Healable, Reconfigurable, Reprocessable Thermoset Shape Memory

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Healable Reconfigurable Reprocessable Thermoset Shape Memory Polymer with Highly Tunable Topological Rearrangement Kinetics Zizheng Fang, Ning Zheng, Qian Zhao, and Tao Xie ACS Appl. Mater. Interfaces, Just Accepted Manuscript • DOI: 10.1021/acsami.7b05713 • Publication Date (Web): 20 Jun 2017 Downloaded from http://pubs.acs.org on June 22, 2017

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ACS Applied Materials & Interfaces

Healable Reconfigurable Reprocessable Thermoset Shape Memory Polymer with Highly Tunable Topological Rearrangement Kinetics Zizheng Fang‡, Ning Zheng‡, Qian Zhao*, Tao Xie* State Key Laboratory of Chemical Engineering, College of Chemical and Biological Engineering, Zhejiang University, 38 Zheda Road, Hangzhou, 310027, P. R. China ABSTRACT: The unique capability of topological rearrangement for dynamic covalent polymer networks has enabled various unusual properties (self-healing, solid-state plasticity, and reprocessability) that are not found in conventional thermosets. Achieving these properties in one network in a synergetic fashion can open up new opportunities for shape memory polymer. To accomplish such a goal, the freedom to tune topological rearrangement kinetics is critical. This is, however, challenging to achieve. In this work, two sets of dynamic bonds (urethane and hindered urea) are incorporated into a hybrid network for synthesizing shape memory poly(ureaurethane). By changing the bond ratio, networks with highly tunable topological rearrangement kinetics are obtained. Combining self-healing, solid-state plasticity, and reprocessability in such one shape memory network leads to unusual versatility in its shape shifting performance. Keywords: dynamic covalent bond, thermoset, shape memory polymer, self-healing, reprocessing, plasticity

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The thermal and mechanical robustness of thermoset polymers has sustained great industrial and academic interests in the history of synthetic polymers. However, the permanent crosslinked nature, while essential to the stability, is unfavorable for designing materials with dynamic properties. An increasingly growing interest focuses on achieving adaptive properties by introducing dynamic covalent linkages into polymer networks.1-3 Dynamic covalent bonds, which typically possess high bond strength relative to supramolecular bonds, can be activated by appropriate external stimulation to undergo bond breaking and reforming. As such, they open up new opportunities to design mechanically robust materials with unique adaptive properties that are not found in conventional thermosets, including self-healing,4-10 reprocessability,11-19 and solid-state plasticity.20-25 Self-healing is based on reforming covalent bonds at fracture interfaces with minimal (if any) interference by external forces. In contrast, both reprocessability and solidstate plasticity rely on dynamic bond exchange to allow topological rearrangement throughout the network. While similar, reprocessing requires pushing the network equilibrium to reach a liquid state to redefine the shape through a mold whereas plasticity allows permanent reshaping in the solid state in a mold free fashion.1 An interesting opportunity is to combine the solid-state plasticity with conventional entropic elasticity in a shape memory network26,27 to yield a new class of shape memory polymer (SMP) called thermadapt SMP.1 As explained in a dedicated section in a recent progress report,1 thermadapt SMP differs from thermoplastic SMP and thermoset SMP in both the structural adaptability of the network and the permanent shape reconfigurability via solid-state plasticity.2022

The unique characteristics of this type of SMP has led to new possibilities in fabricating SMP

devices with geometrically complex permanent shapes.20-22 Since self-healing, reprocessability, and plasticity share their common roots in dynamic covalent bonds, we deduce that even more


