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  5. Boosted Outputs and Robustness of Polymeric Tribovoltaic Nanogenerator Through Secondary Doping

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Preprint
en
2024

Boosted Outputs and Robustness of Polymeric Tribovoltaic Nanogenerator Through Secondary Doping

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en
2024
DOI: 10.2139/ssrn.4697340

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Jia Meng
Chuntao Lan
Chongxiang Pan
+3 more

Abstract

The semiconductor-based tribovoltaic nanogenerator (TVNG) garners distinctive characteristics of direct current output at low internal impedance, rendering it great potentials for self-powered electronics. We present a polymeric TVNG for achieving enhanced electrical outputs and robustness through secondary doping strategy. By utilizing the dimethyl sulfoxide (DMSO) as a dopant, the transport properties of semiconducting poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) can be modulated. We then demonstrate that the tribovoltaic outputs can be significantly enhanced due to the improved conductivity of the PEDOT:PSS and the enlarged Schottky barrier at the dynamic metal-semiconductor interfaces. The quantity of the transferred charge per motion cycle can reach 150.48 mC m-2 in contact-separation mode and 225.73 mC m-2 in sliding mode. Meantime, the dopant improves the flexibility of the PEDOT:PSS and hence the mechanical robustness of TVNG, allowing stable outputs for ~100,000 contact-separation operations cycles or ~36,000 bending cycles. Furthermore, the device also demonstrates exceptional humidity resistance, but confirms a coupling of tribovoltaic effect and electrochemical effect in high-humidity conditions (relative humidity over 90%). Therefore, our findings provide insightful strategies for future optimization of practical tribovoltaic devices.

How to cite this publication

Jia Meng, Chuntao Lan, Chongxiang Pan, Jun Yang, Xiong Pu, Zhong Lin Wang (2024). Boosted Outputs and Robustness of Polymeric Tribovoltaic Nanogenerator Through Secondary Doping. , DOI: https://doi.org/10.2139/ssrn.4697340.

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Publication Details

Type

Preprint

Year

2024

Authors

6

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.2139/ssrn.4697340

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