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Dual nozzle electrospinning based on piezoelectric-conductive composites preparation: simulation and experiment

  • Jingtao Hu , Yan Xu EMAIL logo , Zhengyang Jin and Xujing Zhang EMAIL logo
Published/Copyright: October 24, 2025
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Abstract

Electrospinning is a simple and versatile technique for producing nanofibers. However, conventional single-nozzle devices have inherent limitations in preparing functional composite nanofiber materials. Although dual-nozzle electrospinning provides a direct method for fabricating composite fibers, the integration of dual nozzles into the electrospinning system distorts the electric field distribution. This distortion induces Coulomb repulsion between the uniformly charged jets, resulting in an uneven distribution of heterogeneous fibers in the composite membrane. This unevenness significantly compromises the structural integrity of the material. In this study, we systematically developed an electrospinning simulation model for electric field analysis and jet trajectory prediction. We elucidated the repulsion mechanism between electric field heterogeneity and Coulomb forces in a single-power-supply dual-nozzle system and proposed a dynamic scanning deposition strategy to address the technical challenges of producing heterogeneous fiber composites. This strategy enabled the formation of composite piezoelectric and conductive polymer nanofibers. The resulting composite nanofibers exhibit both high piezoelectricity (d33 = 21.7 pC/N) and electrical conductivity (0.0646 S/m), confirming the effectiveness of the dynamic scanning deposition approach. Supported by the experimental validation of simulation reliability, this study provides a theoretical basis for optimizing the single-power-supply dual-nozzle electrospinning process to produce heterogeneous nanofiber composites.


Corresponding author: Yan Xu and Xujing Zhang, School of Mechanical Engineering, Xinjiang University, Urumqi, 830017, China, E-mail: (Y. Xu), (X. Zhang)

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: This work was supported by the Natural Science Foundation of Xinjiang Uyghur Autonomous Region (2022D01C34), Xinjiang University Outstanding Doctoral Student Research Innovation Project (XJU2022BS084).

  7. Data availability: Data will be made available on request.

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Received: 2025-07-03
Accepted: 2025-08-23
Published Online: 2025-10-24
Published in Print: 2025-11-25

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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