: January 19, 2017 Accepted: March 8, 2017

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DOI: 10.1021/acsmacrolett.7b00037 ACS Macro Lett. 2017, 6, 326−330

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ACS Macro Letters

an advantageous feature for the triple-shape function shown later in the context. Figure 1c further shows a linear relationship between the cross-linking density and Tg, indicating that any shape transition temperature can be conveniently obtained in this range. Despite the classical “thermoset” chemistry along with their rubbery plateau in the DMA curves (obtained at the frequency of 1 Hz), all the samples exhibit complete stress relaxation at 170 °C (Figure 2a, 10% strain). This typical plasticity behavior

covalent chemistry, our previous work has established catalyzed transcarbamoylation for achieving plasticity.20 We show below that the use of an aromatic diisocyanate in the synthesis of the polyurethanes allows achieving plasticity without requiring any catalyst. The amorphous polyurethane networks were synthesized via the reaction of three commercial precursors (Figure 1a): a diol

Figure 2. Stress relaxation characterization. (a) Stress relaxation behaviors at 170 °C. (b) Correlation between characteristic relaxation time and cross-linking density.

can only originate from the transcarbamoylation of carbamates as no other bonds within the networks are known to possess dynamic characteristics. We note here that transcarbamoylation is a well-known reaction. Under most circumstances, however, it has been deemed to be of little use since transcarbamoylation for carbamates is typically “sluggish” and often requires very high temperature (>200 °C) with detrimental side reactions.28 Introducing ample hydroxyl groups into polyurethane to form polyhydroxyurethane is one effective approach to promote transcarbamoylation.28 Without altering the main polyurethane structure, we have also demonstrated that low cross-linking polyurethane networks can display ideal plasticity behaviors albeit with the assistance of dibutyltin dilaurate.20 In contrast, the current polyurethane networks (PU-1 to PU-4) are catalyst free and much higher cross-linked, and both of these features disfavor the plasticity. Thus, the stress relaxation behaviors shown in Figure 2a are, to some extent, unexpected. The characteristic relaxation time (τ*), defined as the time required for 63% stress relaxation,24 increases linearly with the cross-linking density (Figure 2b). On a qualitative basis, this can be explained by considering the gelation of the dynamic networks. According to the classical gelation theory, the gel point for such a network decreases (Table S1) as the ratio between HPED and PEG (correspondingly the cross-linking density) increases. Under the reasonable assumption that the network samples all reach similar near complete conversion after the curing, higher cross-linking density implies that the network is further away from its gel point. Thus, a much longer time is required for the complete relaxation of such a transient or dynamic network.

Figure 1. Polyurethane precursors and thermomechanical characterization of the synthesized networks. (a) Chemical structures of the precursors. (b) DMA curves. (c) Correlation between glass transition temperature and cross-linking density.

(poly(ethylene glycol)diol with Mn of 200, or PEG), a tetraol (N,N,N′,N′-tetrakis(2-hydroxypropyl)ethylenediamine, or HPED), and an aromatic diisocyanate (4,4′-methylenebis(phenyl isocyanate), or MDI). The precursor mixtures were thermally cured (60 °C for 2 h and 120 °C for 2 h), notably without any catalyst present in the system. A series of polyurethane samples (PU-1, PU-2, PU-3, and PU-4) were obtained by varying the ratio between HPED and PEG, while the amount of MDI was adjusted accordingly to maintain the stoichiometric balance between the hydroxyl groups and the isocynate (Table 1). All the samples possess high gel contents (above 97%), confirming that the reactions did proceed to near completion despite the absence of catalyst. From their DMA curves in Figure 1b, it is evident that all the samples show a rubbery plateau above their corresponding glass transitions. Decreasing the ratio between HPED and PEG (from PU-1 to PU-4) leads to reduction in the rubbery modulus (Er) and the cross-linking density (d) (Table 1), consistent with the original network design intent. The change in glass transition temperature (Tg) follows a similar trend, suggesting that the reduction in crosslinking density favors the activation of the network chain mobility at lower temperatures. All glass transitions occur in a relatively broad temperature range (about 50−60 °C), which is

Table 1. Thermomechanical Properties of Amorphous Polyurethane Networks sample

HPED:PEG

MDI

gel content

Tga (°C)

Er (MPa)

db (103mol/m3)

