Layer-by-layer assembled graphene multilayers on multidimensional surfaces for highly durable, scalable, and wearable triboelectric nanogenerators

2018 
Triboelectric nanogenerators (TENGs) are considered promising next-generation mechanical energy harvesters owing to their desirable attributes such as light weight, portability, eco-friendliness, and low cost. However, cost-effective, scalable, and facile manufacturing methods are still required for the commercialization of TENGs, especially for textile-type TENGs compatible with a variety of textile products. In this work, we report for the first time the layer-by-layer (LbL) assembly of graphene multilayers for low-cost, durable, scalable, and wearable TENGs. The LbL-based graphene multilayers are fabricated on polymer substrates with flat, undulated, and textile surfaces, where graphene multilayers play dual roles as a positive tribo-material and as an electrode. The polymer substrate here is utilized as a negative tribo-material. We identify the optimal number of layers for graphene composites and analyze this outcome using their morphological and electrical properties. Due to the hydrogen bonding-based LbL wet processes, graphene composite multilayers could be well deposited on undulated surfaces as well as on large-scale fabric textiles. These LbL-deposited graphene multilayers yield graphene based-TENGs (G-TENGs) with high durability and high performance. Finally, a graphene multilayer on a textile sample is demonstrated as a scalable and wearable textile-based G-TENG (TG-TENG) operated in a single electrode mode, thereby enabling low-cost manufacturing and high compatibility with textile products such as cloths, curtains, bags and so on. The simple, cost-effective, scalable, and versatile LbL assembly can therefore enable the fabrication of wearable energy harvesting sources for many portable personal microelectronic devices (e.g., self-powered wireless sensors).
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    46
    References
    32
    Citations
    NaN
    KQI
    []