(440j) Rational Design of Capacitive Pressure Sensors Based on Material Properties and Architecture of Pyramidal Microstructures

There is an increasing demand for specialized pressure sensors in various applications, especially health monitoring. For example, cardiovascular monitoring, which has implications in early diagnosis of heart failure.^[1] To date, there have been numerous reports on pressure sensor designs in order to meet the demands of specialized pressure sensors. However, in order to address the growing demand for tailored pressure sensors for such a diverse range of applications, it is important to understand how different sensor parameters impact performance. By quantifying these relationships, we aim to determine design parameters that will enable researchers to predict a priori the specific design needed to achieve the targeted performance. Previously, capacitive pressure sensors have been shown to have wide versatility in use, with a high degree of potential tuning possible through manipulating the geometry or material selection of the dielectric layer. One effective approach first reported by our group to tune the performance of capacitive pressure sensors is microstructuring the dielectric layer, which improves its compressibility and, consequently, its sensitivity.^[2,3] As researchers seek to incorporate novel materials into sensors, there is also a need to understand how various intrinsic material properties, such as compressive modulus, impact sensor performance. Compressive modulus is qualitatively known to affect the sensitivity of the pressure sensor.^[4] Quantifying the relationships between intrinsic material properties, dielectric geometry, and sensor performance will enable a design of pressure sensors and selection of materials to meet requirements for specific applications.

Presented here is an improved fabrication method to achieve simple, tunable, consistent, and reproducible pressure sensors by using a pyramid microstructured dielectric layer along with an added lamination layer to improve consistency and reproducibility. The as-produced sensor performance is matches predicted trends of parameters that can be explicitly controlled. Further, we develop a simple computational model using intrinsic properties of the elastic dielectric layer and experimentally confirmed its efficacy. We then use our model to predict other properties and a wider range within the tested properties to better understand the effect of material property and microstructure geometry on the sensor performance. This would allow us to anticipate trends and sensor performance. Finally, we demonstrate that we can more directly design sensors for a specific application, such as wrist pulse sensing, using our model and fabrication method.

[1] Z. Liu, Y. Ma, H. Ouyang, B. Shi, N. Li, D. Jiang, F. Xie, D. Qu, Y. Zou, Y. Huang, H. Li, C. Zhao, P. Tan, M. Yu, Y. Fan, H. Zhang, Z. L. Wang, Z. Li, Adv. Funct. Mater. 2018, 1807560, 1807560.

[2] S. C. B. Mannsfeld, B. C. K. Tee, R. M. Stoltenberg, C. V. H. H. Chen, S. Barman, B. V. O. Muir, A. N. Sokolov, C. Reese, Z. Bao, Nat. Mater. 2010, 9, 859.

[3] G. Schwartz, B. C.-K. Tee, J. Mei, A. L. Appleton, D. H. Kim, H. Wang, Z. Bao, Nat. Commun. 2013, 4, 1859.

[4] T. Q. Trung, N. E. Lee, Adv. Mater. 2016, 28, 4338.