PU-1 PU-2 PU-3 PU-4

1:0 3:1 1:1 1:3

63.1% 61.9% 60.4% 58.4%

97.2% 98.1% 98.7% 97.5%

76−134 65−120 52−110 32−90

16.3 12.0 8.3 4.8

± ± ± ±

1.58 1.17 0.81 0.47

1.0 1.3 0.3 0.3

a

Obtained from DSC curve shown in Figure S1. bCalculated from rubbery modulus from the corresponding stress−strain curves obtained from tensile experiments. 327

DOI: 10.1021/acsmacrolett.7b00037 ACS Macro Lett. 2017, 6, 326−330

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ACS Macro Letters The complete stress relaxation at high temperatures and the glass transitions at lower temperatures together serve as the basis for SMPs with permanent shape reconfigurability. Unless otherwise noted, we hereafter focus on PU-4 to further probe the plasticity and shape memory performances. Figure 3a

Figure 4. Visual demonstration of the plasticity and triple-shape memory performance of PU-4. (a) Originally molded shapes. (b) Plasticity defined permanent shapes. (c) First set of temporary shapes. (d) Second set of temporary shapes. (e) First recovered temporary shapes. (f) Recovered permanent shapes. Scale bar: 8 mm.

Figure 3. Stress relaxation and shape changing characterization of PU4. (a) Stress relaxation and Arrhenius analysis. (b) Consecutive dualshape memory cycles (deformation and recovery temperature were 120 °C). (c) Combined elasticity (labeled I) and plasticity (labeled II) cycles. (d) A typical triple-shape memory cycle.

programming (Figures 4c and 4d). In the shape recovery steps, the samples were first heated to 80 °C to recover to “SIC” and then to 120 °C to recover to the permanent “SMP”. In experiments not shown here, this triple-shape memory cycle can be repeatedly conducted, and the permanent shapes can be redefined multiple times. As stated earlier, tuning the performance of covalently crosslinked SMP without losing its permanent shape reconfigurability is not an easy task. For the current amorphous polyurethane system, all the other samples (PU-1 to PU-3) show similar plasticity behavior with PU-4 with the shape memory transition temperature tunable within the sample series. Likewise, the recovery stress, which is strongly linked to the cross-linking density, is also tunable. Figure 5a displays a

illustrates that this sample exhibits complete stress relaxation with faster relaxation at higher temperatures, showing an Arrhenius correlation with an activation energy of 183.7 kJ/ mol. This high activation energy indicates a larger difference in relaxation time for a given temperature difference, which is beneficial as it allows us to activate and suppress the dynamic exchange in a narrow temperature range. The sample further displays excellent dual-shape memory behavior (Rf: 99.7%, Rr: 97.9%) and cycle stability (Figure 3b) utilizing its glass transition temperature as the shape memory transition. We note here that a prolonged shape fixing time at 120 °C may induce sufficient relaxation to affect the Rr. However, due to the long characteristic relaxation time (approximately 500 min at 120 °C, calculated from Arrhenius curve), even a fixing time of 20 min does not noticeably deteriorate the Rr (Figure S2). Using 150 °C as the plasticity temperature and 120 °C as the shape memory fixing and recovery temperature, multiple shape memory and plasticity-based shape manipulations can be realized in one combined cycle (Figure 3c). Notably, the shape fixing and recovery in the shape memory cycles and the permanent shape retention due to the plasticity are all near perfect throughout the cycling experiments. Beyond such, the broad glass transition can be explored for achieving a tripleshape function shown in Figure 3d. The shape was first deformed at 120 °C and fixed at 75 °C to fix a first temporary shape. Afterward, this shape was further deformed at 75 °C and fixed a second temporary shape. The shape fixity (Rf) ratios for these two shapes are 53.1% and 99.5%, respectively. Upon reheating to these two temperatures, the shapes recover nicely with their shape recovery (Rr) ratios both reaching near 100%. The combined plasticity and triple-shape memory performance are visually illustrated in Figure 4. Three specimens of PU4 in original rectangular shapes (Figure 4a) were permanently deformed into the letters of “SMP” (Figure 4b). The permanent shapes were then deformed in two steps to temporary shapes of “SIC” and “ZJU” via triple-shape

Figure 5. Recovery stress characterizations. (a) A typical stress recovery curve for PU-4. (b) Correlation between recovery stress and cross-linking density.

typical stress recovery curve for PU-4, obtained after a first shape fixing step (Figure S3) followed by a second fixed-strain recovery. Stress recovery curves for samples PU-1 to PU-3 (Figure S4) were obtained in the same fashion. The correlation between peak recovery stress and the cross-linking density is summarized in Figure 5b. The linear correlation between the peak recovery stress and the cross-linking density suggests that any recovery stress can be realized in this range. A notable 328

