Programmable 4D Printing of Bioinspired Solvent‐Driven Morphing Composites
Source abstract
Abstract
Abstract In this article, we propose a bioinspired programmed 4D printing method. A combination of magnetic orientation process and grayscale 3D printing is used to achieve site‐specific orientation of the reinforcing fibers and gradient crosslinking of the matrix resin in the thickness direction within 2D composite films. After solventization and desolventization, 2D thin film structures can be transformed into complex 3D architectures, and the transformed 3D architectures can be permanently preserved without stimulation sources. Thus, the method can also be considered as an efficient 3D printing process. Then, the influences of crosslinking density, sample geometry, formulation, and fiber architecture on the deformation of the printed objects are investigated. Finally, as a proof of concept, various samples, including a hand, snowflake, and leaf, are printed to verify the feasibility of the proposed 4D printing strategy. This programmed 4D printing method provides a simple and effective reversible strategy, taking the full advantages of 4D printing and expanding the design space of 4D printing.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Materials and Mechanics · Advanced Sensor and Energy Harvesting Materials · Additive Manufacturing and 3D Printing Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Key Laboratory of Bionic Engineering (Ministry of Education) Jilin University Changchun 130022 P. R. China
School of Mechanical Aerospace and Civil Engineering University of Manchester Manchester M13 9PL UK
Funding
National Natural Science Foundation of China
References (45)
Show source-provided references
1. 10.1126/science.126096010.1126/science.1260960 ↗
2. 10.1038/ncomms909210.1038/ncomms9092 ↗
3. 10.1126/science.aad268810.1126/science.aad2688 ↗
4. 10.1002/adma.20150109910.1002/adma.201501099 ↗
5. 10.1126/science.aaa239710.1126/science.aaa2397 ↗
6. Ge Q. · Smart Mater. Struct. · 2014 · 23 · 15
7. Ding Z. · Sci. Adv. · 2017 · 3 · 6
8. 10.1038/srep0742210.1038/srep07422 ↗
9. 10.1002/mame.20160021210.1002/mame.201600212 ↗
10. 10.1002/advs.20200100010.1002/advs.202001000 ↗
11. 10.1021/acsami.7b1807910.1021/acsami.7b18079 ↗
12. 10.3390/ma1208135310.3390/ma12081353 ↗
13. 10.1016/j.matdes.2016.02.01810.1016/j.matdes.2016.02.018 ↗
14. 10.1126/science.aap775310.1126/science.aap7753 ↗
15. 10.1002/adma.20170616410.1002/adma.201706164 ↗
16. Bodaghi M. · Smart Mater. Struct. · 2019 · 28
17. 10.1038/nmat454410.1038/nmat4544 ↗
18. Yang H. · Adv. Mater. · 2017 · 29
19. 10.1039/C5MH00212E10.1039/C5MH00212E ↗
20. 10.1021/acsami.6b0738810.1021/acsami.6b07388 ↗
21. 10.1002/adfm.20190328010.1002/adfm.201903280 ↗
22. 10.1038/s41586-018-0185-010.1038/s41586-018-0185-0 ↗
23. 10.1126/sciadv.abc641410.1126/sciadv.abc6414 ↗
24. 10.1039/C7SM01796K10.1039/C7SM01796K ↗
25. Huang L. M. · Adv. Mater. · 2017 · 29 · 6
26. 10.1021/acsami.0c0203810.1021/acsami.0c02038 ↗
27. 10.1038/3774510.1038/37745 ↗
28. 10.1126/science.120387410.1126/science.1203874 ↗
29. 10.1016/j.jsb.2008.06.00810.1016/j.jsb.2008.06.008 ↗
30. 10.1126/science.114009710.1126/science.1140097 ↗
31. 10.1007/s10853-016-0341-610.1007/s10853-016-0341-6 ↗
32. 10.1002/adma.20140180410.1002/adma.201401804 ↗
33. 10.1073/pnas.171515711510.1073/pnas.1715157115 ↗
34. 10.1038/s41467-018-08055-310.1038/s41467-018-08055-3 ↗
35. 10.1038/ncomms964110.1038/ncomms9641 ↗
36. 10.1038/ncomms964310.1038/ncomms9643 ↗
37. Li X. · Adv. Funct. Mater. · 2020
38. Ma C. · ACS Appl. Mater. Interfaces · 2020
39. 10.1038/ncomms266610.1038/ncomms2666 ↗
40. 10.1038/ncomms1391210.1038/ncomms13912 ↗
41. 10.1002/adma.20160575010.1002/adma.201605750 ↗
42. Yang Y. · Sci. Adv. · 2019 · 5 · 4
43. 10.1016/S1369-7021(11)70056-610.1016/S1369-7021(11)70056-6 ↗
44. 10.1007/s10853-018-2447-510.1007/s10853-018-2447-5 ↗
45. Ren L. · Adv. Mater. Technol. · 2019 · 4