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4D printing of PLA/PCL shape memory composites with controllable sequential deformation

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Abstract

Shape memory polymers (SMPs) are a promising class of materials for biomedical applications due to their favorable mechanical properties, fast response, and good biocompatibility. However, it is difficult to achieve controllable sequential shape change for most SMPs due to their high deformation temperature and the simplex deformation process. Herein, shape memory composites based on polylactic acid (PLA) matrix and semi-crystalline linear polymer polycaprolactone (PCL) are fabricated using 4D printing technology. Compared with pure PLA, with the rise of PCL content, the 4D-printed PLA/PCL composites show decreased glass transition temperature (Tg) from 67.2 to 55.2 °C. Through the precise control of the deformation condition, controllable sequential deformation with an outstanding shape memory effect can be achieved for the PLA/PCL shape memory composites. The response time of shape recovery is less than 1.2 s, and the shape fixation/recovery rates are above 92%. In order to simulate sequential petal opening and sequential drug releasing effects, a double-layer bionic flower and a drug release device, respectively, are presented by assembling PLA/PCL samples with different PLA/PCL ratios. The results indicate the potential applications of 4D-printed PLA/PCL composites in the field of bio-inspired robotics and biomedical devices.

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References

  1. Zhou C, Chen Y, Yang Z et al (2013) Digital material fabrication using mask-image-projection-based stereolithography. Rapid Prototyp J 19(3):153–165. https://doi.org/10.1108/13552541311312148

    Article  MathSciNet  Google Scholar 

  2. Khoo ZX, Teoh JEM, Liu Y et al (2015) 3D printing of smart materials: a review on recent progresses in 4D printing. Virtual Phys Prototyp 10(3):103–122. https://doi.org/10.1080/17452759.2015.1097054

    Article  Google Scholar 

  3. Wei H, Wan X, Liu Y et al (2018) 4D printing of shape memory polymers: research status and application prospects. Sci Sin Technol 48(1):2–16. https://doi.org/10.1002/adfm.202100257

    Article  Google Scholar 

  4. Liu TZ, Zhou TY, Yao YT et al (2017) Stimulus methods of multi-functional shape memory polymer nanocomposites: a review. Compos Part A Appl Sci Manuf 100:20–30. https://doi.org/10.1016/j.compositesa.2017.04.022

    Article  Google Scholar 

  5. Mu T, Liu L, Lan X et al (2018) Shape memory polymers for composites. Compos Sci Technol 160:169–198. https://doi.org/10.1016/j.compscitech.2018.03.018

    Article  Google Scholar 

  6. Wu JJ, Huang LM, Zhao Q et al (2018) 4D printing: history and recent progress. Chin J Polym Sci 36(5):563–575. https://doi.org/10.1007/s10118-018-2089-8

    Article  Google Scholar 

  7. Senatov FS, Niaza KV, Zadorozhnyy MY et al (2016) Mechanical properties and shape memory effect of 3D-printed PLA-based porous scaffolds. J Mech Behav Biomed 57:139–148. https://doi.org/10.1016/j.jmbbm.2015.11.036

    Article  Google Scholar 

  8. Zhang X, Geven MA, Grijpma DW et al (2017) Tunable and processable shape memory composites based on degradable polymers. Polymer 122:323–331. https://doi.org/10.1016/j.polymer.2017.06.066

    Article  Google Scholar 

  9. Lipton JI, Cutler M, Nigl F et al (2015) Additive manufacturing for the food industry. Trends Food Sci Technol 43(1):114–123. https://doi.org/10.1016/j.tifs.2015.02.004

    Article  Google Scholar 

  10. Patra S, Young V (2016) A review of 3D printing techniques and the future in biofabrication of bioprinted tissue. Cell Biochem Biophys 74(2):93–98. https://doi.org/10.1007/s12013-016-0730-0

    Article  Google Scholar 

  11. Wendel B, Rietzel D, Kühnlein F et al (2018) Additive processing of polymers. Macromol Mater Eng 293(10):799–809. https://doi.org/10.1002/mame.200800121

    Article  Google Scholar 

  12. Zocca A, Colombo P, Gomes CM et al (2015) Additive manufacturing of ceramics: issues, potentialities, and opportunities. J Am Ceram Soc 98(7):1983–2001. https://doi.org/10.1111/jace.13700

    Article  Google Scholar 

  13. Ho CMB, Ng SH, Yoon YJ (2015) A review on 3D printed bioimplants. Int J Precis Eng Manuf 16(5):1035–1046. https://doi.org/10.1007/s12541-015-0134-x

