High-Performance Triboelectric Nanogenerators Based on Commercial Textiles: Electrospun Nylon 66 Nanofibers on Silk and PVDF on Polyester
Source abstract
Abstract
A high-performance textile triboelectric nanogenerator is developed based on the common commercial fabrics silk and polyester (PET). Electrospun nylon 66 nanofibers were used to boost the tribo-positive performance of silk, and a poly(vinylidene difluoride) (PVDF) coating was deployed to increase the tribo-negativity of PET. The modifications confer a very significant boost in performance: output voltage and short-circuit current density increased ∼17 times (5.85 to 100 V) and ∼16 times (1.6 to 24.5 mA/m2), respectively, compared with the Silk/PET baseline. The maximum power density was 280 mW/m2 at a 4 MΩ resistance. The performance boost likely results from enhancing the tribo-positivity (and tribo-negativity) of the contact layers and from increased contact area facilitated by the electrospun nanofibers. Excellent stability and durability were demonstrated: the nylon nanofibers and PVDF coating provide high output, while the silk and PET substrate fabrics confer strength and flexibility. Rapid capacitor charging rates of 0.045 V/s (2 μF), 0.031 V/s (10 μF), and 0.011 V/s (22 μF) were demonstrated. Advantages include high output, a fully textile structure with excellent flexibility, and construction based on cost-effective commercial fabrics. The device is ideal as a power source for wearable electronic devices, and the approach can easily be deployed for other textiles.
Source: europepmc · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Conducting polymers and applications · Electrospun Nanofibers in Biomedical Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Materials and Manufacturing Research Group, James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, U.K.
Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, U.K.
Funding
Engineering and Physical Sciences Research Council
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