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Optimal performance of poly-hybrid nanocomposites promoted with carbon fibers and nano silicon carbide particles via compression associated with hot pressing: characterization study

  • Venkatesh Rathinavelu EMAIL logo , Sakthivel Perumal , Mohana Krishnan Amarnath , Subramaniam Prabagaran , Sekar Subramani , Manzoore Elahi Mohammad Soudagar , Vinayagam Mohanavel , Sami Al Obaid and Saleh Hussein Salmen
Published/Copyright: June 12, 2025
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Abstract

Polycarbonate (PC) – fiber composites are high heat resistant, and thermal interface materials for electronics applications because of their good thermal stability, impact resistance, strength, low scratch resistance, and low fracture toughness. This research intends to enhance the functional behaviour of PC poly-fiber composites promoted with unidirectional carbon fibers (CF) and 1–5 wt% of 50 nm silicon carbide (SiC) particles with epoxy-based coupling agent, prepared via compression moulding associated with hot pressing. The synthesized PC composite samples PC1, PC2, PC3, and PC4, containing 0, 1, 3, and 5 wt% of nano SiC, were subjected to mechanical testing and the results are compared to find the optimum behaviour. The PC4 composite sample of PC/5 wt% SiC/CF/PC laminate showed optimal performance, and transmission electron microscope (TEM) analysis confirmed the presence of SiC and CF as effective interfaces to the PC matrix. The PC/5 wt% SiC/CF/PC hybrid nanocomposite provides optimum fracture toughness (5.5 MPa0.5), improved hardness (94HV), superior flexural strength (188 MPa) and enhanced yield (96 MPa) & tensile strength (105 MPa), which are greater than those of pure PC laminates. The PC4 composite is proposed for automotive seat frame applications.


Corresponding author: Venkatesh Rathinavelu, Department of Mechanical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, 602105, Tamilnadu, India, 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: Not applicable.

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Received: 2024-11-14
Accepted: 2025-04-15
Published Online: 2025-06-12

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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