Strategy for Constructing Shape-Memory Dynamic Networks through

SMPs have great potential for aerospace, intelligent textiles, biomedical .... PEG4K-Np@6PEG30K-MV, 48:52, 52.8 ± 5.2, 4885 ± 71, 99.5 ± 0.2, 96.9 ...
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Letter Cite This: ACS Macro Lett. 2018, 7, 705−710

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Strategy for Constructing Shape-Memory Dynamic Networks through Charge-Transfer Interactions Dan Liu, Wu-Cheng Nie, Zhi-Bin Wen, Cheng-Jie Fan, Wen-Xia Xiao, Bei Li, Xu-Jing Lin, Ke-Ke Yang,* and Yu-Zhong Wang Center for Degradable and Flame-Retardant Polymeric Materials (ERCEPM-MOE), College of Chemistry, National Engineering Laboratory of Eco-Friendly Polymeric Materials (Sichuan), State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu 610064, China S Supporting Information *

ABSTRACT: Recently, charge transfer (CT) interactions have received attention for the fabrication of supramolecular architectures due to their inherent compatibilities, directional nature and solvent tolerance. In this study, we report a shapememory dynamic network constructed by the CT interaction between π-electron-rich naphthalene embedded in poly(ethylene glycol) (PEG-Np) and π-electron-poor six-arm methyl-viologen-ended poly(ethylene glycol) (6PEG-MV), which was verified by ultraviolet−visible spectroscopy (UV− vis), fluorescence spectra and swelling tests. Interestingly, the mechanical properties of this CT complex were dramatically enhanced compared with the control without CT interaction. Moreover, the excellent shape-memory effect (SME) was realized due to the good crystallization of the PEG segment and stable netpoints based on the CT interaction. In addition, as we expected, this supramolecular polymer network is self-healable and reprocessable.

A

(butadiene) phase that actuated by heat, light and chemical stimuli.27 In our previous work, we reported a series of shapememory dynamic networks based on the Diels−Alder reaction,28 hydrogen bonding29 and metal−ligand coordination,30 which not only exhibit a desirable shape-memory effect (SME) but also are self-healing and able to be reprocessed. However, it remains a challenge to develop dynamic networks that can balance association strength and reversibility. Charge transfer (CT) interactions, which are driven by one unique noncovalent bond that alternately stacks aromatic donor (D) and acceptor (A) chromophores, have been attracting attention due to their inherent compatibilities, directional nature and solvent tolerance.31 CT complexes with various D− A pair structures can be found in vesicles, micelles, films, nanofibers, and host-stabilized supramolecular assemblies.32−38 Burattini et al. designed healable elastomeric supramolecular polymers by blending chain-folding polyimides with π-electrondeficient diimide groups and polyurethane terminated with πelectron-rich pyrenyl groups; this interpolymer was compatibilized by the CT interaction between diimide and pyrene.39 Jiang et al. reported a multifunctional polymer hybrid with tunable a CT interaction between π-electron-poor naphthalene−diimide-containing polyimide (PI-NDI) and π-electronrich anthracene−telechelic polyhedral oligomer silsesquioxane (POSS-AN), which can be on−off controlled due to the

s a unique class of smart materials, shape-memory polymers (SMPs) have attracted attention for both academic and industrial applications due to their structural diversity and ability to be customized. SMPs have great potential for aerospace, intelligent textiles, biomedical engineering, and sensors1−5 due to their shape changing abilities when exposed to predetermined external stimuli, such as light, heat, moisture and magnetic fields.6−12 Dual-shape memory polymers (DSPs), considered the simplest SMPs, can “remember” one temporary shape in a memory cycle; the temporary shape is commonly achieved via a molecule switch, while a netpoint dominates the permanent shape from the point of view of the working mechanism.13,14 Thermally induced SMPs (TSMPs)15 have been investigated extensively due to the ability of heat to directly actuated shape recovery. The molecular switch can be initiated by the thermal transitions of polymers, such as glass transition, melting transition and isotropic−anisotropic transition,16−18 while the netpoint, due to the irreversible nature of chemical cross-linking networks based on covalent bonding (which imposes many restrictions on applications), is currently being focused for its diverse dynamic bonds, including host−guest,19 Diels− Alder,20,21 hydrogen,22,23 metal−ligand coordination,24 disulfide bonds.25 Guo et al. prepared a physical cross-linking hydrogel via UPy quadruple hydrogen bonding units within hydrophilic PEG, which shows high resilience and strength upon deformation when responding to water and heat.26 Kumpfer et al. reported a multiresponsive shape-memory material consisting of a hard metal−ligand phase and soft poly© XXXX American Chemical Society

