With the rapid development of the Internet of Things (IoT), the number of sensors utilized for the IoT is expected to exceed 200 billion by 2025. Thus, sustainable energy supplies without the recharging and replacement of the charge storage device have become increasingly important. Among various energy harvesters, the triboelectric nanogenerator (TENG) has attracted considerable attention due to its high instantaneous output power, broad selection of available materials, eco-friendly and inexpensive fabrication process, and various working modes customized for target applications. The TENG harvests electrical energy from wasted mechanical energy in the ambient environment. Three types of operational modes based on contact-separation, sliding, and freestanding are reviewed for two different configurations with a double-electrode and a single-electrode structure in the TENGs. Various charge transfer mechanisms to explain the operational principles of TENGs during triboelectrification are also reviewed for electron, ion, and material transfers. Thereafter, diverse methodologies to enhance the output power considering the energy harvesting efficiency and energy transferring efficiency are surveyed. Moreover, approaches involving not only energy harvesting by a TENG but also energy storage by a charge storage device are also reviewed. Finally, a variety of applications with TENGs are introduced. This review can help to advance TENGs for use in self-powered sensors, energy harvesters, and other systems. It can also contribute to assisting with more comprehensive and rational designs of TENGs for various applications.

Fabric-based electronic textiles (e-textiles) are the fundamental components of wearable electronic systems, which can provide convenient hand-free access to computer and electronics applications. However, e-textile technologies presently face significant technical challenges. These challenges include difficulties of fabrication due to the delicate nature of the materials, and limited operating time, a consequence of the conventional normally on computing architecture, with volatile power-hungry electronic components, and modest battery storage. Here, we report a novel poly(ethylene glycol dimethacrylate) (pEGDMA)-textile memristive nonvolatile logic-in-memory circuit, enabling normally off computing, that can overcome those challenges. To form the metal electrode and resistive switching layer, strands of cotton yarn were coated with aluminum (Al) using a solution dip coating method, and the pEGDMA was conformally applied using an initiated chemical vapor deposition process. The intersection of two Al/pEGDMA coated yarns becomes a unit memristor in the lattice structure. The pEGDMA-Textile Memristor (ETM), a form of crossbar array, was interwoven using a grid of Al/pEGDMA coated yarns and untreated yarns. The former were employed in the active memristor and the latter suppressed cell-to-cell disturbance. We experimentally demonstrated for the first time that the basic Boolean functions, including a half adder as well as NOT, NOR, OR, AND, and NAND logic gates, are successfully implemented with the ETM crossbar array on a fabric substrate. This research may represent a breakthrough development for practical wearable and smart fibertronics.

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A self‐powered artificial mechanoreceptor module is demonstrated with a triboelectric nanogenerator (TENG) as a pressure sensor with sustainable energy harvesting and a biristor as a neuron. By mimicking a biological mechanoreceptor, it simultaneously detects the pressure and encodes spike signals to act as an input neuron of a spiking neural network (SNN). A self‐powered neuromorphic tactile system composed of artificial mechanoreceptor modules with an energy harvester can greatly reduce the power consumption compared to the conventional tactile system based on von Neumann computing, as the artificial mechanoreceptor module itself does not demand an external energy source and information is transmitted with spikes in a SNN. In addition, the system can detect low pressures near 3 kPa due to the high output range of the TENG. It therefore can be advantageously applied to robotics, prosthetics, and medical and healthcare devices, which demand low energy consumption and low‐pressure detection levels. For practical applications of the neuromorphic tactile system, classification of handwritten digits is demonstrated with a software‐based simulation. Furthermore, a fully hardware‐based breath‐monitoring system is implemented using artificial mechanoreceptor modules capable of detecting wind pressure of exhalation in the case of pulmonary respiration and bending pressure in the case of abdominal breathing.