    Article  Google Scholar 

  14. Guo N, Leu MC (2013) Additive manufacturing: technology, applications and research needs. Front Mech Eng 8(3):215–243. https://doi.org/10.1007/s11465-013-0248-8

    Article  Google Scholar 

  15. Quan ZZ, Wu A, Keefe M et al (2015) Additive manufacturing of multi-directional preforms for composites: opportunities and challenges. Mater Today 18(9):503–512. https://doi.org/10.1016/j.mattod.2015.05.001

    Article  Google Scholar 

  16. Ngo TD, Kashani A, Imbalzano G et al (2018) Additive manufacturing (3D printing): a review of materials, methods, applications and challenges. Compos Part B Eng 143:172–196. https://doi.org/10.1016/j.compositesb.2018.02.012

    Article  Google Scholar 

  17. Momeni F, Seyed MMHN, Liu X et al (2017) A review of 4D printing. Mater Des 122:42–79. https://doi.org/10.1016/j.matdes.2017.02.068

    Article  Google Scholar 

  18. Ren LQ, Li BQ, Song ZY et al (2019) Bioinspired fiber-regulated composite with tunable permanent shape and shape memory properties via 3d magnetic printing. Compos Part B 164:458–466. https://doi.org/10.1016/j.compositesb.2019.01.061

    Article  Google Scholar 

  19. Ren LQ, Zhou XL, Liu QP et al (2018) 3D magnetic printing of bio-inspired composites with tunable mechanical properties. J Mater Sci 53(20):14274–14286. https://doi.org/10.1007/s10853-018-2447-5

    Article  Google Scholar 

  20. Ma SQ, Zhang YP, Wang M et al (2020) Recent progress in 4D printing of stimuli-responsive polymeric materials. Sci China Technol Sci 63(4):532–544. https://doi.org/10.1007/s11431-019-1443-1

    Article  Google Scholar 

  21. Ma S, Zhang Y, Liang Y et al (2020) High-performance ionic-polymer-metal composite: toward large-deformation fast-response artificial muscles. Adv Funct Mater 30(7):1908508. https://doi.org/10.1002/adfm.201908508

    Article  Google Scholar 

  22. Yu K, Ritchie A, Mao YQ et al (2015) Controlled sequential shape changing components by 3D printing of shape memory polymer multimaterials. Procedia IUTAM 12:193–203. https://doi.org/10.1016/j.piutam.2014.12.021

    Article  Google Scholar 

  23. Li XJ, Pan Y, Deng JN et al (2017) Multiscale-structuring of rapid response shape memory polymers based on self-assembly reverse micelles. React Funct Polym 121:1–7. https://doi.org/10.1016/j.reactfunctpolym.2017.10.002

    Article  Google Scholar 

  24. Maurath J, Willenbacher N (2017) 3D printing of open-porous cellular ceramics with high specific strength. J Eur Ceram Soc 37(15):4833–4842. https://doi.org/10.1016/j.jeurceramsoc.2017.06.001

    Article  Google Scholar 

  25. Sobota M, Jurczyk S, Kwiecien M et al (2017) Crystallinity as a tunable switch of poly(L-lactide) shape memory effects. J Mech Behav Biomed 66:144–151. https://doi.org/10.1016/j.jmbbm.2016.11.009

    Article  Google Scholar 

  26. Zhang L, Lin Z, Zhou Q et al (2020) PEEK modified PLA shape memory blends: towards enhanced mechanical and deformation properties. Front Mater Sci 14(2):177–187. https://doi.org/10.1007/s11706-020-0502-z

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Project of National Key Research and Development Program of China (Nos. 2018YFB1105100 and 2018YFC2001300), the National Natural Science Foundation of China (Nos. 5167050531, 51822504, 91848204, 91948302, and 52021003), the Key Scientific and Technological Project of Jilin Province (No. 20180201051GX), the Program for JLU Science and Technology Innovative Research Team (No. 2017TD-04), and the Scientific Research Project of Education Department of Jilin Province (No. JJKH20211084KJ).

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Authors and Affiliations

  1. The Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun, 130025, China

    Suqian Ma, Zeyu Jiang, Meng Wang, Lan Zhang, Yunhong Liang, Zhihui Zhang, Lei Ren & Luquan Ren

  2. Department of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester, M13 9PL, UK

    Lei Ren

Authors
  1. Suqian Ma
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  2. Zeyu Jiang
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  3. Meng Wang
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  4. Lan Zhang
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  5. Yunhong Liang
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  6. Zhihui Zhang
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  7. Lei Ren
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  8. Luquan Ren
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Contributions

SQM and ZYJ performed the experiments and wrote the draft of manuscript; MW and LZ analyzed the data; YHL and LR proposed the project and critical comments on the writing of the manuscript; ZHZ and LQR provided some additional suggestions on experiments. SQM, ZYJ, MW, LZ, YHL, ZHZ, LR, and LQR contributed to the general discussion.

