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Enhancement of mode I/II fracture toughness in basalt/Kevlar hybrid composites via multiwall carbon nanotube integration

  • Mehmet Bulut EMAIL logo , Ömer Yavuz Bozkurt , Ahmet Erkliğ and Atban R. Abdo
Published/Copyright: October 14, 2025
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Abstract

This study investigates the synergistic enhancement of Mode I and Mode II interlaminar fracture toughness (GIC and GIIC) in basalt/Kevlar hybrid fiber-reinforced epoxy composites through multiwall carbon nanotube (MWCNT) integration. Hybrid laminates with two stacking sequences (B5K10B5 and K5B10K5) were fabricated via vacuum-assisted resin transfer molding (VARTM), incorporating MWCNTs at 0.1, 0.25, and 0.5 wt.% in the epoxy matrix. Fracture behavior was characterized using double cantilever beam (DCB) and end-notched flexure (ENF) tests, complemented by scanning electron microscopy (SEM) to elucidate failure mechanisms. Key results demonstrate that 0.1 wt.% MWCNTs optimally enhanced fracture resistance: K5B10K5 composites achieved 32.9 % GIC improvement (923.42 J m−2) and 22.9 % GIIC increase in mode II fracture toughness. B5K10B5 composites exhibited superior baseline GIC (998.61 J m−2 vs. K5B10K5’s 619.47 J m−2) but lower GIIC sensitivity to MWCNTs. Performance degradation occurred at 0.5 wt.% MWCNTs due to agglomeration, reducing GIC by 22.5 % in B5K10B5. Conversely, K5B10K5 showed exceptional GIIC (4481.06 J m−2) at 0.5 wt.%, attributed to crack bifurcation and shear-induced frictional sliding. SEM analysis confirmed that MWCNTs enhance toughness via crack bridging and fiber-matrix interlocking at optimal dispersion (0.1 wt.%), while agglomerates degraded Mode I performance but partially increased Mode II resistance.


Corresponding author: Mehmet Bulut, Sivas Technical Sciences Vocational School Machine Department, 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: 2025-08-18
Accepted: 2025-09-17
Published Online: 2025-10-14
Published in Print: 2025-11-25

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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