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<p>This study presents a nonlinear dynamic model of an unsealed capacitive pressure sensor featuring a clamped–clamped microbeam suspended over a viscoelastic PDMS-based gap filler. The formulation integrates geometric nonlinearity from mid-plane stretching, nonlinear viscoelasticity of the elastomer, and electrostatic actuation under DC bias. A Galerkin-based reduced-order model is developed and solved using an energy-based weak formulation for steady-state harmonic response. Results show that high-permittivity nanocomposite fillers (BaTiO₃–PDMS) significantly enhance capacitive sensitivity while viscoelastic damping suppresses higher harmonics, yielding stable, near-linear dynamics. The beam’s stiffness buffers applied pressure, moderating elastomer deformation and extending the operational range beyond pull-in. The model is validated against benchmark air-gap data, confirming its predictive accuracy for dynamic pressure sensing in biomedical and industrial applications.</p>

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Nonlinear dynamic modeling of an unsealed capacitive pressure sensor with a viscoelastic PDMS gap-filled dielectric

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月1日

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  • Abdollahzadeh Masumeh,
  • Shabani Rasoul,
  • Rezazadeh Ghader

摘要

This study presents a nonlinear dynamic model of an unsealed capacitive pressure sensor featuring a clamped–clamped microbeam suspended over a viscoelastic PDMS-based gap filler. The formulation integrates geometric nonlinearity from mid-plane stretching, nonlinear viscoelasticity of the elastomer, and electrostatic actuation under DC bias. A Galerkin-based reduced-order model is developed and solved using an energy-based weak formulation for steady-state harmonic response. Results show that high-permittivity nanocomposite fillers (BaTiO₃–PDMS) significantly enhance capacitive sensitivity while viscoelastic damping suppresses higher harmonics, yielding stable, near-linear dynamics. The beam’s stiffness buffers applied pressure, moderating elastomer deformation and extending the operational range beyond pull-in. The model is validated against benchmark air-gap data, confirming its predictive accuracy for dynamic pressure sensing in biomedical and industrial applications.