High performance of synergistic reinforced natural rubber with kewda fiber (Pandanus odoratissimus) and carbon black: thermal, morphological and statistically optimized mechanical studies
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Sumit Kumar
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Sanjiv Arora
Abstract
This work describes the preparation of kewda fiber and carbon black reinforced natural rubber composites by the milling process. The alkali-treated fiber is incorporated in the rubber matrix as 0, 1, 3, and 5 per hundred rubber with varying amounts of carbon black 0, 10, and 15 phr. Response surface methodology was used to examine the optimum tensile (20.83 MPa) and tear strength (56.00 N mm−1) along with elongation at break (2,546 %) at the corresponding composition of 1.50 phr of fiber and 8.94 phr of carbon black, which are higher than that of the neat sample, 12.71 MPa and 24.56 N mm−1. Tensile strength was diminished from 20.83 MPa to 13.19 MPa as the fiber amount was increased beyond 1 phr up to 5 phr. Likewise, the addition of carbon black beyond 10 per hundred rubber causes a decrease in mechanical properties. Scanning electron microscopy and Fourier transform infrared spectroscopy were used to study the morphology and inter-component interactions of composites. The thermal stability of the composites was improved from 320 °C to 335 °C and the maximum weight loss occurs at a higher temperature with the addition of carbon black. Thus, the reinforcement of fiber and carbon black in the rubber matrix improved the mechanical and thermal properties of the final material.
Acknowledgments
The author is thankful to the funding agency CSIR New Delhi for financial support in this work. Special thanks to the Chairpersons, Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana and Chaudhary Charan Singh University, Meerut, Uttar Pradesh for the needed laboratory facilities and machinery.
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Research ethics: Not Applicable.
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Informed consent: Not Applicable.
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Author contributions: The author has accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The author(s) declared no potential competing of interest with respect to the research, authorship, and/or publication of this article.
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Research funding: This work was supported by the CSIR New Delhi under the award number 09/105(2018)-EMR-I to Sumit Kumar.
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Data availability: All data analysed or generated from this study are included in this research article.
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