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(6) Leng, J. S.; Lan, X.; Liu, Y. J.; Du, S. Y. Shape-memory polymers and their composites: Stimulus methods and applications. Prog. Mater. Sci. 2011, 56, 1077−1135. (7) Sun, L.; Huang, W. M.; Ding, Z.; Zhao, Y.; Wang, C. C.; Purnawali, H.; Tang, C. Stimulus-responsive shape memory materials: A review. Mater. Eng. 2012, 33, 577−640. (8) Xie, T. Tunable polymer multi-shape memory effect. Nature 2010, 464, 267−270. (9) He, Z.; Satarkar, N.; Xie, T.; Cheng, Y. T.; Hilt, J. Z. Remote controlled multishape polymer nanocomposites with selective radiofrequency actuations. Adv. Mater. 2011, 23, 3192−3196. (10) Bellin, I.; Kelch, S.; Langer, R.; Lendlein, A. Polymeric tripleshape materials. Proc. Natl. Acad. Sci. U. S. A. 2006, 103, 18043−18047. (11) Zhou, J.; Sheiko, S. S. Reversible shape-shifting in polymeric materials. J. Polym. Sci., Part B: Polym. Phys. 2016, 54, 1365−1380. (12) Behl, M.; Kratz, K.; Zotzmann, J.; Nochel, U.; Lendlein, A. Reversible bidirectional shape-memory polymers. Adv. Mater. 2013, 25, 4466−4469. (13) Meng, Y.; Jiang, J. S.; Anthamatten, M. Shape actuation via internal stress-induced crystallization of dual-cure networks. ACS Macro Lett. 2015, 4, 115−118. (14) Huang, L. M.; Jiang, R. Q.; Wu, J. J.; Song, J. Z.; Bai, H.; Li, B. G.; Zhao, Q.; Xie, T. Ultrafast digital printing toward 4D shape changing materials. Adv. Mater. 2017, 29, 1605390. (15) Mao, Y. Q.; Yu, K.; Isakov, M. S.; Wu, J. T.; Dunn, M. L.; Qi, H. J. Sequential self-folding structures by 3D printed digital shape memory polymers. Sci. Rep. 2015, 5, 13616. (16) Zou, W. K.; Dong, J. T.; Luo, Y. W.; Zhao, Q.; Xie, T. Dynamic covalent polymer networks: From old chemistry to modern day innovations. Adv. Mater. 2017, 1606100. (17) Zhao, Q.; Zou, W. K.; Luo, Y. W.; Xie, T. Shape memory polymer network with thermally distinct elasticity and plasticity. Sci. Adv. 2016, 2, e1501297. (18) Defize, T.; Riva, R.; Thomassin, J. M.; Alexandre, M.; Herck, N. V.; Prez, F. D.; Jérôme, C. Reversible TAD chemistry as a convenient tool for the design of (re)processable PCL-based shape-memory materials. Macromol. Rapid Commun. 2017, 38, 1600517. (19) Zhang, G. G.; Zhao, Q.; Yang, L. P.; Zou, W. K.; Xi, X. Y.; Xie, T. Exploring dynamic equilibrium of diels−alder reaction for solid state plasticity in remoldable shape memory polymer network. ACS Macro Lett. 2016, 5, 805−808. (20) Zheng, N.; Fang, Z. Z.; Zou, W. K.; Zhao, Q.; Xie, T. Thermoset shape-memory polyurethane with intrinsic plasticity enabled by transcarbamoylation. Angew. Chem., Int. Ed. 2016, 55, 11421−11425. (21) Kloxin, C. J.; Scott, T. F.; Adzima, B. J.; Bowman, C. N. Covalent adaptable networks (CANs): A unique paradigm in crosslinked polymers. Macromolecules 2010, 43, 2643−2653. (22) Denissen, W.; Winne, J. M.; Prez, F. D. Vitrimers: Permanent organic networks with glass-like fluidity. Chem. Sci. 2016, 7, 30−38. (23) Denissen, W.; Rivero, G.; Nicolaÿ, R.; Leibler, L.; Winne, J. M.; Prez, F. D. Vinylogous urethane vitrimers. Adv. Funct. Mater. 2015, 25, 2451−2457. (24) Montarnal, D.; Capelot, M.; Tournilhac, F.; Leibler, L. Silica-like malleable materials from permanent organic networks. Science 2011, 334, 965−968. (25) Brutman, J. P.; Delgado, P. A.; Hillmyer, M. A. Polylactide vitrimers. ACS Macro Lett. 2014, 3, 607−610. (26) Yang, Y.; Pei, Z.; Li, Z.; Wei, Y.; Ji, Y. Making and remaking dynamic 3D structures by shining light on flat liquid crystalline vitrimer films without a mold. J. Am. Chem. Soc. 2016, 138, 2118− 2121. (27) Pei, Z.; Yang, Y.; Chen, Q.; Wei, Y.; Ji, Y. Regional shape control of strategically assembled multishape memory vitrimers. Adv. Mater. 2016, 28, 156−160. (28) Fortman, D. J.; Brutman, J. P.; Cramer, C. J.; Hillmyer, M. A.; Dichtel, W. R. Mechanically activated, catalyst-free polyhydroxyurethane vitrimers. J. Am. Chem. Soc. 2015, 137, 14019−14022.

