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Updated: Sep 11, 2025

An Additive Manufacturing Technique for the Facile and Rapid Fabrication of Hydrogel-based Micromachines with Magnetically Responsive Components
Published on: July 18, 2018
MXene-Assisted Rapid Gelation and Foaming of Gradient Hydrogel as Human-Machine Interfaces with Regulated Charge
Zengyu Hui1,2, Yurong Wang2, Zhao Zhang3
1Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, 710072, P. R. China.
Abstract:
Hydrogel-based tactile sensors, typically featuring a sandwiched structure of electrode/hydrogel/electrode with charges (electrons and ions) accumulated at both interfaces forming electrical double layers (EDLs), are considered promising candidates as human-machine interfaces (HMIs); however, the continuous dense structure limits the deformability of bulk hydrogels, hindering the achievement of high-sensitivity and wide detectable range. Moreover, the state-of-the-art strategies for enhancing sensitivity predominantly focus on enlarging the changes of hydrogel/electrode contact area upon external force, while overlooking the design of interfacial properties and ion transport kinetics. Herein, polyacrylamide/chitosan/MXene (PAM/CS/MXene, PCM) hydrogels with gradient porosity and tailorable softness are developed following the phase-transition-induced foaming mechanism. Ti3C2TX MXene plays critical roles in promoting rapid gelation, stabilizing bubbles, regulating ion transport kinetics, and modulating charge accumulation/dissipation at the hydrogel/electrode interfaces. As a consequence, the obtained PCM foam (PCMF) tactile sensor exhibits an optimal sensitivity of 4267 kPa-1 together with a wide detectable pressure range up to 100 kPa. Proof-of-concept applications are demonstrated for acquiring and identifying gesture commands to control a robotic arm for swab sampling and the movement of an electric car in a maze game by integrating PCMF sensors with a digital camera for visual feedback and machine learning for accurate recognition, respectively.

