Graphene-based materials on wearable electronics and bendable displays have received considerable attention for the mechanical flexibility, superior electrical conductivity, and high surface area, which are proved to be one of the most promising candidates of stretching and wearable sensors. However, polarized electric charges need to overcome the barrier of graphene sheets to cross over flakes to penetrate into the electrode, as the graphene planes are usually parallel to the electrode surface. By introducing electron-induced perpendicular graphene (EIPG) electrodes incorporated with a stretchable dielectric layer, a flexible and stretchable touch sensor with “in-sheet-charges-transportation” is developed to lower the resistance of carrier movement. The electrode was fabricated with porous nanostructured architecture design to enable wider variety of dielectric constants of only 50-μm-thick Ecoflex layer, leading to fast response time of only 66 ms, as well as high sensitivities of 0.13 kPa⁻¹ below 0.1 kPa and 4.41 MPa⁻¹ above 10 kPa, respectively. Moreover, the capacitance-decrease phenomenon of capacitive sensor is explored to exhibit an object recognition function in one pixel without any other integrated sensor. This not only suggests promising applications of the EIPG electrode in flexible touch sensors but also provides a strategy for internet of things security functions.

The rapid rise of the Internet of things (IoT) have brought the progress of electronic skin (e-skin). E-skin is used to imitate or even surpass the functions of human skin. Thermoregulating is one of the crucial functions of human skin, it is significant to develop a universal way to realize e-skin thermoregulating. Here, inspired by the sweat gland structure in human skin, we report a simple method for achieving dynamic thermoregulating, attributing to the temperature of microencapsulated paraffin remains unchanged when phase change occurs. Combining with the principle of triboelectric nanogenerator, a deep learning model is employed to recognize the output signals of handwriting different letters on ME-skin, and the recognition accuracy reaches 98.13%. Finally, real-time recognition and display of handwritings are successfully implemented by the ME-skin, which provides a general solution for thermoregulating e-skin and application direction for e-skin in the field of IoT.