DataVXACS Applied Materials & InterfacesPaper
journal article·2024-03-20

A Fully Self-Powered Ocean Wave Observation System Empowered by Natural Light Modulated by a Friction-Driven Polymer Network Liquid Crystal

Xingwen Chen, Jiaqi Wang, Cuiling Meng, Pengcheng Liu, Yifan Sun, Xinghui Qin, Shixing Xie

ACS Applied Materials & Interfaces· Volume 16· Issue 11
Publisher page
Source abstract

Abstract

Conventional ocean wave observation instruments are powered by batteries, limiting the continuous observation time. Besides, the waste of batteries brings environmental contaminations. Triboelectric nanogenerators (TENGs) can reveal ocean wave information through their electrical output, taking the triboelectric charge as the information carrier. However, charge amplification is necessary, consuming additional energy. Herein, taking the photons rather than electrons as the information carrier, we developed a fully self-powered natural light-enabled sensing system for ocean wave monitoring by coupling two rotary-freestanding sliding TENGs (RFS-TENGs) and a polymer network liquid crystal (PNLC)-triggered optical system. The natural light is modulated by the PNLC driven by ocean wave-induced friction. With the assistance of a one-way bearing, the rise and fall of the wave will trigger different RFS-TENGs to power the PNLC in different voltage drops, leading to different transmitted natural light intensities. The wave height information can be obtained through the number of pulse signals with the same trough light intensity, while the wave period can be obtained through the duration between the same two sets of pulse signals. The effectiveness of the developed sensing paradigm in practical applications was verified by flume-based experiments, with the highest accuracies of 90.7% in wave height and 99.8% in wave period.
Source: openalex_inverted_index · Quality: baseline

Research context

Energy storagePolymersNanomaterials

OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Tactile and Sensory Interactions · Advanced Fiber Optic Sensors

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Marine Sciences, Sun Yat-Sen University, Zhuhai 519082, China

Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China

Pearl River Estuary Marine Ecosystem Research Station, Ministry of Education, Zhuhai 519082, China

Guangdong Provincial Key Laboratory of Marine Resources and Coastal Engineering, Zhuhai 519082, China

Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China

State Key Laboratory of Advanced Displays and Optoelectronics Technologies, The Hong Kong University of Science and Technology, Kowloon, Hong Kong, China

Funding

Guangdong Provincial Department of Science and Technology · National Key Research and Development Program of China · Basic and Applied Basic Research Foundation of Guangdong Province · National Natural Science Foundation of China

References (38)

Show source-provided references
1. 10.1002/qj.4971094601110.1002/qj.49710946011 ↗
2. 10.1016/j.oceaneng.2021.10875710.1016/j.oceaneng.2021.108757 ↗
3. 10.1016/j.marstruc.2018.05.00710.1016/j.marstruc.2018.05.007 ↗
4. 10.1038/s41893-019-0389-910.1038/s41893-019-0389-9 ↗
5. Cavaleri L. · Oceanol. Acta · 1980 · 3 · 339
6. 10.1088/0964-1726/21/11/11503010.1088/0964-1726/21/11/115030 ↗
7. 10.1016/j.oceaneng.2020.10804310.1016/j.oceaneng.2020.108043 ↗
8. 10.1109/ACCESS.2020.297171110.1109/ACCESS.2020.2971711 ↗
9. Zuo Q. · Ocean Eng. · 2008 · 26 · 124
10. 10.1126/science.aay588110.1126/science.aay5881 ↗
11. 10.1016/S0013-4686(99)00312-610.1016/S0013-4686(99)00312-6 ↗
12. 10.1002/adfm.20190179810.1002/adfm.201901798 ↗
13. 10.1016/j.nantod.2010.09.00110.1016/j.nantod.2010.09.001 ↗
14. 10.1016/j.nanoen.2012.01.00410.1016/j.nanoen.2012.01.004 ↗
15. 10.1016/j.mattod.2016.12.00110.1016/j.mattod.2016.12.001 ↗
16. 10.1016/j.nanoen.2019.10427210.1016/j.nanoen.2019.104272 ↗
17. 10.1016/j.nanoen.2014.10.03410.1016/j.nanoen.2014.10.034 ↗
18. 10.1039/C5EE01532D10.1039/C5EE01532D ↗
19. 10.1002/aenm.20180290610.1002/aenm.201802906 ↗
20. 10.1021/acsnano.9b1014210.1021/acsnano.9b10142 ↗
21. 10.1016/j.nanoen.2021.10588710.1016/j.nanoen.2021.105887 ↗
22. 10.1002/adma.20220506410.1002/adma.202205064 ↗
23. 10.1016/j.mattod.2017.10.00610.1016/j.mattod.2017.10.006 ↗
24. 10.1021/acsnano.0c0182710.1021/acsnano.0c01827 ↗
25. 10.1002/admt.20210109810.1002/admt.202101098 ↗
26. 10.1016/j.nanoen.2023.10868510.1016/j.nanoen.2023.108685 ↗
27. 10.1126/sciadv.abi675110.1126/sciadv.abi6751 ↗
28. Delsing, J.; Ekman, J.; Johansson, J.; Sundberg, S.; Backstrom, M.; Nilsson, T. In Susceptibility of Sensor Networks to Intentional Electromagnetic Interference, 17th International Zurich Symposium on Electromagnetic Compatibility; IEEE, 2006; pp 172–175.10.1109/EMCZUR.2006.214897 ↗
29. Hoang, T.; Kirichek, R.; Paramonov, A.; Houndonougbo, F.; Koucheryavy, A. In Adaptive Routing in Wireless Sensor Networks under Electromagnetic Interference, International Conference on Information Networking (ICOIN); IEEE, 2017; pp 76–79.10.1109/ICOIN.2017.7899479 ↗
30. 10.1016/j.nanoen.2022.10719610.1016/j.nanoen.2022.107196 ↗
31. 10.1016/j.nanoen.2019.10414010.1016/j.nanoen.2019.104140 ↗
32. 10.1002/advs.20200497010.1002/advs.202004970 ↗
33. 10.1021/acsami.1c0404810.1021/acsami.1c04048 ↗
34. 10.1021/acsnano.0c0010710.1021/acsnano.0c00107 ↗
35. Du L. · China Mech. Eng. · 2017 · 28 · 1387
36. 10.1016/j.eurpolymj.2008.05.00910.1016/j.eurpolymj.2008.05.009 ↗
37. 10.1038/s41467-020-20045-y10.1038/s41467-020-20045-y ↗
38. 10.1016/j.rser.2014.11.00110.1016/j.rser.2014.11.001 ↗