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versatility in shape shifting is likely if all three properties are combined in a synergetic way in one SMP. To realize such a goal, it is highly desirable to design a dynamic network for which the topological rearrangement can be activated in a tunable temperature range. This is by no means a straightforward task. A variety of dynamic covalent bonds have been reported that can be thermally activated at different temperature ranges. Esters,20 hydroxyl-esters,10-11,14-15,23,28 urethane,22 and hydroxyl-urethane bonds17 represent dynamic bonds requiring relatively high temperatures (>120 °C) to activate. Those that show dynamic characteristics at ambient temperature include olefin metathesis,12 hindered urea,8 thiuram disulfide,7 and imine.5 Other bonds that can be activated at intermediate temperatures (~50-120 °C) include Diels-Alder moieties,4,21 siloxane exchange,6 and anhydride.24 For catalyzed systems, the catalyst type and loading allow altering the activation temperature,22,28 but such tunability is usually narrow. In principle, adjusting steric hindrance and electronic effect of a particular dynamic bond also allows tuning its activation temperature. With the exception of hindered urea8 and boronic esters29, however, such a strategy has not been adopted in designing dynamic polymer networks, in part due to the extensive effort typically required. In addition, the advance in dynamic covalent networks has historically centered on self-healing and reprocessing,1 efforts to take advantage of diverse dynamic covalent chemistry to expand the design freedom by incorporating plasticity has been limited to transesterication20 and transcarbamoylation.22 Recognizing this deficiency, our attention was drawn to urethane bond exchange (transcarbamoylation) and hindered urea bond exchange, occurring at a vastly different temperature range (above 100 °C and ambient temperature, respectively). These two types of bonds can be formed with similar chemistries, through the reaction of isocyanate and hydroxyl groups and hindered amine, respectively. Thus, they can be conveniently introduced into a single


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network to form poly(urethane-urea). We hypothesized that in such a hybrid network, varying their bond ratio can lead to great flexibility in tuning the network topological rearrangement kinetics to allow reprocessing and plasticity to be achieved in a wide temperature range, much in the same way as copolymerization of two different monomers allows continuous tuning of the glass transition temperature of the resulting copolymer. Such a strategy in principle offers significant practical values due to the commercial availability of precursors. We note that a previous report from Cheng’s group studied the self-healing and reprocessing capability of a poly(urethane-urea) network.19 However, plasticity and shape memory functions were not investigated. More importantly, no effort was made in tuning the network rearrangement kinetics. Hereafter, we describe our effort in designing dynamic networks based on this hypothesis and the versatile shape shifting behavior of the resulting materials by synergizing self-healing, reconfiguration via plasticity, and reprocessability in a shape memory network.


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Scheme 1. Chemical structure and bond exchange mechanisms for the hybrid dynamic network. The poly(urea-urethane) networks were synthesized by reacting hexamethylene diisocyanate (HDI) with a mixture of a diol, a hindered amine, and a triol crosslinker, with the presence of ditin butyl dilaurate (DBTDL) as the catalyst. Specifically, the diol and triol were poly(ethylene glycol)diol (PEG, Mn=2,000) and glycerin (GLY) and the hindered amine was N,N'-di-tertbutylethylenediamine (TBEA) (Scheme 1). The network chemistry design enabled the freedom to tune the crosslinking density (Ve) and the ratio between hindered urea and urethane bonds (Rb), two critical parameters for the network topological rearrangement. A series of samples with


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variations in these two parameters were obtained (Table 1). Their gel contents are all above 95%, verifying the integrity of the networks. Table 1. Formulations and crosslinking densities of the poly(urethane-urea) networks. Samplea

Rb

Hydroxyl ratiob

Ec [MPa]

Ved [mol/m3]

PUU1

8/40

27/13

0.55±0.03

66.1

PUU2

14/39

26/13

0.52±0.04

62.0

PUU3

39/27

14/13

0.43±0.02

51.6

PUU4

51/20

7/13

0.61±0.01

73.1

PUU5

39/27

14/39

1.17±0.05

141.3

PUU6

39/27

14/26

0.99±0.07

120.0

PUU7

39/27

14/6.5

0.25±0.02

30.0

a

The total amount of alcohols and amine was maintained in a stoichiometric balance with the isocyanate. bMolar ratio of hydroxyl groups between PEG and GLY. cObtained from tensile tests. dCalculated from the rubbery moduli using Equation 1 in the Supporting Information. The network topological rearrangement kinetics were evaluated in iso-strain stress relaxation experiments. Figure 1a shows that, when Rb is identical, larger Ve leads to slower stress relaxation. The characteristic relaxation time (τ*), corresponding to the time for 37% stress relaxation, increases with the Ve in an approximately linear fashion (Figure 1b). Such a trend is consistent with theoretical prediction based on Flory gel point theory.30 To evaluate the impact of Rb on the rearrangement kinetics, it is thus necessary to keep Ve constant. This can be done by adjusting the formulation (PUU1 to PUU4 in Table 1). However, the different reactivities of amine and alcohol along with the intrinsically heterogeneous gelation process during the reactions make it difficult to obtain networks with precisely targeted Ve. Putting aside this unavoidable slight variation in Ve (PUU1 to PUU4 in Table 1), Figure 1c illustrates that increase in Rb results in faster stress relaxation. More specifically, the drop in τ* with Rb (Figure 1d) is


