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Effect of graphene nanoplatelets (GnPs) on low velocity impact properties of hybrid kevlar/basalt fiber reinforced epoxy based composites

  • Mehmet Bulut ORCID logo EMAIL logo , Atban Rafea Abdo ORCID logo , Bahjat Hardan Sulaiman ORCID logo , Ömer Yavuz Bozkurt ORCID logo and Ahmet Erkliğ ORCID logo
Published/Copyright: April 15, 2025
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Abstract

In the present study, the effects of hybridization of Basalt and Kevlar fibers in conjunction with the incorporation of Graphene nanoplatelets (GnPs) on the performance under low-velocity impact were experimentally examined. Two different hybrid structural configurations, reinforced with Basalt and Kevlar composites, were fabricated utilizing a hand lay-up technique followed by vacuum bag molding, with variations that included and excluded GnPs. The synergistic effects of hybridization coupled with the modification by GnPs on the impact characteristics of the Basalt/Kevlar hybrid composites were analyzed under an impact energy of 30 J while varying the GnPs loading. The optimal enhancement observed in the peak load and absorbed energy of the specimens was recorded as a 9.4 % and 45.7 % improvement, respectively, when Kevlar fibers were positioned in the outer layers; conversely, the specimens demonstrated enhancements of 13.35 % and 20.36 % in peak load and absorbed energy with the incorporation of 1 wt% and 3 wt% GnPs when Basalt fibers were situated in the outer layers. The increase of the impact properties of the hybrid structures was ascribed to the robust interfacial interactions established among the fibers, epoxy, and GnPs nanoparticles, which facilitated a notable enhancement in performance and optimal stress transfer from the fibers and nanoparticles to the matrix.


Corresponding author: Mehmet Bulut, Sİvas Vocational School of Technical Sciences, Sivas Cumhuriyet University, 58140 Sivas, Türkiye, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: The 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: None declared.

  7. Data availability: The raw data can be obtained on request from the corresponding author.

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Received: 2024-10-24
Accepted: 2025-02-18
Published Online: 2025-04-15
Published in Print: 2025-05-26

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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