Cost-Effective Tunable Iontronic Pressure Sensors Based on a Performance Prediction Model

The iontronic pressure sensor has broad prospects in smart wearable devices, health monitoring, and many other fields. However, it is challenging to evaluate sensor performance and tune the best configuration in a given application scenario because systematic regulation theory and performance prediction models are lacking. In this work, we construct a performance prediction model by combining Persson’s contact theory and the finite element analysis (FEA) method and realize the qualitative and quantitative predictions of the sensor’s performance within the selected pressure range. The optimized sensor based on sandpaper gains high sensitivity (97.5 kPa $^{-{1}}$ ), low detection limit (4.3 Pa), fast response and relaxation times (40/70 ms), wide linear sensing range (0–311.1 kPa), and mechanical stability of 10000 cycles. This cost-effective iontronic pressure sensor with fully adjustable performance can be further applied in daily health and sports monitoring. The performance prediction method has the possibility of further development in the theoretical prediction and performance analysis for a more complicated microstructure sensor. This work provides a new idea and strategy for realizing low-cost, large-scale manufacturing and applying pressure sensors.