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quite significant at the low Rb range but becomes tapered in the higher Rb range. Although both Rb and Ve have significant impact on the topological rearrangement kinetics, the freedom to tune the latter is often limited by other application requirements such as the need to maintain appropriate Ve to ensure a certain level of recovery stress for SMP. Thus, having the flexibility to tune the network rearrangement kinetics using Rb is non-trivial.

Figure 1. Stress relaxation behaviors of the poly(urethane-urea) networks. (a) The stress relaxation curves at 80 °C for samples of different crosslinking densities. (top to bottom: PUU5, PUU6, PUU3, and PUU7) (b) The correlation between the characteristic relaxation time τ* and Ve. (c) The stress relaxation curves at 80 °C for samples of different dynamic bond ratio Rb. (top to bottom: PUU1, PUU2, PUU3, and PUU4) (d) The correlation between the characteristic relaxation time τ* and Rb.


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The highly tunable kinetics allows achieving self-healing, reprocessing, and plasticity across a wide temperature range for the poly(urethane-urea) shape memory networks. We chose PUU3 to evaluate these individual properties due to its appropriate exchange kinetics at an intermediate temperature of 80 °C. For this sample, dynamic mechanical analysis (DMA) reveals a melting temperature of 43 °C, which is 37 °C by differential scanning calorimetry analysis (DSC) measurement. This melting transition arises from the PEG chain segments in the network (Figure S1). With the deformation and recovery temperature of 50 °C, the sample shows excellent shape memory performance utilizing the melting transition as the shape memory transition, with both Rf and Rr exceeding 95% in cyclic testing (Figure 2a). The stress relaxation curves of PUU3 at different temperatures (60-90 °C) and corresponding Arrhenius curve were presented in Figure S2. Complete stress relaxation can be reached in all cases, indicating that permanent shape reconfiguration via plasticity can be realized under these conditions. Thus, the sample can undergo cyclic temporary shape memory (at 50 °C) and permanent shape change (80 °C) in an on demand manner (Figure 2b), with near perfect distinction between the two opposite behaviors (shape retention ratio Rret20 for permanent reconfiguration is around 98%). Multiple cycles of shape memory and plasticity were presented in Figure S3, showing excellent cyclic performance. We note that although plasticity can be achieved at temperatures lower than 80 °C, the relaxation kinetics were slower. For instance, complete stress relaxation at 60 °C takes about 250 minutes (Figure S2), which is advantageous in ensuring network stability for shape memory experiments that typically occurs at a much shorter time-scale of roughly 10 minutes. At temperatures lower than the melting temperature of 43 °C, the bond exchange reaction is further quenched due to the freeze of the chain mobility.


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Figure 2. (a) The consecutive shape memory cycles of PUU3. (b) The combined shape memory and plasticity cycles of PUU3. (c) The stress relaxation curves of samples corresponding to different mass ratios (Rms). (top: PUU1, bottom: PUU3). (d) The correlation between the characteristic relaxation time τ* and Rm. For self-healing, PUU3 was cut into two halves, which were then pushed together gently and allowed to heal at 80 °C without any external forces. Herein, for shape shifting performance, we focus on the most relevant parameter: the strain-at-break (Ɛmax) in the rubbery state. At 40 min healing time which corresponds to the complete stress relaxation in the plasticity experiment, the Ɛmax of the healed sample was 103±11%, only 27% of the original sample. Prolonging the healing time to 4 h, however, led to Ɛmax of 276±53% (about 71% recovery) (Figure S4). For reprocessability, a sample of PUU3 was cut into many small pieces (about 1 mm in dimension)


