Pushing Pressure Detection Sensitivity to New Limits by Modulus-Tunable Mechanism.

Only microstructures are used to improve the sensitivity of iontronic pressure sensors. By modulating the compressive modulus, a breakthrough in the sensitivity of the iontronic pressure sensor is achieved. Furthermore, it allows for programmatic tailoring of sensor performance according to the requirements of different applications. Such a new strategy pushes the sensitivity up to a record-high of 25 548.24 kPa-1 and expands the linear pressure range from 15 to 127 kPa. Additionally, the sensor demonstrates excellent mechanical stability over 10 000 compression-release cycles. Based on this, a well-controlled robotic hand that precisely tracks the pressure behavior inside a balloon to autonomously regulate the gripping angle is developed. This paves the way for the application of iontronic pressure sensors in precise sensing scenarios.

Non-equilibrium compression achieving high sensitivity and linearity for iontronic pressure sensors.

Flexible pressure sensors with high sensitivity and linearity are highly desirable for robot sensing and human physiological signal detection. However, the current strategies for stabilizing axial microstructures (e.g., micro-pyramids) are mainly susceptible to structural stiffening during compression, thereby limiting the realization of high sensitivity and linearity. Here, we report a bending-induced non-equilibrium compression process that effectively enhances the compressibility of microstructures, thereby crucially improving the efficiency of interfacial area growth of electric double layer (EDL). Based on this principle, we fabricate an iontronic flexible pressure sensor with vertical graphene (VG) array electrodes. Ultra-high sensitivity (185.09 kPa-1) and linearity (R2 = 0.9999) are realized over a wide pressure range (0.49 Pa-66.67 kPa). It also exhibits remarkable mechanical stability during compression and bending. The sensor is successfully employed in a robotic gripping task to recognize the targets of different materials and shapes based on a multilayer perception (MLP) neural network. It opens the door to realizing haptic sensing capabilities for robotic hands and prosthetic limbs.