Highly aligned nickel fiber conductive structure for high sensitivity and low consumption in polymer‐based strain sensors with negative resistance‐strain effect
Source abstract
Abstract
Abstract Conductive polymer composites (CPCs) have attracted considerable interest in strain sensing applications due to their excellent conductivity and high sensitivity. However, CPCs exhibiting a positive resistance‐strain effect often suffer from high energy consumption owing to their low initial resistance, which limits their widespread application. Here, we present a novel CPC that exhibits high sensitivity and a negative resistance‐strain effect, prepared using a liquid composite molding (LCM) method with highly aligned nickel nanofiber arrays (NFAs). A bilayer NFAs/polydimethylsiloxane (PDMS) composite structure was achieved through the infiltration of PDMS into the NFAs. Unlike CPCs produced by traditional blending methods, the 2D structure of the LCM‐derived CPCs demonstrates a positive resistance‐strain effect, while the resulting 3D structure displays a unique negative resistance‐strain effect. Notably, these composites with a negative resistance‐strain effect exhibit a strain‐sensing sensitivity as high as 26,895. Furthermore, compared to most literature‐reported sensors, our approach reduces energy consumption by three orders of magnitude. These findings offer a new perspective for the development of high‐performance, low‐power composites and broaden the application scope of strain sensors. Highlights Novel aligned Ni nanofiber arrays/PDMS composite fabricated via LCM. NFAs composites evolve from 2D to 3D networks with increased NF density. 2D and 3D networks show positive and negative resistance–strain, respectively. 3D network composites enable high sensitivity and low energy consumption.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Conducting polymers and applications · Gas Sensing Nanomaterials and Sensors
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of New Energy and Materials China University of Petroleum Beijing China
Funding
National Natural Science Foundation of China
References (46)
Show source-provided references
1. 10.1016/j.compscitech.2018.01.01910.1016/j.compscitech.2018.01.019 ↗
2. 10.1016/j.coco.2023.10169310.1016/j.coco.2023.101693 ↗
3. 10.1016/j.compositesb.2023.11075910.1016/j.compositesb.2023.110759 ↗
4. 10.1016/j.polymer.2024.12728610.1016/j.polymer.2024.127286 ↗
5. 10.1016/j.compositesb.2023.11102210.1016/j.compositesb.2023.111022 ↗
6. 10.1002/pc.2834110.1002/pc.28341 ↗
7. 10.1021/nn501204t10.1021/nn501204t ↗
8. 10.1016/j.cej.2020.12480510.1016/j.cej.2020.124805 ↗
9. 10.1016/j.compscitech.2018.06.03010.1016/j.compscitech.2018.06.030 ↗
10. 10.1016/j.compscitech.2018.11.03410.1016/j.compscitech.2018.11.034 ↗
11. 10.1016/j.cej.2019.03.22310.1016/j.cej.2019.03.223 ↗
12. 10.1016/j.coco.2022.10108710.1016/j.coco.2022.101087 ↗
13. 10.1002/pc.2875910.1002/pc.28759 ↗
14. 10.1016/j.jmrt.2024.04.03910.1016/j.jmrt.2024.04.039 ↗
15. 10.1002/pc.2816410.1002/pc.28164 ↗
16. 10.1021/acsami.6b1307410.1021/acsami.6b13074 ↗
17. 10.1016/j.compositesa.2017.06.00310.1016/j.compositesa.2017.06.003 ↗
18. 10.1016/j.compscitech.2016.12.01410.1016/j.compscitech.2016.12.014 ↗
19. 10.1021/acsami.8b0396710.1021/acsami.8b03967 ↗
20. 10.1039/C6TC01925K10.1039/C6TC01925K ↗
21. 10.1016/j.compscitech.2007.10.05610.1016/j.compscitech.2007.10.056 ↗
22. 10.1016/j.compscitech.2018.02.03210.1016/j.compscitech.2018.02.032 ↗
23. 10.1016/j.compscitech.2022.10952010.1016/j.compscitech.2022.109520 ↗
24. 10.1016/j.compscitech.2019.03.00210.1016/j.compscitech.2019.03.002 ↗
25. 10.1016/j.polymer.2023.12570210.1016/j.polymer.2023.125702 ↗
26. 10.1016/j.compositesa.2019.10571210.1016/j.compositesa.2019.105712 ↗
27. 10.1016/j.compscitech.2018.10.02510.1016/j.compscitech.2018.10.025 ↗
28. 10.1007/s10854‐024‐12474‐y10.1007/s10854‐024‐12474‐y ↗
29. 10.1038/s41598‐021‐00307‐510.1038/s41598‐021‐00307‐5 ↗
30. 10.1088/0957‐4484/27/20/20550210.1088/0957‐4484/27/20/205502 ↗
31. 10.1002/adfm.20150475510.1002/adfm.201504755 ↗
32. 10.1021/acsami.7b1143110.1021/acsami.7b11431 ↗
33. 10.1002/admt.20220109310.1002/admt.202201093 ↗
34. 10.1016/j.compscitech.2022.10936610.1016/j.compscitech.2022.109366 ↗
35. 10.1016/j.jmrt.2023.10.09910.1016/j.jmrt.2023.10.099 ↗
36. 10.1016/j.compositesa.2022.10711310.1016/j.compositesa.2022.107113 ↗
37. 10.1021/acsaelm.4c0048010.1021/acsaelm.4c00480 ↗
38. 10.1016/j.compscitech.2018.05.01710.1016/j.compscitech.2018.05.017 ↗
39. 10.1088/1674‐1056/abd6f510.1088/1674‐1056/abd6f5 ↗
40. 10.1016/j.compscitech.2018.11.03310.1016/j.compscitech.2018.11.033 ↗
41. 10.1088/2399‐1984/acc6ab10.1088/2399‐1984/acc6ab ↗
42. 10.1021/acsami.9b0926510.1021/acsami.9b09265 ↗
43. 10.1002/adfm.20231631410.1002/adfm.202316314 ↗
44. 10.1016/j.jmat.2020.01.00910.1016/j.jmat.2020.01.009 ↗
45. 10.1016/j.snb.2012.09.03610.1016/j.snb.2012.09.036 ↗
46. 10.1088/1361‐6528/abe6c710.1088/1361‐6528/abe6c7 ↗