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and compression molded at 80 °C for 40 min. The remolded sample was then tensile tested and compared to the original sample. While low molding pressures led to poor mechanical properties, the Ɛmax recovered 113% at the mold pressure of 0.5 MPa, reaching 439±20% (Figure S4). More details on the self-healing and reprocessing performance can be found in Figure S4 in the supporting information. We note that most studies on self-healing and reprocessing focus on recycling of thermosets, in which cases full restoration of the mechanical properties is important. In our case, our goal is to manipulate complex shapes for functional (shape memory) instead of structural purposes. Therefore, modulus is less important, instead, recovery of maximum strains (i.e. shape deformability) is more relevant. The reprocessability paves a new way to manipulate topological rearrangement kinetics in a modular manner. To demonstrate this possibility, two samples of different rearrangement kinetics (PUU1 and PUU3) were cut into small particles. The two sets of particles of different mass ratios (Rms) were then well mixed and compression molded into new samples. Figure 2c shows that, the stress relaxation kinetics of the reprocessed sample can be adjusted based on the mass ratio. The monotonic dependence of the characteristic relaxation time on Rm (Figure 2d) suggests that any characteristic relaxation time between 4 min (τ* for PUU3) and 24 min (τ* for PUU1) can be obtained. The above modular approach to tune the network rearrangement kinetics may be called “blending” reprocessing given its similarity with conventional polymer blending. In further reflecting the versatility, two rectangular thin films of PUU1 and PUU3 can be bonded together via cross-healing to form a single composite with two spatially different regions of different rearrangement kinetics (Figure S5). For such a composite, plasticity conducted at 80 and 100 °C can selectively activate the PUU3 region and the entire sample, respectively.


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Demonstration of the shape shifting behavior involving the spatio-selective plasticity can be found in Figure S6. Figure 3 illustrates how reprocessability, self-healing, and plasticity can be combined synergistically to yield versatile shape shifting capability for the SMP. The sample in the form of particles can be compression molded into a selected shape with desired size and thickness. Inspired by “Tangram”, the reprocessed square film was cut into seven building blocks including triangles and parallelograms. These building blocks can then be reassembled via self-healing to form a variety of shapes such as the illustrated butterfly. Subsequently, the two dimensional butterfly was reconfigured into a stereoscopic one by solid-state plasticity. The sample, in its permanent three dimensional butterfly shape, can then undergo temporary shape fixing and permanent shape recovery. Overall, combining reprocessing, self-healing, and plasticity allows simultaneous manipulation of a variety of shape parameters (e.g. thickness, layout, and bending angles), which is a capability not offered by any single property alone. We emphasize here that our strategy focuses more on the manipulation of the complex permanent shapes through the combined reprocessability, self-healing, and plasticity. This is different from a previous study10 that utilizes dynamic bond for welding SMPs of different glass transitions to manipulate the temporary shape fixing.


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Figure 3. Manipulation of the permanent shape for PUU3 via synergistic reprocessing, selfhealing, and plasticity, followed by conventional shape memory cycle (fixing and recovering). Scale bars, 10 mm. In summary, we designed and synthesized a series of shape memory poly(urea-urethane) hybrid networks with variations in the crosslinking density and the ratio between the dynamic urethane and hindered urea bonds. Adjusting these two molecular parameters allow continuous fine tuning of the topological rearrangement kinetics, a task that is otherwise difficult for networks with only one type of dynamic bond. The dynamic networks exhibit self-healing, reprocessability, and solid-state plasticity, three adaptive behaviors not found in conventional thermosets. The wide tunability of the topological rearrangement kinetics facilitates combining all these unusual properties in one shape memory network in a synergistic way, leading to versatile shape shifting behaviors beyond currently known SMP.

ASSOCIATED CONTENT Supporting Information The Supporting Information is available free of charge on the ACS Publications websites.


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Additional information on the experimental descriptions, thermos-mechanical characterization, shape memory and plasticity cycles, representative stress-strain curves, and demonstration of shape memory behaviors of poly(urea-urethane) with different permanent shapes

AUTHOR INFORMATION Corresponding Authors *E-mail: [email protected]. *E-mail: [email protected]. ORCID: 0000-0003-0222-9717 Author Contributions ‡

Z. F. and N. Z. contributed equally to this work.

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 Founds for Distinguished Young Scholar (no. 21625402); National Natural Science Foundation of China (no. 21474084 and 51673169); and Chinese central government’s Recruitment Program of Global Experts.


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Table of Contents


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Healable, Reconfigurable, Reprocessable Thermoset Shape Memory

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