Self-powered sensing platform based on triboelectric nanogenerators towards intelligent mining industry
Abstract Gold’s crucial role in economic and technological developments has driven the industry towards underground mining, with air quality concerns challenging workers’ safety. Currently, commercial solutions to assess air quality and safety in underground mines often suffer from low accuracy, hig...
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-06-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-60418-9 |
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| Summary: | Abstract Gold’s crucial role in economic and technological developments has driven the industry towards underground mining, with air quality concerns challenging workers’ safety. Currently, commercial solutions to assess air quality and safety in underground mines often suffer from low accuracy, high installation and maintenance costs, without providing data on noxious gases. To address these limitations, we developed a triboelectric self-powered sensing-platform (TESS) employing two distinct triboelectric nanogenerators (TENGs) modules to achieve power generation and wind-speed sensing function, with an ultra-low starting wind speed (0.32 m s−1), capable of operating for up to 3 months in underground mining tunnels. Wind-sensing capabilities are accrued by a horizontal turbine based on non-contact TENGs. Meanwhile, the TESS is powered by a distinct array of TENGs that operates via a new working mode, balancing the advantages of contact-separation and free-standing modes. Assisted by an optimized self-driven power management system, the TESS attains a charging power density of 16.36 mW m−2; this power is delivered every 166 s to a sensor node (temperature, relative humidity, pressure, and concentrations of CO, NO2, NH3), a data processing unit, and a LoRa transmitter. This work represents a leap forward in developing robust, cost-effective, battery-free, and wireless TENG-based environmental sensing platforms. |
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| ISSN: | 2041-1723 |