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A self-powered and self-modulated infrared wireless communications system is developed via integrating a triboelectric nanogenerator and mechanical modulation protocols.

Optical emission from gas molecules is considered an excellent solution to gas detection because of its fairly high sensitivity and selectivity. However, its further development is constrained by its large size, high energy consumption, and security risks. Herein, a self‐powered gas sensing solution is proposed by taking advantage of the optical emission of triboelectric discharge (TD). Based on the triboelectric effect, the high output voltage of ≈kV can lead to producing optical emissions through gas discharges, including emission spectra and discharge images, which are affected by gas species and pressure. By means of machine learning, the optical emission signals process the ability to recognize 60 gas atmospheres simultaneously, with an accuracy of 97.67%. With the assistance of the mobile phone or closed‐circuit television, a self‐powered gas sensing system based on TD may serve as a portable detecting device and a vital component of the Internet of Things system.

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The development of the human-machine interface (HMI) is endeavored to find effective approaches to interact with machines by applying emerging technologies. Triboelectric nanogenerator (TENG) can convert mechanical stimuli to electricity, which not only shows great potential in sensing but also is widely used in various HMI applications. This paper proposed a TENG-based hexagonfractal touchpad (HTPad) system using two channels to realize 18 sliding patterns from 3 different modes and a signal recognition module. A one-dimensional convolution neural network (1D CNN) model is proposed for the recognition of the sliding direction signal with 96.5% accuracy, and handwriting digit signals collected by the touchpad can be recognized with a modified model with 99% accuracy. The proposed TENG-based hexagon-fractal touchpad is easy to fabricate, scalable, and with high sensitivity. Furthermore, the recognition model can serve as a unified platform for different recog.nition tasks with little computational cost, which reveals great potential in HMI applications.