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<p>Experimental and machine learning approaches to investigate the effects of potassium permanganate (KMnO<sub>4</sub>) concentration using as filler on the structural, thermal, and dielectric properties of potassium ion-conducting polymer electrolyte membranes are discussed. Polyvinylidene fluoride-hexa-fluoropropylene (PVdF-HFP)-based polymer electrolyte membranes (PEM) with different weight percentage concentrations of KMnO<sub>4</sub> (0&#xa0;wt%, 2&#xa0;wt%, 3&#xa0;wt% and 4&#xa0;wt%) were prepared using the solution-casting method. X-ray diffraction (XRD) studies revealed change in crystalline character with variation of KMnO<sub>4</sub> concentration in PEMs. Fourier-transform infrared (FTIR) spectroscopy showed ion–polymer interactions within the prepared polymer electrolyte system. The dielectric constants, dielectric losses, tangent delta (tan δ) losses, and AC conductivity of samples have been comprehensively evaluated, and the results revealed that 3&#xa0;wt% KMnO<sub>4</sub> concentration demonstrated a high dielectric constant and higher AC conductivity compared to others. The thermal properties are influenced by the varying concentration of KMnO<sub>4</sub> in the PVdF-HFP polymer matrix. Machine learning models, including random forest (RF), decision tree (DT), and light gradient boosting machines (GBM), have been applied to predict the dielectric properties of the prepared specimens. The Stacked Model, which combines the predictions of these individual models, demonstrated superior predictive accuracy, as measured by performance metrics such as <i>R</i>-squared, mean absolute error (MAE), and root mean squared error (RMSE). The use of the Stacked Model provided more robust and reliable predictions, enabling efficient tuning of the materials for enhanced performance. These findings, coupled with the predictive capabilities of machine learning models, suggest that tuning the electrical characteristics properties of potassium ion-conducting polymer electrolytes are crucial for their optimized application in potassium ion-conducting batteries.</p>

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Influence of KMnO4 Concentration on the Physical and Dielectric Properties of Potassium Ion Conducting PVdF-HFP Polymer Electrolytes

  • 期刊论文

  • 发表日期：

    2025年7月30日

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  • Mahendra Singh Rathore,
  • Akshay Pratap Singh,
  • Vaishali Madhani,
  • Sanketsinh Thakor,
  • Prince Jain,
  • Anand Joshi,
  • Chandan R. Vaja

摘要

Experimental and machine learning approaches to investigate the effects of potassium permanganate (KMnO4) concentration using as filler on the structural, thermal, and dielectric properties of potassium ion-conducting polymer electrolyte membranes are discussed. Polyvinylidene fluoride-hexa-fluoropropylene (PVdF-HFP)-based polymer electrolyte membranes (PEM) with different weight percentage concentrations of KMnO4 (0 wt%, 2 wt%, 3 wt% and 4 wt%) were prepared using the solution-casting method. X-ray diffraction (XRD) studies revealed change in crystalline character with variation of KMnO4 concentration in PEMs. Fourier-transform infrared (FTIR) spectroscopy showed ion–polymer interactions within the prepared polymer electrolyte system. The dielectric constants, dielectric losses, tangent delta (tan δ) losses, and AC conductivity of samples have been comprehensively evaluated, and the results revealed that 3 wt% KMnO4 concentration demonstrated a high dielectric constant and higher AC conductivity compared to others. The thermal properties are influenced by the varying concentration of KMnO4 in the PVdF-HFP polymer matrix. Machine learning models, including random forest (RF), decision tree (DT), and light gradient boosting machines (GBM), have been applied to predict the dielectric properties of the prepared specimens. The Stacked Model, which combines the predictions of these individual models, demonstrated superior predictive accuracy, as measured by performance metrics such as R-squared, mean absolute error (MAE), and root mean squared error (RMSE). The use of the Stacked Model provided more robust and reliable predictions, enabling efficient tuning of the materials for enhanced performance. These findings, coupled with the predictive capabilities of machine learning models, suggest that tuning the electrical characteristics properties of potassium ion-conducting polymer electrolytes are crucial for their optimized application in potassium ion-conducting batteries.