Flexible Strain Sensor Based on Copper/Graphene Composite Films
Source abstract
Abstract
This study analyzed a flexible strain sensor with torsional low interference, waterproofing, and self-adhesive properties using a copper/graphene microcrack composite film as a strain-sensitive layer and a self-adhesive silica gel as a waterproofing and self-adhesive layer. The sensor exhibits a wide response range (with a strain capability of up to 110%), high sensitivity (with a gauge factor of 847.87 (92% < ε < 110%)), and satisfactory sensing durability (with response cycles of 10000 under 25% strain) and can detect ultrasmall strains (0.2% strain). In addition, the sensor exhibits similar real-time dynamic responses at different strain levels (0%–40%) and states (untwisted, twisted by 90°, and twisted by 180°), indicating its characteristic of torsional low interference. Moreover, the self-adhesive silica gel allows the sensor to directly adhere to the skin surface, reducing motion artifacts and improving the accuracy of monitoring complex strain states during underwater activities. Therefore, the flexible strain sensor holds potential as a next-generation flexible strain sensor for underwater use.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Tactile and Sensory Interactions · Dielectric materials and actuators
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China
Cangnan Research Institute, Wenzhou University, Wenzhou 325800, China
Funding
Basic Research Project of Wenzhou City · Department of Education of Zhejiang Province · Natural Science Foundation of Zhejiang Province
References (54)
Show source-provided references
1. 10.1126/scirobotics.abc813410.1126/scirobotics.abc8134 ↗
2. 10.1016/j.nanoen.2022.10696710.1016/j.nanoen.2022.106967 ↗
3. 10.1002/smll.20190570710.1002/smll.201905707 ↗
4. 10.1002/advs.20180128310.1002/advs.201801283 ↗
5. 10.1016/j.nanoen.2020.10454610.1016/j.nanoen.2020.104546 ↗
6. 10.1016/j.compscitech.2022.10950510.1016/j.compscitech.2022.109505 ↗
7. 10.1016/j.jallcom.2021.16046610.1016/j.jallcom.2021.160466 ↗
8. 10.1016/j.sna.2022.11393910.1016/j.sna.2022.113939 ↗
9. 10.1016/j.cclet.2022.07.01010.1016/j.cclet.2022.07.010 ↗
10. 10.1016/j.ymssp.2023.11067910.1016/j.ymssp.2023.110679 ↗
11. 10.1016/j.compscitech.2020.10858310.1016/j.compscitech.2020.108583 ↗
12. 10.1016/j.nanoen.2018.04.02210.1016/j.nanoen.2018.04.022 ↗
13. 10.1038/nature1652110.1038/nature16521 ↗
14. 10.1002/adma.20220082310.1002/adma.202200823 ↗
15. 10.1016/j.colsurfa.2020.12549310.1016/j.colsurfa.2020.125493 ↗
16. 10.1002/smll.20210054210.1002/smll.202100542 ↗
17. 10.1016/j.compscitech.2018.11.03410.1016/j.compscitech.2018.11.034 ↗
18. 10.1109/LED.2022.316232710.1109/LED.2022.3162327 ↗
19. 10.1038/nnano.2011.18410.1038/nnano.2011.184 ↗
20. 10.1038/nmat283410.1038/nmat2834 ↗
21. 10.1002/adma.20200848610.1002/adma.202008486 ↗
22. 10.1002/smll.20190477410.1002/smll.201904774 ↗
23. 10.1109/TBME.2019.290022410.1109/TBME.2019.2900224 ↗
24. 10.1002/adfm.20190310010.1002/adfm.201903100 ↗
25. Yang, Y.; Jia, J.; Liu, C.; Mao, D. Decoupling and decomposition analysis on the CO 2 emissions of tourism industry: A case study of Hainan. In E3S Web of Conferences; EDP Sciences: 2019; Vol. 118.10.1051/e3sconf/201911804042 ↗
26. 10.1126/scirobotics.aas902010.1126/scirobotics.aas9020 ↗
27. 10.1002/adfm.20200749510.1002/adfm.202007495 ↗
28. 10.1016/j.nanoen.2019.10414910.1016/j.nanoen.2019.104149 ↗
29. 10.1016/j.cej.2022.13719210.1016/j.cej.2022.137192 ↗
30. 10.1002/admi.20160086210.1002/admi.201600862 ↗
31. 10.1007/s10854-016-5954-710.1007/s10854-016-5954-7 ↗
32. 10.1002/adma.20180330910.1002/adma.201803309 ↗
33. 10.1002/adma.20220365010.1002/adma.202203650 ↗
34. 10.1016/j.optlastec.2020.10669410.1016/j.optlastec.2020.106694 ↗
35. 10.1002/adfm.20200039810.1002/adfm.202000398 ↗
36. 10.1021/acsomega.0c0289710.1021/acsomega.0c02897 ↗
37. 10.1016/j.cej.2019.05.04510.1016/j.cej.2019.05.045 ↗
38. 10.1002/adma.20200149610.1002/adma.202001496 ↗
39. 10.1016/j.compscitech.2022.10946010.1016/j.compscitech.2022.109460 ↗
40. 10.1007/s10854-022-07960-010.1007/s10854-022-07960-0 ↗
41. 10.3390/s1912283410.3390/s19122834 ↗
42. 10.1021/acsami.2c1022610.1021/acsami.2c10226 ↗
43. 10.1021/acsami.7b0298810.1021/acsami.7b02988 ↗
44. 10.1021/acsami.5b0500110.1021/acsami.5b05001 ↗
45. 10.1021/acsami.0c2282310.1021/acsami.0c22823 ↗
46. 10.1007/s10118-021-2500-810.1007/s10118-021-2500-8 ↗
47. 10.1039/C8NR02528B10.1039/C8NR02528B ↗
48. 10.1016/j.compositesb.2023.11057410.1016/j.compositesb.2023.110574 ↗
49. 10.1088/1361-6528/ac5ee610.1088/1361-6528/ac5ee6 ↗
50. 10.1016/j.carbon.2022.01.02010.1016/j.carbon.2022.01.020 ↗
51. 10.1002/mame.20220003410.1002/mame.202200034 ↗
52. 10.1021/acsanm.3c0161410.1021/acsanm.3c01614 ↗
53. 10.1007/s12274-022-4680-010.1007/s12274-022-4680-0 ↗
54. 10.1016/j.compositesa.2021.10666510.1016/j.compositesa.2021.106665 ↗