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Self-powered and speed-adjustable sensor for abyssal ocean current measurements based on triboelectric nanogenerators
The monitoring of currents in the abyssal ocean is an essential foundation of deep-sea research. The state-of-the-art current meter has limitations such as the requirement of a power supply for signal transduction, low pressure resistance, and a narrow measurement range. Here, we report a fully inte...
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Published in: | Nature communications 2024-07, Vol.15 (1), p.6133-10, Article 6133 |
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Main Authors: | , , , , , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | The monitoring of currents in the abyssal ocean is an essential foundation of deep-sea research. The state-of-the-art current meter has limitations such as the requirement of a power supply for signal transduction, low pressure resistance, and a narrow measurement range. Here, we report a fully integrated, self-powered, highly sensitive deep-sea current measurement system in which the ultra-sensitive triboelectric nanogenerator harvests ocean current energy for the self-powered sensing of tiny current motions down to 0.02 m/s. Through an unconventional magnetic coupling structure, the system withstands immense hydrostatic pressure exceeding 45 MPa. A variable-spacing structure broadens the measuring range to 0.02–6.69 m/s, which is 67% wider than that of commercial alternatives. The system successfully operates at a depth of 4531 m in the South China Sea, demonstrating the record-deep operations of triboelectric nanogenerator-based sensors in deep-sea environments. Our results show promise for sustainable ocean current monitoring with higher spatiotemporal resolution.
This study shows a self-powered deep-sea current measurement system using a triboelectric nanogenerator (TENG) that measures currents from 0.02 to 6.69 m/s and withstands over 45 MPa pressure. Successful operation at 4531 m depth in the South China Sea is demonstrated. |
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ISSN: | 2041-1723 2041-1723 |
DOI: | 10.1038/s41467-024-50581-w |