Superhydrophobic Triboelectric Structural Materials Enabled by Hierarchical Spatial Assembly

Abstract

Non‐contact intelligent sensing technology is a key player in the data acquisition and environmental monitoring framework of the Internet of Everything, laying the groundwork for the realization of highly integrated intelligent network systems. However, environmental factors continue to be a major challenge in the process of real‐time monitoring and feedback of objects, constraining the stability and accuracy of the sensing signals. In this study, a hierarchical spatial assembly strategy is implemented to effectively integrate an intermediate energy storage layer with superhydrophobic surfaces (CA = 162 ± 2.5°), resulting in the development of a moisture‐resistant triboelectric structural materials with stable monitoring and tracking capabilities. The moisture‐resistant self‐powered sensor constructed from this triboelectric structural material operates effectively in extreme humidity conditions (99% RH) with a full‐scale output retention rate of 95.2%, and is capable of accurately sensing human activity from the distance of 3 m. Importantly, the sensor can also effectively detect vehicle states during operation and perform real‐time monitoring of the vehicle's surrounding environment. This study not only overcomes the long‐standing challenges of accuracy and stability in high humidity for sensors but also advances the development of sustainable, interconnected intelligent systems for the Internet of Everything in extreme environmental conditions.

Directional Moisture-Wicking Triboelectric Materials Enabled by Laplace Pressure Differences

“…The self-powered respiratory sensor realizes wireless monitoring of respiratory status, which is crucial for the construction of smart medical treatment. − The directional moisture-wicking sensor provides two monitoring modes, one is a wired connection with the intelligent control terminal, and the other is a connection with the wireless mobile terminal. − In the wired mode, the sensor can easily distinguish between different states of respiration, such as depth and rate (Figure f). Furthermore, specific to sports, running and walking can be distinguished efficiently (Figure S9).…”