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In this work, the dielectric properties of PMMA composite have been investigated by adding MWCNT, OH-functionalized MWCNT, and COOH-functionalized MWCNT to observe the effect of incorporating nanomaterials into the PMMA matrix. The samples were prepared using a twin-screw extruder and a 3D mixer. The dielectric properties were evaluated across a frequency range of 200 MHz to 20 GHz. The findings offer new insights into these composites’ polarization mechanisms and structural attributes, emphasizing their potential for improved dielectric performance in microwave frequency applications. Moreover, various machine learning tools were applied in this study to predict dielectric properties and it is observed that in the Ensemble model, the highest R-squared and the lowest MAE are achieved proving it to be the most accurate predictive tool.

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Dielectric Properties and Machine Learning-Based Prediction of PMMA/MWCNT Composites in the Microwave Frequency Region

  • 会议论文

  • 发表日期：

    2026年

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  • Sanketsinh Thakor,
  • Prince Jain,
  • Pranav Rathi,
  • Unnati Joshi,
  • Anand Joshi,
  • Payel Deb

摘要

In this work, the dielectric properties of PMMA composite have been investigated by adding MWCNT, OH-functionalized MWCNT, and COOH-functionalized MWCNT to observe the effect of incorporating nanomaterials into the PMMA matrix. The samples were prepared using a twin-screw extruder and a 3D mixer. The dielectric properties were evaluated across a frequency range of 200 MHz to 20 GHz. The findings offer new insights into these composites’ polarization mechanisms and structural attributes, emphasizing their potential for improved dielectric performance in microwave frequency applications. Moreover, various machine learning tools were applied in this study to predict dielectric properties and it is observed that in the Ensemble model, the highest R-squared and the lowest MAE are achieved proving it to be the most accurate predictive tool.