advantage of the amorphous system lies in that the cross-linking density (thus recovery stress) can be much higher than semicrystalline-based SMP. For a quick comparison, we demonstrated in Figure S5 that PU-4, as the sample with the lowest recovery stress (∼1 MPa) within the current work, is superior to previously reported semicrystalline polyurethane (recovery stress of 0.46 MPa, Figure S420) in terms of the constrained recovery. In summary, we designed and investigated an amorphous thermoset polyurethane SMP system with permanent shape reconfigurability and shape memory performance tunability, a task that was otherwise challenging. By adjusting the crosslinking density, recovery stress and shape memory transition temperatures can be readily tuned, importantly without sacrificing the plasticity. Beyond that, the system shows a range of attractive features including catalyst-free, commercial availability of the precursors, ease of synthesis, and versatile triple-shape memory function. Our work expands the covalently cross-linked polyurethane system as attractive candidates for future device applications. On the more fundamental side, the work suggests that aromatic carbamates are excellent building units for polymers with permanent shape reconfigurability.



ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsmacrolett.7b00037. Experimental details and characterization data (PDF)



AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected]. *E-mail: [email protected]. ORCID

Tao Xie: 0000-0003-0222-9717 Notes

The authors declare no competing financial interest.



ACKNOWLEDGMENTS We would like to thank the following programs for their financial support: National Key Basic Research Program of China (no. 2015CB351903); National Natural Science Funds for Distinguished Young Scholar (no. 21625402); National Natural Science Foundation of China (nos. 21474084 and 21474090); and Chinese central government’s Recruitment Program of Global Experts.



REFERENCES

(1) 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. (2) Lawton, M. I.; Tillman, K. R.; Mohammed, H. S.; Kuang, W.; Shipp, D. A.; Mather, P. T. Anhydride-based reconfigurable shape memory elastomers. ACS Macro Lett. 2016, 5, 203−207. (3) Mather, P. T.; Luo, X.; Rousseau, I. A. Shape memory polymer research. Annu. Rev. Mater. Res. 2009, 39, 445−471. (4) Behl, M.; Razzaq, M. Y.; Lendlein, A. Multifunctional shapememory polymers. Adv. Mater. 2010, 22, 3388−3410. (5) Hu, J. L.; Zhu, Y.; Huang, H. H.; Lu, J. Recent advances in shapememory polymers: Structure, mechanism, functionality, modeling and applications. Prog. Polym. Sci. 2012, 37, 1720−1763. 329

DOI: 10.1021/acsmacrolett.7b00037 ACS Macro Lett. 2017, 6, 326−330

Letter

ACS Macro Letters (29) Kuang, X.; Liu, G.; Dong, X.; Wang, D. J. Correlation between stress relaxation dynamics and thermochemistry for covalent adaptive networks polymers. Mater. Chem. Front. 2017, 1, 111−118. (30) Zhang, G. G.; Zhao, Q.; Zou, W. K.; Luo, Y. W.; Xie, T. Unusual aspects of supramolecular networks: Plasticity to elasticity, ultrasoft shape memory, and dynamic mechanical properties. Adv. Funct. Mater. 2016, 26, 931−937. (31) Zheng, N.; Fang, G. Q.; Cao, Z. L.; Zhao, Q.; Xie, T. High strain epoxy shape memory polymer. Polym. Chem. 2015, 6, 3046−3053. (32) Voit, W.; Ware, T.; Dasari, R. R.; Smith, P.; Danz, L.; Simon, D.; Barlow, S.; Marder, S. R.; Gall, K. High-strain shape-memory polymers. Adv. Funct. Mater. 2010, 20, 162−171.

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