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September 16, 2025Polymer Composites4 citations

Empirical Modeling and Experimental Analysis of Heat‐Treated Silicone Rubber‐TiC Composites for Advanced Piezoelectric Nanogenerators

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SASiraj AzamVKVineet KumarSPSang‐Shin Park

Key Points

  • The TiC-50 composite showed optimal compressive modulus of 4.5 MPa under heat treatment at 50°C.
  • Mechanical and electrical testing demonstrated consistent voltage generation peaking at ±150 mV during biomechanical response tests.
  • An empirical predictive model with R² = 0.9999 accurately classified the properties of heat-treated silicone rubber and titanium carbide composites.
  • Heat treatment significantly enhances energy storage properties, achieving a peak energy density of 0.462 μJ/m³, supporting applications in wearable electronics.

Abstract

ABSTRACT Silicone rubber (SR) is a flexible, thermally stable material with promising applications in piezoelectric nanogenerators (PENGs). This study examines the impact of heat treatment on SR and its composites reinforced with 5 phr titanium carbide (TiC) through the experimental analysis and empirical modeling. Eight samples were vulcanized and heat treated at 25°C, 50°C, 75°C, and 100°C. Mechanical, electrical, and biomechanical testing revealed that the TiC‐50 composite exhibited optimal performance, with a compressive modulus of 4.5 MPa and tensile strength of 2.8 MPa at 50°C. Cyclic loading tests confirm stable voltage output (±10 mV) and excellent durability. Capacitance analysis demonstrated superior energy storage properties, with a peak energy density of 0.462 μJ/m 3 and charge density of 4.202 μC/m 3 . Biomechanical response tests indicated consistent voltage generation, peaking at ±150 mV under finger pressing. An empirical correlation model, formulated using a cubic polynomial equation, accurately predicted mechanical and energy storage properties (R 2 = 0.9999, RMSE = 0.4212, MAE = 0.1985). This study underscores the role of heat treatment in optimizing SR‐TiC composites and highlights the TiC‐50 composite as a multifunctional material for wearable electronics and self‐powered devices, while the predictive model aids in material optimization and energy harvesting applications.

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Cite This Study

Azam et al. (2025) studied this question.

synapsesocial.com/papers/68d453a431b076d99fa59863https://doi.org/10.1002/pc.70457
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