A conformal van der Waals graphene coating enabled high-performance piezo-ionic sensor for spatial, gesture, and object recognition

Transforming mechanical forces into electrical signals is vital in self-powered strain sensors. Compared to piezoelectric materials, piezo-ionic materials show great potential in self-powered strain sensors because of the passive and direction identification characteristics arising from the ion gradient under pressure. However, the low signal output (less than 5 mV) of piezo-ionic sensors might be influenced by the bioelectricity when used in practical applications. In addition, the normally used metal electrodes face fracture under high and long-term strain. Here, we used a dry coating method to prepare highly conformal, highly electrically conductive (minimum sheet resistance of 16 ohm sq(-1)), and highly deformed graphene electrodes with a van der Waals (vdW) structure. Such conformal vdW graphene (vdWGR) electrodes could enhance the signal output to 90.3 mV and resist a large strain of 50% bending variation with 10 000 cycles, avoiding bioelectricity interference and electrode fracture during use. Interestingly, the vdWGR-assisted piezo-ionic sensor exhibited a special bouncing back peak when touched by soft objects, which could be utilized for object recognition. We demonstrated its applications in spatial, gesture, and object recognition by utilizing the piezo-ionic strain sensor's differences in the peak value, directionality, and shapes. The preparation method for high-performance vdW graphene electrodes can be extended to other flexible electrodes or electronics. Besides, the piezo-ionic effect identifying the softness or hardness of objects might assist piezo-ionic sensors in more applications.

Automated summary

The preparation of vdW graphene electrodes through dry coating method enhances the signal output of piezo-ionic strain sensors and resists deformation, bioelectricity interference, and electrode fracture. The sensor exhibits a bouncing back peak when touched by soft objects, which can be used for object recognition. Its applications in spatial, gesture, and object recognition rely on differences in peak value, directionality, and shapes.