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Waste polyethylene-coated fabrics for dual-mode interfaces triboelectrification for self-powered sensors
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- Title
- Waste polyethylene-coated fabrics for dual-mode interfaces triboelectrification for self-powered sensors
- Issued Date
- 2025-12
- Citation
- Results in Engineering, v.28
- Type
- Article
- Author Keywords
- Liquid-solid ; Solid–solid ; Triboelectric ; Waste Textile ; Cotton ; Energy Harvesting ; Environmental Protection ; Food Safety ; Motion Sensors ; Plastic Recycling ; Sustainable Development ; Textiles ; Wearable Sensors ; Circular Economy ; Cotton Textiles ; Energy ; Liquid Solids ; Nanogenerators ; Recycled Polyethylene ; Reuse ; Solid-solid ; Waste Textiles ; Triboelectricity
- ISSN
- 2590-1230
- Abstract
-
The reuse of waste cotton textiles and coating them with recycled polyethylene provides a route to improve environmental sustainability, reducing our landfill burden and supporting the circular economy through effective implementation of the 3Rs: Reduce, Reuse, and Recycle. This approach extends material life and minimizes resource consumption. This study presents a sustainable strategy for energy harvesting and sensor applications by repurposing worn-out cotton textiles that are coated with recycled polyethylene via a simple immersion method. The modified textiles are integrated into two triboelectric nanogenerator (TENG) configurations: a solid–solid TENG (S–S TENG) and a liquid–solid TENG (L–S TENG). The S–S TENG, paired with a polydimethylsiloxane (PDMS) elastomer, achieves a peak output of 250 V, 1.01 µA, and a power output of 83.7 µW at 2 Hz when subject to a 5 N compression. The device exhibits long-term stability and charges a 10 µF capacitor to 3.3 V, with sufficient energy to power light-emitting diodes (LEDs). For the L–S TENG, deionised water droplets interacting with the polyethylene-coated surface generate up to 45 nW for a 50 MΩ load, with a saturated charge of 1.3 nC. When used as a sensor, the device is employed in real-time motion tracking, integrated with an artificial neural network, and in milk adulteration detection. These results demonstrate a low-cost, flexible, and eco-friendly platform for multifunctional energy harvesting and self-powered sensing, advancing circular economy principles and enabling new applications in healthcare, food safety, and wearable electronics.
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- Publisher
- Elsevier
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