A Broad Range Triboelectric Stiffness Sensor for Variable Inclusions Recognition - Zhao, Ziyi; Quan, Zhentan; Tang, Huaze; Xu, Qinghao; Zhao, Hongfa; Wang, Zihan; Song, Ziwu; Li, Shoujie; Dharmasena, Ishara; Wu, Changsheng; Ding, Wenbo (Nano-Micro Letters, 2023)
With the development of artificial intelligence, stiffness sensors are extensively utilized in various fields, and their integration with robots for automated palpation has gained significant attention. This study presents a broad range self-powered stiffness sensor based on the triboelectric nanogenerator (Stiff-TENG) for variable inclusions in soft objects detection. The Stiff-TENG employs a stacked structure comprising an indium tin oxide film, an elastic sponge, a fluorinated ethylene propylene film with a conductive ink electrode, and two acrylic pieces with a shielding layer. Through the decoupling method, the Stiff-TENG achieves stiffness detection of objects within 1.0 s. The output performance and characteristics of the TENG for different stiffness objects under 4 mm displacement are analyzed. The Stiff-TENG is successfully used to detect the heterogeneous stiffness structures, enabling effective recognition of variable inclusions in soft object, reaching a recognition accuracy of 99.7%. Furthermore, its adaptability makes it well-suited for the detection of pathological conditions within the human body, as pathological tissues often exhibit changes in the stiffness of internal organs. This research highlights the innovative applications of TENG and thereby showcases its immense potential in healthcare applications such as palpation which assesses pathological conditions based on organ stiffness. Highlights: 1 We propose a broad range triboelectric sensor system employing elastic sponge and shielding layers, which can realize fast stiffness recognition within 1.0 s at a low cost. 2 A novel algorithm is proposed for rapid stiffness identification by extracting signal characteristics, effectively reducing demand of computing resources. 3 The proposed sensor system can identify the multi-layer stiffness structure of objects, enabling effective recognition of variable inclusions in soft objects with an accuracy of 99.7%.