Received: April 10, 2018 Accepted: May 31, 2018

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DOI: 10.1021/acsmacrolett.8b00256 ACS Macro Lett. 2018, 7, 705−710

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ACS Macro Letters Table 1. Results of Feed Ratio, Swelling Test in Chloroform, and the SME Evaluation samples

PEG-Np/6PEG-MV (w/w)

G (%)

S (%)

Rf (%)

Rr (%)

6

34:66 48:52 68:32

66.1 ± 2.1 52.8 ± 5.2 36.4 ± 2.7

3327 ± 145 4885 ± 71 5523 ± 531

99.6 ± 0.1 99.5 ± 0.2 99.7 ± 0.8

97.3 ± 2.4 96.9 ± 1.1 95.4 ± 0.2

PEG2K-Np@ PEG30K-MV PEG4K-Np@6PEG30K-MV PEG6K-Np@6PEG30K-MV

Figure 1. (A) Schematic strategy of PEG-Np@6PEG-MV. (B) Photographs of PEG6K-Np, 6PEG30K-MV, and PEG6K-Np @6PEG30K-MV.

Figure 2. UV−vis spectra of CT complex mPEG2K-Np@MV and mPEG2K-Np (A); the fluorescence spectra of mPEG2K-Np@MV and mPEG2K-Np (B); the DSC curves of PEG-Np, 6PEG30K-MV and PEG-Np@6PEG30K-MV in cooling run (C) and subsequent healing run (D) at a rate of 5 °C/ min.

dynamic photodimerization of functional group AN when exposed to UV light and heat.40 The elaborate design endows

the hybrid with preferable self-healing performance and rewritable fluorescent behavior. 706

DOI: 10.1021/acsmacrolett.8b00256 ACS Macro Lett. 2018, 7, 705−710

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ACS Macro Letters To the best of our knowledge, the unique CT interaction has not been used to date in the architecture of SMPs. In this study, we propose a new strategy to build a shape-memory supramolecular polymer network based on the CT interaction between π-electron-poor methylviologen (MV) and π-electronrich naphthalene (Np). Poly(ethylene glycol) (PEG) segments, with their strong crystalline tendency, were chosen to act as the molecular switch, while the netpoint falls into the CT interaction that not only produces an excellent shape-memory effect and desirable mechanical performance but also enables self-healing and reprocessability. To fabricate supramolecular networks, we first synthesized two PEG precursors, PEG with pendant naphthalene groups (PEG-

Figure 3. DMA curves of SME of PEG6K -Np@6PEG30K-MV (A); the digital photos of the SME process of PEG6K-Np@6PEG30K-MV (B).

Figure 4. Tensile properties of PEG-Np@6PEG30K-MV and controls (A); the photographs of reprocessing of PEG6K-Np@6PEG30K-MV (B); stress−strain curves of PEG-MV@6PEG30K-MV and reprocessed samples (C).

Np) and six-arm methylviologen-ended PEG (6PEG-MV), which were decorated with Np and MV groups, respectively. The synthetic approach is shown in the Supporting Information, Scheme S1, and all details for the synthetic procedures are addressed in the Supporting Information, Experimental Methods. The structure of all intermediate products and the obtained precursors were characterized by 1 H NMR (Supporting Information, Figure S1), and the molecular weight of PEG-Np is summarized in the Supporting Information, Table S1. Target network PEG-Np@6PEG-MV was facilely prepared by mixing the colorless PEG-Np solution with the pale yellow 6 PEG-MV solution (with N,Ndimethylformamide as the solvent) in an equimolar ratio of the Np and MV groups (the feed ratio of all samples is addressed in Table 1). A dark red film was obtained after casting. Figure 1 depicts the schematic strategy for this fabrication (A) and photographs of the precursors and the final product (B). The formation of the CT complex was directly confirmed by UV−vis and fluorescence emission spectra. Considering the low concentration of Np and MV in the networks, to get a more distinct signal in the UV−vis spectra of the CT interaction, the CT complex of mPEG2KNp@MV was prepared by thrice dissolving and drying mPEG2K-Np (Np-terminated PEG) and small MV molecules in deionized water as described in the Supporting Information, Experimental Methods. Compared with mPEG2K-Np, as shown in Figure 2A, a new absorption band between 350 and 500 nm appeared in the UV−vis spectra of the CT complex.41

