Resistive random access memory (RRAM) devices are receiving increasing extensive attention due to their enhanced properties such as fast operation speed, simple device structure, low power consumption, good scalability potential and so on, and are currently considered to be one of the next-generation alternatives to traditional memory. In this review, an overview of RRAM devices is demonstrated in terms of thin film materials investigation on electrode and function layer, switching mechanisms and artificial intelligence applications. Compared with the well-developed application of inorganic thin film materials (oxides, solid electrolyte and two-dimensional (2D) materials) in RRAM devices, organic thin film materials (biological and polymer materials) application is considered to be the candidate with significant potential. The performance of RRAM devices is closely related to the investigation of switching mechanisms in this review, including thermal-chemical mechanism (TCM), valance change mechanism (VCM) and electrochemical metallization (ECM). Finally, the bionic synaptic application of RRAM devices is under intensive consideration, its main characteristics such as potentiation/depression response, short-/long-term plasticity (STP/LTP), transition from short-term memory to long-term memory (STM to LTM) and spike-time-dependent plasticity (STDP) reveal the great potential of RRAM devices in the field of neuromorphic application.

The demand for portable and rapid identification of liquids has challenged traditional laboratory methods. Here, we propose a high-accuracy liquid identification strategy that integrates water droplet mechanics and solid–liquid interface contact electrification. By applying non-Hookean mechanical properties of droplets, we fabricate a lotus leaf-inspired ZnO–Polydimethylsiloxane (PDMS) superhydrophobic solid–liquid sensor. Based on the special mechanical–electric coupling interface, it achieves the highest droplet pressure sensitivity of 281 mV/Pa. We have made a breakthrough in detecting diverse solution composition with a high monitoring resolution of 5 nM metal ions and 0.1% of alcohol concentration. Through the design of double-stacked devices, triboelectric signals are able to be decoupled into mechanical and contact electrification dual-mode signals. With the integration of a gated recurrent unit (GRU) model, intelligent identification of ten liquids has reached an ultrahigh accuracy of 99%, opening up a pathway for portable liquid monitoring.

Developing fluid velocity sensors with high accuracy, high stability and self-power has practical significance. Triboelectric sensors provide a strategy to realize high-performance self-powered active sensing. In this work, we present a fully enclosed self-powered active spherical triboelectric sensor (SASTS) to detect the fluid velocity. Based on the electrification at the contact interface, resulting in the electrostatic induced electrons transferring between two interdigitated electrodes, SASTS effectively harvests the fluid flow energy to electric signal. After Fourier transform processing of the output signal, the motion frequency of the SASTS can be distinguished and the fluid velocity can be further calculated. By optimizing the structure parameter, it features high precision with average standard deviation less than 4, wide range speed from 2 m/s to 18 m/s of fluid flow, and proved lifetime more than half a year. This technique can be applied to measurement of fluid flow speed, even potentially to other rotation movements, which could have wide applications, such as environment monitoring, transportation and so on.

The components in traditional human–machine interaction (HMI) systems are relatively independent, distributed and low-integrated, and the wearing experience is poor when the system adopts wearable electronics for intelligent control. The continuous and stable operation of every part always poses challenges for energy supply. In this work, a triboelectric technology-based all-in-one self-powered HMI system for wireless remote telemetry and the control of intelligent cars is proposed. The dual-network crosslinking hydrogel was synthesized and wrapped with functional layers to fabricate a stretchable fibrous triboelectric nanogenerator (SF-TENG) and a supercapacitor (SF-SC), respectively. A self-charging power unit containing woven SF-TENGs, SF-SCs, and a power management circuit was exploited to harvest mechanical energy from the human body and provided power for the whole system. A smart glove designed with five SF-TENGs on the dorsum of five fingers acts as a gesture sensor to generate signal permutations. The signals were processed by the microcontroller and then wirelessly transmitted to the intelligent car for remote telemetry and control. This work is of paramount potential for the application of various terminal devices in self-powered HMI systems with high integration for wearable electronics.