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The fusion of the Internet of Things and artificial intelligence demands a broad sensor network. However, traditional sensors rely on external power sources and has challenges such as high maintenance, cost, and environmental issues. Recent research focuses on self-powering sensors, especially triboelectric nanogenerators (TENG), as promising energy harvesters. Nevertheless, conventional TENG-based force sensors are material dependent which impacts to the sensor accuracy. Addressing this, we propose the Pantograph structure-based self-powered force sensor (PF-TENG) system. PFTENG converts vertical input into horizontal movement, measuring force through peak count for material-independent accuracy. Its dynamic range adjusts via spring selection, achieving 92.7% sensing accuracy. Introducing lubricant oil extends its lifespan, demonstrating durability even after 225,000 cycles. Additionally, PF-TENG showcases potential as a tactile sensor, achieving 92% accuracy in recognizing varying Young's modulus of material. This multimodal capability makes PF-TENG promising for diverse applications. The PF-TENG system represents a significant advancement, offering precise force measurement across a wide dynamic range, non-material-dependent operation, and enhanced durability. The deep-learning approach further enhances its utility, allowing accurate tactile recognition. This research presents a novel method for developing non-material dependent TENG sensors, enabling interaction with diverse material surfaces and offering solutions in cutting-edge technology.
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