A multimodal electronic skin integrating triboelectric nanogenerators and capacitive pressure sensors for material detection

• Main Author: Yan, Hanlong
• Other Authors: Yifan Wang
• Format: Thesis-Master by Coursework
• Language: English
• Published: Nanyang Technological University 2024
• Subjects: Chemistry; Physics; Triboelectric nanogenerator; Iontronic pressure sensor; Pressure measurements; Material identification
• Online Access: https://hdl.handle.net/10356/175947
• Institution: Nanyang Technological University

The development of electronic skins (e-skins) with multimodal sensing capabilities, including material detection and pressure measurement capabilities, is critical to promoting the evolution of intelligent sensing systems. Here, a multimodal electronic skin (AMES) composed of a triboelectric nanogenerator (TENG) and an iontronic pressure sensor that can accurately identify materials and detect pressure levels is proposed. As each material has a unique ability to acquire or release electrons, TENG harness this distinctive electrical signal signature produced when contacting diverse materials for accurate material identification. Simultaneously, iontronic pressure sensors offer sensitive and accurate pressure measurements, enhancing the ability of the system to interpret tactile information and helping the triboelectric signal without the effects of pressure. The relationship between the output voltage signal from the TENG and the capacitance signal from the iontronic pressure sensor is utilized to enable precise material identification and accurate pressure measurement. A multimodal electronic skin (AMES) can accurately identify materials and precisely measure pressure. This study successfully recognized five materials (conductive fabric, PI, cloth fabric, finger, and resin) at three pressure levels (1Kpa, 5Kpa and 10 Kpa). This integrated system not only bridges the gap among current tactile sensing technologies but also expands the potential of e-skin for applications in robotics and interaction technologies. In the future, pixel-level recognition of surfaces composed of multiple materials can be achieved by arraying ASEMs.