Multi-Scale Modelling and Simulation of Graphene–PDMS and CNT–PDMS Flexible Capacitive Pressure Sensors for Enhanced Sensitivity

In this study, the multi-scale (meso and macro) modelling was used to predict the electric response of the material. Porosity was introduced through a sugar-templating process to enhance compressibility and sensitivity. Mean-field homogenization was employed to predict the electrical conductivity of the nanocomposites, which was validated experimentally through I–V characterisation, confirming stable Ohmic behavior. The homogenised material parameters were incorporated into COMSOL Multiphysics to simulate diaphragm deflection and capacitance variation under applied pressure. Experimental results showed a linear and stable capacitance response at the force magnitude of 0–7 N. The Graphene nanoplatelets (GnP)–Polydimethylsiloxane (PDMS) sensor demonstrated superior sensitivity (0.0032 pF/N) compared to the CNT–PDMS sensor (0.0019 pF/N), attributed to improved filler dispersion and higher effective surface area of GnP. Finite element simulations were further conducted to evaluate stress distribution in a GnP–PDMS-based capacitive sensor integrated into a shoe insole for gait analysis. The results correlated well with experimental capacitance changes, validating the sensor’s mechanical reliability and pressure sensitivity. This comparative study establishes the GnP–PDMS composite as a more effective candidate for low-cost, biocompatible, and high-performance flexible pressure sensors in wearable biomedical and gait monitoring applications.