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Nanocomposites made of epoxy have become popular increasingly due to their enhanced performance characteristics that support their use in energy storage platforms as well as leading-edge electronic systems. This work is focusing on the investigation of dielectric properties of neat epoxy and nano-epoxy nanocomposites containing 1 wt% nanofiller including of MWCNTs and Ag with Fe2O3 and GO. The nano-fillers were mixed in epoxy by manual mixing method. The dielectric constant together with dielectric loss were measured over frequency range between 1 kHz and 2 MHz. The dielectric behavior improves through nanofiller combinations because they produce enhanced interfacial polarization effects and accelerated charge transport rate. Additionally, Machine learning techniques such as Light BGM, random Forest, Extra Trees and weighted Ensembles were used to predict the dielectric properties. The weighted ensembled model showed outstanding performance among other models. The article provides basic information regarding high-performance nanodielectric materials based on epoxy that will be utilized in modern technological systems.

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Machine Learning Enhanced Dielectric Study for Epoxy – MWCNT/Ag/Fe2O3/GO Nano over Frequency Range of 1 kHz to 2 MHz

  • 会议论文

  • 发表日期：

    2026年

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  • Meenu Khan,
  • Sanketsinh Thakor,
  • Prince Jain,
  • Anand Y. Joshi,
  • Unnati A. Joshi

摘要

Nanocomposites made of epoxy have become popular increasingly due to their enhanced performance characteristics that support their use in energy storage platforms as well as leading-edge electronic systems. This work is focusing on the investigation of dielectric properties of neat epoxy and nano-epoxy nanocomposites containing 1 wt% nanofiller including of MWCNTs and Ag with Fe2O3 and GO. The nano-fillers were mixed in epoxy by manual mixing method. The dielectric constant together with dielectric loss were measured over frequency range between 1 kHz and 2 MHz. The dielectric behavior improves through nanofiller combinations because they produce enhanced interfacial polarization effects and accelerated charge transport rate. Additionally, Machine learning techniques such as Light BGM, random Forest, Extra Trees and weighted Ensembles were used to predict the dielectric properties. The weighted ensembled model showed outstanding performance among other models. The article provides basic information regarding high-performance nanodielectric materials based on epoxy that will be utilized in modern technological systems.