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Highly Stable and Sensitive Paper-Based Bending Sensor Using Silver Nanowires/Layered Double Hydroxides Hybrids

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journal contribution
posted on 08.07.2015, 00:00 authored by Yong Wei, Shilong Chen, Fucheng Li, Yong Lin, Ying Zhang, Lan Liu
Highly sensitive flexible piezoresistive materials using silver nanowires (AgNWs) composites have been widely researched due to their excellent electrical, optical, and mechanical properties. Intrinsically, AgNWs tend to aggregate in polymer matrix because of the intense depletion-induced interactions, which seriously influence the percolation threshold of the composites. In this study, we report a highly stable and sensitive paper-based bending sensor using the AgNWs and layered double hydroxides (LDHs) to construct a hybrid conductive network in waterborne polyurethane that is easy to destruct and reconstruct under bending deformation. The nonconductive 2D LDH nanosheets are embedded into AgNWs network and assist dispersion of AgNWs, which depends on the hydrogen bonding between the two nanostructures. The percolation threshold of the composites decreases from 10.8 vol % (55 wt %) to 3.1 vol % (23.8 wt %), and the composites reaches a very low resistivity (10–4 Ω·cm) with a small amount of AgNWs (8.3 vol %) due to the dispersion improvement of AgNWs with the effect of LDH nanosheets. The as-prepared conductive composites with low percolation threshold can be manufactured on paper via various methods such as rollerball pen writing, inkjet printing, or screen printing. The bending sensor prepared by manufacturing the composites on paper shows low-cost, excellent conductivity, flexibility (>3000 bending cycles), sensitivity (0.16 rad–1), fast response (120 ms) and relaxation time (105 ms), and nontoxicity. Therefore, a simple but efficient wearable sensor is developed to monitor the human motions (such as fingers and elbow joints movements) and presents good repeatability, stability, and responsiveness, making the bending sensor possibly able to meet the needs in numerous applications for robotic systems.