Moreover, a decreased intensity and faded color (Supporting Information, Figure S2) were observed within a range from 20 to 80 °C, which reveals that the formed CT interaction between the Np and MV groups was susceptible to temperature and weakened when temperature increased. Commonly, chromophores tend to exhibit a fluorescence quenching effect in their aggregation state,42 which can also be used to demonstrate the existence of the CT interaction between Np and MV moieties in the current system. In Figure 2B, the fluorescence intensity of mPEG2K-Np@MV decreased compared with that of mPEG2K-Np since the CT interaction between Np and MV moieties at a short distance induced the formation of complementary π−π stacking complex at room temperature. To characterize the relationship between the CT interaction and the performance of the network, the swelling test was performed to evaluate the network. In this study, chloroform was chosen as the solvent because of its excellent solubility for both precursors. the results were summarized in Table 1. Apparently, the gel fraction (G)43,44 decreased from 66.1% to 36.4% with the increase in the molecular weights of the PEGNp prepolymer. However, the swelling ratio (S) showed an opposite trend, ranging from 3327% to 5523%, which indicated that introducing more functional group Np into the system results in a more complete network. Parts C and D of Figure 2 illustrate the DSC curves of the PEG-Np, 6PEG30K-MV precursors and PEG-Np@6PEG30K-MV networks; the relevant thermal characteristics are listed in 707

DOI: 10.1021/acsmacrolett.8b00256 ACS Macro Lett. 2018, 7, 705−710

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ACS Macro Letters Table 2. Tensile Properties of PEG-MV@6PEG30K-Np, the Relevant Control, and Reprocessed Samples original

control

reprocessed

samples

δ (MPa)

ε (%)

δ (MPa)

ε (%)

δ (MPa)

ε (%)

6

13.06 ± 0.58 16.53 ± 1.03 22.95 ± 1.54

501 ± 41 640 ± 93 834 ± 28

9.15 ± 1.43 14.52 ± 0.41 13.7 ± 1.79

11.7 ± 1.9 36.7 ± 4.3 48.9 ± 7.1

10.41 ± 0.33 11.74 ± 0.23 21.64 ± 2.26

136 ± 32 378 ± 17 1021 ± 143

PEG2K-Np@ PEG30K-MV PEG4K-Np@6PEG30K-MV PEG6K-Np@6PEG30K-MV

To disclose the dominant role of the CT interaction on the mechanical performance of the material, tensile tests of the networks and the relevant controls (the blends of PEG-Np and 6 PEG30K without the MV group prepared using the same procedure with the same composition) were conducted. Stress−strain curves are displayed in Figure 4A and the relevant tensile strength (δ) and elongation at break (ε) are summarized in Table 2. Compared with the control networks, networks based on CT interaction had excellent mechanical properties, with δ ranging from 13 to 23 MPa, ε from 501% to 834% and the molecular weight of the PEG segment in PEGNp increasing from 2 to 6 K. This variation in mechanical properties may be ascribed to the different degrees of crystallinity and cross-linking. The control networks had very poor mechanical performance, with ε ranging from 11.7% to 48.9%, which strongly suggested that CT interaction significantly enhanced the mechanical performance of the networks. The desirable mechanical properties of SME proven this network and the reversible nature of the CT interaction inspired us to explore the reprocessability and self-healing capacity of this network. First, to evaluate reprocessability, the original film was broken into small pieces and reprocessed using a compression molding press at 140 °C for 20 min to yield a new sheet sample (Figure 4B). The stress−strain curves of all three original and reprocessed networks are depicted in Figure 4C, and the relevant data are listed in Table 2. Comparatively, the network with the longer PEG segment chain length and lower cross-linking degree had better reprocessability, which can be attributed to better chain mobility and CT complex recovery ratio. Compared with δ of the original sample (approximately 22.95 MPa), δ of the reprocessed PEG6KNp@6PEG30K-MV was almost fully maintained (21.64 MPa). Moreover, the slightly unrecovered CT complex may cause a lower cross-linking degree, resulting in an increase in ε from the original sample. Comparing the covalent cross-linking networks, which are hard to reprocess, the CT interaction gives us a new network construction choice with a good balance of strength and reprocessability. The self-healing behaviors of a typical PEG6K-Np@6PEG30KMV sample, which showed strong reprocessability, were observed via optical microscopy (Figure S6 in Supporting Information). First, the sample was damaged by a razor blade to create a crack (A). Then, the sample was healed at 140 °C for 1 h (B) and 5 h (C); the crack almost disappeared after 1 h, and it became completely homogeneous after 5 h at 140 °C. A series of supramolecular networks based on the CT interaction between 6PEG-MV and PEG-Np were successfully fabricated. UV−vis spectra, fluorescence emission spectra, swelling tests and tensile tests strongly confirmed the formation of CT complexes from different perspectives. Samples with different PEG precursors molecular weights possessed desirable thermal dual-shape memory performance and excellent mechanical properties, which can be ascribed to the CT interaction. Compared to brittle control films with an ε of

