journal article·2018-12

Sliding-mode triboelectric nanogenerator based on paper and as a self-powered velocity and force sensor

Kequan Xia, Chaolin Du, Zhiyuan Zhu, Rongji Wang, Hongze Zhang, Zhiwei Xu

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Abstract

Abstract With an objective to harvest ambient mechanical power for self-powered systems, triboelectric nanogenerators (TENGs) based on paper have been currently put forward as an extensively effective, affordable as well as a sturdy methodology for the generation of power from mechanical vibrations, which are based on coupling between triboelectrification and electrostatic induction. Nevertheless, previous paper TENGs are built by vertical disintegration of the triboelectric-charged planes, requiring intricate structures of the device for ensuring that they are sufficiently resilient towards the charge disintegration; or else, no output current is observed. In this work, we firstly proposed a sliding paper TENG (SP-TENG) that was built based on in-plane charge disintegration mechanism, which utilizes related sliding between two interacting facades. Paper and Teflon tape served as the triboelectric pairs, and carbon ink was used as the conductive electrode. The proposed SP-TENG brings forth an open-circuit voltage of approximately 1000 V, a short-circuit current of approximately 42 μA and a peak power density of approximately 18.4 W/m2. The output performance can instantaneously drive 200 high-powered light-emitting diodes (LEDs). In comparison with the prevailing vertical-touching based TENGs, the advantages of elevated effectiveness, simple manufacture and appropriateness for different kinds of mechanical triggering are exhibited by SP-TENG. Moreover, a stacked SP-TENG is proposed to improve output performance. The proposed SP-TENG has potential applications as self-powered velocity sensor and force sensor.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Conducting polymers and applications · Tactile and Sensory Interactions

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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