Corresponding author

Correspondence to Yunhong Liang.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This study does not contain any studies with human or animal subjects performed by any of the authors.

Appendix

Appendix

DW 3D printer

The homemade DW 3D printer is illustrated in Fig. A1. The printing head of the DW printer is a syringe with nozzle diameter of 640 μm. The printing route is controlled by a three-dimensional moving stage, and the printing platform is a Teflon plate with a smooth surface.

Fig. A1
figure 10

Photograph of the homemade DW 3D printer

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Material preparation

The material components of the printing ink are shown in Table A1. The PLA/PCL composites are named as PLA1−x/PCLx, where x is the mass ratio of PCL in PLA and PCL, respectively. The PLA/PCL powder and nano-silica were added to CH2Cl2 solvent with a mass ratio of PLA/PCL:nano-silica:CH2Cl2 = 1:0.0005:3.

Table A1 Material components of the PLA/PCL composite ink for 4D printing
Full size table

Molding method for PLA sample preparation

The schematic of molding is depicted in Fig. A2. For pure PLA samples, the solution was prepared according to the material content of PLA1.00/PCL0.00. The molding method includes the following steps: (1) pour the PLA solution into a Teflon mold and scrape the top of the solution to be flat; (2) shake the mold to get rid of air bubbles; (3) dry the PLA solution at room temperature for 36 h; (4) peel the PLA samples off the Teflon mold.

Fig. A2
figure 11

Schematic of the fabrication process of molding

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Shape fixation/recovery rate of U-shape

The shape fixation rate (Rf) is calculated according to the following equation:

$${R}_{\mathrm{f}}=\frac{180^{\circ} -{\theta }_{1}}{180^{\circ} -{\theta }_{0}}\times 100\%.$$
(A1)

The shape recovery rate (Rr) is calculated according to the following equation:

$${R}_{\mathrm{r}}=\frac{{\theta }_{2}-{\theta }_{1}}{180^{\circ} -{\theta }_{1}}\times 100\%,$$
(A2)

where θ0 represents the angle with external force once the sample has been fixed to the temporary U-shape, θ1 represents the angle after cooling down the sample and releasing the external force, and θ2 represents the angle after heating the sample up again and recovering it to the original shape.

Shape fixation/recovery rate of helical shape

The shape fixation rate (Rf) is calculated according to the following equation:

$${R}_{\mathrm{f}}=\left(1-\frac{{d}_{1}-{d}_{0}}{{d}_{0}}\right)\times 100\%,$$
(A3)

where d0 represents the outer diameter of the helical shape with external force once the sample has been fixed to the temporary helical shape, and d1 represents the outer diameter after cooling down the sample and releasing the external force.

The shape recovery rate (Rr) is calculated according to the following equation:

$${R}_{\mathrm{r}}=\frac{{S}_{1}}{{S}_{0}}\times 100\%,$$
(A4)

where S0 represents the original rectangle area of the sheet-like sample, and S1 represents the projected area of the recovered sample.

Shape memory properties when fixed to a helical shape

The shape memory properties, including the shape fixation rate, shape recovery rate, and shape recovery time of the 4D-printed PLA/PCL samples fixed to a helical shape, are summarized in Table A2.

Table A2 Summary of the shape memory properties of the 4D-printed PLA/PCL composites when fixed to a helical shape
Full size table

Force measurement of drug release devices

The force measurement was performed on a one-dimensional force transducer (500 mN, NBIT, Nanjing, China). As shown in Fig. A3a, the samples with a temporary “closed” state were placed on a hot plate 2 mm away from the force transducer, with one end pasted on the hot plate. When the samples were heated above Tg, they tended to recover to their original “open” state and then touch the force transducer, which could record the forces generated by the samples. The measured forces shown in Fig. A3b can be transferred into the mass of the pellets according to the equation of F = mg, where F denotes the force, m represents the mass, and g is 9.8 N/kg.

Fig. A3
figure 12

a Photographs of force measurement recorded using a 500-mN force transducer. b Shape recovery forces of the drug release devices based on PLA1.00/PCL0.00, PLA0.94/PCL0.06, and PLA0.70/PCL0.30. Scale bar: 1 cm

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Ma, S., Jiang, Z., Wang, M. et al. 4D printing of PLA/PCL shape memory composites with controllable sequential deformation. Bio-des. Manuf. 4, 867–878 (2021). https://doi.org/10.1007/s42242-021-00151-6

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  • DOI: https://doi.org/10.1007/s42242-021-00151-6

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