A flexible capacitive pressure sensor with a double-sawtooth structure for high resolution pressure sensing

Soft capacitive pressure sensors have been widely utilized in the fields of flexible electronics and smart devices. While expanding its pressure sensing range, enhancing the sensitivity of sensors has attracted attention. Currently, the problems that need further solution also include improving the pressure sensing linearity and force detection resolution, enabling sensors to identify tiny pressure changes under high pressure. To address this issue, this study presents a strategy utilizing direct ink writing (DIW) 3D printing technology to construct a double-sided sawtooth structure, which can significantly enhance sensitivity while improving pressure resolution. Traditional flexible dielectric layers are mostly bulk polymer. As the applied pressure increases, the compression of the dielectric layer reduces the distance change between the electrode plates under the same pressure, making it difficult to achieve linear variation of capacitance. This paper designs a double-sided sawtooth structure, which squeezes out the air in the dielectric layer with the increase of pressure to realize linear detection of a wide range of pressures. The thermal expansion microspheres (TEM) introduces micro-pores in PDMS to further expand the linear detection range. In addition, the interfacial polarization effect of CB is utilized and improve the sensitivity. Comparing the double-sided sawtooth structured sensors based on PDMS, PDMS&TEM and PDMS&TEM&CB, the results show that the PDMS&TEM&CB sensor exhibits the highest sensitivity (6.8 × 10 −3  kPa −1 ), excellent linear response across a wide pressure detection range of up to 150 kPa, ultra-low detection limits (∼3.2 Pa), and outstanding dynamic response and repeatability (over 8000 cycles). Additionally, the high pressure resolution of the sensor renders it exceptionally suitable for various applications, including minute pressure detection, spatial pressure mapping, Braille recognition, and mapping changes in object curvature.