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Characteristic study of intra woven green fibers for structural application

  • Sasmita Kar , Sarojrani Pattnaik ORCID logo EMAIL logo and Mihir Kumar Sutar
Published/Copyright: January 7, 2025
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Abstract

The current study uses three engineered fibres: white flax, brown flax, and jute fibres to form plain intra-woven fabrics. The fabrics were alkali treated with a 5 % NaOH solution and mechanical, physical, and thermal tests were conducted to characterize the individual fabrics. It was observed that treated woven fabrics significantly enhanced the overall mechanical strength compared to untreated fabrics. White and brown flax interlaced fabrics increased the tensile strength by 157 % and 574 %, Young’s modulus by 36 % and 26 %, and breaking strain by 58 % and 89 % than jute interlaced brown and white flax, respectively. Thermo-gravimetric analysis revealed that the fabric containing white flax fibre exhibited a maximum degradation temperature of 364.45 °C and after treatment, each fabric revealed a higher degradation temperature. The change in chemical composition of the green fibres after treatment was studied through Fourier transform infrared spectroscopy analysis.


Corresponding author: Sarojrani Pattnaik, Department of Mechanical Engineering, Veer Surendra Sai University of Technology, Burla, Odisha, India, E-mail:

Acknowledgments

The authors render their sincere gratitude to the Department of Mechanical Engineering, VSSUT Burla and Veer Surendra Sai University of Technology Burla for providing support for conducting experiments and for the infrastructural support.

  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. Sasmita Kar has conceptualized the current research theme, did the experimentation and draft manuscript preparation. Sarojrani Pattnaik supervised the experimentation and research, reviewed and corrected the draft manuscript. Mihir Kumar Sutar contributed towards the review and correction of the draft version of the manuscript and for overall typological corrections.

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

  5. Conflict of interest: The authors declare no conflicts of interest with any other authors. This research work is not supported by any funding.

  6. Research funding: None declared.

  7. Data availability: Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

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Received: 2024-01-31
Accepted: 2024-12-05
Published Online: 2025-01-07
Published in Print: 2024-11-26

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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