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Performance enhancement of ternary epoxy hybrid composites with rice husk bio-filler

  • Basithrahman Abbas , Rathinavel Subbiah EMAIL logo , Vijay Kumar Mahakur , Rajagopal Ramkumar , Lakshminarayanan Oblisamy and Jasleen Kour
Published/Copyright: October 30, 2025
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Abstract

The study explores the effect of rice husk biofiller on the hydration, mechanical, and wear properties of ternary fiber-reinforced epoxy hybrid composites composed of jute, kevlar, and basalt fibers. Composites were fabricated via hand layup and compression, with varying stacking sequences and biofiller content (2 % and 4 %). FTIR analysis confirmed that biofiller addition and fiber stacking variations did not alter composite functional groups. Water absorption tests showed that the composite with stacking sequence K-J-B-K-J-J-K-B-J-K and 4 % biofiller (H4) exhibited the lowest water uptake at 3.2 %, indicating enhanced hydration resistance. Mechanical testing revealed that H4 also achieved the highest tensile strength (137.9 N/mm2), flexural strength (225.9 N/mm2), and impact strength (179 kJ/m2), demonstrating superior load-bearing and toughness characteristics. Conversely, the composite with stacking sequence B-J-K-B-J-J-B-K-J-B and 4 % biofiller (H3) displayed the highest hardness (86.2 Shore D) and the lowest coefficient of friction (0.30), signifying excellent wear resistance. These results suggest that the synergistic combination of ternary fiber stacking and rice husk biofiller optimizes composite performance, making H3 and H4 promising candidates for lightweight, durable automotive interior and exterior components, advancing sustainable composite material development.


Corresponding author: Rathinavel Subbiah, Department of Mechanical Engineering, SRM Institute of Science and Technology (Deemed to be University) – Tiruchirappalli Campus, Tiruchirappalli, 621 105, Tamil Nadu, India, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors contributed equally to this manuscript. 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: None declared.

  7. Data availability: Any data related to this manuscript can be made available on request.

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Received: 2025-05-13
Accepted: 2025-09-12
Published Online: 2025-10-30

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