Supporting Information Table S2. As can be seen, all precursors exhibit acceptable crystallinity. For the PEG-Np precursors, it makes sense that samples with higher PEG molecular weights have better crystallinity with higher melting temperature (Tm) and enthalpy (ΔHm). Compared with the precursors, the crystalline temperature (Tc) of the networks during the cooling run were slightly raised, while the Tm of the network recorded in the heating run was found to be much closer to the higher one of the precursors. According to the corresponding ΔHm, we can conclude that the networks likely maintain their crystallinity because the two precursors have the same crystalline segment and the CT complex has no obvious impact on crystallization. Before SME evaluation, DMA was employed to analyze the thermomechanical properties of the networks. According to storage modulus (E′) curves (Supporting Information, Figure S3), an obvious decline in E′ was observed when temperature crossed Tm. The ratio of the storage modulus was calculated at 0 and 70 °C (r(E′0/E′70)), as shown in Table S3. Apparently, the networks (especially those with high degrees of crosslinking) have high r(E′0/E′70) values, which make the networks easy to program at temperatures above Tm and fixed at temperatures below Tm (characteristic of a good SME).45,46 On the basis of these results, a quantitative test of the SME was conducted using the controlled-force mode of DMA. The programming procedures are as follows. First, the sample was brought to equilibrium at Thigh = 70 °C, which is above Tm. Then, the sample was deformed with an external force and temperature was decreased to 0 °C, which is below Tc of the PEG segment for fixing the temporary shape. Later, the sample was again brought to equilibrium at 70 °C to recover its original shape. The cyclic thermomechanical curve of PEG 6KNp@6PEG30K-MV is shown in Figure 3A as an example; others are shown in Supporting Information, Figures S4 and S5. The fixing ratio (Rf) and recovery ratio (Rr) of all samples were calculated according to eqs S3 and S4 based on cycles 2−5; the results are listed in Table 1. Herein, the crystalline PEG segment served as the switching domain, while the CT complex acted as the netpoint. On one hand, DSC analysis indicates that all networks exhibit good crystallinity, which endows them with good fixing performance, as demonstrated by the extremely high Rf (all beyond 99.5%). On the other, Rr of all networks was found to be above 95%, which indicates that a CT complex with an alternate stacking architecture is strong enough to provide stable netpoints to remember the original shape. Meanwhile, it makes sense that Rr increased with cross-linking degree. Thus, we can conclude that the design of shapememory networks constructed via CT interaction is successful and that an excellent dual-shape memory effect has been realized in current system. The thermally induced dual-shape memory behavior of the PEG6K-Np@6PEG30K-MV network was also recorded by digital photos (Figure 3B); the details are addressed in the Supporting Information, Experimental Methods. 708

DOI: 10.1021/acsmacrolett.8b00256 ACS Macro Lett. 2018, 7, 705−710

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ACS Macro Letters 11.7%, the PEG-Np@6PEG-MV sample exhibits excellent flexibility with an ε of 501%. SME evaluation showed Rr increased from 95% to 98% due to an increase in cross-linking degree. Meanwhile, the strong crystallization of the PEG segment allows for an Rf above 99.5% regardless of the polymer chain length. Furthermore, the reversibility of the CT interaction endows the sample with great self-healing and reprocessing performance and a suitable architecture.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsmacrolett.8b00256. Experimental methods, synthesis, characterization, photographs of CT complex, thermal behaviors data, SME characterization, and optical microscope images (PDF)

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AUTHOR INFORMATION

Corresponding Author

*(K.-K.Y.) E-mail: [email protected]. ORCID

Ke-Ke Yang: 0000-0002-7019-6059 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported financially by the National Natural Science Foundation of China (51473096, 51721091) and the International S&T Cooperation Project of Sichuan Province (2017HH0034).

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DOI: 10.1021/acsmacrolett.8b00256 ACS Macro Lett. 2018, 7, 705−710