@Article{cmc.2026.076136,
AUTHOR = {Rama Gautam, Nikhil Marriwala, Reeta Devi, Dhariya Singh Arya},
TITLE = {Multi-Scale Modelling and Simulation of Graphene–PDMS and CNT–PDMS Flexible Capacitive Pressure Sensors for Enhanced Sensitivity},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {87},
YEAR = {2026},
NUMBER = {2},
PAGES = {--},
URL = {http://www.techscience.com/cmc/v87n2/66644},
ISSN = {1546-2226},
ABSTRACT = {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>I</i>–<i>V</i> 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.},
DOI = {10.32604/cmc.2026.076136}
}