4.8 Article

Piezo-triboelectric hybridized nanogenerator embedding MXene based bifunctional conductive filler in polymer matrix for boosting electrical power

Journal

NANO ENERGY
Volume 105, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.nanoen.2022.108018

Keywords

Triboelectric nanogenerator; Piezoelectric nanogenerator; MXene; Work function; Human motion manipulation system; Material detection system

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This study presents a hybrid generator that combines triboelectric and piezoelectric technology, showing promising potential in addressing the energy crisis and global warming. The generator exhibits excellent electrical performance and can be used in various applications such as controlling robotic hands and material detection.
With an emerging energy crisis and global warming, energy harvesting technologies have attracted an attention as an alternative to replace the fossil fuel-based energy generation methods because they can generate the carbon-free and sustainable energy. Among various energy harvesting technologies, mechanical energy harvesters are regarded as an attractive harvesting method because they can convert the abundant mechanical energy surrounding us to electrical energy. However, the low electrical performance of the mechanical energy harvester is hindering its practical utilization. Hybridization of the two different mechanical energy harvesters can provide the solution for this issue because the electrical performance of the energy harvester can be improved by harvesting the applied mechanical energy in two harvesters, simultaneously. Herein, a triboelectric and piezoelectric hybridized generator is fabricated by embedding MXene and barium titanate ceramic filler in the polydimethylsiloxane matrix (HG-MBP). The role of MXene as the bifunctional conductive filler is theoretically and experimentally investigated and the optimum point of MXene content is investigated and the high open-circuit voltage of 80 V, short-circuit current of 14 mu A, and a power density of 13.5 W/m2 are obtained. As an application, a 3D printer modeled robot hand is successfully controlled based on finger joint movements of a real hand to which the HG-MBPs are attached. Moreover, the object detection system is developed with the aid of k-mean clustering method and different materials are distinguished with a high classification accuracy of 93.33%. These results reveal the excellent potential of the proposed HG-MBP as a human gesture manipulation system and as a material detection sensor, which can be expected to be utilized as a next-generation e-skin in the human-machine interface.

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