Due to its excellent flexibility, graphene has an important application prospect in epidermal electronic sensors. However, there are drawbacks in current devices, such as sensitivity, range, lamination, and artistry. In this work, we have demonstrated a multilayer graphene epidermal electronic skin based on laser scribing graphene, whose patterns are programmable. A process has been developed to remove the unreduced graphene oxide. This method makes the epidermal electronic skin not only transferable to butterflies, human bodies, and any other objects inseparably and elegantly, merely with the assistance of water, but also have better sensitivity and stability. Therefore, pattern electronic skin could attach to every object like artwork. When packed in Ecoflex, electronic skin exhibits excellent performance, including ultrahigh sensitivity (gauge factor up to 673), large strain range (as high as 10%), and long-term stability. Therefore, many subtle physiological signals can be detected based on epidermal electronic skin with a single graphene line. Electronic skin with multiple graphene lines is employed to detect large-range human motion. To provide a deeper understanding of the resistance variation mechanism, a physical model is established to explain the relationship between the crack directions and electrical characteristics. These results show that graphene epidermal electronic skin has huge potential in health care and intelligent systems.

We report an artificial eardrum using an acoustic sensor based on two-dimensional MXene (Ti 3 C 2 T x ), which mimics the function of a human eardrum for realizing voice detection and recognition. Using MXene with a large interlayer distance and micropyramid polydimethylsiloxane arrays can enable a two-stage amplification of pressure and acoustic sensing. The MXene artificial eardrum shows an extremely high sensitivity of 62 kPa −1 and a very low detection limit of 0.1 Pa. Notably, benefiting from the ultrasensitive MXene eardrum, the machine-learning algorithm for real-time voice classification can be realized with high accuracy. The 280 voice signals are successfully classified for seven categories, and a high accuracy of 96.4 and 95% can be achieved by the training dataset and the test dataset, respectively. The current results indicate that the MXene artificial intelligent eardrum shows great potential for applications in wearable acoustical health care devices.

Single-crystal (SC) perovskite is currently a promising material due to its high quantum efficiency and long diffusion length. However, the reported perovskite photodetection range (<800 nm) and response time (>10 μs) are still limited. Here, to promote the development of perovskite-integrated optoelectronic devices, this work demonstrates wider photodetection range and shorter response time perovskite photodetector by integrating the SC CH3NH3PbBr3 (MAPbBr3) perovskite on silicon (Si). The Si/MAPbBr3 heterojunction photodetector with an improved interface exhibits high-speed, broad-spectrum, and long-term stability performances. To the best of our knowledge, the measured detectable spectrum (405–1064 nm) largely expands the widest response range reported in previous perovskite-based photodetectors. In addition, the rise time is as fast as 520 ns, which is comparable to that of commercial germanium photodetectors. Moreover, the Si/MAPbBr3 device can maintain excellent photocurrent performance for up to 3 months. Furthermore, typical gray scale face imaging is realized by scanning the Si/MAPbBr3 single-pixel photodetector. This work using an ultrafast photodetector by directly integrating perovskite on Si can promote advances in next-generation integrated optoelectronic technology.