journal article·2021-12

High performance piezoelectric nanogenerator with silver nanowires embedded in polymer matrix for mechanical energy harvesting

Fang Wang, Huajun Sun, Huiling Guo, Huiting Sui, Qi Wu, Xiaofang Liu, Duanping Huang

Ceramics International· Volume 47· Issue 24
Publisher page

Abstract

Abstract Organic-inorganic piezoelectric nanogenerators (PNGs) have attracted extensive attention for transforming mechanical energy into electricity in the field of self-powered wearables. Generally, the output electrical performance of PNGs can be enhanced by choosing various materials. Herein, the flexible PNG is fabricated by embedding the Sm-doped Pb(Mg1/3Nb2/3)O3–PbTiO3 (Sm-PMN-PT) polycrystalline piezoelectric ceramics into polyvinylidene fluoride-trifluoroethylene (P(VDF-TrFE)) composite film. The ceramics with high piezoelectric voltage coefficient g33 of Sm-PMN-PT are selected as piezoelectric fillers, and the silver nanowires (Ag NWs) with high aspect ratio are embedded as conductive fillers. The optimized electrical properties are achieved for the Ag NWs/15 wt%Sm-PMN-PT/P(VDF-TrFE) PNG, with the open-circuit voltage of up to 83.5 V and instantaneous power density of 7.48 μW/cm2. In particular, the good mechanical stability up to 6000 bending–releasing cycles are obtained after aging for two months. The finite element analysis is utilized to calculate the electric potential of piezoelectric composite materials. Moreover, the Ag NWs/15 wt%Sm-PMN-PT/P(VDF-TrFE) PNG could harvest mechanical energy from different types of human motion, such as taping, bending and pressing. All these results indicated that the Ag NWs/15 wt%Sm-PMN-PT/P(VDF-TrFE) PNG can serve as a promising candidate for the harvest mechanical energy in the surrounding environment.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Innovative Energy Harvesting Technologies · Conducting polymers and applications

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

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