MXene‐Triggered 3D Printing of Gradient‐Modulus Iontronic Pressure Sensors for Adaptive Robotic Grasping

Abstract

Abstract Flexible capacitive pressure sensors are critical components in emerging applications such as electronic skin, human‐machine interfaces, and soft robotics. However, achieving a balance between high sensitivity and a wide linear range remains a key challenge. Here, a synergistic strategy is reported that integrates printable gradient‐modulus hydrogels with microstructured architectures to mitigate this performance trade‐off. It is revealed that MXene plays a critical role in fine‐tuning the elastic moduli of the hydrogel inks via the so‐called MXene triggering chemistry, the latter greatly boosts the radical generation for rapid polymerization kinetics. This enables the minutes‐scale printing of vertically modulus‐graded (stiff‐medium‐soft layers from top to bottom) microdome structures. Such a rational design effectively delays the structural densification and achieves progressive, layer‐by‐layer deformation under pressure, leading to a high sensitivity of 538 kPa −1 across a broad pressure range (up to 440 kPa). The great potential of the rapid‐printed pressure sensing arrays in recognizing object stiffness is further demonstrated, and provides real‐time spatial capacitance feedback during grasping tasks by integrating into a robotic gripper. This work offers a scalable and programmable strategy for innovating material modulus and structural geometry, opening new pathways toward high‐performance tactile sensors for intelligent sensing and adaptive robotics.