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August 18, 2025Advanced Materials28 citations

MXene‐Assisted Rapid Gelation and Foaming of Gradient Hydrogel as Human‐Machine Interfaces with Regulated Charge Accumulation/Dissipation and Enhanced Tactile Sensing Capability

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ZHZengyu HuiYWYurong WangZZZhao Zhang

Key Points

  • The PCM foam tactile sensor achieved a remarkable sensitivity of 4267 kPa −1 and a wide detectable pressure range up to 100 kPa.
  • MXene plays a key role in the hydrogel's rapid gelation, bubble stabilization, and enhanced ion transport kinetics.
  • These innovative sensors can effectively recognize and control robotic arm gestures, showcasing their practical applications in technology.
  • The unique gradient porosity and tailored softness of the hydrogel improve tactile feedback and performance considerably.

Abstract

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. Ti 3 C 2 T X 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.

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Cite This Study

Hui et al. (2025) studied this question.

synapsesocial.com/papers/68af4314ad7bf08b1ead1793https://doi.org/10.1002/adma.202510285
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