A comprehensive review on triboelectric sensors and AI-integrated systems

[Display omitted] Triboelectric sensors, derived from triboelectric nanogenerators, generate electrical signals in response to mechanical stimuli. Its remarkable advantages of inherent self-powering, and ease of manufacture, combined with flexible electronics technologies, pave the way for the trill...

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Bibliographic Details
Published in:Materials today (Kidlington, England) England), 2024-11, Vol.80, p.450-480
Main Authors: Duan, Shengshun, Zhang, Huiyun, Liu, Lei, Lin, Yu, Zhao, Fangzhi, Chen, Pinzhen, Cao, Shuze, Zhou, Kai, Gao, Changjiang, Liu, Zhengfeng, Shi, Qiongfeng, Lee, Chengkuo, Wu, Jun
Format: Article
Language:English
Subjects:
Machine learning
Multimodal sensors
Neuromorphic devices
Triboelectric nanogenerators
Triboelectric sensors
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Summary:[Display omitted] Triboelectric sensors, derived from triboelectric nanogenerators, generate electrical signals in response to mechanical stimuli. Its remarkable advantages of inherent self-powering, and ease of manufacture, combined with flexible electronics technologies, pave the way for the trillion-node IoT mission. Integration of machine learning into triboelectric sensing systems enables effective learning from sensory data and enhances task execution with increased intelligence. This comprehensive review explores the latest scientific and technological advancements in triboelectric sensors, providing insightful analyses in materials, physics, design principles, manufacturing strategies, monomodal and multimodal sensors, von Neumann architecture-based AI systems, and human-like neuromorphic systems. The discussion also covers diverse technological applications, including biomedicine, robotics, prosthetics, human–machine interfaces, AR/metaverse, smart homes, intelligent sports, and intelligent transportation. The narrative concludes by addressing existing challenges, contemplating potential applications, and outlining prospects in this burgeoning field. Covering from fundamental device physics, and AI integration strategies, to system applications, this review aims to illuminate the burgeoning field of triboelectric sensors, inspiring further innovation in self-powered AI-integrated systems and advanced applications, accelerating the paradigm shift toward the era of self-powered artificial intelligence of things.
ISSN:1369-7021
DOI:10.1016/j.mattod.2024.08.013