Cortical‐Folding‐Inspired Multifunctional Reduced Graphene Oxide Microarchitecture Arrays on Curved Substrates
Source abstract
Abstract
Abstract Although controlled wrinkling is demonstrated to be a powerful tool for micro/nanofabrication, large‐area fabrication of microarchitecture arrays on curved substrates by surface wrinkling still remains challenging. Inspired by the cortical folding, a facile method for transforming graphene oxide (GO) patterns into multiscale microarchitecture arrays on curved substrates is developed. Mass production of hierarchical GO papillae arrays can be realized by homogeneous compression of patterned GO/rubber bilayers. The reduced GO (rGO) papillae arrays show superhydrophobicity with tunable adhesion to water, enabling good performance in microdroplet manipulation. Besides, the papillae are capable of regulating the formation of microcracks in the rGO films upon stretching. Similar to the mechanical sensing system of spiders, a crack‐based strain sensor with high gauge factor (≈81), good linearity (0–15%), and fast responding speed (<40 ms) is demonstrated. In addition, the strain sensor is capable of detecting and recognizing multiple deformation modes including stretching, poking, and touching. This work reveals novel cortical folding morphology and provides a simple fabrication method for multifunctional hierarchical microarchitectures on curved substrates, which may find applications in biomimetic microstructures, smart wetting surfaces, and wearable electronics.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Advanced Materials and Mechanics · Surface Modification and Superhydrophobicity
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Beijing Interdisciplinary Research Center National University of Defense Technology Changsha 410073 P. R. China
College of Liberal Arts and Science National University of Defense Technology Changsha 410073 P. R. China
Funding
National Natural Science Foundation of China
References (58)
Show source-provided references
1. 10.1002/adfm.20200838010.1002/adfm.202008380 ↗
2. 10.1002/admt.20200121810.1002/admt.202001218 ↗
3. 10.1016/j.cej.2020.12638810.1016/j.cej.2020.126388 ↗
4. 10.1002/admt.20190023010.1002/admt.201900230 ↗
5. 10.1002/adhm.20210042710.1002/adhm.202100427 ↗
6. 10.1038/nature1695610.1038/nature16956 ↗
7. 10.1002/smll.20160299210.1002/smll.201602992 ↗
8. 10.1002/adfm.20190026610.1002/adfm.201900266 ↗
9. 10.1021/acs.langmuir.0c0363010.1021/acs.langmuir.0c03630 ↗
10. 10.1002/admt.20180034310.1002/admt.201800343 ↗
11. 10.1002/adma.20180010310.1002/adma.201800103 ↗
12. 10.1002/adma.20170491210.1002/adma.201704912 ↗
13. Tan Y. · Acta Polym. Sin. · 2016 · 11 · 1508
14. 10.1002/smll.20180480510.1002/smll.201804805 ↗
15. 10.1007/s40820-020-00436-y10.1007/s40820-020-00436-y ↗
16. 10.1002/adma.20200447710.1002/adma.202004477 ↗
17. 10.1002/adfm.20170213410.1002/adfm.201702134 ↗
18. 10.1016/j.cej.2019.12379410.1016/j.cej.2019.123794 ↗
19. 10.1002/adma.20207005110.1002/adma.202070051 ↗
20. 10.1002/anie.20151079610.1002/anie.201510796 ↗
21. 10.1021/acs.accounts.8b0062310.1021/acs.accounts.8b00623 ↗
22. 10.1002/adma.20190671210.1002/adma.201906712 ↗
23. 10.1021/acs.nanolett.6b0116910.1021/acs.nanolett.6b01169 ↗
24. 10.1002/smll.20200488610.1002/smll.202004886 ↗
25. 10.1002/adfm.20210106810.1002/adfm.202101068 ↗
26. 10.1002/smll.20150057710.1002/smll.201500577 ↗
27. 10.1021/acsnano.7b0193710.1021/acsnano.7b01937 ↗
28. 10.1016/j.carbon.2019.06.05810.1016/j.carbon.2019.06.058 ↗
29. 10.1002/adma.20190093310.1002/adma.201900933 ↗
30. 10.1073/pnas.141155911210.1073/pnas.1411559112 ↗
31. 10.1002/adma.20140140310.1002/adma.201401403 ↗
32. 10.1021/acs.jpcb.5b0720810.1021/acs.jpcb.5b07208 ↗
33. 10.1038/srep0571010.1038/srep05710 ↗
34. 10.1039/D0RA07288E10.1039/D0RA07288E ↗
35. 10.1002/adma.20180541710.1002/adma.201805417 ↗
36. 10.1021/acsnano.7b0348010.1021/acsnano.7b03480 ↗
37. 10.1007/s10118-021-2515-110.1007/s10118-021-2515-1 ↗
38. 10.1021/acsami.8b1814310.1021/acsami.8b18143 ↗
39. 10.1021/acsami.9b0450910.1021/acsami.9b04509 ↗
40. 10.1016/j.matt.2020.10.00510.1016/j.matt.2020.10.005 ↗
41. 10.1039/c0sm00401d10.1039/c0sm00401d ↗
42. 10.1115/1.403038410.1115/1.4030384 ↗
43. 10.1073/pnas.140601511110.1073/pnas.1406015111 ↗
44. 10.1103/PhysRevLett.106.23430110.1103/PhysRevLett.106.234301 ↗
45. 10.1038/srep0888710.1038/srep08887 ↗
46. 10.1021/acsami.6b0164810.1021/acsami.6b01648 ↗
47. 10.1021/acsami.5b0772210.1021/acsami.5b07722 ↗
48. 10.1038/nmat354210.1038/nmat3542 ↗
49. 10.1016/j.jbiomech.2005.05.03110.1016/j.jbiomech.2005.05.031 ↗
50. 10.1038/nature0860310.1038/nature08603 ↗
51. 10.1039/D0NH00241K10.1039/D0NH00241K ↗
52. 10.1002/adma.20160242510.1002/adma.201602425 ↗
53. 10.1038/nature1400210.1038/nature14002 ↗
54. 10.1126/science.abd364310.1126/science.abd3643 ↗
55. 10.1126/sciadv.150066110.1126/sciadv.1500661 ↗
56. 10.1021/nn505953t10.1021/nn505953t ↗
57. 10.1126/scirobotics.aau691410.1126/scirobotics.aau6914 ↗
58. 10.1021/ja01539a01710.1021/ja01539a017 ↗