journal article·2021-11Open access

Energy-dissipative dual-crosslinked hydrogels for dynamically super-tough sensors

Funian Mo, Zifeng Wang, Ruijuan Jiang, Weiming Gai, Qing Li, Shuangkun Lv, Chunyi Zhi

Science China Materials· Volume 64· Issue 11

Abstract

In the fields of electronic skin and soft wearable sensors, intrinsically stretchable conductors undergo rapid development; however, practical applications of artificial skinlike materials/devices have not been realized because of the difficulty in combining the electromechanical properties and sensing performance. Contrarily, insoluble inorganic conductive domains in the hydrogel matrix are generally incompatible with surrounding elastic networks, decreasing the mechanical strength. Usually, the hydrogels are vulnerable either to severe mechanical stimuli or large deformation, especially when notches are induced. In this study, based on an energy-dissipative dual-crosslinked conductive hydrogel, a mechanically durable and super-tough strain sensor was developed. The highly soft yet dynamically tough hydrogel demonstrated high ionic conductivity (30.2 mS cm−1), ultrastretchability (>600% strain), and superior linear dependence of strain sensitivity with a maximum gauge factor of 1.2 at 500% strain. Because of these advantageous synergistic effects, the resultant hydrogel strain sensor demonstrated reliable and stable detection of a large range of human motion and subtle vibrations. Moreover, it impressively exhibited super toughness that could endure consecutive treading pressure and even retain normal operation after 20 times of car run-over on the road. These demonstrations highly confirm the sensor’s superior mechanical durability and reliability, displaying great potential in developing next-generation mechanically adaptable sensors.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Conducting polymers and applications · Tactile and Sensory Interactions

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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